Medical information digital management system and management method thereof

Abstract

The invention relates to the technical field of medical information management, in particular to a medical information digital management system and a management method thereof. The method comprises the following steps: obtaining patient basic information, patient medical history information, patient diagnosis and treatment record information and patient medical image information corresponding to different systems of a medical institution, carrying out information time sequence block integration, and carrying out FHIR formatting and digital file conversion at the same time; generating a medical information digital subfile corresponding to each time block; and storing the medical information digital sub-files corresponding to the time blocks in the corresponding distributed storage blocks on the cloud computing platform in a distributed manner, carrying out block link storage management, obtaining medical information access authorization from the cloud computing platform by using an intelligent contract, and carrying out authorization access management based on the medical information access authorization. And executing corresponding medical information access data sharing management. The efficiency and safety of medical information data management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and in particular to a medical information digital management system and a management method thereof. Background Art

[0002] As an important means to improve the efficiency of medical services, reduce management costs, and enhance the patient's medical experience, digital management of medical information has become an indispensable part of the modern medical industry. In addition, many medical institutions have begun to introduce technologies such as electronic medical records (EMR), health information management systems (HIMS), and medical information platforms. These systems record patients' medical records, diagnosis and treatment information, test results and other data through digital means, which greatly improves the efficiency and accuracy of medical data management. At the same time, digital management of medical information has gradually shifted from data recording to data analysis, intelligent decision-making and resource optimization. However, although information systems such as electronic medical records can store a large amount of patient data, there are still major obstacles to data sharing and intercommunication between different medical institutions and departments, resulting in serious information islands, which makes it impossible to comprehensively improve the efficiency and quality of medical information management. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a medical information digital management system and a management method thereof to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for digital management of medical information includes the following steps:

[0005] Step S1: Obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information corresponding to different systems of the medical institution, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain the medical information data sub-items corresponding to each timestamp block;

[0006] Step S2: FHIR formatting the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; performing digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block;

[0007] Step S3: Distribute and store the medical information digitized sub-files corresponding to each time block in the corresponding distributed storage blocks on the cloud computing platform, and perform block link storage management on each corresponding distributed storage block on the cloud computing platform to generate a medical information digitized block storage sequence;

[0008] Step S4: Use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorized access management on each distributed storage block in the medical information digital block storage sequence to perform corresponding medical information access data sharing management.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: Obtaining corresponding basic information of the patient from the corresponding medical information subsystem of the medical institution;

[0011] Step S12: Obtaining corresponding patient medical history information from the corresponding electronic medical record subsystem of the medical institution;

[0012] Step S13: Obtaining corresponding patient diagnosis and treatment record information from the corresponding medical record information subsystem of the medical institution;

[0013] Step S14: obtaining corresponding patient medical imaging information from the corresponding medical imaging subsystem of the medical institution;

[0014] Step S15: Integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain medical information data sub-items corresponding to each timestamp block.

[0015] Further, step S15 includes the following steps:

[0016] Step S151: Synchronize and integrate the patient's basic information, the patient's medical history information, the patient's diagnosis and treatment record information, and the patient's medical imaging information in a time series to generate a corresponding patient medical information time series set in the same time dimension;

[0017] Step S152: performing data cleaning processing on the corresponding patient medical information time series set in the same time dimension to remove redundant and erroneous medical information, and obtaining the patient medical information time series cleaned set;

[0018] Step S153: Obtain the corresponding patient's first medical treatment time node and the patient's last medical treatment time node through the patient's medical information time series cleaning set, and determine the medical timestamp range segment of each patient according to the patient's first medical treatment time node and the patient's last medical treatment time node;

[0019] Step S154: Based on the medical timestamp range segments of each patient, the patient medical information time series cleaning set is divided into information time series blocks and integrated to obtain the medical information data sub-items corresponding to each timestamp block.

[0020] Further, step S2 includes the following steps:

[0021] Step S21: performing cross-dimensional correlation analysis on the medical information data sub-items corresponding to each timestamp block to obtain cross-dimensional correlation sub-items of medical information corresponding to each timestamp block;

[0022] Step S22: extracting information resource attributes of the cross-dimensional associated sub-items of medical information corresponding to each timestamp block, so as to obtain information resource attribute elements of the medical information sub-items corresponding to each timestamp block;

[0023] Step S23: Based on the information resource attribute element of the medical information sub-item corresponding to each timestamp block, perform FHIR format semantic mapping on each sub-data in the corresponding cross-dimensional associated sub-item of the medical information, and obtain the FHIR format resource object mapping field of the medical information sub-item corresponding to each timestamp block;

[0024] Step S24: performing FHIR formatted output processing on the cross-dimensional associated sub-items of medical information corresponding to each timestamp block based on the FHIR format resource object mapping fields of the medical information sub-items corresponding to each timestamp block, so as to obtain the FHIR standard sub-items of medical information corresponding to each time block;

[0025] Step S25: Perform digital file conversion on the FHIR standard sub-item of medical information corresponding to each time block to generate a digital sub-file of medical information corresponding to each time block.

[0026] Further, step S25 includes the following steps:

[0027] Extract FHIR metadata for the FHIR standard sub-items of medical information corresponding to each time block to obtain the FHIR metadata of medical information corresponding to each time block;

[0028] Based on the medical information FHIR metadata corresponding to each time block, the corresponding medical information FHIR standard sub-item is extracted to obtain the medical information FHIR summary corresponding to each time block;

[0029] Based on the FHIR summary of the medical information corresponding to each time block, the corresponding FHIR standard sub-item of the medical information is processed by FHIR semantic layering to obtain the FHIR sub-item-level hierarchical document of the medical information corresponding to each time block;

[0030] The medical information FHIR sub-item-level hierarchical documents corresponding to each time block are converted into digital files to generate digital sub-files of medical information corresponding to each time block.

[0031] Further, step S3 includes the following steps:

[0032] Step S31: generating a distributed storage block on the cloud computing platform, and distributively storing the digital medical information sub-files corresponding to each time block in the corresponding distributed storage block on the cloud computing platform;

[0033] Step S32: obtaining the creation time, modification time and version information of each corresponding distributed storage block on the cloud computing platform, and generating a block identity identifier based on the creation time, modification time and version information to perform block identity identification processing on the corresponding distributed storage block to generate a distributed storage identity identification block;

[0034] Step S33: performing fault-tolerant recovery dynamic link processing on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information distributed storage dynamic chain;

[0035] Step S34: Based on the block identity identifier link sequence corresponding to the medical information distributed storage dynamic chain, each corresponding distributed storage identity block on the cloud computing platform is managed by block link storage to generate a medical information digital block storage sequence.

[0036] Further, step S33 includes the following steps:

[0037] Perform medical information association mining analysis on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information association connection relationship between each distributed identification block;

[0038] Based on the medical information association connection relationship between each distributed identification block, the block identity identifier corresponding to the distributed storage identity block on the cloud computing platform is pointed to the chain link processing to generate a medical information distributed storage association relationship chain;

[0039] Perform storage fault tolerance and disaster recovery dynamic evaluation and analysis on each distributed storage identity block corresponding to the medical information distributed storage association chain to obtain the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes;

[0040] Based on the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes, the storage fault tolerance recovery capacity calculation formula is used to perform capacity quantification calculation on each distributed storage identity identification block corresponding to the medical information distributed storage association chain, so as to obtain the storage fault tolerance recovery capacity value corresponding to each distributed identification block at different dynamic time nodes;

[0041] According to the preset storage fault tolerance recovery capability threshold, the storage fault tolerance recovery capability value corresponding to each distributed identification block at different dynamic time nodes is compared and judged. If the storage fault tolerance recovery capability value is greater than or equal to the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are explicitly scheduled and linked; if the storage fault tolerance recovery capability value is less than the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are implicitly scheduled and linked to generate a medical information distributed storage dynamic chain.

[0042] Furthermore, the storage fault tolerance recovery capability calculation formula is specifically:

[0043]

[0044]

[0045]

[0046] In the formula, C f (t) is the storage fault tolerance recovery capability value corresponding to the distributed identification block at the dynamic time node t, t0 is the starting time point corresponding to the distributed identification block, t1 is the ending time point corresponding to the distributed identification block, S f (t) is the storage fault tolerance efficiency of the distributed identification block corresponding to the dynamic time node t, T o (t) is the normal recovery time of the distributed identification block corresponding to the fault at the dynamic time node t, T l (t) is the total system operation time corresponding to the distributed identification block at the dynamic time node t, α is the fault tolerance efficiency adjustment coefficient, R d (t) is the information disaster recovery efficiency of the distributed identification block corresponding to the dynamic time node t, T g (t) is the target recovery duration of the distributed identification block corresponding to the dynamic time node t, T a (t) is the target data loss duration corresponding to the distributed identification block at the dynamic time node t, P g (t) is the amount of data required for target recovery corresponding to the distributed identification block at the dynamic time node t, P a (t) is the target data loss amount corresponding to the distributed identification block at the dynamic time node t, β is the disaster recovery efficiency adjustment coefficient, τ is the integral time variable parameter, F(τ) is the redundant storage space occupation ratio corresponding to time τ, H(τ) is the maximum number of failures supported by the redundant strategy corresponding to time τ, γ is the redundant strategy contribution adjustment coefficient, and η is the correction coefficient of the storage fault tolerance recovery capability value.

