Intelligent security transmission method and system for medical data

Abstract

The application provides a kind of intelligent safe transmission method and system of medical data, the method includes: through intelligent tag analysis mechanism, medical data and context information are collected, and the structured description record containing type, sensitive level and permission label is generated;Call strategy matching engine to analyze the description record, adaptively select desensitization and encryption strategy and generate encrypted encapsulation data, and calculate its encryption complexity level at the same time;Extract meta information and permission structure from encrypted encapsulation data, build access index path and register to alliance ledger to realize traceable management;Index path drives channel scheduling module to select transmission mode, and controls edge node to complete relay binding and encryption instruction issuing;After receiving party initiates request, system activates federal authentication mechanism to analyze identity token and task parameters, completes permission verification and context confirmation;Audit module combines authorization result and behavior log, scores transmission behavior and rule matching situation and calculates compliance risk indicators.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to an intelligent and secure transmission method and system for medical data. Background Technology

[0002] With the continuous development of medical information systems, a large amount of sensitive data involving patient diagnosis, testing, imaging, and medication records are frequently transferred between different terminals and systems. Traditional medical data transmission methods mostly rely on fixed rules and static access control, which are difficult to dynamically adapt to diverse access scenarios and risk levels. This can easily lead to security risks due to insufficient data anonymization, invalid access verification, and exposed transmission paths.

[0003] On the other hand, existing solutions generally lack a linkage mechanism between data encapsulation, link scheduling, and behavior auditing. They cannot flexibly select encryption strategies based on task context, nor can they achieve fine-grained tracking and compliance assessment of the entire transmission process. Therefore, there is an urgent need for an intelligent medical data transmission method with structured label description, adaptive encryption processing, and traceable authentication capabilities to improve data utilization efficiency while ensuring its security and compliance during transmission. Summary of the Invention

[0004] This invention provides an intelligent and secure transmission method for medical data, comprising:

[0005] S10. Collect medical data and contextual information through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags;

[0006] S20. Call the strategy matching engine to analyze the description record, adaptively select the de-identification and encryption strategy and generate encrypted encapsulated data, while calculating its encryption complexity level.

[0007] S30. Extract metadata and permission structure from encrypted encapsulated data, construct access index path and register it in the alliance ledger to achieve traceable management;

[0008] S40, the index path-driven channel scheduling module selects the transmission mode and controls the edge nodes to complete relay binding and encryption command issuance;

[0009] S50. After the receiver initiates a request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes the permission verification and context confirmation.

[0010] S60. The audit module combines authorization results and behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators.

[0011] The intelligent secure transmission method for medical data described above includes collecting medical data and contextual information through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags, including:

[0012] S101. Extract medical data and its associated contextual information, construct initial data blocks, and parse their semantic fields and source paths;

[0013] S102. Generate corresponding type identifiers, sensitivity level codes, and access permission tags based on the preset tag rule library, and output structured description records.

[0014] The intelligent secure transmission method for medical data described above includes: invoking a policy matching engine to analyze and describe records; adaptively selecting de-identification and encryption strategies and generating encrypted encapsulated data; and simultaneously calculating its encryption complexity level, including:

[0015] S201. Perform a strategy lookup on the field types and sensitivity levels in the structured description records to match the available de-identification and encryption strategy sets;

[0016] S202. Calculate the data encryption complexity level based on the number of fields and encryption strength parameters, and output the encapsulated encrypted data structure.

[0017] The intelligent secure transmission method for medical data, as described above, includes extracting metadata and permission structures from encrypted and encapsulated data, constructing access index paths, and registering them in a consortium ledger for traceable management.

[0018] S301. Extract the metadata fields and permission structure content from the encrypted encapsulated data, and construct the task access index path;

[0019] S302. Register the access index path and associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

[0020] The intelligent secure transmission method for medical data described above includes an index path-driven channel scheduling module that selects the transmission mode and controls edge nodes to complete relay binding and encryption command issuance, comprising:

[0021] S401. Match available transmission channels based on the index path content, and the channel scheduling module selects the optimal transmission method.

