A hospital archives management system and method based on a cloud archive

Abstract

The application belongs to the field of medical information system, and provides a hospital archives management system and method based on cloud archives, which comprises an archives storage module, an archives query and retrieval module, an archives sharing and cooperation module, an archives security management module, an archives update and maintenance module and a system management module.

Description

Technical Field

[0001] The present invention belongs to the field of medical information systems, and in particular relates to a hospital archive management system and method based on a cloud archive library. Background Art

[0002] With the advancement of digital hospital management, hospital archive management systems have shifted from traditional paper archives to electronic archives, significantly improving hospitals' ability to process patient information. However, with the continuous growth of patient data, hospital archive management systems face many challenges in practical application, including archive classification, storage, backup, security management, and retrieval efficiency.

[0003] Due to the limitations of its design, traditional hospital archive management systems often encounter the following problems: the hospital's electronic archive data volume is huge, and the data types are complex and diverse, including text, images, videos, diagnostic records, etc. Distributed storage often makes data management inconvenient and difficult to manage uniformly. Different users (such as doctors, nurses, managers, etc.) have different access requirements for archives. Traditional systems are difficult to fine-tune control permissions, and are prone to data leakage or insufficient access. Hospital patient information needs to be updated at any time. The traditional system's update and synchronization mechanism for archives is relatively lagging, which can easily lead to data inconsistencies or omissions. Patient archive information is complex, and doctors in different departments focus on different things. Traditional archive retrieval systems are difficult to provide accurate query results based on doctors' needs, which increases the burden on doctors and reduces work efficiency. Summary of the Invention

[0004] In order to solve the problems in the prior art, the present invention provides a hospital archive management system based on a cloud archive library, which includes the following modules:

[0005] The archive storage module is used to classify, store and back up the hospital's electronic archives through cloud storage technology;

[0006] Archives query and retrieval module, used to achieve efficient query of hospital archives based on intelligent retrieval methods;

[0007] The archive sharing and collaboration module is used to allow users within the hospital and related institutions to access archives according to their permissions, thus realizing the sharing and collaborative processing of archive information;

[0008] The file security management module is used to implement a multi-level security protection mechanism to ensure the security of files during upload, download and use;

[0009] The file update and maintenance module is used to automatically monitor and manage file updates to ensure the real-time and accuracy of file data;

[0010] The system management module is used to manage the operation and maintenance of the entire archive management system.

[0011] Furthermore, the archive storage module includes an archive classification function, which uses a multi-level classification algorithm to automatically classify data according to the content characteristics of the archive.

[0012] Furthermore, the archive storage module includes an archive storage function, which adopts distributed cloud storage technology to store archive data on different storage nodes according to its type and frequency of use. High-frequency used archives are stored in high-speed response storage devices, while low-frequency used historical archives are stored in low-cost storage layers.

[0013] Furthermore, the archive storage module includes an archive backup function, which adopts a periodic backup strategy to regularly back up hospital archive data to cloud storage, and the backup process uses incremental backup.

[0014] Furthermore, the intelligent retrieval method implementation method includes:

[0015] Each doctor generates a feature vector based on his / her department, title, professional direction, and historical search records. This feature vector includes the doctor's professional background, department category, and historical search records.

[0016] Based on the doctor's historical query behavior, the system calculates the access frequency of each file type through weighted statistics to form the doctor's query preference distribution, which represents the doctor's attention to different file types;

[0017] The patient's current condition is generated by extracting medical records, test results, and diagnostic records to generate a feature vector, which contains core information about the current condition, such as the latest diagnosis results, medication information, and imaging examinations;

[0018] By comparing with historical archive data, the system identifies whether the patient's condition has changed; for important changes in the condition, the system will adjust the priority of the search results;

[0019] Use the cosine similarity function to calculate the similarity between the doctor's feature vector and the patient's feature vector to obtain the correlation score between the file and the doctor's focus;

[0020] Based on the similarity score, the system prioritizes the recommended files with high scores;

[0021] By calculating the similarity of the feature vectors of the current doctor and other doctors, we can find a group of doctors with similar behaviors.

