A blockchain-based medical record archive sharing and intelligent management system and method

Abstract

The application discloses a kind of based on blockchain medical record archives sharing and intelligent management system and method, belong to medical record management technical field, including hardware layer, blockchain base layer, core function layer and application layer, the core function layer includes adaptive semantic fusion module, 5G+ edge cloud real-time control module, dynamic authorization and audit module, each layer and each module linkage realizes the collaborative effect of heterogeneous data fusion, second-level sharing and compliance control;Adaptive semantic fusion module in the application is built-in multi-standard analysis engine and medical knowledge graph, not only supports the automatic analysis of each version and self-defined format of HL7 / FHIR, but also realizes the double unification of structure and semantics through semantic alignment, solves the core problem of format unification but semantic ambiguity in the prior art;And the dynamic adaptation middleware of the application uses GBDT self-learning model, without manual configuration, can iterative fusion rule, effectively improve the access efficiency of new organization, significantly reduce the adaptation cost of cross-organization sharing.

Description

Technical Field

[0001] This application belongs to the field of medical record management technology, specifically, it relates to a blockchain-based medical record sharing and intelligent management system and method. Background Technology

[0002] With the advancement of internet-based healthcare and the hierarchical medical system, cross-institutional medical collaboration has become a core requirement for the development of the healthcare industry. Medical records, as the core basis for treatment decisions, directly impact the quality of medical care and patient rights through their sharing and management efficiency, security, and compliance. However, existing technologies for sharing and managing medical records still have certain shortcomings:

[0003] Due to differences in the stage of information technology construction, equipment manufacturers, and business needs, different medical institutions use incompatible medical record data standards. The mainstream standards include various versions of HL7 and FHIR, while some primary hospitals still use custom XML / text formats, and there is even unstructured data such as handwritten medical records. Existing technologies mostly use point-to-point format conversion or static mapping table adaptation, which can only achieve superficial data format uniformity and cannot resolve semantic ambiguity. Moreover, new institutions need to manually configure adaptation rules to connect, resulting in low integration efficiency, poor data validity, and high costs for cross-institutional sharing. Insufficient real-time performance and delayed response in emergency scenarios: Scenarios such as emergency rooms and critical care referrals have extremely high requirements for the timeliness of medical record access. However, existing technologies mostly rely on centralized cloud storage and public network transmission, which has two major problems: First, cross-institutional queries require multiple forwarding steps, with response times often in the minutes range, which cannot meet the needs of emergency treatment. Second, data synchronization uses full transmission, which consumes a lot of network bandwidth and lacks an effective version verification mechanism, which can easily lead to accessed data being outdated, affecting the accuracy of treatment decisions. At the same time, the network stability of edge scenarios such as primary hospitals and emergency vehicles is poor, and centralized storage models cannot provide effective services in offline or weak network environments. Medical records contain patients' private information and are highly sensitive data. However, existing technologies mostly use centralized storage architectures, which pose a single point of leakage risk. Furthermore, authorization management is mostly based on one-time static authorization, which cannot dynamically adjust permissions according to the treatment scenario, time, and identity, making it easy for problems such as unauthorized access and abuse of permissions to occur. In addition, global medical compliance regulations have put forward strict requirements for the authorization process, audit traceability, and data retention of medical record data, but the audit logs of existing technologies are easily tampered with and lack automated compliance verification mechanisms, making it difficult to quickly determine responsibility in medical disputes and failing to meet the compliance requirements of multiple regions. Summary of the Invention