[0022] S402, The system controls the edge nodes to complete the corresponding relay binding task and simultaneously issues encrypted transmission instructions.

[0023] The intelligent secure transmission method for medical data described above includes the following steps: After the recipient initiates a request, the system activates a federated authentication mechanism to parse the identity token and task parameters, completing permission verification and context confirmation, including:

[0024] S501, The recipient submits an identity token and task parameters, and the system parses and extracts role information and access scope;

[0025] S502: Call the federal authentication mechanism to complete the authorization comparison and context confirmation process, and output a verification pass flag.

[0026] The intelligent secure transmission method for medical data described above includes an audit module that combines authorization results and behavior logs to score the matching degree of transmission behavior with rules and calculate compliance risk indicators, including:

[0027] S601. Extract system authorization results, behavior logs, and transmission session records to construct an evaluation sample set;

[0028] S602. Calculate compliance scoring indicators based on rule matching degree and historical risk labels, and output compliance risk level assessment results.

[0029] This invention also provides an intelligent and secure transmission system for medical data, comprising:

[0030] The data acquisition module is used to collect medical data and contextual information, and generate structured description records containing type, sensitivity level and permission tags;

[0031] The strategy processing module is used to analyze structured description records, adaptively match desensitization and encryption strategies and generate encrypted encapsulated data, while calculating the encryption complexity level.

[0032] The index registration module is used to extract metadata and permission structures from the encapsulated data, construct access index paths, and register them to the alliance ledger.

[0033] The channel scheduling module is used to select the transmission mode based on the index path and control the edge nodes to complete relay binding and command issuance;

[0034] The identity authentication module is used to parse the identity token and task parameters submitted by the recipient to complete permission verification and context confirmation;

[0035] The audit assessment module is used to analyze authorization results and behavior logs, score the compliance of transmission behavior, and output the compliance risk level.

[0036] The beneficial effects achieved by this invention are as follows: This invention provides an intelligent and secure transmission method for medical data. It can accurately describe data type, sensitivity level, and access permissions based on a structured tagging mechanism. Through a policy matching engine, it achieves dynamic adaptation of de-identification and encryption strategies. Combined with mechanisms such as access index registration, edge scheduling execution, federated identity authentication, and behavior audit scoring, it constructs a secure transmission closed loop that spans the entire process of collection, encryption, transmission, verification, and auditing. This method not only improves the transmission efficiency and flexibility of medical data in multiple scenarios but also significantly enhances the security, controllability, compliance, and traceability of the entire data flow. It is suitable for the secure exchange and authorized transfer of highly sensitive medical data between multiple terminals and institutions. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0038] Figure 1 This is a flowchart of an intelligent and secure transmission method for medical data provided in Embodiment 1 of this application;

[0039] Figure 2 This is a schematic diagram of an intelligent and secure transmission system for medical data provided in Embodiment 2 of this application. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1

[0042] like Figure 1 As shown, Embodiment 1 of this application provides an intelligent and secure transmission method for medical data, comprising the following steps:

[0043] S10. Collect medical data and contextual information through an intelligent tag parsing mechanism to generate structured description records containing type, sensitivity level, and permission tags.

[0044] To support structured labeling of medical data and subsequent access control strategies, the system introduces an intelligent tag parsing mechanism during the data access phase. This mechanism automatically extracts data fields and their contextual paths, completes type identification, sensitivity level coding, and access permission definition, ultimately forming an information set with complete semantic tags. This provides a foundation for subsequent policy matching and access index construction. Specifically, it includes the following sub-steps:

[0045] S101. Extract medical data and its associated contextual information, construct initial data blocks, and parse their semantic fields and source paths;

[0046] The system receives and parses transmitted medical data through the access layer, identifies the original fields and their semantic context, and constructs initial structured data blocks. Each data field's basic attribute is represented as a triple: ,in, This represents the i-th original field (such as patient ID, diagnosis conclusion, etc.). The collection timestamp for this field; This represents the source path identifier for the field (e.g., HIS system, PACS module, interface number, etc.); n represents the total number of fields to be processed in the current task. This is the initial set of fields built for the system, preserving the original values ​​and source information. This structure is used to characterize the basic outline and semantic environment of the fields to be processed, and serves as a prerequisite input for tag assignment and permission mapping.