[0022] Based on the query behavior of similar doctors, the collaborative filtering score of the recommended profiles is calculated and a weighted sum is performed on each profile;

[0023] The profile selected by the doctor will be given a positive reward, and the unselected profile will be given a negative feedback, updating the reinforcement learning strategy;

[0024] Through the reinforcement learning model, the system continuously updates the ranking score of the profile;

[0025] According to the score optimized by reinforcement learning, the final profile ranking score is the weighted sum of content recommendation, collaborative filtering and reinforcement learning score.

[0026] Further, the profile sharing and collaboration module comprises:

[0027] The permission control unit is used to classify, set and manage the permissions of different users, ensuring that only users with corresponding permissions can access specified profile data;

[0028] The profile sharing unit is used to realize the sharing and collaborative processing of profiles within the scope of user permissions;

[0029] The collaborative processing unit is used for multiple users to collaboratively process the same profile.

[0030] Further, the profile security management module comprises:

[0031] The data encryption transmission unit is used to encrypt data during profile uploading, downloading and transmission, ensuring the confidentiality and security of data during network transmission;

[0032] The access control unit is used to strictly control the access permissions of different users, ensuring that only authorized users can access the profile;

[0033] The operation audit and log recording unit is used to record all user operations on the profile, ensuring the traceability and transparency of the operation process of the profile during its entire life cycle;

[0034] The data backup and recovery unit is used to periodically backup profile data to prevent data loss or damage, and ensure the recoverability of the profile in emergency situations.

[0035] Further, the profile update and maintenance module comprises:

[0036] The automatic update monitoring unit is used to monitor the changes of hospital profile data in real time, and when the profile is added, modified or deleted, the system can automatically capture and record these changes, ensuring timely update of profile data;

[0037] The version control unit is used to manage different versions of each profile, ensuring that each version of data can be accurately saved during multiple updates of the profile, and allowing users to view or restore historical versions.

[0038] Automatic synchronization unit, used to ensure synchronous update of archive data in a multi-user, cross-system environment. When archives are accessed and modified by different users or systems, the system can automatically coordinate and synchronize these changes to ensure data consistency;

[0039] The data consistency check unit is used to check the integrity and consistency of the archive data during the archive update process to prevent the inconsistency or loss of archive data due to network failures, system errors or unexpected situations;

[0040] The notification and reminder unit is used to promptly notify relevant users or systems when archives are updated, ensuring that archive users can obtain the latest archive information.

[0041] Furthermore, the system management module includes: managing the operation and maintenance of the entire archive management system through system monitoring, user management, resource allocation, logging and fault diagnosis functions.

[0042] On the other hand, the present invention also provides a hospital archive management method based on a cloud archive library, which uses the aforementioned system to manage hospital archives.

[0043] The cloud archive-based hospital archive management system of the present invention achieves the following beneficial effects through the integration of a series of innovative functional modules:

[0044] The system leverages distributed cloud storage technology to centrally store massive amounts of hospital archival data. It manages these data in layers based on file type and frequency of use, storing high-frequency data on fast storage nodes and low-frequency data on cold storage nodes. This ensures fast data access while reducing storage costs. Furthermore, the use of data compression and a hybrid cloud storage architecture ensures the secure storage of sensitive data and enhances the system's flexibility and scalability.

[0045] The system features built-in automatic update monitoring and version control, enabling real-time monitoring of archive changes and ensuring data consistency across multiple users and systems through automatic synchronization. Furthermore, the system's data backup and recovery capabilities ensure rapid recovery of archive data in the event of an emergency, ensuring data integrity and business continuity.

[0046] The system automatically generates a doctor's query feature vector based on their professional background, historical query behavior, and the patient's current condition. By calculating similarity with the patient's profile feature vector, it intelligently recommends the most relevant profiles. Furthermore, the system combines collaborative filtering and reinforcement learning algorithms to continuously optimize recommendations based on other doctors' query behavior and feedback from their actual choices. Ultimately, this achieves personalized profile sorting and prioritized display, enabling doctors to quickly access the profile information most relevant to their current work, significantly improving retrieval efficiency and reducing the burden on doctors.