[0047] Further, step S4 includes the following steps:

[0048] Step S41: Obtaining medical information access authorization from the cloud computing platform using smart contracts;

[0049] Step S42: Based on the medical information access authorization, the authorization access control rule is configured and designed for each distributed storage block in the medical information digital block storage sequence to generate an authorization access control policy rule corresponding to each medical information distributed storage block, wherein the authorization access control policy rule includes access conditions, authorization levels, and execution time windows;

[0050] Step S43: Based on the authorization access control policy rules corresponding to each medical information distributed storage block, the corresponding distributed storage blocks in the medical information digital block storage sequence are managed for authorization access, and the medical information authorization access frequency and access period are dynamically adjusted to execute corresponding medical information access data sharing management.

[0051] Furthermore, the present invention also provides a medical information digital management system for executing the medical information digital management method as described above, the medical information digital management system comprising:

[0052] The medical information time-series block integration module is used to obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information corresponding to different systems of medical institutions, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information in time-series blocks, so as to obtain the medical information data sub-items corresponding to each timestamp block;

[0053] The medical information block digital conversion module is used to perform FHIR formatting on the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; and to perform digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block;

[0054] The medical information block link storage management module is used to store the medical information digitized sub-files corresponding to each time block in a distributed storage block corresponding to the cloud computing platform, and to perform block link storage management on each corresponding distributed storage block on the cloud computing platform, thereby generating a medical information digitized block storage sequence;

[0055] The medical information authorization access management module is used to use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorization access management on each distributed storage block in the medical information digital block storage sequence to execute corresponding medical information access data sharing management.

[0056] Beneficial effects of the present invention:

[0057] 1. Compared with the prior art, the method for digital management of medical information proposed in the present invention has the beneficial effect of obtaining the corresponding patient basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information from the corresponding medical information subsystem of the medical institution, which can ensure the comprehensive and accurate collection of the patient's comprehensive medical information, thereby providing a solid foundation for subsequent medical services and decision-making. The patient's basic information usually includes but is not limited to name, gender, date of birth, contact information, home address, etc., the medical history information includes but is not limited to past hospitalization records, surgical history, allergy history, family medical history, etc., the diagnosis and treatment record information includes various examinations, tests, diagnosis and treatment records, etc. received by the patient during the medical process, and the imaging data usually includes X-rays, CT scans, MRI images, ultrasonic images, etc., thereby providing basic data guarantee for the subsequent digital processing of medical information. At the same time, by integrating the patient's basic information, patient medical history information, patient treatment record information, and patient medical imaging information in time series, it can help build a time-series patient health record, which provides more comprehensive and efficient data support for medical information management. By integrating different types of information in time series, it can not only achieve data sharing and intercommunication between different medical institutions and departments, but also clearly display the medical record process at each stage, thereby further improving the efficiency and quality of medical information digital management services. Secondly, by formatting the medical information data sub-items corresponding to each timestamp block with FHIR, it can ensure that all medical data complies with the FHIR international standard and can circulate between different medical information platforms. This output is not only the conversion of data format, but also includes the standardized processing of data content. By following the FHIR standard, it can ensure that the structure, field, coding and other elements of medical information are aligned with globally recognized specifications, thereby enhancing the versatility and compatibility of data. It can enable medical information systems to seamlessly connect with different technical platforms and applications, and improve the credibility and security of medical information management. By converting the FHIR standard sub-items of medical information corresponding to each time block into digital files, the FHIR standard sub-items can be converted into digital files, which can be stored, transmitted and processed more conveniently. The digital file conversion not only improves the usability of the data, but also ensures the long-term preservation and convenient query of the information. In the process of digitization of medical information, the reliability, integrity and traceability of medical data are further guaranteed. For the data of each time block, the generation of digital files can effectively integrate data from different sources, provide support for the analysis, statistics and display of medical information, make clinical work, data statistics and information sharing more efficient, and help promote the comprehensive upgrade of medical information data utilization.Then, by distributing and storing the digital sub-files of medical information corresponding to each time block in the corresponding distributed storage blocks on the cloud computing platform, it is helpful to achieve efficient management and secure storage of data. By dividing the data into multiple distributed storage blocks, each block can be stored independently and support horizontal expansion, thereby optimizing the storage capacity and system performance, while providing convenient support for cross-regional access and sharing of medical information. By also performing block link storage management on each corresponding distributed storage block on the cloud computing platform, accurate sequential management and efficient retrieval of stored data can be achieved. The link sequence of each distributed storage identity block forms a structured storage sequence, which enables the rapid location and access of the required data according to the block's identity identifier. This sequential management not only improves the orderliness of data storage, avoids repeated storage of redundant data, ensures the efficient operation of the storage process and the continuity of data, but also helps to achieve standardized, intelligent and automated management of medical information. Finally, by using smart contracts to obtain medical information access authorization from the cloud computing platform, an automated and secure mechanism is provided to manage access control. Smart contracts are an automatic execution protocol based on blockchain technology. They set and automatically verify rules through code to ensure that all operations are executed under predetermined rules and conditions. Any authorization request must undergo preset verification and condition checks so that only authorized users and systems can access specific medical information data blocks. In addition, by authorizing access management for each distributed storage block in the storage sequence of the medical information digitization block based on medical information access authorization, the authorized access frequency and access period of medical information can be dynamically adjusted to ensure that the access to medical information is not only carried out within the scope of compliance, but also can be flexibly responded to according to actual needs. The authorized access control strategy of each medical information digitization block provides a multi-dimensional management method for data access based on preset access conditions, authorization levels and execution time windows. Through the dynamic adjustment of access frequency and access period, the use of data can be optimized according to the actual needs of data sharing, making the shared management of medical information more intelligent and efficient, while not losing the strict protection of medical information data.

[0058] 2. The medical information digital management system proposed in the present invention is generally composed of a medical information time-series block integration module, a medical information block digital conversion module, a medical information block link storage management module and a medical information authorization access management module. It can implement any medical information digital management method described in the present invention, and is used to combine the operations between computer programs running on various modules to implement the medical information digital management method. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient medical information digital management process, thereby simplifying the operating procedures of the medical information digital management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0060] Figure 1 A schematic diagram of the steps of the method for digital management of medical information according to the present invention;

[0061] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0062] Figure 3 for Figure 2 Detailed step flow chart of step S15 in FIG. DETAILED DESCRIPTION

[0063] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0064] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for digital management of medical information, the method comprising the following steps:

[0065] Step S1: Obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information corresponding to different systems of the medical institution, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain the medical information data sub-items corresponding to each timestamp block;

[0066] Step S2: FHIR formatting the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; performing digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block;

[0067] Step S3: Distribute and store the medical information digitized sub-files corresponding to each time block in the corresponding distributed storage blocks on the cloud computing platform, and perform block link storage management on each corresponding distributed storage block on the cloud computing platform to generate a medical information digitized block storage sequence;

[0068] Step S4: Use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorized access management on each distributed storage block in the medical information digital block storage sequence to perform corresponding medical information access data sharing management.

[0069] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the method for digital management of medical information of the present invention. In this example, the method for digital management of medical information includes the following steps:

[0070] Step S1: Obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information corresponding to different systems of the medical institution, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain the medical information data sub-items corresponding to each timestamp block;

[0071] In the embodiment of the present invention, the basic information of the patient is extracted by exchanging data with the medical information subsystem of the medical institution. This operation is completed through API calls. The basic data of the patient, including name, gender, age, address, contact information, insurance information, etc., is queried from the medical information database using the patient's unique identity identifier (such as the patient number or ID number), thereby obtaining the basic information of the patient. The medical history information of the patient is obtained through the electronic medical record subsystem of the medical institution. The specific operation is that the system calls the electronic medical record database interface, uses the patient identifier (such as the medical card number or the hospitalization number) as the query condition, and retrieves the historical medical record of the patient. After the data is extracted, the disease diagnosis, treatment history, allergic reaction, family history and other information in the medical record are structured and stored, thereby obtaining the patient's medical history information. At the same time, when extracting the patient's medical record information from the medical record information subsystem, the patient's medical record, diagnosis results, treatment plan and medication information are obtained by querying the patient's medical record database. This operation is performed through SQL query, using the patient's ID for accurate retrieval. The medical records are divided into medical events at different time nodes, including diagnosis date, attending physician, treatment measures and follow-up records, etc., so as to obtain the patient's medical record information. The medical imaging information of the patient is also obtained by calling the medical imaging subsystem API. The specific steps are to access the image repository according to the patient's identity information, extract the patient's historical imaging data, including X-rays, CT scan results, MRI images, etc. These imaging data are usually stored in DICOM format, so as to obtain the patient's medical imaging information. Then, all data are sorted by timestamp, and the patient's basic information, medical history information, medical record information and medical imaging information are integrated in time series blocks according to the timeline. The data of each time node (such as basic information, medical history information, medical record, imaging information) is summarized as a data sub-item. The structure of the data sub-item is usually a timestamp and the corresponding medical information field. The integrated data is associated through a specific algorithm (such as time series analysis) to ensure that all data are arranged neatly in chronological order, and finally the medical information data sub-items corresponding to each timestamp block are obtained.