[0047] S102. Generate corresponding type identifiers, sensitivity level codes, and access permission tags based on the preset tag rule library, and output structured description records.

[0048] The system uses a preset tag rule library The tag generation rules in the initial field set Each field in Perform tag annotation. Define the tag function as follows: ,in, For field type identifier, ; Indicates the sensitivity level. A larger value indicates greater sensitivity. This indicates the subset of roles that this field allows access to. , This defines the complete set of roles for the system. The final result is a set of structured description records. This collection comprehensively records the original value, collection time, source path, and 3D label attributes of each field, providing semantic support and access boundary definition for subsequent execution strategy matching and encrypted encapsulation.

[0049] S20. Call the strategy matching engine to analyze the description record, adaptively select the de-identification and encryption strategy and generate encrypted encapsulated data, while calculating its encryption complexity level.

[0050] After the structured description record is constructed, to achieve differentiated desensitization and encryption processing for different fields, the system calls the policy matching engine to perform multi-dimensional parsing of the marked fields. Based on their type identifier and sensitivity level, a policy lookup is performed, and the optimal policy set is adaptively selected for subsequent encapsulation processing, taking into account factors such as policy adaptability, computational cost, and data retention. Finally, the system outputs the encapsulated encrypted data structure and calculates the corresponding encryption complexity level for reference in subsequent dynamic policy adjustments and resource scheduling. Specifically, this includes the following sub-steps:

[0051] S201. Perform a strategy lookup on the field types and sensitivity levels in the structured description records to match the available de-identification and encryption strategy sets;

[0052] The system first loads the policy rule base. Each strategy It defines the supported field types, applicable sensitivity levels, supported data operation scope, and expected computational costs. This applies to structured description records. Each field in The system outputs the result based on its label function. Execute strategy matching: ,in, For the i-th field; Used to identify the type of a field, such as structured text, medical images, etc. This represents the sensitivity level of the field; a higher value indicates greater sensitivity. This is the matching function for the strategy engine. The set of strategies matched for the field.

[0053] The system according to The suitability of the strategies is initially screened, and unusable or conflicting strategies are eliminated to form a subset of candidate strategies for subsequent encryption decisions, which are then input into the next processing unit.

[0054] S202. Calculate the data encryption complexity level based on the number of fields and encryption strength parameters, and output the encapsulated encrypted data structure.

[0055] The system processes each field Corresponding policy candidate set To perform a complexity quantification evaluation, factors such as encryption algorithm strength, key length, execution rounds, and data retention weights are introduced, and the encryption complexity scoring function is defined as follows: ,in, For set The number of elements; For strategy The encryption algorithm strength coefficient reflects the security level of the algorithm itself; This is the quantized value after key length normalization; For strategy The standard number of execution rounds; To retain the complexity adjustment factor; For strategy Retention rate of field information (0~1); The maximum retention rate under the current policy set, used for normalization; It is a logarithmic function with base 2, which enhances the complexity differences between strategies.

[0056] After evaluating the complexity of each field, the system determines the appropriate level of sensitivity based on the field. With encryption complexity Adaptively select the optimal set of de-identification strategies that balances security and performance. With encryption policy set Encapsulate and generate the final encrypted data structure: ,in, For fields The processed ciphertext value; , The final selected de-identification and encryption strategy; Z represents the encryption complexity score for the corresponding field; Z is the encapsulated set of encrypted data structures, which will serve as input data for the subsequent construction of dynamic encryption control strategies.

[0057] S30. Extract metadata and permission structure from encrypted encapsulated data, construct access index paths and register them in the alliance ledger to achieve traceable management.

