Archives full life cycle management system and method based on cloud computing

Abstract

The present invention relates to the field of archive management technology, specifically to an archive full life cycle management system and method based on cloud computing; specifically: an intelligent classification module based on format adaptability function and semantic density analysis, a five-stage life cycle management module, and a permission scheduling module integrating RBAC / ABAC permission control and blockchain auditing, based on a matrix decomposition archiving static coding mechanism, achieving document security storage and parsing through orthogonal matrices and pseudo-inverse operations, and improving data security in long-term archiving scenarios; dynamically optimizing cloud storage resources based on document life cycle status and access frequency, and reducing storage costs in combination with hot and cold tiering strategies; introducing AI algorithms, automatically evaluating archive retention value based on regulations and semantic analysis, and achieving compliance destruction decisions. The present invention significantly improves the efficiency and security of full-process management through multi-model fusion metadata extraction, dynamic policy binding, and automated state migration.

Description

Technical Field

[0001] The present invention relates to the technical field of archive management, and in particular to an archive full life cycle management system and method based on cloud computing. Background Art

[0002] With the continuous deepening of digital office and information management, archive management is gradually transforming from traditional paper archives to electronic archives. The types, quantity and complexity of archival data are showing explosive growth. How to achieve efficient, intelligent and standardized management of the entire life cycle of electronic archives from generation, classification, storage, retrieval, approval, backup to destruction has become an important issue facing various enterprises, institutions, government agencies and social organizations.

[0003] Publication number CN117671714A discloses a method and system for building a full-life cycle smart archive management method; the method includes: collecting archival materials and creating a primary archive library; organizing archives based on the primary archive library, generating an ultimate archive library and storing them; retrieving and sharing archives by establishing retrieval rules; recording the operation history and access history of archives and marking and tracing them; establishing archiving requirements and archiving and backing up them; setting archive destruction rules and regularly destroying and updating them; establishing a full-cycle archive management from archive collection to archive destruction by collecting, identifying, organizing, storing, retrieving, counting and destroying archival information resources, thereby realizing the integrated utilization and management of archival resources; thereby improving the efficiency and quality of archive management and promoting information sharing and collaboration.

[0004] At present, cloud computing technology has been widely used in the construction of various information systems due to its elastic resource scheduling, distributed storage and service capabilities, providing strong support for the centralized management, unified scheduling, secure access and remote sharing of archival data. At the same time, the integrated application of emerging technologies such as artificial intelligence and blockchain has also provided new technical paths for intelligent identification, intelligent circulation and compliance auditing in the life cycle of archives. Summary of the Invention

[0005] The purpose of the present invention is to address the problems existing in the background technology and propose a cloud computing-based archive full life cycle management system and method.

[0006] The technical solution of the present invention is a cloud computing-based archive lifecycle management method, which includes the following specific implementation steps:

[0007] S1. Collect archival data, evaluate its compatibility with standard archives based on a format compatibility function, determine the archiving value of unstructured documents by combining optical character recognition and semantic density analysis, define policy functions based on compatibility thresholds and semantic density thresholds, and reconstruct and map the data into a unified standard format.

[0008] S2 uses a text vector representation fused with TF-IDF and BERT to extract metadata, performs multi-label semantic classification after weighted fusion, and generates reconstructed labels by combining label level completion and cross-inference;

[0009] S3, dynamically divides the initial status into draft, review, archive, and delete stages based on document metadata tags and identifiers, and automatically binds access rights and storage policies based on the status;

[0010] S4. Dynamically adjust the encryption method and storage strategy based on the document lifecycle status. Draft status uses AES encoding, review status uses RSA encoding, and archive status uses a matrix decomposition archive static encoding mechanism. Resources are intelligently scheduled based on access frequency to optimize storage costs and secure access efficiency.

[0011] S5. Dynamically assign user permissions, set fine-grained access policies based on the document lifecycle status, record user behavior, operation types, and results in real-time to generate audit logs, and detect abnormal access to trigger alarm mechanisms;

[0012] S6. Automatically migrate archive status based on user behavior, time rules, and the policy engine. Configure migration rules and verify permissions and status consistency through a conditional trigger mechanism. Combine automated migration with event-driven trigger policy adjustments to simultaneously update storage locations and audit records.

[0013] S7. Real-time assessment of operational compliance through a periodic audit mechanism, combined with anomaly detection and risk assessment to generate early warning reports and automatically lock abnormal files;

[0014] S8. Automatically trigger a compliance review at the end of the archive retention period, adaptively select and execute a destruction strategy based on the type, and generate a traceable audit log after verifying the destruction results.

[0015] Preferably, the implementation process of defining the strategy function decision processing action based on the adaptability threshold and the semantic density threshold is as follows:

[0016] S21. Based on a unified data access model, connect to several data sources, automatically identify the input data format, define the format adaptability function, and evaluate its compatibility with the standard file format:

[0017] ;

[0018] Among them, F adapt Indicates the overall adaptability of archival data to the standard format; f i Indicates the format of the i-th field in the data source of the archive data; F std Represents a predefined standard archive metadata template; sim(f i ,Fstd ) represents the field format similarity matching function; n represents the total number of input fields; α i Indicates the importance weight of the field, set according to business rules;

[0019] S22. Perform OCR recognition and semantic density analysis on unstructured documents, define a semantic density function, and determine whether they are worth archiving:

[0020] ;

[0021] Among them, D sem represents the semantic density score; w j represents the jth semantic keyword; Indicates the semantic weight of the keyword in the document; β j represents the preset semantic keyword weight factor; L represents the number of words in the document;

[0022] S23. Define the strategy function , the access action is determined based on the format adaptability and semantic density, specifically:

[0023] ;

[0024] Among them, θ1 represents the format adaptability threshold; θ2 represents the semantic density threshold; sourceType represents the data source type; criticalList represents the list of non-discarded sources.

[0025] Preferably, the classification process of multi-label semantic classification is as follows:

[0026] S31. Use TF-IDF+BERT word embedding model fusion representation:

[0027] ;

[0028] in, Represents the statistical word frequency weight vector; Represents the statistical word frequency weight vector; represents the context embedding vector; α represents the weighted fusion coefficient;

[0029] S32: Extract metadata using a fusion of three channels: structural rule channel, semantic understanding channel, and named entity recognition channel, and fuse the results of each channel.