[0047] Through permission control mechanisms and encrypted transmission technology, the system enables secure sharing and collaborative processing of records within the hospital and between external institutions. The system supports multiple users collaborating on the same record simultaneously, ensuring data consistency. It also provides version control and real-time communication capabilities, facilitating collaborative discussions among users while processing records.

[0048] In general, the intelligent retrieval function of the present invention significantly improves the query efficiency of the hospital archive system, avoids information overload and invalid queries, and can achieve fast and accurate archive acquisition, especially in the case of large-scale archival data, providing strong support for doctors' diagnosis and treatment decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0050] Figure 1 It is a system block diagram of the method of the present invention. DETAILED DESCRIPTION

[0051] Below, the invention is preferably described with reference to the accompanying drawings and specific embodiments.

[0052] This embodiment solves the above problem through the following steps:

[0053] In one embodiment, reference Figure 1 The present invention provides a cloud-based hospital archive management system. This system aims to address at least one of the challenges associated with hospital archive management, such as decentralized archive storage, inadequate access rights management, delayed data updates, or inconvenient data retrieval, by leveraging cloud-based storage, intelligent management, and security technologies.

[0054] The cloud archive described in this invention is an archive storage and management platform built on cloud computing technology. This platform utilizes distributed storage, virtualization, and data backup mechanisms to store hospital archive data on remote cloud servers, enabling reliable storage, management, and anytime, anywhere access to massive amounts of data. The cloud archive not only significantly increases data storage capacity and processing efficiency, but also ensures data confidentiality and integrity through multiple security mechanisms.

[0055] The hospital archive management system described in this invention is a computerized system dedicated to the collection, storage, management, query, sharing, and updating of hospital archival information. By integrating multiple functional modules, this system aims to address challenges inherent in traditional hospital archive management, such as fragmented archive storage, difficult retrieval, and the vulnerability of data loss or corruption. The system supports the unified management of various types of hospital archival data, including but not limited to patient medical records, medical examination reports, imaging data, and medication records.

[0056] The archive storage module is used to classify, store and back up the hospital's electronic archives through cloud storage technology.

[0057] Cloud storage technology is used to classify, store, and back up hospital electronic records. Based on the complexity and sensitivity of hospital records, this module specifically improves traditional classification, storage, and backup processes to ensure data security, scalability, and efficient retrieval. Specifically, it includes the following features:

[0058] File classification function

[0059] Archive classification is a fundamental step in hospital archive management, determining the efficiency of subsequent storage and retrieval. Because hospitals' electronic archives contain a wide variety of data types, including text, images, videos, diagnostic records, and other formats, the classification process must be highly flexible and accurate.

[0060] The system uses a multi-level classification algorithm to automatically categorize data based on the content characteristics of the files (such as basic patient information, medical records, examination reports, and imaging materials). During the classification process, natural language processing (NLP) technology is combined to analyze text data, and image recognition algorithms are used to identify and classify image data.

[0061] Introducing an automatic classification model based on machine learning during the classification process allows for continuous learning and optimization based on historical classification data, improving classification accuracy and efficiency. For imaging data, a deep learning image classification algorithm is used to automatically classify images based on different examination types (such as CT, MRI, and X-ray), reducing human intervention.

[0062] The classification function can handle data in various formats, and automated classification reduces manual operation time and error rates. By continuously optimizing classification rules through machine learning models, the system can adapt to the dynamic changes in hospital file types, improving classification accuracy and efficiency. This classification method makes subsequent retrieval and storage more organized, requiring only filtering by specific category during retrieval, shortening search time.

[0063] Archive storage function

[0064] Hospital electronic archives contain not only a massive amount of data but also diverse data formats, encompassing both important textual data and image data that takes up significant storage space. Therefore, storage capabilities must be flexible, scalable, and capable of efficient data management.

[0065] The system uses distributed cloud storage technology to store archival data on different storage nodes based on its type and frequency of use. Frequently used archives (such as a patient's recent medical records) are stored in high-speed storage devices, while less frequently used historical archives are stored in low-cost storage tiers (such as cold storage).

