Intelligent safe transmission method and system for medical data

Abstract

The invention provides an intelligent safe transmission method and system for medical data, and the method comprises the steps: collecting the medical data and context information through an intelligent label analysis mechanism, and generating a structural description record containing a type, a sensitivity level and an authority label; calling a strategy matching engine to analyze the description record, adaptively selecting a desensitization and encryption strategy, generating encrypted encapsulation data, and calculating an encryption complexity level of the encrypted encapsulation data; extracting meta-information and an authority structure from the encrypted and packaged data, constructing an access index path, and registering the access index path to an alliance account book to realize traceable management; the index path drives the channel scheduling module to select a transmission mode, and controls the edge node to complete relay binding and encryption instruction issuing; after a receiver initiates a request, the system activates a federal authentication mechanism to analyze the identity token and the task parameters, and authority verification and context confirmation are completed; and the auditing module combines an authorization result with a behavior log, scores a transmission behavior and rule matching condition and calculates a compliance risk index.

Description

Technical Field

[0001] The present 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 Art

[0002] With the continuous development of medical information systems, large amounts of sensitive data, including patient diagnosis, testing, imaging, and medication records, are frequently transferred between different terminals and systems. Traditional medical data transmission methods rely on fixed rules and static permission controls, making it difficult to dynamically adapt to diverse access scenarios and risk levels. This can easily lead to security risks due to insufficient data desensitization, failed permission verification, and exposed transmission paths.

[0003] On the other hand, existing solutions generally lack linkage mechanisms for data encapsulation, link scheduling, and behavior auditing. This makes it difficult to flexibly select encryption strategies based on task context, and it also makes it difficult to 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 that combines 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] The present invention provides a method for intelligent and secure transmission of medical data, which includes: S10. Collect medical data and context information through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags; S20. Calling the policy matching engine to analyze the description record, adaptively selecting the desensitization and encryption strategies, and generating encrypted encapsulated data, while calculating its encryption complexity level. S30. Extract metadata and permission structure from the encrypted and encapsulated data, construct an access index path, and register it in the alliance ledger to achieve traceable management. S40, the index path drives the channel scheduling module to select a transmission mode and control the edge node to complete the relay binding and encryption instruction issuance; S50: After the recipient initiates the request, the system activates the federated authentication mechanism to parse the identity token and task parameters, completing the authority verification and context confirmation; S60. The audit module combines the authorization results with the behavior logs to score the transmission behavior and rule matching and calculate the compliance risk indicators.

[0005] The method for intelligent and secure transmission of medical data as described above, wherein medical data and context information are collected through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags, includes: S101, extracting medical data and its associated context information, constructing an initial data block and parsing its semantic fields and source paths; S102: Generate corresponding type identification, sensitivity level code and access permission label according to the preset label rule library, and output structured description record.

[0006] The intelligent and secure transmission method for medical data described above, wherein a policy matching engine is called to analyze description records, adaptively select desensitization and encryption strategies, generate encrypted encapsulated data, and calculate its encryption complexity level, includes: S201. Perform policy lookup on the field type and sensitivity level in the structured description record to match the available desensitization and encryption policy sets; S202: Calculate the data encryption complexity level based on the number of fields and the encryption strength parameter, and output the encapsulated encrypted data structure.

[0007] The intelligent and secure transmission method for medical data described above, wherein metadata and permission structures are extracted from the encrypted and encapsulated data, an access index path is constructed, and the access index path is registered in the alliance ledger to achieve traceable management, includes: S301, extracting the meta information field and permission structure content in the encrypted encapsulated data, and constructing a task access index path; S302: Register the access index path and the associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

[0008] The intelligent and secure transmission method for medical data described above, wherein the index path drives the channel scheduling module to select the transmission mode and controls the edge node to complete the relay binding and encryption instruction issuance, includes: S401, matching available transmission channels based on index path content, and scheduling the channel scheduling module to select the optimal transmission mode; S402: The system controls the edge node to complete the corresponding relay binding task and simultaneously issues the encryption transmission instruction.

[0009] In the above-described intelligent and secure transmission method for medical data, 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: S501: The recipient submits the identity token and task parameters, and the system parses and extracts the role information and access scope; S502: Call the federated authentication mechanism to complete the authority comparison and context confirmation process, and output a verification pass mark.

[0010] In the above-mentioned intelligent and secure transmission method for medical data, the audit module combines the authorization results and behavior logs to score the matching of transmission behavior with rules and calculate compliance risk indicators, including: S601. Extract system authorization results, behavior logs, and transmission session records to construct an evaluation sample set; S602. Calculate compliance scoring indicators based on the rule matching degree and historical risk tags, and output compliance risk level assessment results.