[0030] S34. Combine the extracted metadata M and perform multi-label semantic classification: ;

[0031] Among them, W represents the classification weight matrix; b represents the bias term; σ represents the Sigmoid activation function; C represents the archive label set.

[0032] Preferably, the process of extracting metadata by fusing the three channels of structural rule channel, semantic understanding channel and named entity recognition channel is as follows:

[0033] Structural rule channel: parses data based on existing template rules, extracts metadata from documents of known formats, matches them according to predefined rules, and identifies key fields in documents, namely structured metadata M. struct ;

[0034] Semantic extraction channel: feature vector Input into the pre-trained model BERT, perform contextual semantic analysis, use text classification methods to identify meaningful metadata items, and output semantic metadata M sem ;

[0035] Named entity recognition channel: Based on the pre-trained bidirectional long short-term memory network BiLSTM-conditional random field CRF model, combined with the context information in the feature vector, named entity recognition is performed and named entity metadata M is output. NER ;

[0036] Fuse the results of each channel: ;

[0037] Where M represents the fused metadata; λ1, λ2, and λ3 represent the weighting coefficients of each channel.

[0038] Preferably, the implementation process of the matrix decomposition archiving static coding mechanism is as follows:

[0039] S51. Initialization: Select two positive integers n and m, satisfying m>n, and choose a finite field F. q ;q=251;

[0040] Define two special matrix sets: one element is an n×n reversible matrix that satisfies A matrix set S of n×m matrices, and a matrix set rank M whose elements are n×m matrices and satisfy rank(M)=n;

[0041] Where I is the identity matrix, T is the matrix transpose operation; rank(M) represents the rank of the matrix M;

[0042] Archive static encoding and archive static analysis generation: randomly select an orthogonal matrix A∈S and a full-rank matrix B∈M; and calculate the archive static analysis code Pd: ;

[0043] S52, archive document encoding operation: extract the document to be archived, perform binary encoding doc on it, and perform primary encoding on it to obtain encoded data data = H (doc); H is a predefined hash function;

[0044] Calculate the encoding transition parameter Et: , where u is randomly selected vector, satisfying , solving the equation gives Among them, B + is the pseudo-inverse of matrix B;

[0045] in, is a finite field F q m-dimensional vector space on ;

[0046] The output is the archive static code u, and {u, data} is archived;

[0047] S53, archive static analysis operation: obtain {u, data}, extract archive data data and archive static code u, calculate analysis transition parameters ;

[0048] like =data, it is confirmed that the archived data data has been extracted, parsed, and restored.

[0049] Preferably, the destruction strategy formulation process is as follows:

[0050] S61. Get the creation time D of the file created and the corresponding retention period P time , and according to the current time T curr , calculate the archive retention period T exp Has it ended? ;

[0051] If the conditions are met, the destruction process will be entered;

[0052] S62. Before destroying the files, according to the type of files and the corresponding compliance rules rule , perform compliance checks, and generate destruction authorization C for files that meet the destruction conditions compliance =1, otherwise marked as “needs further review”;

[0053] S63. Automatically select the appropriate destruction strategy based on the file type, sensitivity, and compliance requirements: ;

[0054] Among them, S strategy Indicates the destruction strategy; Typedoc Indicates the file type; A audit Indicates the audit record requirements and whether a destruction audit report needs to be generated; S() indicates the destruction policy formulation mechanism.

[0055] Preferably, the destruction strategy mechanism is:

[0056] Select the destruction method based on the file type: for common files, select the logical destruction strategy; for sensitive files, select a more secure destruction strategy;

[0057] Compliance requirement adjustment: If the compliance check shows that the archive cannot be destroyed, the destruction policy returns to "delayed destruction" and the retention period is re-evaluated.

[0058] Preferably, the evaluation process of anomaly detection and risk assessment is as follows:

[0059] S81. Dynamic Compliance Detection: By monitoring the real-time data stream of document operations, it automatically determines whether the current operation meets compliance requirements: ;

[0060] Among them, C com Indicates the result of the legality assessment. A value of 1 indicates that the operation complies with the compliance requirements, and a value of 0 indicates that the operation does not comply with the compliance requirements. rule Represents the compliance rules of the application; C() represents the core function of compliance assessment;

[0061] S82. Build an intelligent compliance assessment model to evaluate document operations in real time. By analyzing historical operation data, learn and predict potential compliance risks and provide intelligent assessment reports: ;

[0062] Among them, C context Represents the context information of the current operation, including but not limited to the time, user, device, and geographic location of the operation; M() represents a risk assessment function, which is used to assess the compliance risk of the current document based on the document's historical operations, the current operation context, and the characteristics of the document itself.

[0063] The technical solution of the present invention is a cloud computing-based archive lifecycle management system, which is used to implement the above-mentioned cloud computing-based archive lifecycle management method, including:

[0064] The file collection module is used to support the automatic collection of files in multiple source formats, and provide optical character recognition (OCR) and content structured analysis;

[0065] Cloud archiving and classification module, used for automatic classification and archiving based on intelligent tags and knowledge graphs, supports user-defined metadata models, and realizes the standardization of archive structures;

[0066] The lifecycle management module introduces a four-stage management mechanism: creation, review, archiving, and destruction; each stage is bound to access rights, processing policies, and automatic audit rules;

[0067] The cloud-based permission scheduling and auditing module, based on dynamic permission access control and blockchain evidence storage, records operation logs in real time and has backtracking and error correction functions;

[0068] The intelligent scheduling and storage optimization module automatically optimizes storage resources based on file popularity, permission level, and lifecycle, leveraging the elasticity of cloud computing for dynamic resource allocation.

[0069] The destruction and archive retention judgment module automatically executes the archive retention and destruction judgment procedure.