[0072] Step S2: FHIR formatting the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; performing digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block;

[0073] In an embodiment of the present invention, cross-dimensional association analysis is performed on the medical information data sub-items corresponding to each timestamp block to achieve structured processing of the medical information data. Each data item is distinguished based on the timestamp. When performing cross-dimensional association analysis, it is necessary to use association rule mining technology, such as the Apriori algorithm or the FP-growth algorithm, to mine the potential correlation between different data items based on the temporal nature of the medical data and the relationship between dimensions, and to extract information resource attributes from the medical information associated sub-items corresponding to each timestamp block. This step requires parsing the data in each cross-dimensional associated sub-item to extract its related metadata attributes. For example, if a certain associated sub-item contains the patient's medication information, diagnostic information and test results, then each information point needs to be semantically annotated and classified first. The attributes of the medication information include the drug name, dosage, frequency, start time and end time, etc.; the attributes of the diagnostic information include the diagnosis Diagnosis type, symptom description, clinical manifestations, etc.; the attributes of the test results include test items, test values, reference ranges, etc. The medical information resource attributes corresponding to each timestamp block are extracted and formed into information resource attribute elements. At the same time, FHIR format semantic mapping is performed based on the information resource attribute elements corresponding to each timestamp block. At this time, the resource model defined by the FHIR standard is used for data conversion, and non-standardized medical information is mapped to FHIR resource objects. For example, the patient's diagnosis information can be mapped to the FHIR Condition resource, the drug treatment information can be mapped to the MedicationRequest resource, and the test results can be mapped to the Observation resource. In this process, data is filled based on the predefined fields in the FHIR standard, and data type and FHIR formatted output processing are performed at the same time. At this time, each semantically mapped FHIR resource object is converted into a standard FHIR. JSON or XML formatted data files are formatted and output. This process uses conversion tools supported by FHIR, such as FHIR servers or FHIR conversion services (such as HAPI FHIR servers), to output different types of medical information resources as data files that comply with the FHIR standard, and generate standard JSON or XML files according to the requirements of the FHIR format. For example, if a patient's diagnosis information is converted into a Condition resource object, the resource will be output according to the FHIR JSON specification to ensure that each field (such as diagnosis code, symptom description, date, etc.) is accurately filled in the corresponding position, so as to obtain the medical information FHIR standard sub-items corresponding to each time block.Then, digital file conversion is performed on the FHIR standard sub-items of medical information corresponding to each time block. At this time, the FHIR format data (JSON or XML) is converted into a digital file format suitable for storage, transmission and management by using a digital file conversion tool. Common conversion methods include compressing FHIR JSON or XML data into PDF, CSV, or encrypted ZIP file formats for storage and backup. During the conversion process, metadata can also be added to the digital files, such as timestamps, file versions, document classifications, etc., to ensure that the final digital sub-files meet the data management requirements of the enterprise or medical institution, and ensure the security and privacy of the data during transmission and storage. Finally, the digital sub-files of medical information corresponding to each time block are converted.

[0074] Step S3: Distribute and store the medical information digitized sub-files corresponding to each time block in the corresponding distributed storage blocks on the cloud computing platform, and perform block link storage management on each corresponding distributed storage block on the cloud computing platform to generate a medical information digitized block storage sequence;

[0075] In an embodiment of the present invention, by generating distributed storage blocks on a cloud computing platform and distributing and storing the digitized sub-files of medical information in corresponding blocks, it is first necessary to create several storage blocks through the distributed storage system of the platform. Each storage block will be used to store a certain range of medical information sub-files. These digitized data files are distributed and stored on the cloud computing platform. Through the distributed storage block technology of the platform, the data is automatically distributed to multiple physical storage devices to ensure the reliability and scalability of storage. The specific storage location of the distributed storage block will be dynamically allocated according to the data size, storage strategy and load balancing algorithm to ensure that the medical information data is evenly distributed among multiple nodes, while ensuring that the storage blocks can be quickly accessed and efficiently retrieved. By obtaining and processing the creation time, modification time and version information of each distributed storage block on the cloud computing platform, and generating a unique block identity identifier (Block ID) for each storage block. The generated block identifier will be associated with the corresponding distributed storage block. Then, the storage block will be managed by block linking based on the linking sequence of the identifiers corresponding to the medical information distributed blocks. Distributed ledger technology (such as blockchain technology) will be used to maintain the order and link relationship of each distributed storage identity block. Each storage block will be assigned a sequence number to form a chain structure in the order of timestamp or version control. The links between blocks are associated through block identifiers. Specifically, a sequence number will be generated according to metadata such as the creation time and modification time of each block, and it will be ensured that the link relationship of each block is correct and smooth. For example, when medical information data is first stored, the first block will be generated according to the creation time, and subsequent blocks will be connected in sequence according to information such as modification time and version number to form an ordered storage chain. In the process of linking storage management, a global block storage sequence will be maintained, and finally the storage will generate a medical information digitization block storage sequence.

[0076] Step S4: Use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorized access management on each distributed storage block in the medical information digital block storage sequence to perform corresponding medical information access data sharing management.

[0077] In an embodiment of the present invention, smart contracts are used to interact with a cloud computing platform to ensure that access authorization to medical information is obtained legally and automatically. First, the smart contract is deployed on a blockchain network to communicate with the API interface of the cloud computing platform. The smart contract contains rules and conditions for authorized access. These rules can be dynamically set based on the type and sensitivity of the medical information and the identity authentication of the access requester. When a user (such as a doctor, medical institution or patient) makes an access request, the smart contract verifies the identity and authority of the requester through an on-chain mechanism. By querying the authentication service module of the cloud platform, the smart contract can confirm whether the requester is qualified to access the requested medical information, thereby obtaining the corresponding medical information access authorization. After obtaining the authorization to access medical information, a specific authorization access control strategy is designed and configured for each medical information data block. First, the medical information is stored in a distributed storage system in a digital form. Each information data block is assigned a unique identifier. For the access control of each data block, a corresponding access control strategy is generated based on the previously obtained access authorization. The configuration of this strategy covers multiple aspects, including access conditions, authorization levels, and execution time windows. The access conditions involve the identity authentication of the requesting party (such as an identity authentication code or biometric information), specific medical scenarios (such as medical record query, medical record access, etc.), and environmental factors of access (such as device security or network security status); the authorization level is divided into different levels according to the type and permission of the access request, such as read-only, modifiable, deletable, etc.; and the execution time window controls the timeliness of access, stipulates that the access permission is valid within a certain period of time, and prevents unauthorized behavior. For example, a doctor can access a certain The data of some patients can be read and modified, but they have no right to access during non-duty hours. By applying this fine-grained control strategy to each storage block, access management for each medical information data block is implemented to achieve dynamic sharing and access control of medical information. Whenever the requester makes an access request to a specific medical data block, real-time access verification will be performed according to the configured authorization access control policy. During the verification process, the identity information of the requester, the access level requested, and the time of the request will be compared to see if they meet the set conditions. For example, when a doctor tries to access the medical records of patient A, the blockchain will be used to verify whether the doctor is within the authorization time window, whether he meets the authorization access level, and whether the access conditions (such as doctor role, patient consent, etc.) are met. If all conditions are met, access will be allowed; if not, the access request will be rejected and the reason for rejection will be recorded to ensure that the access rules of all relevant parties are always consistent, and ultimately the corresponding medical information access data sharing management process will be implemented.

[0078] Further, as an embodiment of the present invention, refer to Figure 2 As shown, Figure 1Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0079] Step S11: Obtaining corresponding basic information of the patient from the corresponding medical information subsystem of the medical institution;

[0080] In an embodiment of the present invention, the basic information of the patient is extracted by interacting with the medical information subsystem of the medical institution. This operation is completed through an API call. The patient's unique identity (such as patient number or ID number) is used to query the patient's basic data from the medical information database, including name, gender, age, address, contact information, insurance information, etc. After the data is extracted, this information is stored in the memory cache for subsequent use, and the patient's basic information is finally obtained.

[0081] Step S12: Obtaining corresponding patient medical history information from the corresponding electronic medical record subsystem of the medical institution;

[0082] In an embodiment of the present invention, the patient's medical history information is obtained through the electronic medical record subsystem of the medical institution. The specific operation is that the system calls the electronic medical record database interface, uses the patient identifier (such as medical card number or hospitalization number) as the query condition, and retrieves the patient's historical medical record. After the data is extracted, the disease diagnosis, treatment history, allergic reaction, family medical history and other information in the medical record are structured and stored, and time is sorted according to the timestamp of the record to ensure the timing correctness during subsequent integration, and finally the patient's medical history information is obtained.

[0083] Step S13: Obtaining corresponding patient diagnosis and treatment record information from the corresponding medical record information subsystem of the medical institution;

[0084] In an embodiment of the present invention, when extracting the patient's medical record information from the medical record information subsystem, the patient's medical record, diagnosis results, treatment plan and medication information are obtained by querying the patient's medical record database. This operation is performed through SQL query, and the patient's ID is used for accurate retrieval. The medical records are divided into medical events at different time nodes, including diagnosis date, attending physician, treatment measures and follow-up records, etc., which are stored in chronological order and associated with the patient's medical history information to provide a complete medical record track, and finally the patient's medical record information is obtained.