[0066] Data compression and tiered storage algorithms are introduced, employing different compression strategies for different data types. For example, lossless compression is used for text data, while deep learning-based compression technology is used to reduce storage space for image data. Furthermore, a hybrid cloud storage architecture is employed, storing sensitive data in a private cloud and general data in a public cloud, ensuring a balance between data security and cost-effectiveness.

[0067] Distributed storage improves system scalability, enabling dynamic expansion of storage capacity based on the growth of hospital archive data. Data compression and tiered storage technologies significantly reduce storage space usage and, through the rational allocation of resources, lower storage costs.

[0068] The hybrid cloud architecture can meet the hospital's security needs for sensitive data, while making full use of public cloud storage resources to improve the overall storage flexibility and cost-effectiveness.

[0069] File backup function

[0070] Backing up hospital archival data is an important step in ensuring data security, especially when dealing with accidental data loss, equipment failure or malicious attacks (such as ransomware). The backup system must be able to quickly restore data to ensure the continuity of hospital operations.

[0071] The system adopts a periodic backup strategy, regularly backing up hospital archive data to cloud storage. The backup process uses incremental backup technology, that is, only backing up the data that has changed since the last backup, to reduce backup time and storage space.

[0072] Blockchain technology is incorporated into the backup process for data verification, ensuring the integrity and immutability of backup data. Furthermore, to address the timeliness requirements of hospital records, the system supports real-time backup. Through automated monitoring mechanisms, backups are immediately initiated when important data (such as emergency patient records) changes, ensuring critical data is not lost. Furthermore, a disaster recovery backup system replicates backup data to storage nodes in multiple geographical locations, further improving backup reliability.

[0073] Incremental backup technology significantly reduces backup time and storage space usage, improving backup efficiency. Blockchain technology ensures the integrity and tamper-proofing of backup data, enhancing data security. Real-time backup ensures the security of high-priority data, making it particularly suitable for medical scenarios requiring rapid response. Disaster recovery systems improve data recovery availability through multi-location backup, enhancing the system's resilience to natural disasters and man-made attacks.

[0074] The archive query and retrieval module is used to achieve efficient query of hospital archives based on intelligent retrieval technology.

[0075] A patient's files may contain a lot of information, and doctors in different departments may have different focuses. If all the files are retrieved at once, it will affect the doctor's selection process. On the other hand, some medical imaging data is huge, and reading all the files will increase the burden on the system and network. In view of the characteristics of hospital files, this module adopts intelligent retrieval methods to ensure that query and retrieval operations can be completed quickly and accurately when dealing with challenges such as diverse file types, real-time requirements, and huge data volumes. Specifically, the implementation method of intelligent retrieval technology is as follows:

[0076] Each doctor generates a feature vector V based on his / her department, title, professional direction, and historical records. 医生 This feature vector includes the doctor’s professional background, department category, and historical query records.

[0077] V 医生 =[Department, Title, Professional Field, Query Preference]

[0078] Based on the doctor's historical query behavior, the system calculates the access frequency of each file type through weighted statistics to form the doctor's query preference distribution, which represents the doctor's attention to different file types.

[0079]

[0080] Among them, P 医生 (d i ) indicates that the doctor has i The frequency of visits, Indicates the file d i Number of visits, Indicates the total number of times the archive has been accessed.

[0081] The patient's current condition is generated by extracting medical records, examination results, diagnosis records, etc. to generate a feature vector V 患者 , which contains core information about the current condition, such as the latest diagnosis results, medication information, imaging examinations, etc.

[0082] V 患者 =[diagnosis, examination results, medication information, imaging data]

[0083] By comparing the data with historical archive data, the system can identify changes in the patient's condition and adjust the priority of the search results for important changes.

[0084] △V 病情 =V 历史 -V 当前

[0085] Among them, △V 病情 Indicates changes in condition, V 历史 Indicates historical condition, V 当前 Indicates the current condition.