[0070] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0071] This paper designs a cloud computing-based archive lifecycle management system and method. Through modular integration and technological innovation, it significantly improves the efficiency and security of the entire archive management process:

[0072] (1) Full-process automation and improved classification accuracy: An intelligent access mechanism based on format adaptation function and semantic density analysis, combined with a metadata extraction method that integrates TF-IDF and BERT multi-models, enables accurate classification and standardized processing of multi-source heterogeneous archives, effectively solving the problems of inefficiency and classification errors caused by the traditional system's reliance on manual intervention;

[0073] (2) Dynamic security and compliance assurance: Through the integration of blockchain evidence storage and RBAC / ABAC permission control mechanism, the immutability of operation logs and fine-grained permission management are ensured. Combined with the AI-driven compliance destruction judgment module (based on regulations and semantic analysis), the risk of illegal operations is significantly reduced, meeting the security and legal requirements of enterprise-level document management.

[0074] (3) Storage resource optimization and cost control: The intelligent scheduling module dynamically allocates high-performance storage, encrypted storage, or low-cost cold storage resources based on the document lifecycle status (draft, review, archive) and access frequency. It achieves efficient and secure storage of archived data through a matrix decomposition static coding mechanism, taking into account both storage cost optimization and data recovery reliability.

[0075] (4) Static coding and parsing: By utilizing the linear algebraic properties of orthogonal matrices (A) and full-rank matrices (B), the binary coded data (data) of the document to be archived is combined with a random vector (u) to generate an irreversible archive static code. The pseudo-inverse operation and the irreversibility of the orthogonal matrix ensure that the data can only be parsed by the legally authorized party. This mechanism uses the dual processing of matrix decomposition and hash function during data storage to ensure data confidentiality (to avoid unauthorized recovery) and ensure the integrity and tamper-proofness of the archived data by verifying the consistency of the transition parameters with the original data. At the same time, its mathematical architecture based on finite fields optimizes storage efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 This is a system architecture diagram of a cloud computing-based archive lifecycle management system proposed by the present invention;

[0077] Figure 2 This is a flow chart of a method for managing the entire life cycle of archives based on cloud computing proposed by the present invention. DETAILED DESCRIPTION

[0078] Example 1, as Figure 1 As shown, the present invention proposes a cloud computing-based archive life cycle management system, which includes: an archive collection module, a cloud archiving and classification module, a life cycle management module, a cloud permission scheduling and audit module, an intelligent scheduling and storage optimization module, and a destruction and archive retention judgment module.

[0079] The file collection module supports automatic collection of files in multiple source formats (including but not limited to PDF, images, audio and video), and provides OCR (Optical Character Recognition) recognition and content structured analysis;

[0080] The cloud-based archiving and classification module automatically classifies and archives files based on intelligent tags and knowledge graphs, supports user-defined metadata models, and achieves standardized file structures.

[0081] The lifecycle management module introduces a five-stage management mechanism: creation, use, storage, maintenance, and destruction; each stage is bound to access rights, processing policies, and automatic audit rules;

[0082] The cloud-based permission scheduling and auditing module, based on role-based access control (RBAC) and blockchain evidence storage, records operation logs in real time and has backtracking and error correction functions;

[0083] The intelligent scheduling and storage optimization module automatically optimizes storage resources based on file popularity (access frequency), permission level, and lifecycle, leveraging the elasticity of cloud computing for dynamic resource allocation.

[0084] The destruction and archive retention judgment module introduces AI algorithms to determine whether archives can be destroyed or should be retained for a long time (combined with regulations / unit policies), and provides automatic backup and compliance reminders.

[0085] Example 2, as Figure 2 As shown, the present invention proposes a cloud computing-based archive life cycle management method, which is applied to the cloud computing-based archive life cycle management system proposed in Example 1. The specific implementation steps are as follows:

[0086] S1. The archive collection module automatically identifies, standardizes, and structures multi-source heterogeneous archive data, providing a unified entry point for subsequent archive classification, management, and analysis. The specific implementation process is as follows:

[0087] S11. Based on a unified data access model, connect to several data sources {including but not limited to: local document directories (file systems); third-party business systems (OA, ERP, CRM); mail servers (Exchange, SMTP); scanner or mobile terminal input (images / scans)}, automatically identify the input data format, and evaluate its compatibility with standard file formats. Specifically:

[0088] Define the format fitness function: ;

[0089] Among them, F adapt Indicates the overall adaptability of archival data to the standard format; f i Indicates the format of the i-th field in the data source of the archive data; F std Represents a predefined standard archive metadata template; sim(f i ,F std ) represents the field format similarity matching function, which calculates the similarity of the type, length, and format rules of the input field and the standard field; n represents the total number of input fields; α i Indicates the importance weight of the field, which is set according to business rules (for example, the weight of "File Number" is higher than the weight of "Remarks");

[0090] S12. Perform OCR recognition and semantic density analysis on unstructured documents (including but not limited to PDF, Word, and scanned documents) to determine whether they have archiving value and define the semantic density function:

[0091] ;

[0092] Among them, D sem It represents the semantic density score, reflecting the effective information density per unit length of the document; w jrepresents the jth semantic keyword (including but not limited to "contract", "project number", "approver"); Represents the semantic weight of the keyword in the document, which is derived based on the TF-IDF semantic vector in this embodiment; β j represents the preset semantic keyword weight factor; L represents the number of words in the document;

[0093] S13. Define the strategy function , the access action is determined based on the format adaptability and semantic density, specifically:

[0094] ;

[0095] Where θ1 represents the format adaptability threshold; θ2 represents the semantic density threshold; sourceType represents the data source type (including but not limited to: approval system, official document system); criticalList represents the list of sources that cannot be discarded (including but not limited to: official documents, financial budget documents);

[0096] S14. Map the received or reconstructed data into a unified standard file format.

