File digital efficient processing system fused with AI technology

Abstract

The invention relates to the technical field of archive management systems, and particularly discloses an AI technology-fused archive digital efficient processing system, which comprises an intelligent acquisition preprocessing module, an AI deep identification analysis module, an intelligent classification archiving module, an AI retrieval optimization module and a security encryption module, automatic collection and unified processing of multiple types of archives are achieved through the intelligent collection and preprocessing module, operations such as denoising, deviation correction and format standardization are completed with the help of an AI algorithm, dependence on manual operation is avoided, and the archive preprocessing period is greatly shortened; through deep integration of the AI technology and the archive management service, the efficiency, accuracy and safety problems in existing archive digital processing are comprehensively solved, the archive management cost is reduced, the archive utilization value is improved, and reliable technical support is provided for archive digital management in various fields.

Description

Technical Field

[0001] This invention relates to the field of archive management system technology, specifically to an efficient archive digitization processing system that integrates AI technology. Background Technology

[0002] As an important carrier of information, archives encompass various types, including paper, electronic, and image formats. Their standardized management and efficient utilization are core needs for enterprises and research institutions. With the acceleration of informatization, archive digitization has become a key path to overcome the limitations of traditional archive management; however, the current process of archive digitization still faces many problems that urgently need to be solved. Traditional archival processing relies heavily on manual operation. From data collection and organization to classification and archiving, it not only consumes a large amount of manpower but also suffers from low processing efficiency and high error rates. Image optimization of scanned paper documents often relies on simple filter processing, which is difficult to accurately remove noise and correct tilt, leading to difficulties in subsequent text recognition. Electronic documents have complex formats and lack a unified standardized processing mechanism, resulting in chaotic data storage and poor compatibility. In the information extraction and classification stages, traditional methods often rely on simple categorization based on keyword matching, failing to deeply analyze the semantic information of archives and struggling to cope with the complexity and diversity of archive content. This results in low classification accuracy, with many archives unable to be quickly matched to the corresponding directories. During the retrieval process, existing systems often suffer from problems such as large discrepancies between search results and user needs, and delayed response times due to fixed weight settings and a lack of dynamic optimization mechanisms, failing to meet the need for efficient and accurate information retrieval. In terms of security, traditional archival systems mostly employ fixed access control and static encryption, making it difficult to identify abnormal access behavior in real time. They also suffer from delayed responses to malicious attacks or unauthorized operations, posing security risks such as data leakage and tampering. Therefore, there is an urgent need for an archival digitization system that integrates advanced technologies to address the core pain points of existing technologies, such as low efficiency, poor accuracy, and insufficient security, and to promote the transformation of archival management towards intelligence, efficiency, and security. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a high-efficiency archival digitization system that integrates AI technology, thus solving the problems mentioned in the background section.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a high-efficiency digital archival processing system integrating AI technology, comprising: an intelligent acquisition and preprocessing module, an AI deep recognition and analysis module, an intelligent classification and archiving module, an AI retrieval and optimization module, and a security encryption module; The intelligent acquisition and preprocessing module is used to acquire multiple types of original archives, and completes the denoising, correction, and format standardization of archive images through AI algorithms, and extracts the basic visual feature set of the archives. The AI ​​deep recognition and analysis module is based on AI visual recognition and natural language processing technology. It performs accurate text recognition, fuzzy semantic completion and key information extraction on the pre-processed archives to generate structured data. The intelligent classification and archiving module extracts multi-dimensional semantic and relational features of archives through a deep learning model, constructs a dynamic classification system, and realizes automatic classification and intelligent archiving of archives. The AI ​​search optimization module dynamically adjusts search weights based on the correlation between user search behavior data and file features, generating accurate search results and optimizing response efficiency. The security encryption module uses AI abnormal behavior monitoring and dynamic encryption technology to achieve access control, transmission encryption and storage security protection of archival data, ensuring the integrity and security of digital archives.

[0005] Preferably, the intelligent acquisition and preprocessing module includes an archive acquisition unit, an intelligent preprocessing unit, and a basic feature extraction unit; The archive acquisition unit is used to acquire scanned copies of paper archives, photographed images of physical archives, and various electronic format archives through a high-definition scanner, image acquisition device, and electronic archive import interface to form an original archive dataset. The intelligent preprocessing unit is used to perform AI-driven preprocessing operations on the original archival dataset. The specific steps are as follows: S11. Parse the original archive dataset to distinguish between image archives and electronic document archives; S12. For image files, use adaptive median filtering to remove noise pixel by pixel and calculate the noise removal rate. ; S13. Detecting image tilt angle using Hough transform. It automatically rotates and corrects itself to a horizontal position; S14. Convert the corrected image files and electronic document files into the standard PDF / A format; S15. Substitute into the formula to calculate the sharpness optimization factor. ,like Return to step S2 and repeat the noise removal process until... ; Among them, the sharpness optimization coefficient The calculation formula is as follows: ; in, This represents the noise removal rate, ranging from 0 to 1. The more thorough the noise removal, the higher the rate. The closer to 1; This indicates the original tilt angle of the file, in radians, with a value range of 0. ; This is a preprocessing correction constant, ranging from 0.05 to 0.15, used to compensate for algorithm errors; The basic feature extraction unit is used to extract the basic visual features of the preprocessed archive image, including image resolution. Text area ratio Edge contour complexity This forms a set of basic visual features.

[0006] Preferably, the AI ​​deep recognition and analysis module includes an AI text recognition unit, a semantic analysis unit, and a structured data generation unit; The AI ​​text recognition unit employs an improved OCR algorithm, combined with a sharpness optimization coefficient from the basic visual feature set. Improve the accuracy of text recognition in blurry or faded documents. satisfy: ; in, To ensure a clear baseline recognition rate for archives, To compensate for the recognition rate of blurry archives; The semantic parsing unit, based on a BERT pre-trained model, performs semantic analysis on the recognized text, extracts key information from the archives, and calculates the accuracy rate of key information extraction. The formula is as follows: ; in, The semantic matching degree is represented by the cosine similarity between the keywords and the document's topic, with a value ranging from 0 to 1. A higher matching degree indicates a better match. The closer to 1; , These are the weights for recognition accuracy and semantic matching degree, respectively. This can be set by the user according to the file type; This is a semantic parsing correction constant, with a value range of 0.02-0.08; The structured data generation unit is used to generate standardized structured data, and the specific steps are as follows: S21. Receive key information output by the semantic parsing unit; S22. Verify the completeness of key information. If any information is missing, supplement the missing information based on the NLP semantic completion model. S23. According to the XML preset template, map the key information to the corresponding tag nodes to generate a draft of structured data; S24. Calculate the accuracy rate of key information extraction. ,like Return to the semantic parsing unit to extract again until... ; S25. Perform format verification on the initial draft of the structured data, and generate the final standardized structured data after passing the verification.

[0007] Preferably, the AI ​​text recognition unit calculates the text enhancement contrast for text enhancement processing of faded documents. The formula is as follows: ; In the formula, The original contrast of the text in the archive, with a value range of 0-255. The higher the contrast, the larger the value. This is the contrast enhancement factor, with a value ranging from 1.2 to 2.0. The smaller A larger value indicates a greater need for enhancement to accommodate files of different resolutions. To enhance the correction constant, with a value ranging from 10 to 20, this formula improves the distinction between faded text and the background, thereby optimizing recognition accuracy. .