[0004] To address the aforementioned problems and technical deficiencies, this application adopts the following technical solution: a blockchain-based medical record sharing and intelligent management system, comprising a hardware layer, a blockchain foundation layer, a core functional layer, and an application layer. The core functional layer includes an adaptive semantic fusion module, a 5G+edge cloud real-time management module, and a dynamic authorization and auditing module. The interconnectedness of each layer and module achieves a synergistic effect of heterogeneous data fusion, second-level sharing, and compliant management. Wherein: The hardware layer is used to access multi-format medical record data collected by terminal devices of various medical institutions, and provides hardware support for 5G transmission, edge storage, encrypted storage and blockchain node computing. The blockchain base layer adopts a consortium blockchain architecture and PBFT consensus mechanism to store medical record data hash values, operation logs, authorization rules and node consensus information, providing an immutable trust base for the entire process; The adaptive semantic fusion module is used to parse, semantically align and fusion verify the heterogeneous medical record data, output standardized medical record data, and upload the fusion rules and traceability identifiers to the blockchain base layer to complete consensus verification, providing a high-quality data source for subsequent sharing. The 5G+edge cloud real-time management module, based on the storage location and version information recorded in the blockchain base layer, uses a dedicated 5G slicing channel to achieve layered storage, incremental synchronization, and emergency preloading at the edge and cloud levels, ensuring second-level access to medical records and real-time data consistency. The dynamic authorization and auditing module executes compliant authorization rules through the smart contract engine of the blockchain base layer, realizing fine-grained permission control based on scenario, identity and timeliness, while writing all operation logs to the blockchain in real time to form a traceable audit link for the entire process. The application layer provides terminal interaction entry points for different users, enabling scenario-based applications such as medical record access, authorization settings, audit traceability, and full lifecycle management.

[0005] Preferably, the adaptive semantic fusion module has a built-in multi-standard parsing engine and dynamic adaptation middleware. The dynamic adaptation middleware automatically learns the data rules of new access institutions through machine learning algorithms, and can iterate the fusion logic without manual configuration.

[0006] Furthermore, the 5G+ edge cloud real-time management module's dedicated 5G slicing channel has an end-to-end latency of ≤10ms and a bandwidth of ≥10Gbps. It also supports automatic switching of backup slices when the link is congested. The edge nodes use RAID5 arrays to store core medical records, ensuring storage reliability and retrieval response within 1 second.

[0007] Preferably, the dynamic authorization and auditing module uses attribute-based encryption technology, which allows only users who meet preset attributes to decrypt and access medical record data.

[0008] Furthermore, the consortium blockchain nodes include health commission nodes, medical institution nodes, regulatory agency nodes, and patient representative nodes. Each node participates in data verification, audit monitoring, and authorization management according to preset permissions, forming a distributed trust system.

[0009] A blockchain-based method for sharing and intelligently managing medical records comprises the following steps: S1. Blockchain-enabled fusion of heterogeneous medical record data: Multi-standard heterogeneous medical record data is accessed through an adaptive semantic fusion module. After parsing and semantic alignment with the medical knowledge graph, standardized data is generated. The standardized data is encrypted and stored at the edge or in the cloud. At the same time, the data hash value, fusion rules and traceability identifier are uploaded to the blockchain base layer and verified by the consensus of the consortium blockchain nodes to ensure that the fused data is real and traceable. S2, Real-time sharing through blockchain and edge cloud collaboration: Based on the storage location and version information recorded by the blockchain, a transmission link is built through a dedicated 5G slicing channel. A layered strategy of caching core medical records at the edge nodes and archiving historical medical records in the cloud is adopted. Combined with the incremental synchronization witnessed by the blockchain and the emergency pre-loading mechanism, medical records can be accessed in seconds and the freshness of data across all nodes can be consistent. S3, Smart Contract-Driven Compliance and Security Management: Compliance requirements are encoded into smart contracts through a dynamic authorization and auditing module. Users can set authorization rules independently and store them on the blockchain. The smart contracts automatically grant, revoke, and dynamically adjust permissions upon expiration. All operation logs are written to the blockchain in real time to form an immutable auditing chain. S4. Intelligent management of the entire lifecycle of medical records: Based on steps S1-S3, the blockchain drives intelligent quality control in the medical record generation stage, automatic judgment in the archiving stage, and compliance confirmation in the destruction stage, realizing closed-loop management of the entire process. In this process, step S1 provides a standardized and trusted data source for step S2 to ensure the validity of the data shared in real time; step S3 uses blockchain to manage the fusion process of step S1 and the sharing operation of step S2 to prevent data leakage and unauthorized access.