[0058] The system extracts key metadata fields and access permission structures from the encapsulated and encrypted data content, integrates multi-level feature information to generate a unique access index path identifier, and registers this path and permission relationship to the consortium blockchain ledger, achieving full-process traceability management of medical data access behavior. Specifically, it includes the following sub-steps:

[0059] S301. Extract the metadata fields and permission structure content from the encrypted encapsulated data, and construct the task access index path;

[0060] The system extracts a set of metadata fields from the encrypted encapsulated data structure Z. With permission structure A unique task access index path is constructed through feature transformation, weight encoding, and encrypted concatenation. The calculation formula is as follows: ,in, This serves as the identifier for the constructed access index path; This represents the total number of metadata fields. This is a multi-dimensional hash function used to generate irreversible index digests; This is the j-th metadata field; For fields Weighting coefficients; Encoding function for field features; This is the identifier for the i-th role; This refers to the access operation corresponding to the i-th role; This function concatenates the permission structure and returns an identifier string. The weight parameters for the permission structure pairs; For symmetric encryption functions that use a key; This indicates a string concatenation operation. The access index path I output in this step will serve as the unique index identifier for subsequent calls to the alliance ledger registration and channel scheduling modules.

[0061] S302. Register the access index path and associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

[0062] The system configuration includes access index paths Permission structure P Registration timestamp and complete registration records of multi-node signature sets This is then synchronously written to each consensus node in the consortium ledger, with the following record expression: ,in, For the registration records in the alliance ledger; Identifier for the access index path; The access permission structure includes roles, scope of operation, and sensitivity level; The standard timestamp for the occurrence of the registration act; For the q-th node in the consortium blockchain, this is the digital signature of the index information. This represents the total number of nodes participating in the registration within the consortium blockchain. The expression represents the set of signatures from all nodes to ensure multi-node consensus; || represents the concatenation operation.

[0063] Final output registration record The data is distributed across multiple ledger nodes, ensuring the security, integrity, and end-to-end traceability of the medical data index path, serving as the foundation for subsequent transmission control and behavior auditing.

[0064] S40, the index path-driven channel scheduling module selects the transmission mode and controls the edge nodes to complete relay binding and encryption command issuance.

[0065] The system uses access index path I as the driving signal, and the linkage channel scheduling module automatically performs cross-domain transmission resource matching and communication channel path selection. Combining policy parameters, task level, and network status, it dynamically determines the encrypted relay method and issues encrypted control commands to edge nodes, thus constructing a complete secure communication link. Specifically, it includes the following sub-steps:

[0066] S401. Match available transmission channels based on the index path content, and the channel scheduling module selects the optimal transmission method.

[0067] System resolves index path The extracted field set Extracting the sensitivity level of each field based on the label function And set the overall sensitivity level of the field based on the field weight: ,in, For the j-th field in the index path; Representation field The sensitivity level, with a value range of . The larger the value, the more sensitive it is; The weight coefficient of the field is set according to the field type and frequency of use; The number of fields involved in the evaluation.

[0068] The channel scheduling module is based on this sensitivity level. Task data range For channel sets The process involves filtering and calculating the scheduling score for each available channel to select the optimal channel. Its decision function is defined as follows: ,in, For channel Sensitivity Level The following is a score for secure transmission; For channel The transmission delay; the smaller the value, the faster the speed. For channel For task data range Capacity compatibility; These are the weighting coefficients under the multi-dimensional scheduling strategy, preset according to task requirements.

[0069] S402, The system controls the edge nodes to complete the corresponding relay binding task and simultaneously issues encrypted transmission instructions.

[0070] The system selects the optimal channel. Corresponding communication protocols and relay configuration rules, constructing relay binding instructions. With encryption control command set And send to the edge node. The instruction set structure is represented as follows: ,in, This includes the binding configuration information for relay nodes, such as node address, port, and authentication method; This indicates channel-level encryption control parameters, including protocol type, key index, key rotation strategy, etc. For the task path With transmission channel The jointly generated context binding summary is used for subsequent node identity verification and task tracking.

[0071] Control commands Edge nodes After execution, the relay channel initialization and encrypted transmission control process will be completed, and a path-driven secure data path will be built.

[0072] S50. After the receiver initiates a request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes permission verification and context confirmation.