[0097] S2, the cloud archiving and classification module, performs semantic analysis on the accessed data, extracts key metadata, and automatically categorizes the data into a multi-dimensional archival tag system based on content characteristics and context, providing a data foundation for subsequent lifecycle management and retrieval. The specific implementation process is as follows:

[0098] S21. Use TF-IDF+Word Embedding Model (BERT) to fuse the representation:

[0099] ;

[0100] in, Represents the statistical word frequency weight vector; Represents the statistical word frequency weight vector; represents the context embedding vector (this embodiment uses the output of the pre-trained model BERT); α represents the weighted fusion coefficient;

[0101] S22, using the three-channel fusion of "structural rule channel + semantic understanding channel + named entity recognition channel" to extract metadata:

[0102] Structural rule channel: parses data based on existing template rules, extracts metadata from documents of known formats, matches them according to predefined rules, and identifies key fields in documents, namely structured metadata M. struct : ;

[0103] Among them, fstruct () represents the structured data extraction function, which depends on the structural pattern of the document;

[0104] Semantic extraction channel: feature vector Input into the pre-trained model BERT, perform contextual semantic analysis, use text classification methods to identify meaningful metadata items (including but not limited to date, project number), and output semantic metadata M sem : ;

[0105] Among them, f NLP () represents the semantic extraction function based on the pre-trained model BERT;

[0106] Named entity recognition channel: Based on the pre-trained BiLSTM (bidirectional long short-term memory network)-CRF (conditional random field) model, combined with the context information in the feature vector, named entity recognition is performed and named entity metadata M is output. NER : ;

[0107] After the metadata is processed independently by each channel (structured metadata M struct , semantic metadata M sem , named entity metadata M NER ), assign different weights based on the extraction importance of each channel and fuse the results of each channel: ;

[0108] Where M represents the fused metadata; λ1, λ2, and λ3 represent the weighting coefficients of each channel;

[0109] S23. Combine the extracted metadata M and perform multi-label semantic classification: ;

[0110] Where W represents the classification weight matrix; b represents the bias term; σ represents the Sigmoid activation function; C represents the archive label set;

[0111] S24. Since labels have contextual levels and cross-dimensions, set label fusion strategy: ;

[0112] Among them, F hier () indicates the tag tree automatic completion function, which completes according to the superordinate concept; F cross () represents the label cross inference function; Represents the refactoring tag, that is, the completed tag;

[0113] S25. Output reconstruction label .

[0114] S3, the lifecycle management module initializes the lifecycle status of the document and binds the corresponding lifecycle management policy to ensure the efficient use and protection of the document throughout its lifecycle. The specific implementation process is as follows:

[0115] S31. Build file status initialization division rules and dynamically allocate life cycle status:

[0116] Creation stage: If the document is newly created (the label contains "Creation Date" and has not been reviewed), the initial status is "Draft";

[0117] Review phase: If a document is marked as requiring review (the tag contains "Review"), its lifecycle state is initialized to "In Review";

[0118] Archiving stage: If the document has completed the approval process and meets the archiving conditions (such as the archiving period has expired), it is initialized to "Archived";

[0119] Deletion phase: If a document reaches the preset end date of its lifecycle or is marked as useless, it is initialized to "Deleted";

[0120] S32. Once the lifecycle status of a document is determined, the corresponding lifecycle management policy is automatically bound to the document, including but not limited to access rights and storage requirements. These policies are dynamically set based on the document's lifecycle stage. Specifically,

[0121] Draft status: The document is visible only to the creator and is stored in a temporary area without being backed up;

[0122] Under review: The document is visible to specific reviewers, stored in the review area, and has a set review cycle;

[0123] Archive status: Documents are stored in a long-term archive system with data encryption and backup policies enabled, and can only be modified by specific personnel;

[0124] Deletion status: The document is marked as deleted and automatically cleaned up or completely destroyed after a specified time;

[0125] Based on this: the initial life cycle state of the document is determined by the metadata tag M and the unique identifier of the document, and the corresponding life cycle management strategy P is automatically bound according to the initial life cycle state and the completion tag M, and the life cycle state S of the document is output. init and the corresponding management strategy P.

[0126] S4, the intelligent scheduling and storage optimization module, stores documents in the cloud based on their lifecycle status and management policies, ensuring their security, privacy, and high availability. It also implements flexible scheduling, taking into account different document access requirements and different lifecycle stages, so that documents can automatically adjust their storage and processing resources based on their access frequency and storage requirements. The specific implementation process is as follows:

[0127] S41. Dynamically adjust data encoding methods according to different stages of the document lifecycle to improve document security and reduce encryption complexity as needed:

[0128] Draft status documents: The document is encoded using a symmetric encryption algorithm (including but not limited to AES), the key is stored in a temporary storage area, and the encryption process is completed before the document is uploaded;

[0129] Documents in the review state: Data is encapsulated and encoded, and RSA public and private key encryption algorithms are used to ensure higher security during document transmission;

[0130] Archived documents: When archiving and retrieving archive documents for viewing, they are encoded and parsed based on the matrix decomposition archive static coding mechanism;

[0131] S42. Automatically select different cloud storage resources for storage based on the document's lifecycle status and management policy. Determine the document storage location by analyzing the document's access frequency, storage period, and encryption requirements.

[0132] (1) Draft status document:

[0133] Storage location: Stored in high-performance, fast-response cloud storage areas, including but not limited to SSD storage pools;

[0134] Storage configuration: Enable multi-copy storage to ensure high availability and automatically expand capacity when access is frequent;

[0135] Storage cost: relatively high, but can ensure fast reading and writing;

[0136] (2) Audit status document:

[0137] Storage location: Stored in a high-security, encrypted storage pool using block storage or encrypted object storage services;

[0138] Storage configuration: Enable data access control (DAC) and encryption policies;

[0139] Storage cost: High, ensuring confidentiality during transmission and storage;

[0140] (3) Archive status documents:

[0141] Storage location: Stored in cold storage area or archive storage;

[0142] Storage configuration: Use long-term storage services, including but not limited to low-frequency storage (LRS) or object storage;

[0143] Storage cost: low, suitable for long-term storage but infrequently accessed documents;

[0144] S43. Automatically adjust the allocation of storage and computing resources based on the document lifecycle stage to optimize storage costs and resource utilization: ;

[0145] Among them, R sched Indicates resource scheduling scheme; f() indicates predefined scheduling function; F access Indicates the access frequency of the document (including but not limited to reading frequency and modification frequency); R compute Indicates computing resource requirements; S init Indicates the document lifecycle status (draft, review, archive, delete);

[0146] Specifically:

[0147] Draft documents: Due to their high access frequency, they require faster response times and higher computing resources to handle modifications and reviews, and storage resources must have a higher response speed.

[0148] Archived documents: Access frequency is low, computing and storage resources are reduced, and low-cost, long-term storage solutions are used;

[0149] Audit status documents: This requires higher security in storage and computing resources to support rapid document review and modification.