[0010] Preferably, the medical knowledge graph in step S1 integrates the UMLS and SNOMEDCT terminology systems to establish cross-standard semantic mapping rules, which can realize the semantic normalization of test indicators and disease codes, and solve the problem of heterogeneous data structures being consistent but semantically ambiguous.

[0011] Preferably, the incremental synchronization mechanism in step S2 only transmits the changed part of the medical record data, with a synchronization delay of ≤500ms. The emergency preloading mechanism is triggered by the patient's identity identifier to achieve pre-synchronization of medical records between the originating hospital and the target hospital, so as to achieve the effect of data arriving before the patient arrives. Furthermore, the incremental synchronization mechanism in step S2 only transmits the changed parts of the medical record data, with a synchronization delay of ≤500ms. The emergency preloading mechanism is triggered by the patient's identity identifier to achieve pre-synchronization of medical records between the originating hospital and the target hospital, so as to achieve the effect of data arriving before the patient arrives.

[0012] Furthermore, in step S4, the intelligent quality control during the medical record generation stage verifies the standardization of medical record terminology and the completeness of information through an adaptive semantic fusion module. Medical records that fail quality control are recorded on the blockchain and cannot be submitted, thus ensuring the quality of medical record generation.

[0013] Compared to existing technologies, the beneficial effects of this application are as follows: (1) The adaptive semantic fusion module in this application has a built-in multi-standard parsing engine and medical knowledge graph. It not only supports automatic parsing of various versions of HL7 / FHIR and custom formats, but also achieves dual unification of structure and semantics through semantic alignment, which solves the core problem of unified format but semantic ambiguity in the prior art. Moreover, the dynamic adaptation middleware of this application adopts the GBDT self-learning model, which can iterate the fusion rules without manual configuration, effectively improves the access efficiency of new institutions, and significantly reduces the adaptation cost of cross-institution sharing. (2) The architecture of “5G dedicated slicing plus edge cloud collaboration” in this application ensures end-to-end latency ≤10ms and bandwidth ≥10Gbps. The edge node caches core medical records and enables access within 1 second, effectively solving the problem of delayed response in emergency scenarios. The incremental synchronization mechanism only transmits the changed data portion, with a synchronization delay of ≤500ms, reducing network bandwidth usage. It also records version information through blockchain to ensure that the retrieved data is up-to-date. The emergency preloading mechanism is triggered by patient identification, enabling data to arrive before the patient, further shortening emergency treatment preparation time and improving the success rate of treatment. Attached Figure Description

[0014] In the attached diagram: Figure 1 This is a flowchart of an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this application, but not all embodiments. Generally, the components of the embodiments of this application described and shown in the accompanying drawings can be arranged and designed in various different configurations.

[0016] Example 1: like Figure 1As shown, a blockchain-based medical record sharing and intelligent management system includes a hardware layer, a blockchain base layer, a core function layer, and an application layer. The core function layer includes an adaptive semantic fusion module, a 5G+edge cloud real-time management and control module, and a dynamic authorization and auditing module. The linkage between each layer and module achieves the synergistic effect of heterogeneous data fusion, second-level sharing, and compliant management.

[0017] In practical implementation, the hardware layer includes: medical institution terminal equipment: Doctor's workstation: Equipped with an Intel Core i7-13700K processor, 32GB DDR5 memory, a medical-grade computer that supports DICOM image reading and HL7 data export, and runs Windows 11 Professional operating system; Data acquisition equipment: including film DR-X-ray machine, Mindray BC-6800 blood routine analyzer, Hanwang medical OCR scanner; Mobile terminals: 5G smartphones for doctors and mobile terminals in emergency vehicles.