[0073] Upon receiving a data access request, the system first parses the identity token and task parameters submitted by the recipient, extracting its role information and context tag. Then, the system invokes the federated authentication mechanism, performing permission matching and context consistency verification on the current request based on the permission structure and context records registered in the access index path, and outputs a verification success flag. Specifically, this includes the following sub-steps:

[0074] S501, The recipient submits an identity token and task parameters, and the system parses and extracts role information and access scope;

[0075] The system extracts the identity token from the access request submitted by the recipient. and task parameter set ,in Includes role credentials and timestamp identifiers. This includes the request task context. and target data identifier index The system uses a token decoding function. Parse the identity token to obtain the set of recipient roles: ,in, This indicates the multiple business roles to which the recipient belongs, serving as the primary basis for access control comparison.

[0076] S502: Call the federal authentication mechanism to complete the authorization comparison and context confirmation process, and output a verification pass flag.

[0077] The system is based on structured request information. Invoke the federal authentication mechanism to verify its encryption and encapsulation data structure. The permission structure bound in and contextual tags Perform joint verification. Index k represents the requested task. The corresponding data structure number. The verification logic is as follows: ,in, The set of permissions submitted by the requester; Contextual tags submitted by the requester; This is the k-th encrypted encapsulated data structure; The permission structure registered in the data structure; Context information registered in the data structure; The verification process is as follows: 1 for pass, 0 for rejection. This logic ensures that the requester's permissions and context information simultaneously meet the registered access control requirements, thus ensuring task-level data access security.

[0078] S60. The audit module combines authorization results and behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators.

[0079] After completing the permission verification and context validation processes, the system activates the audit module to conduct a compliance review of the entire task execution process. This process comprehensively analyzes the system authorization results, task behavior logs, and data transmission records to extract a set of behavioral features for risk assessment. These features are then matched with a rule set and historical tags to ultimately output the task's compliance risk level identifier. Specifically, this includes the following sub-steps:

[0080] S601. Extract system authorization results, behavior logs, and transmission session records to construct an evaluation sample set;

[0081] The system first retrieves the permission verification result corresponding to the current task request. Action log during request execution and transmission session records These three elements together constitute a multidimensional audit assessment sample set: ,in, Indicates the verification result; This represents a sequence of behavioral events recorded by the system. ,in, This represents the content of the data segment transmitted in the i-th transmission. This represents the time window for the i-th transmission. This represents the rate statistics for the i-th transmission. The system preprocesses the above samples, standardizing the encoding format, removing redundant behavioral events, and reconstructing the transmission trajectory to provide structured input for subsequent risk scoring.

[0082] S602. Calculate compliance scoring indicators based on rule matching degree and historical risk labels, and output compliance risk level assessment results.

[0083] The system is based on a sample set. Combined with rule base Compared with historical risk label matrix Construct a task compliance scoring function. Define each rule. The corresponding matching strength is The risk weighting factor is Compliance score results It is given by the following formula: ,in, This indicates behavior logs and rules. The degree of matching; This represents the historical risk weight with time decay; Representation rules The correlation coefficient with label dimension i; Representation rules The corresponding historical risk label value under the i-th risk dimension; n and k represent the total number of rules and the total number of risk dimension labels in the rule base, respectively; For the nonlinear transformation function of the risk label; As a normalization factor, ensure .

[0084] Based on the scoring results The system maps the range of the system's compliance risk level identifier to the following: The system will eventually produce the results. Record to encrypted audit structure This ensures that all actions throughout the mission are documented, risks are traceable, and security is controllable.

[0085] Example 2

[0086] like Figure 2 As shown, Embodiment 2 of this application provides an intelligent and secure transmission system for medical data, comprising:

[0087] The data acquisition module 21 is used to collect medical data and contextual information, and generate structured description records containing type, sensitivity level, and permission tags. It includes the following sub-modules:

[0088] The data field extraction submodule 211 is used to receive the raw medical data stream and extract the valid fields and their associated context information to construct an initial structured data block. This submodule preprocesses the data through the access layer component, identifies key fields in the data (such as patient ID, examination results, diagnosis conclusions, etc.), and extracts the collection timestamp and source path of the fields (such as HIS, PACS, interface number, etc.). Finally, each field is expressed as a structured triple, including the field value, collection time, and source identifier, and stored uniformly in the initial field set.