[0085] Step S14: obtaining corresponding patient medical imaging information from the corresponding medical imaging subsystem of the medical institution;

[0086] In an embodiment of the present invention, obtaining the patient's medical imaging information is achieved by calling the medical imaging subsystem API. The specific steps are to access the image repository based on the patient's identity information, extract the patient's historical imaging data, including X-rays, CT scan results, MRI images, etc. These imaging data are usually stored in DICOM format, and the acquired image files are parsed to extract key metadata (such as imaging time, location, image type, etc.), and store the imaging information by timestamp, and finally obtain the patient's medical imaging information.

[0087] Step S15: Integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain medical information data sub-items corresponding to each timestamp block.

[0088] In an embodiment of the present invention, when integrating various types of patient medical information, all data are sorted by timestamp, and the patient's basic information, medical history information, medical record information and medical imaging information are integrated in time series blocks according to the timeline. The data of each time node (such as basic information, medical history information, medical record, imaging information) is summarized as a data sub-item. The structure of the data sub-item is usually a timestamp and a corresponding medical information field. The integrated data is associated through a specific algorithm (such as time series analysis) to form a comprehensive medical file of the patient, and is updated and displayed in the electronic health record system to ensure that all data is neatly arranged in chronological order, and finally the medical information data sub-items corresponding to each timestamp block are obtained.

[0089] Further, as an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S15 in FIG. 1 , in this embodiment, step S15 includes the following steps:

[0090] Step S151: Synchronize and integrate the patient's basic information, the patient's medical history information, the patient's diagnosis and treatment record information, and the patient's medical imaging information in a time series to generate a corresponding patient medical information time series set in the same time dimension;

[0091] In an embodiment of the present invention, the patient's basic information, medical history information, medical record information and medical imaging information that have been previously collected and extracted are integrated in a time-series synchronization manner. The specific implementation method is to extract the following four types of data from the patient information management system for each patient: basic information (such as name, age, gender, etc.), medical history information (such as previous medical history, family history, allergy history, etc.), medical record information (such as outpatient, hospitalization, surgery, examination records, etc.) and medical imaging information (such as X-ray, CT, MRI and other imaging materials). These information are sorted according to timestamps, and different categories of data are integrated into a unified time dimension through time alignment technology (for example, based on timestamp matching, time interval merging, etc.). All data are associated based on the patient's unique identifier to ensure that each piece of data can be accurately matched with the corresponding patient information, and finally generate a corresponding patient medical information time series set under the same time dimension.

[0092] Step S152: performing data cleaning processing on the corresponding patient medical information time series set in the same time dimension to remove redundant and erroneous medical information, and obtaining the patient medical information time series cleaned set;

[0093] In an embodiment of the present invention, data cleaning is performed on a patient medical information time series set that has previously undergone time series synchronization. The main purpose of data cleaning is to remove redundant data and erroneous data. The specific operation process includes identifying and removing duplicate medical records through a rule engine, such as duplicate records of the same examination items, medical operations or drug prescriptions; applying error detection algorithms (such as outlier detection, missing value filling, data consistency check, etc.) to process the medical information time series set to detect and correct outliers in the data. For example, if the medical treatment time of a patient overlaps or is in reverse order, it will be automatically marked and prompted for subsequent manual processing; in addition, for the patient's imaging data, if there is a situation where it cannot be decoded or the format is wrong, these data will be automatically eliminated to ensure the accuracy and completeness of the information, and finally obtain the patient's medical information time series cleaned set.

[0094] Step S153: Obtain the corresponding patient's first medical treatment time node and the patient's last medical treatment time node through the patient's medical information time series cleaning set, and determine the medical timestamp range segment of each patient according to the patient's first medical treatment time node and the patient's last medical treatment time node;

[0095] In an embodiment of the present invention, the patient's first medical time node and last medical time node are obtained based on the patient's medical information time series cleaning set, and the medical timestamp range segments of each patient are delineated according to these time nodes. First, the earliest diagnosis and treatment time is extracted from the patient's cleaned medical information time series set as the patient's first medical time node, and the latest diagnosis and treatment time is extracted as the last medical time node. For example, if the patient's earliest medical record is March 10, 2018, and the last diagnosis and treatment record is August 20, 2022, then the patient's medical time range is from March 10, 2018 to August 20, 2022, and based on the interval between the first medical time node and the last medical time node, the patient's medical timestamp range is automatically calculated to form multiple time segments. The span of each segment can be divided according to the frequency of diagnosis and treatment activities (for example, by month, quarter or year), and can also be customized according to the actual diagnosis and treatment situation to ensure that each segment contains meaningful medical information, and finally obtain the medical timestamp range segments of each patient.

[0096] Step S154: Based on the medical timestamp range segments of each patient, the patient medical information time series cleaning set is divided into information time series blocks and integrated to obtain the medical information data sub-items corresponding to each timestamp block.

[0097] In an embodiment of the present invention, the patient's medical information time series cleansing set is integrated into information time series blocks in combination with the previously determined timestamp range segments. The specific operation is to divide the cleaned medical information into time periods according to the designated timestamp segments, and classify all data items in each time period into a data subset. For example, if the timestamp range segment is from January 1, 2020 to March 31, 2020, all medical records, examination reports, imaging materials and other data within this period will be gathered together to form a medical information data sub-item. Similarly, if a patient receives different treatments or examinations in different time periods, an independent medical information subset will be generated for each time period to ensure that the medical records in each time period correspond to the patient's health status, and the data is not repeated and the logic is clear. These block-integrated data subsets are convenient for subsequent analysis, query and report generation, which improves the data processing efficiency and accuracy, ensures the high quality and high availability of the patient's medical information, and finally obtains the medical information data sub-items corresponding to each timestamp block.

[0098] Further, step S2 includes the following steps:

[0099] Step S21: performing cross-dimensional correlation analysis on the medical information data sub-items corresponding to each timestamp block to obtain cross-dimensional correlation sub-items of medical information corresponding to each timestamp block;

[0100] In an embodiment of the present invention, cross-dimensional correlation analysis is performed on the medical information data sub-items corresponding to each timestamp block to achieve structured processing of the medical information data, divide it according to timestamp blocks, and identify the medical data sub-items contained in each block. For example, for the patient's medical record data, laboratory test data, imaging examination data, etc., each data is distinguished based on the timestamp. When performing cross-dimensional correlation analysis, it is necessary to use association rule mining technology, such as the Apriori algorithm or the FP-growth algorithm, to mine the potential correlation between different data items based on the temporal nature of medical data and the relationship between dimensions. For example, the laboratory test results at a certain point in time are associated with the subsequent imaging examination results, or the drug prescription at a certain point in time is directly related to the subsequent changes in the patient's condition. These correlation information are stored in the database to form cross-dimensional correlation sub-items of each timestamp block, and finally the cross-dimensional correlation sub-items of medical information corresponding to each timestamp block are obtained.

[0101] Step S22: extracting information resource attributes of the cross-dimensional associated sub-items of medical information corresponding to each timestamp block, so as to obtain information resource attribute elements of the medical information sub-items corresponding to each timestamp block;

[0102] In an embodiment of the present invention, information resource attributes are extracted from cross-dimensional associated sub-items of medical information corresponding to each timestamp block. This step requires parsing the data in each cross-dimensional associated sub-item to extract its related metadata attributes. For example, if a cross-dimensional associated sub-item contains the patient's medication information, diagnostic information and test results, then each information point needs to be semantically annotated and classified first. The attributes of medication information include drug name, dosage, frequency, start time and end time, etc.; the attributes of diagnostic information include diagnosis type, symptom description, clinical manifestations, etc.; the attributes of test results include test items, test values, reference ranges, etc. Based on the standardization requirements of medical information, the HL7FHIR (Fast Healthcare Interoperability Resources) data model can be used for attribute annotation. Through automated tools, such as the FHIR toolkit, the medical information resource attributes corresponding to each timestamp block are extracted and information resource attribute elements are formed, and finally the information resource attribute elements of the medical information sub-items corresponding to each timestamp block are obtained.

[0103] Step S23: Based on the information resource attribute element of the medical information sub-item corresponding to each timestamp block, perform FHIR format semantic mapping on each sub-data in the corresponding cross-dimensional associated sub-item of the medical information, and obtain the FHIR format resource object mapping field of the medical information sub-item corresponding to each timestamp block;

[0104] In an embodiment of the present invention, FHIR format semantic mapping is performed based on the information resource attribute element of the medical information sub-item corresponding to each timestamp block. At this time, the resource model defined by the FHIR standard is used for data conversion, and the non-standardized medical information is mapped to the FHIR resource object. For example, the patient's diagnosis information can be mapped to the FHIR Condition resource, the drug treatment information can be mapped to the MedicationRequest resource, and the test result can be mapped to the Observation resource. In this process, data is filled based on the predefined fields in the FHIR standard, and the data type and format are converted at the same time. This step also involves data validation to ensure that the mapped data meets the FHIR format requirements. For example, the time field must comply with the ISO8601 standard, and the numerical field needs to comply with the unit and range defined in FHIR. This process is usually implemented by writing FHIR conversion rules or using the FHIR API, and finally the FHIR format resource object mapping field corresponding to the medical information sub-item of each timestamp block is obtained.