[0086] Use the cosine similarity function to calculate the doctor feature vector V 医生 and patient feature vector V 患者 The similarity between the profile and the doctor's focus is obtained.

[0087]

[0088] According to the similarity score S 内容 , the system gives priority to recommending files with high scores.

[0089] sort(d)=argmax d S 内容 (d)

[0090] By calculating the similarity of the feature vectors of the current doctor i and other doctors j, we can find a group of doctors with similar behaviors.

[0091]

[0092] Calculate the collaborative filtering score S of the recommended profile based on the query behavior of similar doctors 协同 , and a weighted sum is performed on each profile. The score is used to supplement the content-based recommendation results and help doctors discover relevant profiles that may have been overlooked.

[0093]

[0094] The profile selected by the doctor will be given a positive reward R(di ), the unselected files are given negative feedback to update the reinforcement learning strategy.

[0095]

[0096] Through reinforcement learning models (such as Q-learning), the system continuously updates the ranking score Q(d i ) for future search optimization.

[0097] Q(d i )←Q(d i )+α[R(d i )+γmax d Q(d′)-Q(d i )]

[0098] Where α is the learning rate, γ is the discount factor, and max d Q(d') is the maximum score of the subsequent profiles.

[0099] According to the scores optimized by reinforcement learning, the final profile ranking score is the weighted sum of content recommendation, collaborative filtering and reinforcement learning scores.

[0100] S 最终 (d i )=β1S 内容 (d i )+β2S 协同 (d i )+β3Q(d i )

[0101] Among them, β1, β2, and β3 are weight coefficients that control the contribution of each algorithm and can be adjusted according to needs. A preset number of files with the highest score ranking are screened based on the final optimization score.

[0102] The above intelligent retrieval method, combined with the doctor's behavior model and the patient's current condition, realizes intelligent retrieval of hospital files. It also dynamically adjusts according to the doctor's actual selection behavior to ensure that the files finally retrieved best meet the doctor's current needs and are displayed in order of priority, thus achieving efficient query of hospital files.

[0103] The archive sharing and collaboration module is used to allow users within the hospital or other related institutions to access archives according to their permissions, thereby realizing the sharing and collaborative processing of archive information.

[0104] This module includes a permission control unit for classifying, setting and managing the permissions of different users, ensuring that only users with corresponding permissions can access specified archive data. User permissions are divided according to their roles (such as doctors, nurses, administrative staff, etc.) and institutions (such as internal hospitals, external collaborative hospitals, insurance agencies, etc.).

[0105] Specifically:

[0106] All users are classified and a user permission list is generated according to their identity, role, and affiliated institution. This permission list contains the range of archives that each user can access.

[0107] A role-based access control (RBAC) model-based permission system is established to define the mapping relationship between user roles and archive access permissions, ensuring that users can only access archives within their permission range.

[0108] Through multi-level permission settings, access levels for different user roles are defined, such as view-only permissions, edit permissions, and sharing permissions, ensuring that users can only perform corresponding operations within their permission range.

[0109] This module includes an archive sharing unit for implementing archive sharing and collaborative processing within the user's permission range. The sharing mechanism supports intra-hospital and cross-institution user collaboration and ensures secure transmission and access of data between different users.

[0110] Specifically, the implementation method is as follows:

[0111] Define sharing permission rules based on the type, sensitivity of the archive, and identity verification of the recipient to determine whether sharing is allowed. The range of archive sharing is determined based on the permission control system and sharing rules.

[0112] During the sharing process, the system encrypts the transmitted archive data using symmetric or asymmetric encryption algorithms to protect the security of the archive data, ensuring that the data cannot be accessed by unauthorized users during cross-network transmission.

[0113] After the archive is decrypted at the recipient, the system determines the recipient's specific operation permissions based on their permission level. For example, if the recipient only has view-only permissions, they cannot edit or forward the archive.

[0114] This module includes a collaborative processing unit for multiple users to collaboratively process the same archive. Collaborative processing supports functions such as document editing, annotation, discussion, and ensures data consistency and security during simultaneous operations by multiple users.