[0150] Based on this: the intelligent scheduling and storage optimization module is based on the document life cycle status S init , management policy P and document content D, according to the life cycle status and management policy, the document is encrypted accordingly to generate an encrypted document D enc , according to the life cycle status and policy, the storage location and storage type of the document are determined, and according to the access frequency and life cycle status of the document, storage and computing resources are intelligently scheduled to ensure efficient storage and secure access to the document, and then the encrypted storage document and the corresponding resource scheduling plan are output.

[0151] S5. When a user initiates an operation request such as file access, editing, downloading, or forwarding, the cloud access permission scheduling and auditing module is triggered in real time. Combining RBAC (role-based), ABAC (attribute-based), and behavioral audit mechanisms, it determines whether the user has the corresponding operation permissions. At the same time, all access operations are recorded in the blockchain audit chain to ensure that the operation behavior is traceable and cannot be tampered with. The specific implementation process is as follows:

[0152] S51. Predefine roles and assign permissions. The permission assignment table is shown in Table 1:

[0153] Table 1 Permission allocation table

[0154] ;

[0155] In addition, access permissions are dynamically determined based on user, resource, and environment attributes:

[0156] ;

[0157] Among them, A() represents the attribute-based access control function; U role Indicates the user role; P history Represents user behavior history; S status Indicates the life cycle state of the document; F doc Indicates the sensitivity of the document; E env Environmental factors include but are not limited to access time (working hours / non-working hours) and device (company intranet / extranet);

[0158] Resource attributes: including but not limited to the sensitivity level of the document and document labels (including but not limited to financial documents and personal information);

[0159] Environmental attributes: including but not limited to access time, access location, and access device;

[0160] User attributes: including but not limited to user's access history, account status, and department;

[0161] S52. Design fine-grained role and permission management based on the document lifecycle status and management strategy. Documents at different stages have different requirements for user permissions:

[0162] ;

[0163] Among them, C access Represents access control policy; Pe() represents access control function, which generates policy based on life cycle status, user role and access request; S init Indicates the document life cycle state; U roleIndicates user roles (including but not limited to administrator, editor, viewer); R access () indicates the access request type (view, edit, delete);

[0164] Specifically:

[0165] (1) Draft status document:

[0166] Editing permissions: Granted to document owners and authorized editors, allowing them to modify document content;

[0167] View permissions: Allow some high-authorized users (administrators) to view documents, but do not allow external users to access them;

[0168] (2) Audit status document:

[0169] View permissions: Only reviewers and administrators can view;

[0170] Modification permissions: limited to administrators and certain specific roles, preventing ordinary users from modifying documents;

[0171] (3) Archive status documents:

[0172] Viewing permissions: Archived documents can be opened to limited viewers, but editing is not allowed;

[0173] Permission lock: Once archived, modification permissions will be frozen to prevent tampering;

[0174] S53. During the document lifecycle, establish a monitoring mechanism to track document access in real time and audit all user operations, especially monitoring sensitive operations (including but not limited to editing and deletion). Specifically:

[0175] (1) Contents of audit records:

[0176] User information: records who performed the operation (including but not limited to user ID and role);

[0177] Operation type: record the operation type (including but not limited to viewing, editing, deleting, and modifying status);

[0178] Document information: ID, name and type of the document being operated;

[0179] Operation time: timestamp of operation execution;

[0180] Operation result: whether the operation is successful or not; if it fails, the failure reason will be recorded;

[0181] An example of an audit log is shown in Table 2:

[0182] Table 2 Audit log example table

[0183] ;

[0184] (2) When abnormal behavior is detected (such as frequent failed operations or user access to sensitive documents), the system should automatically trigger an alarm mechanism to notify the administrator. The audit alarm formula is:

[0185] ;

[0186] Among them, R alert Represents the alarm trigger mechanism; R() represents the audit alarm mechanism; L audit () indicates audit log; T threshold Indicates the set threshold (consecutive failures exceed 5 times); F anomaly Indicates abnormal behavior (including but not limited to high-profile access within a short period of time).

[0187] S6. The lifecycle management module automatically updates the archive status based on user behavior (including but not limited to editing, archiving, and signing), time rules (including but not limited to reaching the retention period), and the judgment results of the policy rule engine. The archive status is migrated according to the state transition function. The state migration operation will again trigger the adjustment of the encrypted storage location and the audit chain record. The specific implementation process is as follows:

[0188] S61. Build a condition-triggered automatic migration mechanism. This mechanism automatically pushes documents from one state to another when they meet specific conditions. Each state transition must be controlled and the lifecycle state changed using the following mechanisms:

[0189] (1) State transition rule configuration: The lifecycle state changes of each document depend on specific rules; for example:

[0190] (a) From draft status to review status: the document completes preliminary editing and is submitted for review;

[0191] (b) From review status to archive status: the document is reviewed, approved and archived;

[0192] (2) Definition of state transition conditions: Each state transition is accompanied by specific conditions, such as:

[0193] (a) From Draft to Review: All required fields in the document are completed and submitted for review;

[0194] (b) Reviewed to archive: The document has passed the reviewer's review and no revisions have been made;

[0195] S62. To ensure that the document lifecycle migration strictly complies with regulations, a check is performed at each state change to confirm whether the document meets the migration conditions. Specifically:

[0196] (1) Permission verification: Each time a state is transferred, it is verified whether the current user has sufficient permissions to perform the operation;

[0197] (2) State consistency check: When performing state migration, check whether the current state of the document allows the target state migration;

[0198] For example, a document can be moved to the "Approved" status only after it has been reviewed. If the status is inconsistent, the migration request will be automatically rejected;

[0199] S63. To improve the efficiency of document lifecycle management, intelligent automated migration rules are introduced. Specifically:

[0200] (1) Automated migration: Automatically migrate documents from one state to another based on their lifecycle status and internal and external conditions;

[0201] For example: After a document is reviewed by an auditor, the document status is automatically changed from "under review" to "archived";

[0202] (2) Event-triggered migration: Automatically execute state migration by setting a specific event-driven mechanism (including but not limited to timed triggering and business process completion);

[0203] For example: When a document exceeds the storage period, it will be automatically moved to the "archive" state.