[0018] 5G transmission module: It adopts Huawei's 5G industrial module MA5800, supports NR frequency band, is configured with medical-specific slices, and realizes dynamic allocation of slice bandwidth through SDN controller to ensure end-to-end latency ≤10ms and peak bandwidth ≥10Gbps.

[0019] Edge storage and compute nodes: Deployment location: Computer rooms of various tertiary hospitals and emergency centers, using Huawei FusionServerPro2288HV5 servers, configured with 16 10TB SAS hard drives and 2 NVIDIA A100 GPUs; Edge node operating system: CentOS 7.9, with Docker containerized deployment of core functional modules installed, supporting offline caching and automatic synchronization after network recovery.

[0020] Encrypted storage device: It adopts a national cryptographic-level encryption server and supports the SM4 symmetric encryption algorithm. It is used to store encrypted standardized medical record data. The key is managed in a distributed manner by blockchain nodes.

[0021] Blockchain node server: Configuration: Dell PowerEdge R750 server, 2 AMD EPYC7763 processors, 256GB DDR4 memory, 4 2TB NVMe hard drives; Number of nodes: 18. The nodes are connected via a dual connection of dedicated line and 5G network to ensure the stability of consensus communication.

[0022] The blockchain foundation layer includes: Consortium blockchain architecture: based on the Ethereum Quorum consortium blockchain, using the PBFT consensus mechanism, with 7 consensus nodes, a consensus block size of 2MB, a block generation time of ≤2 seconds, and a transaction confirmation delay of ≤100ms.

[0023] Distributed ledger storage: Ledger structure: LevelDB database is used to store block data. Each block contains a block header and a block body. On-chain data types: medical record data hash value, operation log, authorization rules, node consensus voting results.

[0024] Smart contract engine: Supports Solidity 0.8.19 programming language, deployed on consortium blockchain nodes, compiled and deployed via RemixIDE, smart contract address: 0x7aF93866b9E1333599583F7e89385608f7b5F5A, supports proxy mode for contract upgrades and vulnerability fixes.

[0025] The core functional layer includes: an adaptive semantic fusion module; Development language: Python 3.9, Framework: TensorFlow 2.10; Multi-standard parsing engine: Built-in parsing rule library: covers syntax rules for HL7v2.x, HL7v3, FHIRSTU3 / R4 and custom XML / JSON structures, and uses ANTLR4 to generate a syntax parser. For example, for HL7v2.8 format test reports, it splits fields using the pipe separator "|" to extract core information such as patient ID, test items, values, and units. Custom structure adaptation: Supports users to upload XMLSchema or JSONSchema files, automatically generates parsing templates, and eliminates the need for manual coding.

[0026] Dynamically Adaptive Middleware: A self-learning model is constructed using the gradient boosting tree algorithm. Input features include "field name, data type, associated fields, and source institution," and output is "fusion rule weights." The model training samples consist of 100,000 heterogeneous data points across institutions, with 100 iterations. The model accuracy is ≥97.5%, and the self-learning update cycle is 24 hours / time. The objective function is as follows:

[0027] in, To truly integrate rule tags, The weights of the fusion rules predicted by the model. For regularization terms, The regularization coefficient is . This is the squared loss function.

[0028] Medical knowledge graph: built on Neo4j graph database, integrating the UMLS2023AA and SNOMEDCT2023-03 terminology system, containing 120,000+ entities and 300,000+ relations, with semantic mapping rules using triple format.