[0115] Specifically, the implementation method is as follows:

[0116] When multiple users operate on the same file simultaneously, the system uses a distributed locking mechanism or version control mechanism to prevent data conflicts. Each time a user modifies a file, a new version is generated and historical versions are saved to ensure that all modifications are traceable.

[0117] During the collaboration process, users can annotate and discuss archives. The system supports online comments, tagging and discussion through the real-time communication module. All annotations are recorded and bound to the archive data.

[0118] During collaborative processing, the system uses a differentiated synchronization algorithm to ensure that only the modified portions are transmitted when multiple users are editing files simultaneously. The file sharing and collaboration module utilizes a multi-layered design encompassing rights management, file sharing mechanisms, collaborative processing, and auditing capabilities, ensuring secure file sharing and efficient collaboration among different users. The interoperability of these functional units ensures the security, integrity, and efficiency of file sharing and collaborative processing, meeting the needs of file sharing within hospitals and across institutions. This reduces network bandwidth consumption and improves collaboration efficiency.

[0119] The archive security management module is used to implement a multi-level security protection mechanism to ensure the security of archives during upload, download and use.

[0120] This module includes multiple security protection mechanisms to ensure the data security, integrity, and traceability of hospital archives during upload, download, and use. This module forms a comprehensive archive security management system through multi-level security measures such as encrypted transmission, access control, operation auditing, and data backup. Specifically, it includes the following functions:

[0121] Data encryption transmission unit

[0122] The data encryption transmission unit is used to encrypt data during file upload, download and transmission to ensure the confidentiality and security of data during network transmission.

[0123] Access Control Unit

[0124] The access control unit is used to strictly control the access rights of different users, ensuring that only authorized users can access archives. This unit is based on the role-based access control (RBAC) model, allowing users to determine the specific scope of access to archives based on their role and permission level.

[0125] Operational Auditing and Logging Unit

[0126] The operation audit and log recording unit is used to record all users' operations on archives, ensuring the traceability and transparency of the archive operation process throughout its life cycle.

[0127] Data backup and recovery unit

[0128] The data backup and recovery unit is used to regularly back up archive data to prevent data loss or damage and ensure the recoverability of archives in emergency situations.

[0129] Backup strategy: We use a combination of incremental and full backup strategies to regularly back up archive data. Incremental backups record changes to archives since the last backup, reducing backup data volume and time. Full backups are performed regularly to ensure the complete data set is backed up.

[0130] Recovery mechanism: In the event of data damage or loss, the system can quickly restore files through redundant storage and backup data to ensure business continuity and file integrity.

[0131] Data integrity check unit

[0132] The data integrity verification unit is used to check the data integrity of files during upload, download and use to ensure that the data has not been tampered with.

[0133] The archive update and maintenance module is used to automatically monitor and manage archive updates to ensure the real-time and accuracy of archive data.

[0134] This module builds an efficient archive update and maintenance mechanism through version control, real-time update detection, automatic synchronization, and data consistency verification. Specifically, it includes the following functions:

[0135] Automatic update monitoring unit

[0136] The automatic update monitoring unit is used to monitor the changes in hospital file data in real time. When files are added, modified or deleted, the system can automatically capture and record these changes to ensure timely updating of file data.

[0137] The system detects file updates through an event-driven mechanism or a timed polling mechanism. When a user makes any changes to a file (such as adding a new diagnostic record, updating an inspection result, etc.), the system automatically generates an update event and marks the file as "needs to be updated."

[0138] Change record: When changes are detected in archive data, the system will automatically generate an archive update record to record the version changes and modification details of the archive.

[0139] Version Control Unit

[0140] The version control unit is used to manage different versions of each file, ensuring that the data of each version can be accurately saved during multiple updates of the file, and allowing users to view or restore historical versions.

[0141] Version generation mechanism: After each file update, the system automatically creates a new version for the file and saves the historical version in the archive version library. The system uses incremental storage, storing only the difference data from the previous version to reduce storage space usage.

[0142] Historical version management: The system allows users to view or restore a historical version, ensuring that they can restore to the required version in case of misoperation or traceability.