[0204] S7. The lifecycle management module periodically triggers an audit mechanism to conduct compliance assessments on all file operations and status evolution processes. Compliance reports or risk warnings are generated by comparing unit policies and national regulations (including but not limited to data protection laws and archive laws). If abnormal behavior is found (including but not limited to overdue file retention and unauthorized access), the administrator is automatically notified and the file is locked. The specific implementation process is as follows:

[0205] S71. Record and regularly audit every key operation in the document lifecycle, including document creation, editing, review, archiving, and deletion. Detailed logging of these operations ensures that any sensitive operation can be traced and audited, preventing any unauthorized or erroneous actions.

[0206] (1) Keep detailed records of each event in the document life cycle, including but not limited to the time of the event, the user who performed the operation, the type of operation, and the object of the operation;

[0207] That is, every change in the document status must be recorded as an audit log, automatically recording the following:

[0208] ;

[0209] Among them, T time Indicates the timestamp of the operation; R role Indicates the user role that performs the operation; A action Indicates the operation performed (including but not limited to creation, editing, and review); D doc Indicates the document involved in the operation; P para Indicates parameters related to the operation (including but not limited to approval opinions and modification content); AutoLog() indicates the audit log;

[0210] (2) Use hashing algorithm to encrypt the audit log and store it in cRULE to ensure that the log content will not be modified during storage;

[0211] S72. Combine dynamic compliance detection and real-time compliance assessment mechanisms to conduct compliance assessments on document management information:

[0212] (1) Dynamic compliance detection: By monitoring the real-time data flow of document operations, it automatically determines whether the current operation meets the compliance requirements: ;

[0213] Among them, C com Indicates the result of the legality assessment. A value of 1 indicates that the operation complies with the compliance requirements, and a value of 0 indicates that the operation does not comply with the compliance requirements. rule Represents the compliance rules of the application, i.e., predefined regulations, policies, and compliance requirements; C() represents the core function of compliance assessment, which is used to determine whether the current document operation complies with the predetermined compliance rules and standards;

[0214] It should be noted that the core function C() of the compliance assessment calculates the compliance score based on the operation type, user role, document characteristics, and applicable compliance rules. Specifically:

[0215] Operation type matching: For operation type A action , according to the predefined compliance rule set C rule Determine whether the operation is allowed;

[0216] For example, if A action = "edit", and C rule The rule in the code states that editing operations are only allowed to senior users. If the user role R role If the user is not an "Administrator", the compliance score is 0, which means the user does not meet the compliance requirements.

[0217] Document feature matching: Check the document type D doc Whether it complies with the operating rules; for example, if D docIf the file is confidential and the action is deletion, the compliance score will be adjusted based on whether the action approval process is followed;

[0218] Role check: determine the user role R role Whether the operation is allowed. If the role does not meet the requirements, the operation is not compliant.

[0219] Final Compliance Score: If the action meets all rule conditions, then C com =1, otherwise 0;

[0220] (2) Combining big data analysis and machine learning algorithms, we build an intelligent compliance assessment model to evaluate document operations in real time. By analyzing historical operation data, we learn and predict potential compliance risks and provide intelligent assessment reports: ;

[0221] Among them, C context Represents the contextual information of the current operation, including but not limited to the time, user, device, and geographic location of the operation. M() represents a risk assessment function, which is used to assess the compliance risk of the current document based on the document's historical operations, the current operation context, and the characteristics of the document itself.

[0222] S73. Introduce abnormal behavior detection and risk assessment mechanisms to monitor and analyze potential risks in document operations in real time, identify behaviors that do not meet compliance requirements in advance, and prevent violations. Specifically:

[0223] (1) Abnormal behavior detection: Based on user behavior analysis and pattern recognition, it automatically identifies abnormal behaviors in document operations, including but not limited to sudden additions of deletion operations and unusual file modifications. ;

[0224] Among them, E detect () indicates the abnormal behavior detection result, which is 1 if an abnormality is detected, otherwise 0; oper Indicates the behavioral data of the current operation (including but not limited to file modification and deletion); T thr () indicates the set abnormal threshold, which is used to identify whether the behavior is abnormal; Represents an abnormal behavior detection function, which is used to identify and judge abnormal behavior of users during document operations. It detects whether suspicious or non-compliant operations have occurred based on historical behavior patterns and threshold settings. By comparing the deviation between the current operation and the historical record, combined with the threshold, it is determined whether abnormal behavior has occurred. If an abnormality is detected, the function output result is 1; otherwise, it is 0.

[0225] (2) Based on big data analysis, the system evaluates the compliance risk of documents by calculating the risk scores of all operations in the document life cycle: ;

[0226] Among them, R com () represents the compliance risk score; T risk Indicates the set risk threshold; P policy () represents the compliance policy, which is defined according to the laws and regulations on document management; Risk() represents the final compliance risk assessment function, which is used to conduct a comprehensive assessment based on the abnormal behavior detection results, operation thresholds and compliance policies to obtain the final compliance risk score.

[0227] S8, the destruction and archiving retention judgment module, automatically and intelligently determines whether to destroy the archive based on factors such as the archive's type, importance, and legal requirements at the end of the archive retention period, and performs secure destruction operations after confirmation. The specific implementation process is as follows:

[0228] S81. Get the creation time D of the file created and the corresponding retention period P time , and according to the current time T curr , calculate the archive retention period T exp Has it ended? ;

[0229] If the conditions are met, the destruction process will be entered;

[0230] S82. Before destroying records, conduct a review based on compliance requirements to ensure that the destruction complies with relevant laws and regulations and corporate policies. Compliance checks include but are not limited to confirming the sensitivity of the records, legal or regulatory requirements (including but not limited to financial and tax documents), and whether there are other specific prohibitions on destruction, namely, based on the type of records and the corresponding compliance rules. rule , perform compliance checks. If the file is a sensitive file (including but not limited to financial and personal privacy files), reconfirm whether there are any unexpired compliance requirements or whether the retention period needs to be extended. For files that meet the destruction conditions, generate a destruction authorization C compliance =1, otherwise marked as “needs further review”;

[0231] S83. Automatically select appropriate destruction strategies based on the file type, sensitivity, and compliance requirements. Destruction strategies include but are not limited to physical destruction (including but not limited to hard drive destruction) and logical destruction (including but not limited to file erasure and encryption destruction): ;