[0008] Preferably, the intelligent classification and archiving module includes a multi-dimensional feature extraction unit, an AI classification model unit, and an automatic archiving unit; The multi-dimensional feature extraction unit is used to extract semantic features of archives from structured data: topic features. Formation time characteristics and business type characteristics Extracting morphological features from a set of basic visual features: text density and page layout features ; The AI ​​classification model unit uses a convolutional neural network based on transfer learning to construct the classification model, taking multi-dimensional features as input and combining key information to extract accuracy. Calculate classification confidence The formula is as follows: ; in, This is a topic feature, with a value ranging from 0 to 1. The higher the topic matching degree, the larger the value. To form a time feature, the value ranges from 0 to 1, and the higher the degree of fit with the time range of the category directory, the larger the value. This is a business type feature, with a value ranging from 0 to 1. The higher the business type matching degree, the larger the value. This represents the text density, with a value ranging from 0 to 1. The higher the proportion of text, the larger the value. This is a page layout feature, with a value ranging from 0 to 1. The higher the degree of fit with the category layout template, the larger the value. These are the weight coefficients for each feature. It supports dynamic adjustments based on the needs of record management; The classification correction constant has a value range of 0.03-0.09. The automatic archiving unit is used to automatically classify and archive documents. The specific steps are as follows: S31. Set the classification confidence threshold. The value ranges from 0.9 to 0.95 and can be adjusted according to the accuracy requirements of record management. S32. Calculate the classification confidence score. With threshold Perform a comparison; S33, if It automatically matches the corresponding category directory and generates the archive path; S34. Establish a cross-index between the archives and the associated directories, and record the index association relationships; S35. Bind the structured data to the original archive files, store them in the corresponding archiving path, and update the archiving status of the archive management database.

[0009] Preferably, the subject features The metric calculation is performed based on keyword matching, using the following formula: ; in, The number of keywords to match a predefined thesaurus in the archive. The total number of keywords in the preset thesaurus. This represents the uniformity of keyword distribution within the archives, ranging from 0.6 to 1.0. A more uniform distribution indicates a higher uniformity. The closer it is to 1.0; the more characteristic this topic is. As classification confidence The core input parameters.

[0010] Preferably, the AI ​​retrieval optimization module includes an intelligent retrieval unit, a dynamic weight adjustment unit, and a result sorting unit; The intelligent retrieval unit supports keyword retrieval, semantic retrieval, and multi-condition combination retrieval. After receiving a user's retrieval request, it extracts the matching degree between the retrieval keywords and the multi-dimensional features of the archive. ; The dynamic weight adjustment unit is based on user historical search feedback and classification confidence. Calculate the retrieval response efficiency The formula is as follows: ; in, This represents the index update frequency, measured in times per hour, with a value range of 0.5-5. To retrieve the matching degree between keywords and file features, the value ranges from 0 to 1, with a larger value indicating a higher matching degree; , These are the weights for index frequency and matching degree, respectively. ; The correction constant is used for retrieval, with a value range of 0.04-0.12; The result sorting unit is used to prioritize the search results, and the specific steps are as follows: S41. Receive the retrieval response efficiency output by the dynamic weight adjustment unit. ; S42. Extract the matching degree of the search results. Access frequency Update time ; S43. Constructing a ranking weight model: ; In the formula, Indicates the current time. Indicates the file update time; S44, according to The search results are sorted in descending order, and the first 10 results are displayed first. S45. Record user clicks on the sorting results and update the access frequency. And a database of user search behavior.

[0011] Preferably, the security encryption module includes an access control unit, a dynamic encryption unit, and an AI anomaly monitoring unit; The permission management unit is based on the RBAC model and combines AI recognition and account behavior characteristics to assign hierarchical access permissions to different users. The dynamic encryption unit uses the AES-256 algorithm to encrypt the data transmission process of the archives, and uses fragmented encrypted storage for the stored data. The encryption key is dynamically generated by the hardware encryption module. The AI ​​anomaly monitoring unit is used to monitor and handle abnormal access behavior. The specific steps are as follows: S51. Set abnormal access risk threshold The value ranges from 0.7 to 0.85; S52. Collect user access behavior data in real time and calculate the actual access frequency. Permission matching degree ; S53. Substitute into the formula to calculate the abnormal access risk value. The formula is as follows: ; in, This represents the actual access frequency, measured in times per minute. This is the user's historical average access frequency, measured in times per minute, calculated based on data from the past 30 days. This represents the permission matching degree, with a value ranging from 0 to 1. It indicates that the operation permission exactly matches the user-assigned permission. ; This is a risk assessment correction constant, with a value ranging from 0.01 to 0.07. S54, if This allows the access operation to continue and records the access log; S55, if Automatically triggers three levels of alerts: Level 1 alert Restrict export operations; Level 2 warning Editing privileges are frozen, only browsing privileges are retained; Level 3 warning. Force logout, account ban for 1 hour, and send a warning notification to the administrator; S56. After the warning is lifted, record the abnormal access event to the security log and update the user behavior feature database.

[0012] Preferably, the training process of the AI ​​classification model unit includes: Step 1: Collect historical archive data, label it with classification tags, and combine it with a multi-dimensional feature set to form a training dataset; Step 2: Divide the training dataset into training and test sets in a 7:3 ratio, input them into the initial CNN model for training, and calculate the classification accuracy for each training round. ; Step 3: Optimize the model based on transfer learning by introducing pre-trained text classification model parameters and adjusting the weight coefficients. ~ Calculate the iteration coefficients of the model optimization The formula is as follows: ; in, This represents the classification accuracy from the previous training round, with a value ranging from 0 to 1. This is the iteration step size, ranging from 0.05 to 0.15. This is an iterative correction constant, with a value range of 0.02-0.06; Step 4: When Stable within the preset range, When the accuracy is ≥0.95 and the test set accuracy is ≥98%, training is stopped, and the optimized model is used as the final classification model. Step 5: Collect newly archived data quarterly, incrementally train the final classification model, and update the weight coefficients. - Ensure that the model is adapted to the new file type.

[0013] Preferably, the dynamic weight adjustment unit also supports adjustments based on retrieval response efficiency. The index optimization mechanism involves the following steps: S61. Set the search response efficiency threshold. The value ranges from 0.85 to 0.9; S62. Real-time monitoring of search response efficiency ,like Maintain the current weighting coefficients , ; S63, if Extract frequently searched keywords and improve their index priority; S64. Calculate the weight adjustment coefficient. The formula is as follows: ; in, To retrieve user satisfaction scores, the value ranges from 0 to 1 and is calculated based on user feedback ratings. The feature contribution is derived from the matching degree of the search results, and its value ranges from 0 to 1. , These are the weights for satisfaction and contribution, respectively. ; This is a weighting correction constant, with a value ranging from 0.03 to 0.09; S65, according to Adjust the weighting coefficients: , ,make sure ; S66. Update the feature weights of the search index and recalculate the search response efficiency. until ; S67. Record the weight adjustment records and synchronize them to the index optimization log to provide data support for subsequent model iterations.

[0014] This invention provides a high-efficiency archival digitization system that integrates AI technology, which has the following beneficial effects: 1. Improve the efficiency and standardization of archival processing: The system achieves automated collection and unified processing of various types of archives through the intelligent collection and preprocessing module. With the help of AI algorithms, it completes operations such as noise reduction, correction, and format standardization, eliminating the dependence on manual operation and significantly shortening the archive preprocessing cycle. At the same time, it converts various types of archives into a unified standard format, solving the problems of chaotic storage and poor compatibility of traditional archives, and laying a standardized foundation for subsequent data processing.

[0015] 2. Ensuring the accuracy of archival information extraction: The AI ​​deep recognition and analysis module combines visual recognition and natural language processing technologies, and optimizes the recognition algorithm for special archives such as those that are blurry or faded, to achieve accurate text recognition and semantic completion; through in-depth extraction and verification of key archival information, the error rate of information extraction is effectively reduced, and the generated structured data provides high-quality data support for subsequent classification, archiving and retrieval.

[0016] 3. Achieve intelligent and dynamic adaptation of archive classification and archiving: The intelligent classification and archiving module extracts multi-dimensional features of archives based on a deep learning model and constructs a dynamic classification system. It can automatically match the classification catalog according to the characteristics of the archive content, which significantly improves the classification accuracy compared with the traditional keyword matching method. The system supports dynamic adjustment of weight coefficients, which can adapt to the archive management needs of different industries and different scenarios, and enhance the flexibility and adaptability of classification and archiving.