[0105] Step S24: performing FHIR formatted output processing on the cross-dimensional associated sub-items of medical information corresponding to each timestamp block based on the FHIR format resource object mapping fields of the medical information sub-items corresponding to each timestamp block, so as to obtain the FHIR standard sub-items of medical information corresponding to each time block;

[0106] In an embodiment of the present invention, the FHIR format resource object mapping fields of the medical information sub-items corresponding to each timestamp block previously determined are combined to perform FHIR formatted output processing on the corresponding cross-dimensional associated sub-items of the medical information. At this time, each FHIR resource object after semantic mapping is converted into a data file in standard FHIR JSON or XML format, and formatted and output. This process uses a conversion tool supported by FHIR, such as an FHIR server or an FHIR conversion service (such as a HAPI FHIR server), to output different types of medical information resources as data files that comply with the specification in accordance with the FHIR standard. During the output process, the integrity and consistency of the data are automatically verified, and a standard JSON or XML file is generated in accordance with the requirements of the FHIR format. For example, if a patient's diagnosis information is converted into a Condition resource object, the resource will be output in accordance with the FHIR JSON specification to ensure that each field (such as diagnosis code, symptom description, date, etc.) is accurately filled in the corresponding position, and finally the medical information FHIR standard sub-item corresponding to each time block is obtained.

[0107] Step S25: Perform digital file conversion on the FHIR standard sub-item of medical information corresponding to each time block to generate a digital sub-file of medical information corresponding to each time block.

[0108] In an embodiment of the present invention, digital file conversion is performed on the FHIR standard sub-items of medical information corresponding to each time block. At this time, the data in FHIR format (JSON or XML) is converted into a digital file format suitable for storage, transmission and management by using a digital file conversion tool. Common conversion methods include compressing FHIR JSON or XML data into PDF, CSV, or encrypted ZIP file formats for storage and backup. During the conversion process, metadata such as timestamp, file version, document classification, etc. can also be added to the digital file. The key technologies of this step include the selection of file format and the compression and encryption of data. The existing PDF generation library or file compression tool can be used to ensure that the final generated digital sub-file meets the data management requirements of the enterprise or medical institution, and ensure the security and privacy of the data during transmission and storage. Finally, the digital sub-files of medical information corresponding to each time block are converted and generated.

[0109] Further, step S25 includes the following steps:

[0110] Extract FHIR metadata for the FHIR standard sub-items of medical information corresponding to each time block to obtain the FHIR metadata of medical information corresponding to each time block;

[0111] In an embodiment of the present invention, metadata of medical information corresponding to each time block is extracted from the FHIR standard sub-items of medical information corresponding to each time block previously divided. These metadata include basic information of the patient, type of medical service, treatment record, examination result, drug usage, medical imaging information, etc. In a specific operation, the medical record is first divided into blocks according to the time node to form a set of records of multiple time periods, and the FHIR resource type related to the patient's medical record is extracted from the record of each time period, for example, Patient, Observation, Procedure, MedicationRequest, etc. When extracting, the API interface defined in the FHIR standard is used to obtain the resource metadata corresponding to each time block through RESTful calls. These resource metadata will include various fields, such as resource ID, timestamp, resource status, associated medical record, etc., and all FHIR metadata corresponding to each time block are obtained, and finally the FHIR metadata of medical information corresponding to each time block is obtained.

[0112] Preferably, based on the medical information FHIR metadata corresponding to each time block, the corresponding medical information FHIR standard sub-item is subjected to FHIR summary extraction to obtain the medical information FHIR summary corresponding to each time block;

[0113] In an embodiment of the present invention, the key medical information summary is further extracted from the FHIR metadata of each time block. The specific operation is to extract the key medical information sub-items in each time block, such as the patient's chief complaint, diagnosis, treatment plan, examination results, etc., through structural analysis of the FHIR metadata. In terms of technical implementation, the text information in the FHIR resource is processed using natural language processing (NLP) technology to extract summary information. For example, the patient's vital sign data is extracted from the Observation resource, or the name, dosage and usage of the drug is extracted from the MedicationRequest, and these key information are aggregated according to the order of the time blocks to form the FHIR summary of each time block. In order to ensure the accuracy of the summary information, a data mining algorithm is used to compare the medical information of different time periods, eliminate redundant information, retain the most representative medical summary content, generate a medical information FHIR summary file for each time block, and finally obtain the medical information FHIR summary corresponding to each time block.

[0114] Preferably, based on the FHIR summary of the medical information corresponding to each time block, the corresponding FHIR standard sub-item of the medical information is subjected to FHIR semantic layering processing to obtain a FHIR sub-item-level hierarchical document of the medical information corresponding to each time block;

[0115] In the embodiment of the present invention, the corresponding medical information FHIR standard sub-items are processed at the semantic level by combining the previously extracted medical information FHIR summary, with the purpose of classifying and grading the medical information according to the hierarchical structure for subsequent reference and management. First, the FHIR summary information in each time block is semantically analyzed to identify different types of medical information, such as disease diagnosis, treatment methods, drug use, etc., and hierarchical processing is performed according to the semantic relationship of this information. Specifically, domain-specific ontologies (such as medical ontologies) and natural language processing techniques can be used to classify relevant medical information. FHIR sub-items are classified into different semantic levels, for example: level 1 (such as disease name), level 2 (such as drug name, treatment method), level 3 (such as drug dosage, course of treatment), etc. Through semantic analysis and hierarchical modeling, the same type of medical information is classified into the same category, and different categories of medical information are placed at different levels. This process can be completed using ontology building tools (such as OWL) and knowledge graph technology to generate hierarchical FHIR documents, which record the semantic structure and relationship of medical information within the time block, and finally obtain the FHIR sub-item-level hierarchical documents of medical information corresponding to each time block.

[0116] Preferably, the medical information FHIR sub-item-level hierarchical documents corresponding to each time block are converted into digital files to generate digital sub-files of the medical information corresponding to each time block.

[0117] In an embodiment of the present invention, the FHIR document that has been hierarchically processed is converted into a digital format to facilitate storage, query and transmission. The specific operation steps include selecting an appropriate digital file format, such as XML, JSON or Parquet, according to the FHIR sub-item-level hierarchical document structure of each time block. These formats have good compatibility and data storage advantages, and converting the FHIR hierarchical document into a target format by using a corresponding file conversion tool or library (such as xml.etree.ElementTree, json module or Apache Parquet library in Python). During the conversion process, it is ensured that the structure and hierarchical relationship of the medical information are accurately preserved. For example, the medical information summary, hierarchical structure, field content, etc. of each time block should remain complete and accurate after conversion. In order to improve the readability and retrieval efficiency of the digital file, the converted file can be compressed to reduce the file size and accelerate data exchange. The generated digital file will contain the complete medical information of each time block, and finally generate a digital sub-file of medical information corresponding to each time block.

[0118] Further, step S3 includes the following steps:

[0119] Step S31: generating a distributed storage block on the cloud computing platform, and distributively storing the digital medical information sub-files corresponding to each time block in the corresponding distributed storage block on the cloud computing platform;

[0120] In an embodiment of the present invention, by generating distributed storage blocks on a cloud computing platform and distributing and storing the digitized sub-files of medical information in the corresponding blocks, it is first necessary to create several storage blocks through the distributed storage system of the platform, and each storage block will be used to store a certain range of medical information sub-files. The specific operation is to divide the medical information data into multiple time blocks through the distributed storage nodes of the computing platform. The basis for dividing the time blocks is the recording time of the medical information or the time node of the event. The medical information in each time block will be converted into a digital format. For example, the patient's medical records, examination reports, etc. are converted into structured data formats (such as JSON, XML, etc.) to ensure that the data can be easily accessed and processed. These digital data files are distributed and stored on the cloud computing platform. Through the distributed storage block technology of the platform, the data is automatically distributed to multiple physical storage devices to ensure the reliability and scalability of storage. The specific storage location of the distributed storage block will be dynamically allocated according to the data size, storage strategy and load balancing algorithm to ensure that the medical information data is evenly distributed among multiple nodes, while ensuring that the storage blocks can be quickly accessed and efficiently retrieved.

[0121] Step S32: obtaining the creation time, modification time and version information of each corresponding distributed storage block on the cloud computing platform, and generating a block identity identifier based on the creation time, modification time and version information to perform block identity identification processing on the corresponding distributed storage block to generate a distributed storage identity identification block;

[0122] In an embodiment of the present invention, by obtaining and processing the creation time, modification time and version information of each distributed storage block on the cloud computing platform, it is necessary to extract the relevant timestamp and version control information of each storage block according to the metadata management module built into the storage system. The creation time of each distributed storage block is automatically recorded by the system when creating the storage, and the modification time is automatically generated by updating the storage block content. The version information is managed according to the modification history of the file, and a new version identifier is generated for each update. Through these time information, a unique block identifier (Block ID) is generated for each storage block. The identifier will combine the timestamp and version number to ensure the uniqueness of each storage block in the entire system. The method for generating the block identifier can adopt an encrypted hash algorithm, such as SHA-256, to ensure the security and non-tamperability of the identifier. The generated block identifier will be associated with the corresponding distributed storage block as the unique identifier of the block, and finally a distributed storage identity block is generated.