[0089] The tag generation submodule 212 is used to automatically annotate the initial field set based on the tag rule base, generating field type identifiers, sensitivity level codes, and access permission tags. This submodule determines the field data type (such as structured text, images, tables, etc.) through a field type recognition function, classifies the importance of fields according to sensitivity level mapping rules, uses integer codes to represent the level strength, and generates a set of role access restrictions for fields based on the system permission dictionary. Finally, it outputs a set of structured description records, recording the original value, source, time, and three-dimensional tag features of each field, serving as the semantic basis for subsequent calls by the policy engine.

[0090] The strategy processing module 22 analyzes structured description records, adaptively matches de-identification and encryption strategies, generates encrypted encapsulated data, and calculates the encryption complexity level. It includes the following sub-modules:

[0091] The policy matching submodule 221 is used to perform a policy lookup operation on each field in the structured description record to determine the set of de-identification and encryption policies that can be applied to that field. This submodule first loads the policy rule base and extracts information such as the applicable field type, supported sensitivity levels, execution constraints, and expected computational cost for each policy. Then, based on the field's type identifier and sensitivity level, the system uses a policy matching function to filter out a set of policies that meet the conditions, and further eliminates policy items that do not meet system constraints or have conflicts, generating a candidate subset of policies for that field.

[0092] The encryption encapsulation submodule 222 is used to perform complexity evaluation on the candidate strategy set and calculate a comprehensive score based on factors such as encryption strength, retention rate, and execution rounds, selecting the optimal strategy for data encapsulation processing. During the scoring process, this submodule integrates parameters such as encryption algorithm strength, key length, execution rounds, and information retention rate, constructing an encryption complexity scoring model through a weighted function to ensure a balance between security and system load. Finally, the selected strategy is applied to the original fields to generate an encapsulation structure containing the de-identified result and the encrypted ciphertext, while recording the complexity score for each field, providing parameter support for subsequent task level judgment and channel selection.

[0093] The index registration module 23 is used to extract metadata and permission structures from the encapsulated data, construct access index paths, and register them to the alliance ledger. It includes the following sub-modules:

[0094] The index construction submodule 231 is used to extract the set of metadata fields and the corresponding permission structure pairs from the encrypted encapsulated data structure, and construct a unique access index path identifier based on multivariate hash encoding, feature concatenation, and symmetric encryption operations. This submodule extracts the digest of each field according to the field weight parameters and feature encoding functions, and performs feature concatenation and digest encryption in conjunction with the role operation pairs of the permission structure, finally generating a string of access index path identifiers with irreversible properties.

[0095] The consortium registration submodule 232 is used to register the generated access index path and permission structure to multiple consensus nodes in the consortium blockchain ledger, completing the traceable registration of access behavior. This submodule constructs a registration record containing the index path, permission structure, registration timestamp, and node signature, and synchronously writes this record to multiple ledger nodes through the consensus mechanism. After registration, the system can quickly locate the corresponding permission rules through the index path and call the registration information in subsequent access control and behavior auditing stages, ensuring that all access activities have the ability to leave traces on the chain, controllable permissions, and traceable responsibility.

[0096] The channel scheduling module 24 is used to select the transmission mode based on the index path and control the edge nodes to complete relay binding and command issuance. It includes the following sub-modules:

[0097] The channel matching submodule 241 is used to parse the field set in the access index path, calculate the comprehensive sensitivity level of the fields, and filter and score the available transmission channels based on sensitivity, task level, and network status, selecting the optimal transmission path with the highest scheduling score. This submodule calculates the comprehensive score based on the sensitivity level and its set weight in the field labels, and combines the security capabilities, transmission latency, and capacity adaptability of each channel to perform multi-dimensional evaluation using a weighted strategy function, ultimately determining the communication path that best matches the task.