[0017] 4. Optimize search experience and response efficiency: The AI ​​search optimization module dynamically adjusts search weights based on the correlation between user search behavior data and file features. By constructing a comprehensive ranking model, it makes search results more relevant to users' actual needs. At the same time, the index optimization mechanism improves search response efficiency in real time, solving the problems of lag and large result deviation in traditional systems, and helping users quickly obtain target files.

[0018] 5. Enhance the security protection capabilities of archival data: The security encryption module integrates hierarchical access control, dynamic encryption and AI anomaly monitoring technology to form a comprehensive security protection system; through AI algorithms, it identifies abnormal access behavior in real time and triggers hierarchical warnings, which is more proactive and timely than traditional static security protection methods, effectively preventing risks such as data leakage and tampering, and ensuring the integrity and security of archival data.

[0019] In summary, this system, through the deep integration of AI technology and archival management operations, comprehensively solves the efficiency, accuracy, and security issues in existing archival digitization processes, reduces archival management costs, enhances the utilization value of archives, and provides reliable technical support for archival digitization management in various fields. Attached Figure Description

[0020] Figure 1This is a schematic diagram of the process of a high-efficiency digital archival processing system integrating AI technology as described in this invention; Figure 2 This is a block diagram illustrating the principle of an efficient archive digitization system integrating AI technology, as described in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1-2 As shown, the present invention provides a technical solution: a high-efficiency digital archival processing system integrating AI technology, comprising: an intelligent acquisition and preprocessing module, an AI deep recognition and analysis module, an intelligent classification and archiving module, an AI retrieval and optimization module, and a security encryption module; The intelligent acquisition and preprocessing module is used to acquire various types of original archives (paper archives, electronic scans, and image archives). It uses AI algorithms to denoise, correct skew, and standardize the format of the archive images, extracting the basic visual features of the archives. The AI ​​deep recognition and analysis module, based on AI visual recognition and natural language processing technology, performs accurate text recognition, fuzzy semantic completion, and key information extraction on the preprocessed archives, generating structured data. The intelligent classification and archiving module uses a deep learning model to extract multi-dimensional semantic and relational features of the archives, constructing a dynamic classification system to achieve automatic classification and intelligent archiving. The AI ​​retrieval optimization module dynamically adjusts retrieval weights based on the correlation between user retrieval behavior data and archive features, generating accurate retrieval results and optimizing response efficiency. The security and encryption module uses AI abnormal behavior monitoring and dynamic encryption technology to achieve access control, transmission encryption, and storage security protection for archive data, ensuring the integrity and security of digital archives.

[0023] More specifically, the intelligent acquisition and preprocessing module includes an archive acquisition unit, an intelligent preprocessing unit, and a basic feature extraction unit; The archive acquisition unit is used to acquire scanned copies of paper archives, photographed images of physical archives, and various electronic archive formats (PDF, JPG, DOC) through a high-definition scanner, image acquisition equipment, and electronic archive import interface to form the original archive dataset. The intelligent preprocessing unit is used to perform AI-driven preprocessing operations on the original archival dataset. The specific steps are as follows: S11. Parse the original archive dataset to distinguish between image archives and electronic document archives; S12. For image files, use adaptive median filtering to remove noise pixel by pixel and calculate the noise removal rate. ; S13. Detecting image tilt angle using Hough transform. It automatically rotates and corrects itself to a horizontal position; S14. Convert the corrected image files and electronic document files into the standard PDF / A format; S15. Substitute into the formula to calculate the sharpness optimization factor. ,like Return to step S2 and repeat the noise removal process until... ; Among them, the sharpness optimization coefficient The calculation formula is as follows: ; in, This represents the noise removal rate, ranging from 0 to 1. The more thorough the noise removal, the higher the rate. The closer to 1; This indicates the original tilt angle of the file, in radians, with a value range of 0. ; This is a preprocessing correction constant, ranging from 0.05 to 0.15, used to compensate for algorithm errors; The basic feature extraction unit is used to extract basic visual features of the preprocessed archival image, including image resolution. Text area ratio Edge contour complexity This forms a set of basic visual features.

[0024] The intelligent data acquisition and preprocessing module, through the collaborative work of the archive acquisition unit, the intelligent preprocessing unit, and the basic feature extraction unit, achieves fully compatible acquisition and standardized preprocessing of multiple types of archives. Its role is reflected in three aspects: The archive acquisition unit breaks down the barriers between paper, electronic, and physical archives by using high-definition scanning, image capture, and multi-format electronic archive import interfaces, forming a unified original archive dataset and solving the problems of single and poor compatibility of traditional acquisition methods. The intelligent preprocessing unit uses AI algorithms as its core, employing adaptive median filtering for noise reduction, Hough transform for bias correction, and PDF / A format unification, combined with sharpness optimization coefficients. (must meet) The closed-loop verification (the formula for calculating the sharpness optimization factor is) is as follows: This ensures that the archive images are free from noise interference, angular deviation, and format specifications, significantly reducing manual preprocessing costs and shortening the preprocessing cycle. Image resolution extracted by the basic feature extraction unit Text area ratio Edge contour complexity Visual features such as these provide high-quality data support for the subsequent AI deep recognition and analysis module's text recognition, semantic extraction, and intelligent classification and archiving module's feature matching, laying the foundation for accurate processing of the entire process of archive digitization.

[0025] In this example, taking the digitization of enterprise personnel files as an example, the intelligent acquisition and preprocessing module is used for file processing: The file acquisition unit uses an EPSONDS-780N high-definition scanner to acquire scanned copies of paper personnel files (such as labor contracts and employment registration forms), and uses an industrial camera with a resolution of 48 million pixels to capture images of physical files (such as employee ID photos and certificates of honor). Employee electronic files (PDF performance reports and DOC resumes) are imported through the system interface, resulting in a total of 1000 files of different types forming the original file dataset. The intelligent preprocessing unit first executes step S11, parsing out 620 image files (including 450 scanned copies and 170 captured images) and 380 electronic document files. Then, step S12 is executed, using a 3×3 window adaptive median filter to denoise the image files pixel-by-pixel, calculating the noise removal rate. Then, step S13 was executed, and Hough transform was used to detect that 85 of the files were tilted, with the maximum tilt angle being... (i.e., 15°) The system automatically rotates and corrects the skew to horizontal; then, step S14 is executed to convert all files to PDF / A-2a format; finally, step S15 is executed to retrieve the preprocessing correction constant. Substitute into the sharpness optimization coefficient formula Calculation yields ,at this time Returning to step S12, adjust the filtering window to 5×5 and perform noise reduction again. The second calculation yields... Substituting into the formula, we get Still not satisfied; continue adjusting the filter window to 7×7, finally obtaining... Substitute the values ​​into the formula again to calculate: ,satisfy The requirements; the basic feature extraction unit then extracts the basic visual features of the preprocessed archives, including the scanned copies of labor contracts. , , Employee ID photo , , This forms a basic set of visual features, which can then be used by subsequent AI deep recognition and analysis modules and intelligent classification and archiving modules.

[0026] More specifically, the AI ​​deep recognition and analysis module includes an AI text recognition unit, a semantic analysis unit, and a structured data generation unit; The AI ​​text recognition unit employs an improved OCR algorithm, incorporating a sharpness optimization coefficient from a set of basic visual features. Improve the accuracy of text recognition in blurry or faded documents. satisfy: ; in, The baseline recognition rate for clear archives is ≥99% (preset value). To compensate for the recognition rate of fuzzy archives (preset value ≥95%); The semantic parsing unit, based on a BERT pre-trained model, performs semantic analysis on the recognized text, extracts key archival information (including archival number, creation date, subject keywords, and related entities), and calculates the accuracy of key information extraction. The formula is as follows: ; in, The semantic matching degree is represented by the cosine similarity between the keywords and the document's topic, with a value ranging from 0 to 1. A higher matching degree indicates a better match. The closer to 1; , These are the weights for recognition accuracy and semantic matching degree, respectively. This can be set by the user according to the file type; This is a semantic parsing correction constant, with a value range of 0.02-0.08; The structured data generation unit is used to generate standardized structured data. The specific steps are as follows: S21. Receive key information output by the semantic parsing unit (file number, creation date, subject keywords, associated subject); S22. Verify the completeness of key information (no missing required fields). If missing information exists, supplement the missing information based on the NLP semantic completion model. S23. According to the XML preset template, map the key information to the corresponding tag nodes to generate a draft of structured data; S24. Calculate the accuracy rate of key information extraction. ,like Return to the semantic parsing unit to extract again until... ; S25. Perform format verification on the initial draft of the structured data, and generate the final standardized structured data after passing the verification.