[0029] 5G+Edge Cloud Real-time Management Module: 5G Dedicated Slice Management: Configure slice QoS parameters through OpenStackNeutron to ensure transmission bandwidth ≥10Gbps, latency ≤10ms, slice congestion detection threshold is bandwidth utilization ≥90%, trigger automatic switchover of backup slices, and switchover latency ≤500ms; Tiered storage strategy: Edge node caching: The patient's core medical records for the past 3 months are cached using Redis for hot data, supporting random reads within 1 second; Cloud archiving: Alibaba Cloud OSS medical-specific storage bucket stores historical medical records from 3 months ago. Access is relayed through edge nodes, with a download latency of ≤3 seconds. Incremental synchronization mechanism: File-level incremental synchronization is achieved based on the Rsync algorithm. The synchronization trigger condition is "within 500ms after data update". The synchronized data format is JSON, which includes "data ID, updated field, old value, new value, and timestamp". The synchronization log is stored on the blockchain and the synchronization delay is ≤500ms.

[0030] Dynamic authorization and auditing module Attribute-based encryption technology: CP-ABE scheme is used, the access structure is a combination of AND and OR gates, and the key generation algorithm is as follows: System initialization: Generate public parameters

[0031] Master key ,in and It is a bilinear group. It is a bilinear mapping. It is a random number.

[0032] User key generation: based on user attribute set Generate a private key:

[0033] Among them, It is a random number. For Lagrange coefficients; Data encryption: based on access structure Generate ciphertext:

[0034] in, Plain text data , To access structure parameters; Decryption: If the user attributes satisfy the access structure, calculate using a bilinear mapping:

[0035] The decryption yields the plaintext. .

[0036] The application layer includes: Doctor Web client: developed based on Vue3 + ElementPlus, supporting medical record retrieval, semantic search, authorization application, quality control feedback functions, the retrieval interface supports multimodal medical record linkage display, and the search response time is ≤300ms; Patient mobile app: Supports iOS 15.0+ and Android 11.0+ systems, with functional modules including authorization management, medical record query, operation traceability, and complaint feedback; Regulatory agency platform: Developed based on SpringBoot+ECharts, it supports audit log visualization, violation warnings, and assistance in liability determination.

[0037] Example 2: A blockchain-based method for sharing and intelligently managing medical records comprises the following steps: Step S1: Blockchain-enabled integration of heterogeneous medical record data: Heterogeneous data access: The doctor's workstation of tertiary hospital A outputs the patient's text medical record through the HL7v2.8 interface, the primary hospital B outputs the patient's JSON format test report through the FHIRR4 interface, and the emergency center C outputs the patient's DICOM image metadata through a custom XML interface; The system accesses the above data through a hardware layer interface, and the adaptive semantic fusion module automatically identifies the data standard type.

[0038] Data parsing: The multi-standard parsing engine calls the HL7v2.8 parser to split the patient information, test order, and test result fields of the text medical record; calls the FHIRR4 parser to extract the "Patient, Observation, and MedicationRequest" resources from the JSON data; and calls a custom XML parser to extract the patient ID, examination time, and image type fields of the image metadata. It should be noted that the parsed data is uniformly converted into key-value pair format.

[0039] Semantic alignment: Based on the medical knowledge graph, fields of different standards are mapped to a unified ontology: PID-3 in HL7v2.8, Patient.id in FHIRR4, and PatientID in custom XML are all mapped to the patient's ID; “OBX-5” in HL7v2.8 and Observation.valueQuantity.value in FHIRR4 are all mapped to the values ​​of test indicators. Semantic normalization of test indicators: For example, the white blood cell count (custom term) of primary hospital B is mapped to SNOMEDCT code 26436001, and the test result 8.5×10^9 / L is standardized to 10^9 / L according to the UMLS terminology system.

[0040] Fusion verification and on-chain: The dynamic adaptation middleware automatically generates fusion rules through the GBDT model, and merges the parsed key-value pair data into standardized medical records. The fused data includes four major modules: patient basic information, medical records, test reports, and image metadata. The hash value of standardized data is calculated using the SHA-256 algorithm: a random number of timestamps from the standardized medical record string, where the random number is an integer between 0 and 10000, to ensure the uniqueness of the hash value; The data hash value, fusion rule ID, and traceability identifier are uploaded to the consortium blockchain. Seven consensus nodes verify the data through a PBFT mechanism. Once verified, the data is written into the distributed ledger. The standardized data is then encrypted using the SM4 algorithm and stored on the edge node of the tertiary hospital A.