[0143] Automatic synchronization unit

[0144] The automatic synchronization unit is used to ensure the synchronization of archive data in a multi-user, cross-system environment. When archives are accessed and modified by different users or systems, the system can automatically coordinate and synchronize these changes to ensure data consistency.

[0145] Synchronization mechanism: When multiple users work on the same file simultaneously, the system uses a distributed locking mechanism to prevent data conflicts. If users modify a file simultaneously, a conflict detection is triggered when saving, and the system will automatically merge the files or prompt the user to manually resolve them.

[0146] Cross-system synchronization: In a multi-system environment, the system automatically synchronizes files between different systems through API interfaces or middleware to ensure that data remains consistent across systems.

[0147] Data consistency check unit

[0148] The data consistency check unit is used to check the integrity and consistency of archive data during the archive update process to prevent inconsistency or loss of archive data due to network failure, system errors or other unexpected situations.

[0149] Consistency verification mechanism: The system performs consistency verification by generating a hash value of the data. After each update, the system automatically calculates and compares the hash values ​​before and after the update to ensure that the data has not been tampered with or damaged.

[0150] Automatic recovery mechanism: When data inconsistency or corruption is detected, the system automatically restores the archive data from the backup to ensure the integrity and accuracy of the archive.

[0151] Notification and reminder unit

[0152] The notification and reminder unit is used to send timely notifications to relevant users or systems when archives are updated, ensuring that archive users can obtain the latest archive information.

[0153] Notification mechanism: When the file update is completed, the system automatically sends a notification to the user who has the permission to the file. The notification content includes the updated file ID, version number and modification summary.

[0154] Reminder mechanism: For file updates that are not processed in time or conflicting files, the system will regularly send reminder notifications to ensure that the problem is resolved as soon as possible.

[0155] The archive update and maintenance module builds a maintenance mechanism that can ensure the real-time, accuracy and consistency of archive data through functions such as automatic update monitoring, version control, automatic synchronization, data consistency verification and notification reminders. This module ensures efficient update and synchronization of archives in a multi-user and multi-system environment, effectively preventing the risk of data loss or inconsistency.

[0156] The system management module is used to manage the operation and maintenance of the entire archive management system.

[0157] The system management module manages the operation and maintenance of the entire archive management system. Through system monitoring, user management, resource allocation, logging, and fault diagnosis, it ensures the stability, efficiency, and security of the system. This module is designed to address the complexity of the archive management system and provides a comprehensive operation and maintenance mechanism.

[0158] On the other hand, the present invention also discloses a hospital archive management method based on a cloud archive library, which uses the system described above to make inspection project decisions.

[0159] For any module structure not specifically defined in this invention, the prior art shall prevail. The prior art mentioned in the aforementioned background and specific embodiments of this invention may be considered as part of this invention and used to understand the meaning of certain technical features or parameters. The scope of protection of this invention shall be based on the actual content of the claims.

Claims

1. A hospital archive management system based on cloud archives, characterized in that: The system includes the following modules: The archive storage module is used to classify, store and back up the hospital's electronic archives through cloud storage technology; Archives query and retrieval module, used to achieve efficient query of hospital archives based on intelligent retrieval methods; The archive sharing and collaboration module is used to allow users within the hospital and related institutions to access archives according to their permissions, thus realizing the sharing and collaborative processing of archive information; The file security management module is used to implement a multi-level security protection mechanism to ensure the security of files during upload, download and use; The file update and maintenance module is used to automatically monitor and manage file updates to ensure the real-time and accuracy of file data; System management module, used to manage the operation and maintenance of the entire archive management system; The intelligent retrieval method implementation method includes: Each doctor generates a feature vector based on his / her department, title, professional direction, and historical search records. This feature vector includes the doctor's professional background, department category, and historical search records. Based on the doctor's historical query behavior, the system calculates the access frequency of each file type through weighted statistics to form the doctor's query preference distribution, which represents the doctor's attention to different file types; The patient's current condition is generated by extracting medical records, test results, and diagnostic records to generate a feature vector, which contains core information about the current condition, such as the latest diagnosis results, medication information, and imaging examinations; By comparing with historical archive data, the system identifies whether the patient's condition has changed; for important changes in the condition, the system will adjust the priority of the search results; Use the cosine similarity function to calculate the similarity between the doctor's feature vector and the patient's feature vector to obtain the correlation score between the file and the doctor's focus; Based on the similarity score, the system prioritizes the recommended files with high scores; By calculating the similarity of the feature vectors of the current doctor and other doctors, we can find a group of doctors with similar behaviors. Based on the query behavior of similar doctors, the collaborative filtering score of the recommended profiles is calculated and a weighted sum is taken for each profile. This score is used to supplement the content-based recommendation results and help doctors discover relevant profiles that may have been overlooked. The files selected by the doctor will be given positive rewards, and the files not selected will be given negative feedback to update the reinforcement learning strategy; Through the reinforcement learning model, the system continuously updates the ranking scores of the archives; According to the scores optimized by reinforcement learning, the final profile ranking score is the weighted sum of content recommendation, collaborative filtering and reinforcement learning scores.