[0027] The AI ​​deep recognition and analysis module, through the collaborative operation of the AI ​​text recognition unit, semantic analysis unit, and structured data generation unit, fundamentally addresses the pain points of traditional document recognition, such as low accuracy in blurry or faded documents, inaccurate extraction of key information, and inconsistent data formats. The AI ​​text recognition unit utilizes an improved OCR algorithm combined with a clarity optimization coefficient during the preprocessing stage. It dynamically adapts to the recognition needs of blurry and faded documents, overcoming the limitation of traditional OCR's high dependence on document quality and improving text recognition accuracy; the semantic parsing unit deeply mines text semantics based on the BERT pre-trained model, extracting key information to improve accuracy. (The formula is) The quantitative calculation and adjustable weight design of the data ensure the accuracy of extracting core information such as file number and creation date. The structured data generation unit generates standardized XML-formatted structured data through full-process control of "completeness verification, semantic completion, format mapping, accuracy closed-loop verification and format verification". This not only solves the problem of fragmented traditional archival data, but also provides structured data support for feature matching of the subsequent intelligent classification and archiving module and accurate retrieval of the AI ​​retrieval optimization module, realizing the transformation of archival information from identification to usability.

[0028] In this example, taking the digitization of official documents (including blurred and faded 2018 meeting minutes and electronically formatted 2023 approval documents) as an example, the AI ​​deep recognition and analysis module is applied as follows: First, the AI ​​text recognition unit calls the document clarity optimization coefficient output from the preprocessing stage. (Meeting Minutes) Approval documents Set a clear file benchmark recognition rate. fuzzy file compensation recognition rate Substitute into the formula Calculation: Accuracy of text recognition in meeting minutes: ; The approved documents are calculated as follows: ; The semantic parsing unit analyzes and recognizes text based on the BERT model, and sets weights. (Identification accuracy weight) (Semantic matching degree weight), semantic parsing correction constant The semantic matching degree of the meeting minutes was calculated. Approval documents Substitute into the formula ; Meeting Minutes ; Approval documents ; All meet ; Subsequently, the structured data generation unit executes step S21 to receive key information (the meeting minutes include the file number "Politics and Meeting

[2018] No. 15", the theme keyword "urban renewal", and the approval document includes the formation date "2023-06-12"); in S22, it is verified that the meeting minutes are missing the "formation date", and it is supplemented as "2018-09-28" through the NLP semantic completion model in combination with the document signature information; in S23, the information is mapped to the tag nodes according to the XML preset template to generate a preliminary structured data; in S24, recalculation still meets the requirements; in S25, through format verification (tag closing, data type matching), standardized XML structured data is finally generated for use by the subsequent intelligent classification and archiving module.

[0029] More specifically, the AI text recognition unit performs text enhancement processing on faded files and calculates the text enhancement contrast , and the formula is as follows: ; In the formula, is the original contrast of the file text, with a value range of 0-255, and the higher the contrast, the larger the value; is the contrast enhancement coefficient, with a value range of 1.2-2.0, the smaller it is, the larger the value is, which is used to adapt to the enhancement requirements of files with different clarity; is the enhancement correction constant, with a value range of 10-20. Through this formula, the distinction between faded text and the background is improved, and thus the recognition accuracy is optimized .

[0030] The AI text recognition unit aims at the core pain points of low contrast between text and background and blurred edges in faded files, which lead to a decrease in OCR recognition accuracy. Through the dynamic quantization calculation of text enhancement contrast to achieve precise enhancement, and the core effect is reflected in the following points: Based on the original contrast [[ID=3--]] (0-255) of the file text as the basic data, combined with the clarity optimization coefficient and the contrast enhancement coefficient [[ID=3--]] (1.2-2.0) of the linkage adaptation design (when is smaller, that is, the file clarity is worse and the fading is more serious, the value is larger to ensure the enhancement strength for files with a high degree of fading), and the enhancement correction constant (10-20) is introduced to compensate for the error in the low-contrast scenario. Through the formula Scientifically calculated enhancements improve text contrast, effectively widening the grayscale difference between faded text and the background, and strengthening text edge contours. This avoids the problems of "over-enhancement leading to text distortion" or "insufficient enhancement failing to distinguish text from background" found in traditional fixed-parameter enhancement methods. This enhancement process provides highly discriminative text image input for subsequent improved OCR algorithms, directly addressing the key obstacle of blurry text recognition in faded archives and significantly optimizing text recognition accuracy. This provides a reliable textual basis for the semantic parsing unit to accurately extract key information from archives (such as archive number and creation date), ensuring the accuracy and stability of the entire process of archive digitization in low-quality faded archive scenarios.

[0031] More specifically, the intelligent classification and archiving module includes a multi-dimensional feature extraction unit, an AI classification model unit, and an automatic archiving unit; The multi-dimensional feature extraction unit is used to extract semantic features of archives from structured data: topic features. Formation time characteristics and business type characteristics Extracting morphological features from a set of basic visual features: text density and page layout features ; The AI ​​classification model unit uses a convolutional neural network based on transfer learning to construct the classification model. It takes multi-dimensional features as input and combines key information to extract accuracy. Calculate classification confidence The formula is as follows: ; in, This is a topic feature, with a value ranging from 0 to 1. The higher the topic matching degree, the larger the value. To form a time feature, the value ranges from 0 to 1, and the higher the degree of fit with the time range of the category directory, the larger the value. This is a business type feature, with a value ranging from 0 to 1. The higher the business type matching degree, the larger the value. This represents the text density, with a value ranging from 0 to 1. The higher the proportion of text, the larger the value. This is a page layout feature, with a value ranging from 0 to 1. The higher the degree of fit with the category layout template, the larger the value. These are the weight coefficients for each feature. It supports dynamic adjustments based on the needs of record management; The classification correction constant has a value range of 0.03-0.09. The automatic archiving unit is used to automatically classify and archive documents. The specific steps are as follows: S31. Set the classification confidence threshold. The value ranges from 0.9 to 0.95 and can be adjusted according to the accuracy requirements of record management. S32. Calculate the classification confidence score. With threshold Perform a comparison; S33, if It automatically matches the corresponding category directory and generates the archive path; S34. Establish a cross-index between the archives and the associated directories, and record the index association relationship (including archive number, directory ID, and association weight). S35. Bind the structured data to the original archive files, store them in the corresponding archiving path, and update the archiving status of the archive management database.

[0032] The intelligent classification and archiving module addresses the pain points of traditional archive classification, which relies on manual keyword matching, suffers from low accuracy due to its single-dimensional approach, and has low archiving efficiency. It achieves a breakthrough through a collaborative design of "multi-dimensional feature extraction, intelligent model classification, and automatic closed-loop archiving." The effect is manifested in: the multi-dimensional feature extraction unit simultaneously captures the semantic features of the archives (themes...). Formation time Business Type ) and morphological features (character density) Page layout This avoids the one-sidedness of single-feature classification; the AI ​​classification model unit is based on a convolutional neural network using transfer learning, combining key information to improve accuracy. With dynamically adjustable feature weights - Classification confidence is calculated using a formula. To ensure the accuracy and adaptability of classification results (e.g., government archives can be improved). Weighting, company profiles can be improved Weight); Automatic archiving unit through With threshold The comparison of (0.9-0.95) triggers a closed-loop operation, automatically generating the archiving path, establishing a cross-index and binding data storage, completely eliminating manual intervention. This not only solves the problem of "incorrect and missing archiving" in traditional archiving, but also significantly shortens the archiving cycle. At the same time, the cross-index design lays the foundation for efficient retrieval by the subsequent AI retrieval optimization module, realizing the "intelligent, accurate and automated" classification and archiving of archives.