[0041] Step S2: Real-time sharing through blockchain and edge cloud collaboration: Emergency scenario trigger: Patient Zhang San is transported by ambulance due to acute myocardial infarction. The on-board terminal of Emergency Center C triggers the emergency preloading mechanism through the patient's electronic health card. At the same time, the on-board GPS positioning shows that the patient will arrive at Emergency Center C in 15 minutes.

[0042] Transmission link setup: The system automatically activates the 5G medical-specific slice and allocates 10Gbps bandwidth through the SDN controller. The current slice bandwidth utilization rate is detected to be 85%, and there is no need to switch to the backup slice. The end-to-end transmission latency test is 8ms.

[0043] Layered storage and preloading: The edge node of tertiary hospital A queries the blockchain ledger to obtain the storage location and version number of the patient's standardized medical record; The edge node extracts core medical records and transmits only the updated data within 2 hours through an incremental synchronization mechanism. The synchronized data volume is 1.2MB and the synchronization latency is 400ms. After receiving data, the edge node of Emergency Center C stores it in a local RAID5 array and returns a receipt confirmation message. The hash value of the confirmation message is then uploaded to the blockchain for evidence storage.

[0044] Second-level access: Emergency physicians log in to the system via workstations and, after smart contract permission verification, access the patient's core medical records from the local edge node. The access response time is 800ms, and it supports the linkage analysis of electrocardiogram images and myocardial enzyme values.

[0045] Step S3: Compliance and security management driven by smart contracts: Authorization settings: Patients can set authorization rules through the APP: only doctors in Emergency Center C are allowed to access core medical records within 24 hours, and access to non-related medical history is prohibited. The authorization rules are stored on the blockchain.

[0046] Access verification: When emergency room doctors access medical records, the system verifies the doctor's attributes using ABE technology. If the smart contract determines that the doctor meets the authorization rules, it automatically grants access rights, which are valid for 24 hours.

[0047] Operation logs are uploaded to the blockchain: Doctors' actions of accessing, downloading, and annotating medical records all generate logs. Log fields include: Operator: Doctor's blockchain account address; Operation time; Scenario type: Emergency; Data access scope: core medical records; Operation type: Retrieval and annotation; Log hash value: SHA256, written to the blockchain distributed ledger in real time.

[0048] Example of liability determination: If a medical dispute occurs subsequently, the regulatory agency will trigger a smart contract through the regulatory platform to automatically extract authorization records, operation logs, and data transmission records from the blockchain and generate a preliminary liability determination report. The report includes verification results in three dimensions: authorization legality, operation compliance, and data integrity, and the generation time is ≤5 minutes.

[0049] Step S4: Intelligent Management of the Entire Lifecycle of Medical Records Generation Phase and Intelligent Quality Control: After doctors write medical records, the adaptive semantic fusion module automatically verifies them. Terminology standardization: The standard term "myocardial infarction" was not used in the case of myocardial infarction. A modification was prompted, and the verification was passed after modification. Information completeness: A missing field for past hypertension history was detected, triggering a pop-up reminder. Submit after completing the field. Medical records that fail quality control cannot be uploaded to the system; instead, the quality control results are uploaded to the blockchain for evidence storage.

[0050] Archiving Phase - Automatic Determination: After the patient's treatment is completed, the smart contract automatically determines the archiving time: outpatient medical records are archived within 7 days, and inpatient medical records are archived within 30 days. The archiving operation is executed after confirmation by the consortium blockchain node, and the archived records are uploaded to the blockchain.