[0123] Step S33: performing fault-tolerant recovery dynamic link processing on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information distributed storage dynamic chain;

[0124] In an embodiment of the present invention, when implementing fault-tolerant recovery dynamic link processing, it is first necessary to set a redundant storage strategy for each distributed storage identity identification block through the fault-tolerant management module of the cloud computing platform, and quantify the corresponding storage fault-tolerant recovery capability value according to parameters such as the access frequency, importance and data capacity of the storage block, and combine the preset storage fault-tolerant recovery capability threshold to compare and judge the capabilities of each distributed identification block at different time nodes. If the storage fault-tolerant recovery capability value is greater than or equal to the preset threshold, the link relationship of the block on the distributed storage association chain will be explicitly scheduled. In this case, the block will be actively selected as a key node to optimize its storage performance, and ensure efficient processing and rapid recovery of data by accelerating transmission and scheduling strategies. On the contrary, if the storage fault-tolerant recovery capability value is less than the preset threshold, the link relationship of the block on the distributed storage association chain will be implicitly scheduled, and redundant storage or delayed recovery will be adopted to reduce its impact on the performance of the entire chain. This implicit scheduling method can maximize the fault tolerance and recovery capabilities of the entire storage system while ensuring data security, and ultimately generate a dynamic chain of distributed storage of medical information.

[0125] Step S34: Based on the block identity identifier link sequence corresponding to the medical information distributed storage dynamic chain, each corresponding distributed storage identity block on the cloud computing platform is managed by block link storage to generate a medical information digital block storage sequence.

[0126] In an embodiment of the present invention, the storage blocks are managed by block linking based on the block identity identifier linking sequence of the medical information distributed storage dynamic chain. Distributed ledger technology (such as blockchain technology) is used to maintain the order and link relationship of each distributed storage identity block. Each storage block is assigned a sequence number and a chain structure is formed in the order of timestamp or version control. The links between blocks are associated through block identity identifiers. Specifically, a sequence number is generated based on metadata such as the creation time and modification time of each block, and it is ensured that the link relationship of each block is correct and smooth. For example, medical information data During the initial storage, the first block will be generated according to the creation time, and subsequent blocks will be connected in sequence according to the modification time, version number and other information to form an ordered storage chain. During the link storage management process, a global block storage sequence will be maintained to track the order and version changes of all storage blocks to ensure the integrity and consistency of medical information. Each storage block not only contains digitized medical information, but also stores related metadata, version numbers, identity identifiers and other information. Through this orderly link management, it can effectively support version control, traceability and auditing of medical information, and ultimately store and generate a digital block storage sequence for medical information.

[0127] Further, step S33 includes the following steps:

[0128] Perform medical information association mining analysis on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information association connection relationship between each distributed identification block;

[0129] In an embodiment of the present invention, medical information association mining analysis is performed on each distributed storage identity identification block on a cloud computing platform. Specifically, a data mining-based algorithm is used to process the medical information stored in the storage block, so as to extract various medical data such as medical records, medical records, examination results, medical imaging information, etc. in each distributed storage block through natural language processing (NLP) technology. These data are identified through association analysis algorithms, especially cluster analysis, association rule learning or graph mining technology, to identify potential associations between blocks. For example, by analyzing the patient's multiple medical records, it is found that the information in different storage blocks has some medical commonalities (such as the same disease diagnosis, similar treatment plans, etc.), thereby establishing an association relationship between the blocks, and finally generating a medical information association connection relationship between each distributed identification block.

[0130] Preferably, based on the medical information association connection relationship between each distributed identification block, the block identifier of the corresponding distributed storage identity block on the cloud computing platform is processed by pointing to a chain link to generate a medical information distributed storage association relationship chain;

[0131] In an embodiment of the present invention, based on the medical information association connection relationship between each distributed identification block previously obtained, the block identity identifier is then pointed to by chain link processing. In this step, graph database technology is used, with medical information as nodes and identifiers as edges, and the identifiers of distributed storage blocks are dynamically linked according to the association relationship. Specifically, for each pair of associated storage blocks, a pointing chain link is established through the identifier to form a graphical medical information storage association network. A graph database (such as Neo4j) is used for storage and query management, which can effectively query the connection relationship and its changes between blocks. The identifier of each block will point to the identity identifier of another block to form a directed chain, build a distributed storage association relationship chain of medical information, and finally generate a distributed storage association relationship chain of medical information.

[0132] Preferably, a storage fault tolerance and disaster recovery dynamic evaluation analysis is performed on each distributed storage identity identification block corresponding to the medical information distributed storage association chain to obtain the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes;

[0133] In an embodiment of the present invention, a dynamic evaluation and analysis of storage fault tolerance and disaster recovery is performed on each distributed storage identity identification block on the medical information distributed storage association chain. To this end, fault-tolerant storage technologies, such as redundant coding, RAID (redundant disk array) technology and erasure codes, are first used to evaluate the data integrity and recovery capabilities of each block under different environments. In this process, storage blocks are selected for periodic fault tolerance testing to simulate different types of disaster scenarios, such as hardware failures, power outages or network attacks, to evaluate their recovery capabilities. The specific operation is to inject faults into each storage block through automated scripts and monitoring tools, monitor its response time and recovery time, and at the same time, quantitatively evaluate the fault tolerance efficiency and information recovery efficiency based on different dynamic time nodes (such as peak load, normal load and low load time periods), and finally obtain the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes.

[0134] Preferably, based on the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes, the storage fault tolerance recovery capacity calculation formula is used to perform capacity quantification calculation on each distributed storage identity identification block corresponding to the medical information distributed storage association chain, so as to obtain the storage fault tolerance recovery capacity value corresponding to each distributed identification block at different dynamic time nodes;

[0135] In an embodiment of the present invention, a suitable storage fault tolerance recovery capability calculation formula is formed by combining time variable parameters, storage fault tolerance efficiency, normal recovery time after a fault occurs, total system operating time, fault tolerance efficiency adjustment coefficient, information disaster recovery efficiency, target recovery time, target data loss time, target amount of data required for recovery, target amount of data loss, disaster recovery efficiency adjustment coefficient, redundant storage occupied space ratio, maximum number of faults supported by redundant strategy, redundant strategy contribution adjustment coefficient and related parameters to perform capability quantification calculation on each corresponding distributed storage identity identification block on the medical information distributed storage association chain, so as to quantify the storage fault tolerance recovery capability value of each distributed identification block at different time nodes, and finally obtain the storage fault tolerance recovery capability value corresponding to each distributed identification block at different dynamic time nodes.

[0136] Preferably, the storage fault tolerance recovery capability value corresponding to each distributed identification block at different dynamic time nodes is compared and judged according to the preset storage fault tolerance recovery capability threshold. If the storage fault tolerance recovery capability value is greater than or equal to the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are explicitly scheduled and linked; if the storage fault tolerance recovery capability value is less than the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are implicitly scheduled and linked to generate a medical information distributed storage dynamic chain.

[0137] In an embodiment of the present invention, the capacity of each distributed identification block at different time nodes is compared and judged based on the storage fault tolerance recovery capability value obtained by the previous quantitative calculation and combined with the preset storage fault tolerance recovery capability threshold. If the storage fault tolerance recovery capability value is greater than or equal to the preset threshold, the link relationship of the block in the medical information distributed storage association chain will be explicitly scheduled. In this case, the block will be actively selected as a key node to optimize its storage performance, and ensure efficient processing and rapid recovery of data through accelerated transmission and scheduling strategies. On the contrary, if the storage fault tolerance recovery capability value is less than the preset threshold, the link relationship of the block in the medical information distributed storage association chain will be scheduled in an implicit manner, and redundant storage or delayed recovery will be adopted to reduce its impact on the performance of the entire chain. This implicit scheduling method can maximize the fault tolerance and recovery capabilities of the entire storage system while ensuring data security, thereby generating a dynamic medical information distributed storage chain. These implicitly scheduled link relationships can be dynamically adjusted through network load balancing technology to ensure that the storage capacity of different nodes can still operate stably in a changing network environment, and finally generate a dynamic chain of medical information distributed storage.

[0138] Furthermore, the storage fault tolerance recovery capability calculation formula is specifically:

[0139]

[0140]

[0141]

[0142] In the formula, C f (t) is the storage fault tolerance recovery capability value corresponding to the distributed identification block at the dynamic time node t, t0 is the starting time point corresponding to the distributed identification block, t1 is the ending time point corresponding to the distributed identification block, S f (t) is the storage fault tolerance efficiency of the distributed identification block corresponding to the dynamic time node t, T o(t) is the normal recovery time of the distributed identification block corresponding to the fault at the dynamic time node t, T l (t) is the total system operation time corresponding to the distributed identification block at the dynamic time node t, α is the fault tolerance efficiency adjustment coefficient, R d (t) is the information disaster recovery efficiency of the distributed identification block corresponding to the dynamic time node t, T g (t) is the target recovery duration of the distributed identification block corresponding to the dynamic time node t, T a (t) is the target data loss duration corresponding to the distributed identification block at the dynamic time node t, P g (t) is the amount of data required for target recovery corresponding to the distributed identification block at the dynamic time node t, P a (t) is the target data loss amount corresponding to the distributed identification block at the dynamic time node t, β is the disaster recovery efficiency adjustment coefficient, τ is the integral time variable parameter, F(τ) is the redundant storage space occupation ratio corresponding to time τ, H(τ) is the maximum number of failures supported by the redundant strategy corresponding to time τ, γ is the redundant strategy contribution adjustment coefficient, and η is the correction coefficient of the storage fault tolerance recovery capability value.