2. The hospital archive management system based on cloud archive according to claim 1 is characterized in that: The archive storage module includes an archive classification function, which uses a multi-level classification algorithm to automatically classify data according to the content characteristics of the archive.

3. The hospital archive management system based on cloud archive according to claim 1, characterized in that: The archive storage module includes an archive storage function, which adopts distributed cloud storage technology to store archive data on different storage nodes according to its type and frequency of use. Archives that are used frequently are stored in high-speed response storage devices, while historical archives that are used less frequently are stored in low-cost storage layers.

4. The hospital archive management system based on cloud archive according to claim 1, characterized in that: The archive storage module includes an archive backup function, which adopts a periodic backup strategy to regularly back up hospital archive data to cloud storage, and the backup process uses incremental backup.

5. The hospital archive management system based on cloud archives according to claim 1, characterized in that: The file sharing and collaboration module includes: The permission control unit is used to classify, set and manage the permissions of different users to ensure that only users with corresponding permissions can access the specified archive data; The file sharing unit is used to realize file sharing and collaborative processing within the scope of user permissions; Collaborative processing unit, used for multiple users to collaboratively process the same file.

6. The hospital archive management system based on cloud archives according to claim 1, characterized in that: The archive security management module includes: Data encryption transmission unit, used to encrypt data during file upload, download and transmission to ensure the confidentiality and security of data during network transmission; Access control unit, which is used to strictly control the access rights of different users to ensure that only authorized users can access the archives; Operation audit and log recording unit, used to record all user operations on archives, ensuring the traceability and transparency of the operation process of archives throughout their life cycle; The data backup and recovery unit is used to regularly back up archive data to prevent data loss or damage and ensure the recoverability of archives in emergency situations.

7. The hospital archive management system based on cloud archives according to claim 1, characterized in that: The file update and maintenance module includes: Automatic update monitoring unit, used to monitor changes in hospital file data in real time. When files are added, modified or deleted, the system can automatically capture and record these changes to ensure timely update of file data; The version control unit is used to manage different versions of each file, ensuring that the data of each version can be accurately preserved during multiple file updates, and allowing users to view or restore historical versions; Automatic synchronization unit, used to ensure synchronous update of archive data in a multi-user, cross-system environment. When archives are accessed and modified by different users or systems, the system can automatically coordinate and synchronize these changes to ensure data consistency; The data consistency check unit is used to check the integrity and consistency of the archive data during the archive update process to prevent the inconsistency or loss of archive data due to network failures, system errors or unexpected situations; The notification and reminder unit is used to promptly notify relevant users or systems when archives are updated, ensuring that archive users can obtain the latest archive information.

8. The hospital archive management system based on cloud archives according to claim 1, characterized in that: The system management module includes: managing the operation and maintenance of the entire archive management system through system monitoring, user management, resource allocation, log recording and fault diagnosis functions.

9. A hospital archive management method based on cloud archives, characterized in that Hospital archive management is performed using the system as described in any one of claims 1-8.

Images