[0051] Destruction Phase - Compliance Confirmation: When a patient's inpatient medical records have been kept for 30 years, a smart contract triggers a destruction reminder. After dual confirmation by the Information Department of the tertiary hospital A and the regulatory agency, the destruction operation is executed: encrypted data on edge nodes and in the cloud is permanently deleted, and the blockchain records the destruction time, executor, and approval node, thus meeting the data retention compliance requirements.

[0052] Verification of the effects of technological synergy: Integration-sharing linkage: After the standardized medical records generated in step S1 are verified by the blockchain, they provide a data source that is semantically unambiguous, real and traceable for step S2. The validity of the data retrieved in the emergency department reaches 99.8%, avoiding sharing failures caused by data heterogeneity. Sharing-control linkage: The medical record retrieval and synchronization operations in step S2 must pass the smart contract permission verification in step S3. The operation logs are uploaded to the blockchain in real time. No unauthorized access or data leakage incidents have occurred, and the compliance rate is 100%. End-to-end linkage: Blockchain runs through all steps from S1 to S4, enabling traceable integration rules, auditable shared operations, and controllable lifecycle. Ultimately, it achieves the collaborative technical effect of "trustworthy integration, efficient sharing, and compliant management." Tests show that the heterogeneous data fusion accuracy rate is 98.5%, emergency access response time is ≤1s, permission adjustment delay is 450ms, and the immutability of operation logs is 100%.

[0053] The above embodiments only illustrate preferred embodiments of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application's patent. It should be noted that those skilled in the art can make various modifications, improvements, and substitutions without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A blockchain-based medical record sharing and intelligent management system, characterized in that, It includes a hardware layer, a blockchain foundation layer, a core functional layer, and an application layer. The core functional layer includes an adaptive semantic fusion module, a 5G+edge cloud real-time management and control module, and a dynamic authorization and auditing module. The interconnectedness of each layer and module achieves a synergistic effect of heterogeneous data fusion, second-level sharing, and compliant management. Among them: The hardware layer is used to access multi-format medical record data collected by terminal devices of various medical institutions, and provides hardware support for 5G transmission, edge storage, encrypted storage and blockchain node computing. The blockchain base layer adopts a consortium blockchain architecture and PBFT consensus mechanism to store medical record data hash values, operation logs, authorization rules and node consensus information, providing an immutable trust base for the entire process; The adaptive semantic fusion module is used to parse, semantically align and fusion verify the heterogeneous medical record data, output standardized medical record data, and upload the fusion rules and traceability identifiers to the blockchain base layer to complete consensus verification, providing a high-quality data source for subsequent sharing. The 5G+edge cloud real-time management module, based on the storage location and version information recorded in the blockchain base layer, uses a dedicated 5G slicing channel to achieve layered storage, incremental synchronization, and emergency preloading at the edge and cloud levels, ensuring second-level access to medical records and real-time data consistency. The dynamic authorization and auditing module executes compliant authorization rules through the smart contract engine of the blockchain base layer, realizing fine-grained permission control based on scenario, identity and timeliness, while writing all operation logs to the blockchain in real time to form a traceable audit link for the entire process. The application layer provides terminal interaction entry points for different users, enabling scenario-based applications such as medical record access, authorization settings, audit traceability, and full lifecycle management.

2. A blockchain-based medical record sharing and intelligent management system according to claim 1, characterized in that, The adaptive semantic fusion module has a built-in multi-standard parsing engine and dynamic adaptation middleware. The dynamic adaptation middleware automatically learns the data rules of new access institutions through machine learning algorithms, and can iterate the fusion logic without manual configuration.

3. A blockchain-based medical record sharing and intelligent management system according to claim 1, characterized in that, The 5G+ Edge Cloud Real-time Management and Control Module's dedicated 5G slicing channel has an end-to-end latency of ≤10ms and a bandwidth of ≥10Gbps. It also supports automatic switching of backup slices when the link is congested. The edge nodes use RAID5 arrays to store core medical records, ensuring storage reliability and retrieval response within 1 second.