[0033] In this example, taking the digital classification and archiving of corporate financial records (including Q1 2024 travel expense reimbursement forms and the 2023 annual audit report) as an example, the intelligent classification and archiving module is applied as follows: First, the multi-dimensional feature extraction unit extracts semantic features from the structured data; the thematic features of the travel expense reimbursement forms... (Matching the thesaurus of "expense reimbursement") and time characteristics of formation (Matching the time range of the "2024Q1" category directory), business type characteristics (Matching the "Expense Accounting" business type), extract morphological features from the basic visual feature set; text density. (Text occupies 80% of the page), Page layout features (Matches the layout template of the "Expense Reimbursement Form"); Audit Report (Matching the theme "Annual Audit") (In line with the "2023" timeframe) (Matching the "Financial Audit" business type) , .

[0034] Secondly, feature weights are set for each AI classification model unit. ( ), ( ), ( ), ( ), ( () ), accuracy of key information extraction (Both types of files meet the requirements) ), classification correction constant Substitute the values ​​into the formula to calculate the classification confidence level: Travel expense reimbursement form The calculation is as follows: ; Audit Report The calculation is as follows: ; Finally, the automatic archiving unit executes the following steps: S31. Set the classification confidence threshold. S32 comparison yielded two types of files. S33 Automatically matches the directories “Financial Archives - Expense Accounting - 2024Q1” and “Financial Archives - Audit Report - 2023”, generating the archiving paths “ / Group Finance / 2024Q1 / Expense Reimbursement / ” and “ / Group Finance / 2023 / Audit Report / ”; S34 Creates a cross-index (archive number “C202403001” corresponds to directory ID “FY-FYHS-2024Q1”, association weight 0.9586; archive number “C202312008” corresponds to directory ID “FY-SJBG-2023”, association weight 0.9514); S35 Binds the structured data and the original expense reimbursement form / audit report file to the corresponding path and updates the database archiving status to “archived”.

[0035] More specifically, thematic features The metric calculation is performed based on keyword matching, using the following formula: ; in, The number of keywords to match a predefined thesaurus in the archive. The total number of keywords in the preset thesaurus. This represents the uniformity of keyword distribution within the archives, ranging from 0.6 to 1.0. A more uniform distribution indicates a higher uniformity. The closer it is to 1.0; the more characteristic this topic is. As classification confidence The core input parameters.

[0036] The quantitative calculation design addresses the problem of misjudging subject relevance caused by traditional methods that rely solely on the number of matching keywords and ignore the rationality of keyword distribution. It uses a pre-defined thesaurus as a benchmark and... (The number of keywords matching the subject in the archive) and The ratio of (total number of keywords in the thesaurus) first quantifies the basic matching degree of keywords to ensure the standardization of theme judgment; then, the evenness of keyword distribution is introduced. (0.6-1.0) This parameter corrects the bias of "dense local keywords but weak overall relevance to the theme" (e.g., a personnel file only mentions the keyword "finance" in the footnotes). It will decrease due to concentrated distribution, thus pulling down the overall level. ),make Through formula Achieving dual-dimensional quantification of "match quantity and distribution rationality" results in the final output. (Values ​​0-1) can more objectively and accurately reflect the true fit between the archives and the preset theme; as a classification confidence level The core input parameter, The precise quantification directly provides reliable thematic dimension basis for AI classification model units, avoiding the problem of misclassification of archives due to thematic judgment bias, significantly improving the thematic matching accuracy of intelligent classification and archiving modules for different types of archives (such as government documents, corporate contracts, and scientific research reports), and ensuring the consistency between classification results and actual archive content.

[0037] More specifically, the AI ​​search optimization module includes an intelligent search unit, a dynamic weight adjustment unit, and a result ranking unit; The intelligent search unit supports keyword search, semantic search, and multi-condition combination search. After receiving a user's search request, it extracts the matching degree between the search keywords and the multi-dimensional features of the archive. ; The dynamic weight adjustment unit is based on user historical search feedback (satisfaction). ) and classification confidence Calculate the retrieval response efficiency The formula is as follows: ; in, This represents the index update frequency, measured in times per hour, with a value range of 0.5-5. To retrieve the matching degree between keywords and file features, the value ranges from 0 to 1, with a larger value indicating a higher matching degree; , These are the weights for index frequency and matching degree, respectively. ; The correction constant is used for retrieval, with a value range of 0.04-0.12; The results sorting unit is used to prioritize the search results. The specific steps are as follows: S41. Receive the retrieval response efficiency output by the dynamic weight adjustment unit. ; S42. Extract the matching degree of the search results. Access frequency Update time ; S43. Constructing a ranking weight model: ; In the formula, Indicates the current time. Indicates the file update time; S44, according to The search results are sorted in descending order, and the first 10 results are displayed first. S45. Record user clicks on the sorting results and update the access frequency. And a database of user search behavior.

[0038] The AI ​​search optimization module addresses the pain points of traditional document retrieval, such as limited search methods, fixed weights leading to significant result bias, delayed response times, and sorting that doesn't align with user needs. It achieves breakthroughs through a comprehensive design encompassing "multi-mode retrieval, dynamic weight optimization, intelligent sorting, and a feedback loop." Its benefits are manifested in: the intelligent search unit supports keyword, semantic, and multi-condition combined searches, breaking the limitations of traditional single-search methods and adapting to diverse user search habits; the dynamic weight adjustment unit adjusts based on index update frequency. (0.5-5 times / hour) and keyword matching degree (0-1), combined with dynamically adjustable weights , ( ), through formula ( The retrieval response efficiency is quantified by a retrieval correction constant ranging from 0.04 to 0.12. This ensures that retrieval takes into account both index timeliness and matching accuracy; the result sorting unit is constructed... Model( ), integrating response efficiency, matching degree, and access frequency and the timeliness of archives ( For the current time, To avoid the bias of sorting by a single dimension (and to update by recording user click feedback), updates are also made based on the update time. By combining with behavioral databases, a closed loop of "retrieval, sorting, feedback and optimization" is formed, making subsequent search results more relevant to users' actual needs. Ultimately, this achieves "flexible search methods, dynamic weighting, accurate results and personalized experience," significantly reducing the time cost for users to obtain target profiles.

[0039] In an example, taking a networked enterprise HR department's search for "new employee onboarding files in Q1 2024" as an example, the AI ​​search optimization module is applied as follows: First, the intelligent search unit receives the user's multi-condition combined search request (keywords "new employee onboarding" and time range "2024.01.01-2024.03.31"), and extracts the matching degree between the search keywords and the multi-dimensional features of the files. (Files matching characteristics such as "new employee", "employment registration", and "Q1 2024") Partially matched files (between 0.75 and 0.88).

[0040] Secondly, the dynamic weight adjustment unit calls upon users' historical search feedback (satisfaction level). ) and confidence level of archival classification (Target File) ), set the index update frequency Times / hour (HR file index updated twice in the last 24 hours), weight (Index frequency weight) (Matching weight), retrieval correction constant Substitute the values ​​into the formula to calculate the retrieval response efficiency: .

[0041] Finally, the result sorting unit executes the following steps: S41 receives... S42 extracts the search results. (0.92, 0.88, 0.85, etc.), access frequency (Having been visited by the HR department 5 times in the past 30 days) Visited twice Update time (Updated 2024.03.20) Updated 2024.01.15 Current time S43 Build Model calculation: , , For example, the archives; ; but The calculation is as follows: ; Other files are calculated using the same logic. Between 1.52 and 1.78; S44 according to Sort in descending order and take the first 10 items (including the above). The archives are prioritized for display; S45 records the user's feedback on the first 3 results clicked, and displays the corresponding archives. The values ​​were updated to 6, 3, and 4 and synchronized to the user search behavior database to provide data support for subsequent search weight adjustments.