[0098] The relay control submodule 242 is used to generate corresponding relay binding instructions and channel-level encryption control command sets based on the selected optimal transmission channel, and send these command sets to the edge nodes. This submodule constructs a complete command structure containing relay node configuration information (such as address, port, and authentication method) and encryption control parameters (such as protocol type, key index, and rotation strategy), and appends a context binding digest jointly generated by the task path and channel for subsequent node identity verification and task tracking. After instruction execution, the communication channel is initialized and security encryption settings are completed, ensuring the security and consistency of medical data during transmission.

[0099] The identity authentication module 25 is used to parse the identity token and task parameters submitted by the recipient, and to complete permission verification and context confirmation. It includes the following sub-modules:

[0100] The token parsing submodule 251 receives and parses the identity token and task parameters submitted by the requester, extracting key fields such as the role set, timestamp, and context label to construct structured authentication request information. This submodule calls the token decoding function in the system to perform format recognition and content decryption of the identity credential, obtaining the set of business roles possessed by the requester. Simultaneously, it identifies the context of the target task, including request time, data type, and access scope, providing semantic input for subsequent permission comparison.

[0101] The Federated Authentication submodule 252 invokes the federated authentication mechanism to jointly verify the role permissions and context tags submitted by the requester against the permission structure and context records bound to the access index path. This submodule executes strict consistency verification logic to ensure that the permission set is completely contained within the authorized role set and that the context parameters are completely consistent with the registered content. Upon successful verification, it outputs "1" as a flag; otherwise, it outputs "0" and terminates the access process. This mechanism ensures that access behavior must simultaneously meet the requirements of permission control and context consistency, strengthening security boundary control during data access.

[0102] Audit assessment module 26 is used to analyze authorization results and behavior logs, score the compliance of transmission behaviors, and output a compliance risk level. It includes the following sub-modules:

[0103] The sample construction submodule 261 is used to extract system authorization results, behavior logs, and data transmission records during the current task execution process to construct a multi-dimensional audit assessment sample set. This submodule retrieves the permission determination results output during the task verification phase and synchronously loads the behavior log sequence and transmission session information for each data segment, including transmission time window, data rate, and content summary. The system performs standardized preprocessing on the above sample data, including log structure regularization, invalid item removal, and behavior sequence reconstruction, generating a unified input structure for compliance analysis, providing basic data support for rule matching and risk calculation.

[0104] The risk scoring submodule 262 combines rule matching degree with historical risk tags to execute a compliance scoring function and calculate the risk level assessment result of the current task. This submodule performs similarity analysis on each behavior log and entry in the rule base, overlays historical risk tags with time decay weights, constructs a compliance scoring expression, and outputs a standardized score. Based on the range of the scoring result, the system maps it to a discrete risk level identifier, such as "Low," "Medium," or "High," and records the result in an encrypted audit structure, achieving task-level audit traceability and behavioral risk backtracking.

[0105] Corresponding to the above embodiments, the present invention provides a computer storage medium, including: at least one memory and at least one processor;

[0106] The memory is used to store one or more program instructions;

[0107] A processor is used to run one or more program instructions to execute a method for the intelligent and secure transmission of medical data.

[0108] Corresponding to the above embodiments, this embodiment of the invention provides a computer-readable storage medium containing one or more program instructions, which are executed by a processor to provide a method for intelligent and secure transmission of medical data.

[0109] The embodiments disclosed in this invention provide a computer-readable storage medium storing computer program instructions that, when executed on a computer, cause the computer to perform the aforementioned intelligent and secure transmission method for medical data.