[0232] Among them, Sstrategy Indicates the destruction strategy; Type doc Indicates the file type; A audit Indicates the audit record requirements and whether a destruction audit report needs to be generated; S() indicates the destruction policy formulation mechanism;

[0233] It should be noted that the destruction strategy mechanism is:

[0234] (1) Select the destruction method based on the file type:

[0235] For common archives, select a logical destruction strategy (including but not limited to file erasure and encryption destruction);

[0236] For sensitive files (including but not limited to confidential documents), choose a more secure destruction strategy, including but not limited to physical destruction (hard drive destruction, printed document destruction) or encrypted destruction;

[0237] (2) Compliance requirement adjustment: If the compliance check shows that the file cannot be destroyed (including but not limited to extending the retention period), the destruction policy returns to "delayed destruction" and the retention period is re-evaluated;

[0238] (3) Audit record requirements: If audit records are required, include an audit function when selecting a destruction strategy to ensure that each destruction operation can be traced;

[0239] S84: After the destruction strategy is determined, the actual destruction operation is automatically executed. destruction : ;

[0240] Among them, D doc Indicates the files to be operated, that is, the files to be destroyed; T destroy Indicates the timestamp of the destruction operation; O() indicates the destruction mechanism;

[0241] It should be noted that the destruction mechanism O() is specifically as follows: according to the selected destruction strategy, perform the file destruction operation (physical destruction: including but not limited to destroying hard disks and paper files; logical destruction: perform file erasure and encryption destruction to ensure that the files cannot be restored); each destruction operation generates a timestamp T destroy , ensuring that operations are traceable;

[0242] S85. After the destruction operation is completed, the destruction results are verified and an audit record is generated, namely: Verify the destruction results: Use technical means to verify whether the destruction operation is thorough (for physical destruction, verify whether the hard disk or storage medium is shredded or destroyed; for logical destruction, use data recovery tools to check whether the file can be recovered); Audit generation: Record the destruction operation and generate an audit report, recording the destroyed files, destruction time, and executor information;

[0243] S86. Archive the logs and audit records generated during the destruction process and generate a destruction report based on demand. log , the destruction log contains detailed information about each operation: L log ={O destruction ,D doc ,A audit}.

[0244] In the third embodiment, the present invention proposes a cloud computing-based archive lifecycle management method, which also includes a matrix decomposition-based archive static coding mechanism. The specific implementation steps are as follows:

[0245] Initialization settings: select two positive integers n and m, satisfying m>n, and choose a finite field F q (q=251); define two special matrix sets: S (n×n reversible matrix, and satisfy , I is the identity matrix, T is the matrix transpose operation), M (n×m matrix, and satisfying rank(M)=n, rank(M) represents the rank of matrix M).

[0246] Archive static encoding and archive static analysis generation: randomly select an orthogonal matrix A∈S and a full-rank matrix B∈M; and calculate the archive static analysis code Pd: .

[0247] Archive document encoding operation:

[0248] Extract the document to be archived, encode it into binary code doc, and perform primary encoding on it to obtain the encoded data data = H (doc); H is a predefined hash function;

[0249] Calculate the encoding transition parameter Et: , where u is randomly selected vector, satisfying , solving the equation gives Among them, B + is the pseudo-inverse of matrix B;

[0250] Based on this, the archive static code u is obtained, and {u, data} is archived.

[0251] Archive static analysis operation:

[0252] Get {u, data}, extract archive data data and archive static code u, calculate the parsing transition parameters ;

[0253] like =data, it is confirmed that the archived data data has been extracted, parsed, and restored.

[0254] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A cloud computing-based archive life cycle management method, characterized in that: The specific implementation steps include the following: S1. Collect archival data, evaluate its compatibility with standard archives based on a format compatibility function, determine the archiving value of unstructured documents by combining optical character recognition and semantic density analysis, define policy functions based on compatibility thresholds and semantic density thresholds, and reconstruct and map the data into a unified standard format. The implementation process of the policy function decision processing action is defined based on the fitness threshold and the semantic density threshold as follows: Based on a unified data access model, it connects to several data sources, automatically identifies the input data format, defines the format adaptability function, and evaluates its adaptability with the standard archive format: ； Among them, F adapt Indicates the overall adaptability of archival data to the standard format; f i Indicates the format of the i-th field in the data source of the archive data; F std Represents a predefined standard archive metadata template; sim(f i ,F std ) represents the field format similarity matching function; n represents the total number of input fields; α i Indicates the importance weight of the field, set according to business rules; Perform OCR recognition and semantic density analysis on unstructured documents, define semantic density functions, and determine whether they are worth archiving: ； Among them, D sem represents the semantic density score; w j represents the jth semantic keyword; Indicates the semantic weight of the keyword in the document; β j represents the preset semantic keyword weight factor; L represents the number of words in the document; Defining a policy function , the access action is determined based on the format adaptability and semantic density, specifically: ； Among them, θ1 represents the format adaptation threshold; θ2 represents the semantic density threshold; sourceType represents the data source type; criticalList represents the list of non-discardable sources; S2 uses a text vector representation fused with TF-IDF and BERT to extract metadata, performs multi-label semantic classification after weighted fusion, and generates reconstructed labels by combining label level completion and cross-inference; S3, dynamically divides the initial status into draft, review, archive, and delete stages based on document metadata tags and identifiers, and automatically binds access rights and storage policies based on the status; S4. Dynamically adjust the encryption method and storage strategy based on the document lifecycle status. Draft status uses AES encoding, review status uses RSA encoding, and archive status uses a matrix decomposition archive static encoding mechanism. Resources are intelligently scheduled based on access frequency to optimize storage costs and secure access efficiency. The implementation process of the matrix decomposition-based archiving static coding mechanism is as follows: A1. Initialization: Select two positive integers n and m, satisfying m>n, and choose a finite field F. q ;q=251; Define two special matrix sets: one element is an n×n reversible matrix that satisfies A matrix set S of n×m matrices, and a matrix set rank M1 whose elements are n×m matrices and satisfy rank(M1)=n; Where I is the identity matrix, T is the matrix transpose operation; rank(M1) represents the rank of matrix M1; Archive static encoding and archive static analysis generation: randomly select an orthogonal matrix A∈S and a full-rank matrix B∈M1; and calculate the archive static analysis code Pd: ; A2. Archive document encoding operation: extract the document to be archived, perform binary encoding on it (doc), and perform primary encoding on it to obtain encoded data (data = H(doc)). H is a predefined hash function. Calculate the encoding transition parameter Et: , where u is randomly selected vector, satisfying , solving the equation gives Among them, B + is the pseudo-inverse of matrix B; in, is a finite field F q m-dimensional vector space on ; The output is the archive static code u, and {u, data} is archived; A3. Archive static parsing operation: Get {u, data}, extract archive data data and archive static code u, calculate parsing transition parameters ; like =data, then the archive data has been confirmed to be extracted, parsed, and restored; S5. Dynamically assign user permissions, set fine-grained access policies based on the document lifecycle status, record user behavior, operation types, and results in real-time to generate audit logs, and detect abnormal access to trigger alarm mechanisms; S6. Automatically migrate archive status based on user behavior, time rules, and the policy engine. Configure migration rules and verify permissions and status consistency through a conditional trigger mechanism. Combine automated migration with event-driven trigger policy adjustments to simultaneously update storage locations and audit records. S7. Real-time assessment of operational compliance through a periodic audit mechanism, combined with anomaly detection and risk assessment to generate early warning reports and automatically lock abnormal files; S8. Automatically trigger a compliance review at the end of the archive retention period, adaptively select and execute a destruction strategy based on the type, and generate a traceable audit log after verifying the destruction results.