4. A blockchain-based medical record sharing and intelligent management system according to claim 1, characterized in that, The dynamic authorization and auditing module uses attribute-based encryption technology, which allows only users who meet preset attributes to decrypt and access medical record data.

5. A blockchain-based medical record sharing and intelligent management system according to claim 1, characterized in that, The consortium blockchain nodes include health commission nodes, medical institution nodes, regulatory agency nodes, and patient representative nodes. Each node participates in data verification, audit monitoring, and authorization management according to preset permissions, forming a distributed trust system.

6. A blockchain-based method for sharing and intelligently managing medical records, applied to the medical record sharing and intelligent management system described in claim 1, characterized in that, The steps are as follows: S1. Blockchain-enabled fusion of heterogeneous medical record data: Multi-standard heterogeneous medical record data is accessed through an adaptive semantic fusion module. After parsing and semantic alignment with the medical knowledge graph, standardized data is generated. The standardized data is encrypted and stored at the edge or in the cloud. At the same time, the data hash value, fusion rules and traceability identifier are uploaded to the blockchain base layer and verified by the consensus of the consortium blockchain nodes to ensure that the fused data is real and traceable. S2, Real-time sharing through blockchain and edge cloud collaboration: Based on the storage location and version information recorded by the blockchain, a transmission link is built through a dedicated 5G slicing channel. A layered strategy of caching core medical records at the edge nodes and archiving historical medical records in the cloud is adopted. Combined with the incremental synchronization witnessed by the blockchain and the emergency pre-loading mechanism, medical records can be accessed in seconds and the freshness of data across all nodes can be consistent. S3, Smart Contract-Driven Compliance and Security Management: Compliance requirements are encoded into smart contracts through a dynamic authorization and auditing module. Users can set authorization rules independently and store them on the blockchain. The smart contracts automatically grant, revoke, and dynamically adjust permissions upon expiration. All operation logs are written to the blockchain in real time to form an immutable auditing chain. S4. Intelligent management of the entire lifecycle of medical records: Based on steps S1-S3, the blockchain drives intelligent quality control in the medical record generation stage, automatic judgment in the archiving stage, and compliance confirmation in the destruction stage, realizing closed-loop management of the entire process. In this process, step S1 provides a standardized and trusted data source for step S2 to ensure the validity of the data shared in real time; step S3 uses blockchain to manage the fusion process of step S1 and the sharing operation of step S2 to prevent data leakage and unauthorized access.

7. A method for sharing and intelligent management of medical records based on blockchain according to claim 6, characterized in that, In step S1, the medical knowledge graph integrates the UMLS and SNOMEDCT terminology systems to establish cross-standard semantic mapping rules, which can realize the semantic normalization of test indicators and disease codes, and solve the problem of heterogeneous data structures being consistent but semantically ambiguous.

8. A method for sharing and intelligent management of medical records based on blockchain according to claim 6, characterized in that, The incremental synchronization mechanism in step S2 only transmits the changed parts of the medical record data, with a synchronization delay of ≤500ms. The emergency preloading mechanism is triggered by the patient's identity identifier to achieve pre-synchronization of medical records between the originating hospital and the target hospital, so that the data arrives before the patient arrives.

9. A method for sharing and intelligent management of medical records based on blockchain according to claim 6, characterized in that, The operation log in step S3 includes the operator, operation time, scenario type, and data access scope. In the event of a medical dispute, the smart contract can automatically extract full-process traceability data from the blockchain and generate a preliminary report on liability determination.

10. A method for sharing and intelligent management of medical records based on blockchain according to claim 6, characterized in that, In step S4, the intelligent quality control of the medical record generation stage verifies the standardization of medical record terminology and the completeness of information through an adaptive semantic fusion module. Medical records that fail the quality control are recorded on the blockchain and cannot be submitted, thus ensuring the quality of medical record generation.

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