[0143] The present invention obtains a storage fault-tolerance recovery capacity calculation formula by using a specific mathematical model and after verification, which is used to perform capacity quantitative calculation on each distributed storage identity identification block corresponding to the medical information distributed storage association chain. The storage fault-tolerance recovery capacity calculation formula combines storage fault-tolerance efficiency and disaster recovery efficiency, and can dynamically evaluate the fault-tolerance and disaster recovery capabilities of distributed storage blocks at different time nodes. Specifically, the storage fault-tolerance efficiency quantifies the ratio of the fault recovery time to the total system operation time through the ratio, reflecting the recovery speed of the distributed storage after a fault occurs, while the disaster recovery efficiency reflects the efficiency and data recovery situation in the disaster recovery process by combining the target recovery time, the target data loss time, and the ratio of the amount of recovered data to the amount of lost data. The influence of redundant storage and redundant strategy on fault-tolerance recovery capacity in the formula provides a quantitative analysis of storage redundancy schemes and strategies, wherein the redundant storage occupied space ratio and the maximum number of faults supported by the redundant strategy can dynamically evaluate the occupied space of redundant storage and the fault-tolerance capability of the redundant strategy, which has important guiding significance for the optimization of the fault-tolerance strategy. The redundant strategy contribution adjustment coefficient flexibly optimizes the fault-tolerance capability of the system according to the adjustment of the redundant strategy to achieve higher fault recovery capability. In addition, the formula takes into account the changes at different time nodes in the form of an integral, which can more accurately reflect the performance of storage fault tolerance and recovery capabilities in a dynamic environment. The introduction of time integral (especially the embedded time variable) means that storage fault tolerance and recovery capabilities will change during continuous operation. The formula takes these changes into account, thereby obtaining a more accurate fault tolerance and recovery capability assessment. In summary, the formula fully considers the storage fault tolerance and recovery capability value C corresponding to the distributed identification block at the dynamic time node t. f (t), the starting time point t0 corresponding to the distributed identification block, the ending time point t1 corresponding to the distributed identification block, and the storage fault tolerance efficiency S corresponding to the distributed identification block at the dynamic time node t f (t), the normal recovery time T of the corresponding fault of the distributed identification block at the dynamic time node t o (t), the total system running time T corresponding to the distributed identification block at the dynamic time node t l (t), fault tolerance efficiency adjustment coefficient α, information disaster recovery efficiency R corresponding to the distributed identification block at the dynamic time node t d (t), the target recovery time T corresponding to the distributed identification block at the dynamic time node t g (t), the target data loss duration T corresponding to the distributed identification block at the dynamic time node t a (t), the amount of data required for target recovery of the distributed identification block corresponding to the dynamic time node t P g(t), the target data loss amount P corresponding to the distributed identification block at the dynamic time node t a (t), disaster recovery efficiency adjustment coefficient β, integral time variable parameter τ, redundant storage space occupied ratio F(τ) corresponding to time τ, maximum number of failures supported by the redundant strategy corresponding to time τ H(τ), redundant strategy contribution adjustment coefficient γ, storage fault tolerance recovery capability value correction coefficient η, where T o (t) is the normal recovery time of the distributed identification block corresponding to the fault at the dynamic time node t and T l (t) is the total system running time corresponding to the distributed identification block at the dynamic time node t, which constitutes a storage fault tolerance efficiency S corresponding to the distributed identification block at the dynamic time node t. f (t) By combining the target recovery time T corresponding to the distributed identification block at the dynamic time node t g (t), the target data loss duration T corresponding to the distributed identification block at the dynamic time node t a (t), the amount of data required for target recovery of the distributed identification block corresponding to the dynamic time node t P g (t) and the target data loss amount P corresponding to the distributed identification block at the dynamic time node t a (t) constitutes a distributed identification block corresponding to the information disaster recovery efficiency R at the dynamic time node t d (t) According to the storage fault tolerance recovery capability value C corresponding to the distributed identification block at the dynamic time node t f The correlation between (t) and the above parameters constitutes a functional relationship:

[0144]

[0145] This formula can realize the capacity quantification calculation process of each distributed storage identity identification block corresponding to the medical information distributed storage association chain. At the same time, by introducing the correction coefficient η of the storage fault tolerance recovery capability value, it can be adjusted according to the error situation occurring in the calculation process, thereby improving the accuracy and applicability of the storage fault tolerance recovery capability calculation formula.

[0146] Further, step S4 includes the following steps:

[0147] Step S41: Obtaining medical information access authorization from the cloud computing platform using smart contracts;

[0148] In the embodiment of the present invention, the smart contract interacts with the cloud computing platform to ensure that the access authorization of medical information can be obtained legally and automatically. First, the smart contract is deployed on the blockchain network to communicate with the API interface of the cloud computing platform. The smart contract contains the rules and conditions for authorized access. These rules can be dynamically set based on the type and sensitivity of the medical information and the identity authentication of the access requester. When a user (such as a doctor, a medical institution or a patient) makes an access request, the smart contract verifies the identity and authority of the requester through the chain mechanism. By querying the authentication service module of the cloud platform, the smart contract can confirm whether the requester is qualified to access the requested medical information. After verification, the smart contract will send an authorization request to the cloud computing platform and obtain a specific medical information access token (AccessToken). This token will instruct the requester to access specific medical data within a certain time window. At the same time, the validity period and scope of authority of the token will be clearly specified in the smart contract, and finally the corresponding medical information access authorization will be obtained.

[0149] Step S42: Based on the medical information access authorization, the authorization access control rule is configured and designed for each distributed storage block in the medical information digital block storage sequence to generate an authorization access control policy rule corresponding to each medical information distributed storage block, wherein the authorization access control policy rule includes access conditions, authorization levels, and execution time windows;

[0150] In an embodiment of the present invention, after obtaining the medical information access authorization, a specific authorization access control strategy is designed and configured for each medical information data block. The medical information is first stored in a distributed storage system in a digital form, and each information data block is assigned a unique identifier. For the access control of each data block, a corresponding access control strategy is generated based on the previously obtained access authorization. The configuration of the strategy covers multiple aspects, including access conditions, authorization levels, and execution time windows. The access conditions involve the identity authentication of the requesting party (such as an identity authentication code or biometric information), specific medical scenarios (such as medical record query, diagnosis and treatment record access, etc.), and the access Environmental factors (such as device security or network security status); authorization levels are divided into different levels according to the type and permission of the access request, such as read-only, modifiable, and deletable; and the execution time window controls the timeliness of access, stipulates that access rights are valid within a certain period of time, and prevents unauthorized behavior. For example, doctors can read and modify the data of certain patients during their duty hours, but have no access during off-duty hours. By applying this fine-grained control strategy to each storage block, the security and compliance of medical information during storage and access are ensured, and finally the authorization access control policy rules corresponding to each distributed storage block of medical information are designed and generated.

[0151] Step S43: Based on the authorization access control policy rules corresponding to each medical information distributed storage block, the corresponding distributed storage blocks in the medical information digital block storage sequence are managed for authorization access, and the medical information authorization access frequency and access period are dynamically adjusted to execute corresponding medical information access data sharing management.

[0152] In the embodiment of the present invention, after completing the configuration of the authorization access control policy, the access management of each medical information data block is implemented to realize the dynamic sharing and access control of medical information. Whenever the requesting party makes an access request to a specific medical data block, real-time access verification will be performed according to the configured authorization access control policy. During the verification process, the identity information of the requester, the requested access level and the time of the request will be compared to see whether they meet the set conditions. For example, when a doctor tries to access the medical records of patient A, the blockchain will be used to verify whether the doctor is within the authorization time window, whether he meets the authorization access level, and whether the access conditions (such as the doctor's role, the patient's consent form, etc.) are met. ), if all conditions are met, access will be allowed; if not, the access request will be denied and the reason for denial will be recorded. In order to optimize the dynamic management of medical data, the authorization policy can also be automatically adjusted according to the access frequency and time period. For example, in certain specific time periods, it is necessary to increase the access frequency or extend the access time period to support emergency or special medical needs, while in other time periods, the access frequency is restricted or the time period is shortened. Through automated policy adjustment and real-time access control, the security, privacy and compliance of medical information are guaranteed, the risk of information leakage and abuse is avoided, and the access rules of all relevant parties are ensured to be consistent at all times, ultimately achieving the execution of the corresponding medical information access data sharing management process.

[0153] Furthermore, the present invention also provides a medical information digital management system for executing the medical information digital management method as described above, the medical information digital management system comprising:

[0154] The medical information time-series block integration module is used to obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information corresponding to different systems of medical institutions, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information in time-series blocks, so as to obtain the medical information data sub-items corresponding to each timestamp block;

[0155] The medical information block digital conversion module is used to perform FHIR formatting on the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; and to perform digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block;

[0156] The medical information block link storage management module is used to store the medical information digitized sub-files corresponding to each time block in a distributed storage block corresponding to the cloud computing platform, and to perform block link storage management on each corresponding distributed storage block on the cloud computing platform, thereby generating a medical information digitized block storage sequence;

[0157] The medical information authorization access management module is used to use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorization access management on each distributed storage block in the medical information digital block storage sequence to execute corresponding medical information access data sharing management.