[0110] In this embodiment of the invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0111] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0112] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0113] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0114] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0115] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0116] Those skilled in the art will recognize that, in one or more of the examples above, the functions described in this invention can be implemented using a combination of hardware and software. When applied as software, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0117] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made on the basis of the technical solution of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent and secure transmission of medical data, characterized in that, Includes the following steps: S10. Collect medical data and contextual information through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags; S20. Call the strategy matching engine to analyze the description record, adaptively select the de-identification and encryption strategy and generate encrypted encapsulated data, while calculating its encryption complexity level. S30. Extract metadata and permission structure from encrypted encapsulated data, construct access index path and register it in the alliance ledger to achieve traceable management; S40, the index path-driven channel scheduling module selects the transmission mode and controls the edge nodes to complete relay binding and encryption command issuance; S50. After the receiver initiates a request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes the permission verification and context confirmation. S60. The audit module combines authorization results and behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators. The index path-driven channel scheduling module selects the transmission mode and controls the edge nodes to complete relay binding and encryption command issuance, including the following sub-steps: S401. Match available transmission channels based on the index path content, and the channel scheduling module selects the optimal transmission method. S402, The system controls the edge nodes to complete the corresponding relay binding task and simultaneously issues encrypted transmission instructions; After the recipient initiates a request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes permission verification and context confirmation, including the following sub-steps: S501, The recipient submits an identity token and task parameters, and the system parses and extracts role information and access scope; S502: Call the federal authentication mechanism to complete the authorization comparison and context confirmation process, and output a verification pass flag; The audit module combines authorization results with behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators, including the following sub-steps: S601. Extract system authorization results, behavior logs, and transmission session records to construct an evaluation sample set; S602. Calculate compliance scoring indicators based on rule matching degree and historical risk labels, and output compliance risk level assessment results.

2. The intelligent secure transmission method for medical data according to claim 1, characterized in that, Medical data and contextual information are collected through an intelligent tag parsing mechanism to generate structured description records containing type, sensitivity level, and permission tags, including the following sub-steps: S101. Extract medical data and its associated contextual information, construct initial data blocks, and parse their semantic fields and source paths; S102. Generate corresponding type identifiers, sensitivity level codes, and access permission tags based on the preset tag rule library, and output structured description records.

3. The intelligent secure transmission method for medical data according to claim 1, characterized in that, The process involves using a strategy matching engine to analyze the description records, adaptively selecting de-identification and encryption strategies, generating encrypted encapsulated data, and calculating its encryption complexity level. This includes the following sub-steps: S201. Perform a strategy lookup on the field types and sensitivity levels in the structured description records to match the available de-identification and encryption strategy sets; S202. Calculate the data encryption complexity level based on the number of fields and encryption strength parameters, and output the encapsulated encrypted data structure.

4. The intelligent secure transmission method for medical data according to claim 1, characterized in that, Extracting metadata and permission structures from encrypted and encapsulated data, constructing access index paths, and registering them in the consortium ledger for traceable management includes the following sub-steps: S301. Extract the metadata fields and permission structure content from the encrypted encapsulated data, and construct the task access index path; S302. Register the access index path and associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

5. An intelligent and secure transmission system for medical data, characterized in that, include: The data acquisition module is used to collect medical data and contextual information, and generate structured description records containing type, sensitivity level and permission tags; The strategy processing module is used to analyze structured description records, adaptively match desensitization and encryption strategies and generate encrypted encapsulated data, while calculating the encryption complexity level. The index registration module is used to extract metadata and permission structures from the encapsulated data, construct access index paths, and register them to the alliance ledger. The channel scheduling module is used to select the transmission mode based on the index path and control the edge nodes to complete relay binding and command issuance; The identity authentication module is used to parse the identity token and task parameters submitted by the recipient to complete permission verification and context confirmation; The audit assessment module is used to analyze authorization results and behavior logs, score the compliance of transmission behavior, and output the compliance risk level. The channel scheduling module is specifically used for: matching available transmission channels according to the index path content; selecting the optimal transmission method; and having the system control the edge nodes to complete the corresponding relay binding task and synchronously issue encrypted transmission instructions. After the recipient initiates a request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes the permission verification and context confirmation. Specifically, the recipient submits the identity token and task parameters, the system parses and extracts role information and access scope; calls the federated authentication mechanism to complete the permission comparison and context confirmation process, and outputs a verification pass flag. The audit assessment module combines authorization results and behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators. Specifically, it extracts system authorization results, behavior logs, and transmission session records to construct an assessment sample set; calculates compliance scoring indicators based on the degree of rule matching and historical risk labels, and outputs compliance risk level assessment results.

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