[0042] More specifically, the security encryption module includes an access control unit, a dynamic encryption unit, and an AI anomaly detection unit; The access control unit is based on the RBAC (Role-Based Access Control) model, combined with AI recognition and account behavior characteristics, to assign hierarchical access permissions (browsing permission, editing permission, export permission) to different users. The dynamic encryption unit uses the AES-256 algorithm to encrypt the data transmission process of the archives, and uses fragmented encrypted storage for the stored data. The encryption key is dynamically generated by the hardware encryption module. The AI ​​anomaly detection unit is used to monitor and handle abnormal access behavior. The specific steps are as follows: S51. Set abnormal access risk threshold The value ranges from 0.7 to 0.85; S52. Collect user access behavior data in real time and calculate the actual access frequency. Permission matching degree ; S53. Substitute into the formula to calculate the abnormal access risk value. The formula is as follows: ; in, This represents the actual access frequency, measured in times per minute. This is the user's historical average access frequency, measured in times per minute, calculated based on data from the past 30 days. This represents the permission matching degree, with a value ranging from 0 to 1. It indicates that the operation permission exactly matches the user-assigned permission. ; This is a risk assessment correction constant, with a value ranging from 0.01 to 0.07. S54, if This allows the access operation to continue and records the access log; S55, if Automatically triggers three levels of alerts: Level 1 alert Restrict export operations; Level 2 warning Editing privileges are frozen, only browsing privileges are retained; Level 3 warning. Force logout, account ban for 1 hour, and send a warning notification to the administrator; S56. After the warning is lifted, record the abnormal access event to the security log and update the user behavior feature database.

[0043] The security encryption module addresses the core pain points of traditional archive security protection, such as crude access control, static encryption methods, and lagging anomaly detection. It constructs a closed-loop security system through a three-dimensional collaborative design of "hierarchical access control, end-to-end dynamic encryption, and AI real-time anomaly defense." The core effects are reflected in the following: The access control unit is based on the RBAC model and integrates AI recognition and account behavior characteristics (such as frequently used login devices and operating habits) to achieve multi-dimensional identity verification of "roles, biometrics, and behavioral characteristics," accurately allocating hierarchical permissions such as browsing, editing, and exporting, and avoiding the risk of permission misuse caused by single account authentication; The dynamic encryption unit uses the AES-256 algorithm to ensure the security of data transmission. Combined with fragmented encrypted storage and hardware-generated dynamic keys, it solves the problems of easy leakage of traditional fixed keys and easy mass theft of stored data, achieving end-to-end encryption protection for transmission and storage.

[0044] The AI ​​anomaly detection unit calculates the risk value of abnormal access in real time. ; The formula is The actual access frequency Permission matching degree Compared with historical data Correlation, in conjunction with a risk threshold of 0.7-0.85. A three-tiered early warning mechanism enables real-time identification and tiered handling of abnormal behavior, avoiding the lag of traditional manual monitoring. The three mechanisms work together to form a full-process security protection system of "precise authorization before the event, dynamic encryption during the event, and real-time defense after the event," effectively preventing risks such as leakage, tampering, and unauthorized access to archival data, and ensuring the integrity and security of digital archives.

[0045] More specifically, the training process for an AI classification model unit includes: Step 1: Collect historical archive data (no less than 10,000 records), label them with classification tags, and combine them with a multi-dimensional feature set to form a training dataset; Step 2: Divide the training dataset into training and test sets in a 7:3 ratio, input them into the initial CNN model for training, and calculate the classification accuracy for each training round. ; Step 3: Optimize the model based on transfer learning by introducing pre-trained text classification model parameters and adjusting the weight coefficients. ~ Calculate the iteration coefficients of the model optimization The formula is as follows: ; in, This represents the classification accuracy from the previous training round, with a value ranging from 0 to 1. This is the iteration step size, ranging from 0.05 to 0.15. This is an iterative correction constant, with a value range of 0.02-0.06; Step 4: When Stable within the preset range, When the accuracy is ≥0.95 and the test set accuracy is ≥98%, training is stopped, and the optimized model is used as the final classification model. Step 5: Collect newly archived data (no less than 2000 records) every quarter, incrementally train the final classification model, and update the weight coefficients. - Ensure that the model is adapted to the new file type.

[0046] The training process of the AI ​​classification model unit addresses the pain points of traditional CNN models, such as high data dependence, poor adaptability to new types of archives, and difficulty in consistently achieving classification accuracy. Its effects are manifested in the following ways: by limiting the amount of historical archive data (no less than 10,000 records) and labeling them with classification tags, a high-quality training dataset is constructed using multi-dimensional features (semantic and morphological features), laying a data foundation for accurate model training; a 7:3 ratio is used to divide the training and testing sets, ensuring sufficient training data while objectively verifying the model's generalization ability, avoiding the problems of "overfitting during training and distortion during testing"; transfer learning is introduced to reuse pre-trained text classification model parameters, significantly reducing the time cost and data requirements for training the model from scratch, while dynamically adjusting the weight coefficients... ~ This allows the model to adapt to the differences in contributions from multi-dimensional features of archives; training is stopped when the iteration coefficient η ≥ 0.95 and the test set accuracy ≥ 98% is used to quantitatively control the model optimization effect and ensure that the model classification accuracy meets the standard; incremental training and updates are performed every quarter based on no less than 2000 new archive data. ~ This addresses the shortcomings of traditional models that are "static and inflexible, unable to adapt to new types of archives," maintaining high classification accuracy and ultimately providing stable and reliable classification model support for the intelligent classification and archiving module, ensuring classification confidence. To ensure the accuracy of calculations and avoid misclassification or omission of files.

[0047] Example: Taking the training of a classification model for three types of archives in an archive—documentary archives, scientific and technological archives, and accounting archives—as an example, the training process of this AI classification model unit is as follows: Step 1: Collect 15,000 historical archive data entries from the archive over the past 10 years (6,000 documentary archive entries, 4,500 scientific and technological archive entries, and 4,500 accounting archive entries). After manually labeling the corresponding classification tags, compare them with the multi-dimensional features (thematic features) of each archive. Formation time characteristics Business type characteristics Text density Page layout features Step 2: Divide the dataset into a training set of 10,500 records (4,200 documents, 3,150 technical documents, and 3,150 accounting documents) and a test set of 4,500 records in a 7:3 ratio. Input the training set into the initial CNN model (using the VGG-16 lightweight architecture) for training. Calculate the classification accuracy after the first round of training. Step 3: Based on transfer learning, introduce the parameters of the pre-trained "Chinese Archive Text Classification BERT Model" (training corpus is the library's open archive corpus), and initially set the weight coefficients. =0.3 ( ), =0.2 ( ), =0.25 ( ), =0.1 ( ), =0.15 ( (The accuracy rate from the previous training round) Iteration step size Iterative correction constant Substitute the values ​​into the formula to calculate the optimization iteration coefficients of the model: ,because Continue iterative training; after the second round of training ,Adjustment Calculated In the subsequent three rounds of training The accuracy stabilized within the 1.05~1.07 range; Step 4, synchronously verify the test set accuracy. After the 5th round of training, the test set classification accuracy was 98.3% (only 76 test files were misclassified, mostly "cross-category related files"), which meets the requirements. If the test set accuracy is ≥98%, training is stopped, and the optimized model is used as the final classification model; Step 5: In Q3 of that year, the archives added 2800 new types of archives, such as "Rural Revitalization Special Archives" and "Digital Twin Project Archives." After collecting this batch of data, incremental training is performed on the final classification model, and the weight coefficients are updated using the gradient descent algorithm: because the new archives have strong thematic relevance, ( The value was adjusted from 0.3 to 0.33. ( The value was adjusted from 0.25 to 0.27. , , The corresponding fine-tuning values ​​are 0.18, 0.09, and 0.13. After incremental training, the model achieved a classification accuracy of 98.1% for new types of archives, thus meeting the classification requirements of the new archives.