[0158] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for digital management of medical information, characterized in that: The following steps are involved: Step S1: Obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information corresponding to different systems of the medical institution, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain the medical information data sub-items corresponding to each timestamp block; Step S2: FHIR formatting the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; performing digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block; Step S3: Distribute and store the medical information digitized sub-files corresponding to each time block in the corresponding distributed storage blocks on the cloud computing platform, and perform block link storage management on each corresponding distributed storage block on the cloud computing platform to generate a medical information digitized block storage sequence; Step S4: Use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorized access management on each distributed storage block in the medical information digital block storage sequence to perform corresponding medical information access data sharing management.

2. The method for digital management of medical information according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Obtaining corresponding basic information of the patient from the corresponding medical information subsystem of the medical institution; Step S12: Obtaining corresponding patient medical history information from the corresponding electronic medical record subsystem of the medical institution; Step S13: Obtaining corresponding patient diagnosis and treatment record information from the corresponding medical record information subsystem of the medical institution; Step S14: obtaining corresponding patient medical imaging information from the corresponding medical imaging subsystem of the medical institution; Step S15: Integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information, and patient medical imaging information in time series blocks to obtain medical information data sub-items corresponding to each timestamp block.

3. The method for digital management of medical information according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: Synchronize and integrate the patient's basic information, the patient's medical history information, the patient's diagnosis and treatment record information, and the patient's medical imaging information in a time series to generate a corresponding patient medical information time series set in the same time dimension; Step S152: performing data cleaning processing on the corresponding patient medical information time series set in the same time dimension to remove redundant and erroneous medical information, and obtaining the patient medical information time series cleaned set; Step S153: Obtain the corresponding patient's first medical treatment time node and the patient's last medical treatment time node through the patient's medical information time series cleaning set, and determine the medical timestamp range segment of each patient according to the patient's first medical treatment time node and the patient's last medical treatment time node; Step S154: Based on the medical timestamp range segments of each patient, the patient medical information time series cleaning set is divided into information time series blocks and integrated to obtain the medical information data sub-items corresponding to each timestamp block.

4. The method for digital management of medical information according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing cross-dimensional correlation analysis on the medical information data sub-items corresponding to each timestamp block to obtain cross-dimensional correlation sub-items of medical information corresponding to each timestamp block; Step S22: extracting information resource attributes of the cross-dimensional associated sub-items of medical information corresponding to each timestamp block, so as to obtain information resource attribute elements of the medical information sub-items corresponding to each timestamp block; Step S23: Based on the information resource attribute element of the medical information sub-item corresponding to each timestamp block, perform FHIR format semantic mapping on each sub-data in the corresponding cross-dimensional associated sub-item of the medical information, and obtain the FHIR format resource object mapping field of the medical information sub-item corresponding to each timestamp block; Step S24: performing FHIR formatted output processing on the cross-dimensional associated sub-items of medical information corresponding to each timestamp block based on the FHIR format resource object mapping fields of the medical information sub-items corresponding to each timestamp block, so as to obtain the FHIR standard sub-items of medical information corresponding to each time block; Step S25: Perform digital file conversion on the FHIR standard sub-item of medical information corresponding to each time block to generate a digital sub-file of medical information corresponding to each time block.

5. The method for digital management of medical information according to claim 4, characterized in that: Step S25 includes the following steps: Extract FHIR metadata for the FHIR standard sub-items of medical information corresponding to each time block to obtain the FHIR metadata of medical information corresponding to each time block; Based on the medical information FHIR metadata corresponding to each time block, the corresponding medical information FHIR standard sub-item is extracted to obtain the medical information FHIR summary corresponding to each time block; Based on the FHIR summary of the medical information corresponding to each time block, the corresponding FHIR standard sub-item of the medical information is processed by FHIR semantic layering to obtain the FHIR sub-item-level hierarchical document of the medical information corresponding to each time block; The medical information FHIR sub-item-level hierarchical documents corresponding to each time block are converted into digital files to generate digital sub-files of medical information corresponding to each time block.

6. The method for digital management of medical information according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: generating a distributed storage block on the cloud computing platform, and distributively storing the digital medical information sub-files corresponding to each time block in the corresponding distributed storage block on the cloud computing platform; Step S32: obtaining the creation time, modification time and version information of each corresponding distributed storage block on the cloud computing platform, and generating a block identity identifier based on the creation time, modification time and version information to perform block identity identification processing on the corresponding distributed storage block to generate a distributed storage identity identification block; Step S33: performing fault-tolerant recovery dynamic link processing on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information distributed storage dynamic chain; Step S34: Based on the block identity identifier link sequence corresponding to the medical information distributed storage dynamic chain, each corresponding distributed storage identity block on the cloud computing platform is managed by block link storage to generate a medical information digital block storage sequence.

7. The method for digital management of medical information according to claim 6, characterized in that: Step S33 includes the following steps: Perform medical information association mining analysis on each corresponding distributed storage identity block on the cloud computing platform to generate a medical information association connection relationship between each distributed identification block; Based on the medical information association connection relationship between each distributed identification block, the block identity identifier corresponding to the distributed storage identity block on the cloud computing platform is pointed to the chain link processing to generate a medical information distributed storage association relationship chain; Perform storage fault tolerance and disaster recovery dynamic evaluation and analysis on each distributed storage identity block corresponding to the medical information distributed storage association chain to obtain the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes; Based on the storage fault tolerance efficiency and information disaster recovery efficiency corresponding to each distributed identification block at different dynamic time nodes, the storage fault tolerance recovery capacity calculation formula is used to perform capacity quantification calculation on each distributed storage identity identification block corresponding to the medical information distributed storage association chain, so as to obtain the storage fault tolerance recovery capacity value corresponding to each distributed identification block at different dynamic time nodes; According to the preset storage fault tolerance recovery capability threshold, the storage fault tolerance recovery capability value corresponding to each distributed identification block at different dynamic time nodes is compared and judged. If the storage fault tolerance recovery capability value is greater than or equal to the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are explicitly scheduled and linked; if the storage fault tolerance recovery capability value is less than the preset storage fault tolerance recovery capability threshold, the corresponding distributed storage identity identification blocks on the medical information distributed storage association chain are implicitly scheduled and linked to generate a medical information distributed storage dynamic chain.

8. The method for digital management of medical information according to claim 7, characterized in that: The storage fault tolerance recovery capability calculation formula is specifically: In the formula, C f (t) is the storage fault tolerance recovery capability value corresponding to the distributed identification block at the dynamic time node t, t0 is the starting time point corresponding to the distributed identification block, t1 is the ending time point corresponding to the distributed identification block, S f (t) is the storage fault tolerance efficiency of the distributed identification block corresponding to the dynamic time node t, T o (t) is the normal recovery time of the distributed identification block corresponding to the fault at the dynamic time node t, T l (t) is the total system operation time corresponding to the distributed identification block at the dynamic time node t, α is the fault tolerance efficiency adjustment coefficient, R d (t) is the information disaster recovery efficiency of the distributed identification block corresponding to the dynamic time node t, T g (t) is the target recovery duration of the distributed identification block corresponding to the dynamic time node t, T a (t) is the target data loss duration corresponding to the distributed identification block at the dynamic time node t, P g (t) is the amount of data required for target recovery corresponding to the distributed identification block at the dynamic time node t, P a (t) is the target data loss amount corresponding to the distributed identification block at the dynamic time node t, β is the disaster recovery efficiency adjustment coefficient, τ is the integral time variable parameter, F(τ) is the redundant storage space occupation ratio corresponding to time τ, H(τ) is the maximum number of failures supported by the redundant strategy corresponding to time τ, γ is the redundant strategy contribution adjustment coefficient, and η is the correction coefficient of the storage fault tolerance recovery capability value.

9. The method for digital management of medical information according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Obtaining medical information access authorization from the cloud computing platform using smart contracts; Step S42: Based on the medical information access authorization, the authorization access control rule is configured and designed for each distributed storage block in the medical information digital block storage sequence to generate an authorization access control policy rule corresponding to each medical information distributed storage block, wherein the authorization access control policy rule includes access conditions, authorization levels, and execution time windows; Step S43: Based on the authorization access control policy rules corresponding to each medical information distributed storage block, the corresponding distributed storage blocks in the medical information digital block storage sequence are managed for authorization access, and the medical information authorization access frequency and access period are dynamically adjusted to execute corresponding medical information access data sharing management.

10. A digital management system for medical information, characterized in that: Used to execute the method for digital management of medical information as claimed in claim 1, the digital management system for medical information comprises: The medical information time-series block integration module is used to obtain the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information corresponding to different systems of medical institutions, and integrate the patient's basic information, patient medical history information, patient diagnosis and treatment record information and patient medical imaging information in time-series blocks, so as to obtain the medical information data sub-items corresponding to each timestamp block; The medical information block digital conversion module is used to perform FHIR formatting on the medical information data sub-items corresponding to each timestamp block to obtain the medical information FHIR standard sub-items corresponding to each time block; and to perform digital file conversion on the medical information FHIR standard sub-items corresponding to each time block to generate digital medical information sub-files corresponding to each time block; The medical information block link storage management module is used to store the medical information digitized sub-files corresponding to each time block in a distributed storage block corresponding to the cloud computing platform, and to perform block link storage management on each corresponding distributed storage block on the cloud computing platform, thereby generating a medical information digitized block storage sequence; The medical information authorization access management module is used to use smart contracts to obtain medical information access authorization from the cloud computing platform, and based on the medical information access authorization, perform authorization access management on each distributed storage block in the medical information digital block storage sequence to execute corresponding medical information access data sharing management.

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