2. The method for managing archives throughout their lifecycle based on cloud computing according to claim 1, characterized in that: The classification process of multi-label semantic classification is as follows: S31. Use TF-IDF+BERT word embedding model fusion representation: ； in, Represents the statistical word frequency weight vector; Represents the statistical word frequency weight vector; represents the context embedding vector; α represents the weighted fusion coefficient; S32: Extract metadata using three channels: structural rule channel, semantic understanding channel, and named entity recognition channel, and fuse the results of each channel; S33. Combine the fused metadata M and perform multi-label semantic classification: ; Among them, W represents the classification weight matrix; b represents the bias term; σ represents the Sigmoid activation function; C represents the archive label set.

3. The method for managing archives throughout their lifecycle based on cloud computing according to claim 2, characterized in that: The extraction process of metadata by fusing the structural rule channel, semantic understanding channel, and named entity recognition channel is as follows: Structural rule channel: parses data based on existing template rules, extracts metadata from documents of known formats, matches them according to predefined rules, and identifies key fields in documents, namely structured metadata M. struct ; Semantic understanding channel: feature vector Input into the pre-trained model BERT, perform contextual semantic analysis, use text classification methods to identify meaningful metadata items, and output semantic metadata M sem ; Named entity recognition channel: Based on the pre-trained bidirectional long short-term memory network BiLSTM-conditional random field CRF model, combined with the context information in the feature vector, named entity recognition is performed and named entity metadata M is output. NER ; Fuse the results of each channel: ； Where M represents the fused metadata; λ1, λ2, and λ3 represent the weighting coefficients of each channel.

4. The method for managing archives throughout their lifecycle based on cloud computing according to claim 1, wherein: The destruction strategy formulation process is as follows: S61. Get the creation time D of the file created and the corresponding retention period P time , and according to the current time T curr , calculate the archive retention period T exp Has it ended? ； If the conditions are met, the destruction process will be entered; S62. Before destroying the files, according to the type of files and the corresponding compliance rules rule , perform compliance checks, and generate destruction authorization C for files that meet the destruction conditions compliance =1, otherwise marked as "needs further review"; S63. Automatically select the appropriate destruction strategy based on the file type, sensitivity, and compliance requirements: ； Among them, S strategy Indicates the destruction strategy; Type doc Indicates the file type; A audit Indicates the audit record requirements and whether a destruction audit report needs to be generated; S() indicates the destruction policy formulation mechanism.

5. The method for managing archives throughout their lifecycle based on cloud computing according to claim 4, characterized in that: The destruction strategy mechanism is: Select the destruction method based on the file type: for ordinary files, select the logical destruction strategy; for sensitive files, select a more secure destruction strategy; Compliance requirement adjustment: If the compliance check shows that the archive cannot be destroyed, the destruction policy returns to "delayed destruction" and the retention period is re-evaluated.

6. The method for managing archives throughout their lifecycle based on cloud computing according to claim 1, characterized in that: The evaluation process of anomaly detection and risk assessment is as follows: S81. Dynamic Compliance Detection: By monitoring the real-time data stream of document operations, it automatically determines whether the current operation meets compliance requirements: ； Among them, C com Indicates the result of the legality assessment. A value of 1 indicates that the operation complies with the compliance requirements, and a value of 0 indicates that the operation does not comply with the compliance requirements. rule Represents the compliance rules of the application; C() represents the core function of compliance assessment; R role Indicates the user role that performs the operation; A action Indicates the operation to be performed; D doc Indicates the document involved in the operation; S82. Build an intelligent compliance assessment model to evaluate document operations in real time. By analyzing historical operation data, learn and predict potential compliance risks and provide intelligent assessment reports: ； Among them, C context Represents the context information of the current operation; M2() represents the risk assessment function, which is used to assess the compliance risk of the current document based on the document's historical operations, the current operation context, and the characteristics of the document itself; Represents historical operation data; Represents the predicted potential compliance risk.

7. A cloud computing-based archive life cycle management system, which is used to implement the cloud computing-based archive life cycle management method according to any one of claims 1 to 6, characterized in that: include: The file collection module supports automatic collection of files in multiple source formats and provides optical character recognition (OCR) and content structured analysis. Cloud archiving and classification module, used for automatic classification and archiving based on intelligent tags and knowledge graphs, supports user-defined metadata models, and realizes the standardization of archive structures; The lifecycle management module introduces a four-stage management mechanism: creation, review, archiving, and destruction; each stage is bound to access rights, processing policies, and automatic audit rules; The cloud-based permission scheduling and auditing module, based on dynamic permission access control and blockchain evidence storage, records operation logs in real time and has backtracking and error correction functions; The intelligent scheduling and storage optimization module automatically optimizes storage resources based on file popularity, permission level, and lifecycle, leveraging the elasticity of cloud computing for dynamic resource allocation. The destruction and archive retention judgment module automatically executes the archive retention and destruction judgment procedure.

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