[0048] More specifically, the dynamic weight adjustment unit also supports adjustments based on retrieval response efficiency. The index optimization mechanism involves the following steps: S61. Set the search response efficiency threshold. The value ranges from 0.85 to 0.9; S62. Real-time monitoring of search response efficiency ,like Maintain the current weighting coefficients , ; S63, if Extract high-frequency search keywords (keywords with a search frequency of ≥5 times in the past 24 hours) and improve their indexing priority; S64. Calculate the weight adjustment coefficient. The formula is as follows: ; in, To retrieve user satisfaction scores, the value ranges from 0 to 1 and is calculated based on user feedback ratings. The feature contribution is derived from the matching degree of the search results, and its value ranges from 0 to 1. , These are the weights for satisfaction and contribution, respectively. ; This is a weighting correction constant, with a value ranging from 0.03 to 0.09; S65, according to Adjust the weighting coefficients: , ,make sure ; S66. Update the feature weights of the search index and recalculate the search response efficiency. until ; S67. Record the weight adjustment records and synchronize them to the index optimization log to provide data support for subsequent model iterations.

[0049] The dynamic weight adjustment unit's index optimization mechanism fundamentally addresses the retrieval lag problem caused by the fixed weights of traditional retrieval indexes, which cannot dynamically adapt to response efficiency and user needs. Its effectiveness is manifested in setting a retrieval response efficiency threshold. (0.85-0.9) serves as the optimization trigger benchmark, and is monitored in real time. And differentiated processing; Maintaining weights ensures stability. Prioritize the extraction of high-frequency search keywords (≥5 times in the past 24 hours) to improve index priority and quickly respond to high-frequency needs; weight adjustment coefficient The calculation (formula is) Integrating user search satisfaction (0-1, reflecting user subjective experience) and feature contribution (0-1, reflecting the actual value of features to retrieval accuracy), and weighted accordingly. To balance the influence of both and avoid bias in optimization based on a single dimension; then... Dynamic adjustment , And update the index feature weights, forming an index iteration process of "monitoring, optimization, verification, and closed loop" until... This approach not only solves the problem of delayed search response, but also ensures that index optimization aligns with actual search needs by linking user satisfaction with feature contribution. Meanwhile, weight adjustments are recorded and synchronized to the log, providing data support for subsequent model iterations and achieving a dual improvement in the "efficiency and accuracy" of the search index.

[0050] In this example, taking the retrieval scenario of "project document archives" in an internet company as an example, the index optimization mechanism is applied as follows: Step 1, the dynamic weight adjustment unit S61 sets the retrieval response efficiency threshold. S62 real-time monitoring of search response efficiency over the past hour revealed a surge in search volume for keywords such as "2024Q3 project review" and "core product requirements document." S63 extracts frequently searched keywords from the past 24 hours ("2024Q3 Project Review" searched 12 times, "Core Product Requirements Document" searched 9 times, both ≥5 times), and identifies the corresponding file characteristics (thematic characteristics) of the two types of keywords. Business type characteristics The index priority is increased from "normal" to "high"; S64 calculates the weight adjustment coefficient. User search satisfaction is calculated based on user feedback ratings (85 out of nearly 100 search responses are satisfactory). The feature contribution is derived by reverse derivation of the matching degree of the search results (the average matching degree of high-frequency keyword search results is 0.8). Set weights (Satisfaction weight) (Contribution weight), take the weight adjustment constant. Substitute into the formula: ; S65 Adjust Weighting Coefficient: Current (Index frequency weight) (Matching weight), then , (satisfy S66 updates the retrieval index feature weights; adjusts the weights of "2024Q3 Project Review" and "Core Product Requirements Documents" accordingly. , The feature weights were increased from 0.3 and 0.25 to 0.35 and 0.3 respectively, and the retrieval response efficiency was recalculated. Stop optimization; S67 records this weight adjustment (original). High-frequency keyword indexing priority is increased. (From 0.82 to 0.91), synchronized to the index optimization log, providing data reference for subsequent iterations of the Q4 project document retrieval model.

[0051] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A high-efficiency archival digitization system integrating AI technology, characterized in that, include: It includes an intelligent data acquisition and preprocessing module, an AI deep recognition and analysis module, an intelligent classification and archiving module, an AI retrieval and optimization module, and a security encryption module; The intelligent acquisition and preprocessing module is used to acquire multiple types of original archives, and completes the denoising, correction, and format standardization of archive images through AI algorithms, and extracts the basic visual feature set of the archives. The AI ​​deep recognition and analysis module is based on AI visual recognition and natural language processing technology. It performs accurate text recognition, fuzzy semantic completion and key information extraction on the pre-processed archives to generate structured data. The intelligent classification and archiving module extracts multi-dimensional semantic and relational features of archives through a deep learning model, constructs a dynamic classification system, and realizes automatic classification and intelligent archiving of archives. The AI ​​search optimization module dynamically adjusts search weights based on the correlation between user search behavior data and file features, generating accurate search results and optimizing response efficiency. The security encryption module uses AI abnormal behavior monitoring and dynamic encryption technology to achieve access control, transmission encryption and storage security protection of archival data, ensuring the integrity and security of digital archives.

2. The efficient archival digitization system integrating AI technology according to claim 1, characterized in that, The intelligent acquisition and preprocessing module includes an archive acquisition unit, an intelligent preprocessing unit, and a basic feature extraction unit. The archive acquisition unit is used to acquire scanned copies of paper archives, photographed images of physical archives, and various electronic format archives through a high-definition scanner, image acquisition device, and electronic archive import interface to form an original archive dataset. The intelligent preprocessing unit is used to perform AI-driven preprocessing operations on the original archival dataset. The specific steps are as follows: S11. Parse the original archive dataset to distinguish between image archives and electronic document archives; S12. For image files, use adaptive median filtering to remove noise pixel by pixel and calculate the noise removal rate. ; S13. Detecting image tilt angle using Hough transform. It automatically rotates and corrects itself to a horizontal position; S14. Convert the corrected image files and electronic document files into the standard PDF / A format; S15. Substitute into the formula to calculate the sharpness optimization factor. ,like Return to step S2 and repeat the noise removal process until... ; Among them, the sharpness optimization coefficient The calculation formula is as follows: ； in, This represents the noise removal rate, ranging from 0 to 1. The more thorough the noise removal, the higher the rate. The closer to 1; This indicates the original tilt angle of the file, in radians, with a value range of 0. ; This is a preprocessing correction constant, ranging from 0.05 to 0.15, used to compensate for algorithm errors; The basic feature extraction unit is used to extract the basic visual features of the preprocessed archive image, including image resolution. Text area ratio Edge contour complexity This forms a set of basic visual features.

3. The efficient archival digitization system integrating AI technology according to claim 2, characterized in that, The AI ​​deep recognition and analysis module includes an AI text recognition unit, a semantic analysis unit, and a structured data generation unit; The AI ​​text recognition unit employs an improved OCR algorithm, combined with a sharpness optimization coefficient from the basic visual feature set. Improve the accuracy of text recognition in blurry or faded documents. satisfy: ； in, To ensure a clear baseline recognition rate for archives, To compensate for the recognition rate of blurry archives; The semantic parsing unit, based on a BERT pre-trained model, performs semantic analysis on the recognized text, extracts key information from the archives, and calculates the accuracy rate of key information extraction. The formula is as follows: ； in, The semantic matching degree is represented by the cosine similarity between the keywords and the document's topic, with a value ranging from 0 to 1. A higher matching degree indicates a better match. The closer to 1; , These are the weights for recognition accuracy and semantic matching degree, respectively. This can be set by the user according to the file type; This is a semantic parsing correction constant, with a value range of 0.02-0.08; The structured data generation unit is used to generate standardized structured data, and the specific steps are as follows: S21. Receive key information output by the semantic parsing unit; S22. Verify the completeness of key information. If any information is missing, supplement the missing information based on the NLP semantic completion model. S23. According to the XML preset template, map the key information to the corresponding tag nodes to generate a draft of structured data; S24. Calculate the accuracy rate of key information extraction. ,like Return to the semantic parsing unit to extract again until... ; S25. Perform format verification on the initial draft of the structured data, and generate the final standardized structured data after passing the verification.

4. The efficient archival digitization system integrating AI technology according to claim 3, characterized in that, The AI ​​text recognition unit performs text enhancement processing on faded documents, calculating text enhancement contrast. The formula is as follows: ； In the formula, The original contrast of the text in the archive, with a value range of 0-255. The higher the contrast, the larger the value. This is the contrast enhancement factor, with a value ranging from 1.2 to 2.

0. The smaller A larger value indicates a greater need for enhancement to accommodate files of different resolutions. To enhance the correction constant, with a value ranging from 10 to 20, this formula improves the distinction between faded text and the background, thereby optimizing recognition accuracy. .

5. The efficient archival digitization system integrating AI technology according to claim 4, characterized in that, The intelligent classification and archiving module includes a multi-dimensional feature extraction unit, an AI classification model unit, and an automatic archiving unit; The multi-dimensional feature extraction unit is used to extract semantic features of archives from structured data: topic features. Formation time characteristics and business type characteristics Extracting morphological features from a set of basic visual features: text density and page layout features ; The AI ​​classification model unit uses a convolutional neural network based on transfer learning to construct the classification model, taking multi-dimensional features as input and combining key information to extract accuracy. Calculate classification confidence The formula is as follows: ； in, This is a topic feature, with a value ranging from 0 to 1. The higher the topic matching degree, the larger the value. To form a time feature, the value ranges from 0 to 1, and the higher the degree of fit with the time range of the category directory, the larger the value. This is a business type feature, with a value ranging from 0 to 1. The higher the business type matching degree, the larger the value. This represents the text density, with a value ranging from 0 to 1. The higher the proportion of text, the larger the value. This is a page layout feature, with a value ranging from 0 to 1. The higher the degree of fit with the category layout template, the larger the value. These are the weight coefficients for each feature. It supports dynamic adjustments based on document management needs; The classification correction constant has a value range of 0.03-0.

09. The automatic archiving unit is used to automatically classify and archive documents. The specific steps are as follows: S31. Set the classification confidence threshold. The value ranges from 0.9 to 0.95 and can be adjusted according to the accuracy requirements of record management. S32. Calculate the classification confidence level. With threshold Perform a comparison; S33, if It automatically matches the corresponding category directory and generates the archive path; S34. Establish a cross-index between the archives and the associated directories, and record the index association relationships; S35. Bind the structured data to the original archive files, store them in the corresponding archiving path, and update the archiving status of the archive management database.

6. The efficient archival digitization system integrating AI technology according to claim 5, characterized in that, Thematic features The metric calculation is performed based on keyword matching, using the following formula: ； in, The number of keywords to match a predefined thesaurus in the archive. The total number of keywords in the preset thesaurus. This represents the uniformity of keyword distribution within the archives, ranging from 0.6 to 1.

0. A more uniform distribution indicates a higher uniformity. The closer it is to 1.0; the more characteristic this topic is. As classification confidence The core input parameters.

7. The efficient archival digitization system integrating AI technology according to claim 6, characterized in that, The AI ​​search optimization module includes an intelligent search unit, a dynamic weight adjustment unit, and a result sorting unit. The intelligent retrieval unit supports keyword retrieval, semantic retrieval, and multi-condition combination retrieval. After receiving a user's retrieval request, it extracts the matching degree between the retrieval keywords and the multi-dimensional features of the archive. ; The dynamic weight adjustment unit is based on user historical search feedback and classification confidence. Calculate the retrieval response efficiency The formula is as follows: ； in, This represents the index update frequency, measured in times per hour, with a value range of 0.5-5. The value ranges from 0 to 1, indicating the match between keywords and file features. A higher match result corresponds to a larger value. , These are the weights for index frequency and matching degree, respectively. ; The correction constant is used for retrieval, with a value range of 0.04-0.12; The result sorting unit is used to prioritize the search results, and the specific steps are as follows: S41. Receive the retrieval response efficiency output by the dynamic weight adjustment unit. ; S42. Extract the matching degree of the search results. Access frequency Update time ; S43. Constructing a ranking weight model: ； In the formula, Indicates the current time. Indicates the file update time; S44, according to The search results are sorted in descending order, and the first 10 results are displayed first. S45. Record user clicks on the sorting results and update the access frequency. And a database of user search behavior.

8. The efficient archival digitization system integrating AI technology according to claim 7, characterized in that, The security encryption module includes an access control unit, a dynamic encryption unit, and an AI anomaly monitoring unit. The permission management unit is based on the RBAC model and combines AI recognition and account behavior characteristics to assign hierarchical access permissions to different users; The dynamic encryption unit uses the AES-256 algorithm to encrypt the data transmission process of the archives, and uses fragmented encrypted storage for the stored data. The encryption key is dynamically generated by the hardware encryption module. The AI ​​anomaly monitoring unit is used to monitor and handle abnormal access behavior. The specific steps are as follows: S51. Set abnormal access risk threshold The value ranges from 0.7 to 0.85; S52. Collect user access behavior data in real time and calculate the actual access frequency. Permission matching degree ; S53. Substitute into the formula to calculate the abnormal access risk value. The formula is as follows: ； in, This represents the actual access frequency, measured in times per minute. This is the user's historical average access frequency, measured in times per minute, calculated based on data from the past 30 days. This represents the permission matching degree, with a value ranging from 0 to 1. It indicates that the operation permission exactly matches the user-assigned permission. ; This is a risk assessment correction constant, with a value ranging from 0.01 to 0.

07. S54, if This allows the access operation to continue and records the access log; S55, if Automatically triggers three levels of alerts: Level 1 alert Restrict export operations; Level 2 warning Editing privileges are frozen, only browsing privileges are retained; Level 3 warning. Force logout, account ban for 1 hour, and send a warning notification to the administrator; S56. After the warning is lifted, record the abnormal access event to the security log and update the user behavior feature database.

9. The efficient archival digitization system integrating AI technology according to claim 8, characterized in that, The training process of the AI ​​classification model unit includes: Step 1: Collect historical archive data, label it with classification tags, and combine it with a multi-dimensional feature set to form a training dataset; Step 2: Divide the training dataset into training and test sets in a 7:3 ratio, input them into the initial CNN model for training, and calculate the classification accuracy for each training round. ; Step 3: Optimize the model based on transfer learning by introducing pre-trained text classification model parameters and adjusting the weight coefficients. ~ Calculate the iteration coefficients of the model optimization The formula is as follows: ； in, This represents the classification accuracy from the previous training round, with a value ranging from 0 to 1. This is the iteration step size, ranging from 0.05 to 0.

15. This is an iterative correction constant, with a value range of 0.02-0.06; Step 4: When Stable within the preset range, When the accuracy is ≥0.95 and the test set accuracy is ≥98%, training is stopped, and the optimized model is used as the final classification model. Step 5: Collect newly archived data quarterly, incrementally train the final classification model, and update the weight coefficients. - Ensure that the model is adapted to the new file type.

10. The efficient archival digitization system integrating AI technology according to claim 9, characterized in that, The dynamic weight adjustment unit also supports adjustments based on retrieval response efficiency. The index optimization mechanism involves the following steps: S61. Set the search response efficiency threshold. The value ranges from 0.85 to 0.9; S62. Real-time monitoring of search response efficiency ,like Maintain the current weighting coefficients , ; S63, if Extract frequently searched keywords and improve their index priority; S64. Calculate the weight adjustment coefficient. The formula is as follows: ； in, To retrieve user satisfaction scores, the value ranges from 0 to 1 and is calculated based on user feedback ratings. The feature contribution is derived from the matching degree of the search results, and its value ranges from 0 to 1. , These are the weights for satisfaction and contribution, respectively. ; This is a weighting correction constant, with a value ranging from 0.03 to 0.09; S65, according to Adjust the weighting coefficients: , ,make sure ; S66. Update the feature weights of the search index and recalculate the search response efficiency. until ; S67. Record the weight adjustment records and synchronize them to the index optimization log to provide data support for subsequent model iterations.

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