[0011] The present invention also provides an intelligent and secure transmission system for medical data, which includes: The data collection module is used to collect medical data and context information and generate structured description records containing type, sensitivity level and permission labels; The policy processing module is used to analyze structured description records, adaptively match desensitization and encryption strategies, generate encrypted encapsulated data, and calculate the encryption complexity level; The index registration module is used to extract metadata and permission structures from encapsulated data, build access index paths, and register them in the alliance ledger; The channel scheduling module is used to select the transmission mode according to the index path and control the edge nodes to complete the relay binding and instruction 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.

[0012] The beneficial effects achieved by the present invention are as follows: The present invention provides an intelligent and secure transmission method for medical data, which can accurately describe data types, sensitivity levels, and access rights based on a structured labeling mechanism, dynamically adapt desensitization and encryption policies through a policy matching engine, and combine access index registration, edge scheduling execution, federated identity authentication, and behavioral audit scoring mechanisms to build a secure transmission closed loop that runs through the entire collection, encryption, transmission, verification, and audit process. This method not only improves the transmission efficiency and flexibility of medical data in multiple scenarios, but also significantly enhances the security controllability and compliance traceability of the entire data process. It is suitable for the secure exchange and authorized flow of highly sensitive medical data between multiple terminals and multiple institutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 This is a flow chart of a method for intelligent and secure transmission of medical data provided in Example 1 of the present application; Figure 2 This is a schematic diagram of an intelligent and secure transmission system for medical data provided in Example 2 of the present application. DETAILED DESCRIPTION

[0015] The following is a clear and complete description of the technical solutions in the embodiments of the present invention, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0016] Example 1 like Figure 1 As shown, the first embodiment of the present application provides a method for intelligent and secure transmission of medical data, including the following steps: S10. Collect medical data and context information through the intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level and permission tags.

[0017] To achieve structured annotation of medical data and support subsequent access control policies, the system introduces an intelligent label parsing mechanism during the data access phase, automatically extracting data fields and their context paths, completing type identification, sensitivity level coding, and access rights definition, ultimately forming an information set with complete semantic labels, providing basic support for subsequent policy matching and access index construction. Specific sub-steps include: S101, extracting medical data and its associated context information, constructing an initial data block and parsing its semantic fields and source paths; The system receives and parses the transmitted medical data through the access layer, identifies the original fields and their semantic environment, and constructs the initial structured data blocks. Assume that the basic attributes of each data field are expressed as a triple: ,in, Represents the i-th original field (such as patient ID, diagnosis conclusion, etc.); The collection timestamp of the field; is the source path identifier of the field (such as HIS system, PACS module, interface number, etc.); n is the total number of fields to be processed in the current task; This is the initial set of fields constructed for the system, preserving their original values ​​and source information. This structure describes the basic outline and semantic context of the fields to be processed and serves as a pre-input for label assignment and permission mapping.

[0018] S102: Generate corresponding type identification, sensitivity level code and access permission label according to the preset label rule library, and output structured description record.

[0019] The system uses the preset tag rule library The label generation rules in the initial field set Each field in Perform labeling. Define the labeling function as follows: ,in, Identifies the field type. ; Indicates the sensitivity level. , the larger the value, the more sensitive it is; Indicates the subset of roles that are allowed to access this field. , The complete set of roles defined by the system. Finally, a set of structured description records is generated: ,This collection comprehensively records the original value, collection time, source path, and ,three-dimensional label attributes of each field, providing semantic support and access boundary ,definition for subsequent execution strategy matching and encryption encapsulation.

[0020] S20. Call the policy matching engine to analyze the description record, adaptively select the desensitization and encryption strategy, generate encrypted encapsulated data, and calculate its encryption complexity level.

[0021] After the structured description record is constructed, in order to achieve differentiated desensitization and encryption processing for different fields, the system calls the policy matching engine to perform multi-dimensional analysis of the tag field, performs policy search based on its type identification and sensitivity level, and combines dimensions such as policy adaptability, computational cost, and data retention to adaptively select the optimal policy set for subsequent encapsulation processing. Finally, the system outputs the encapsulated encrypted data structure and calculates the corresponding encryption complexity level for reference in subsequent dynamic policy adjustment and resource scheduling. It specifically includes the following sub-steps: S201. Perform policy lookup on the field type and sensitivity level in the structured description record to match the available desensitization and encryption policy sets; The system first loads the policy rule base , where each strategy It defines the supported field types, applicable sensitivity levels, supported data operation scopes, and expected computational costs. Each field in , the system outputs the result according to its label function , perform policy matching: ,in, is the i-th field; Identifies the type of the field, such as structured text, medical image, etc. is the sensitivity level of the field, with higher values ​​indicating greater sensitivity; is the policy engine matching function; The policy set that matches the field.

[0022] System based The adaptability of the strategies in the network is preliminarily screened, unavailable or constraint-conflicting strategies are eliminated, and a subset of candidate strategies for subsequent encryption decisions is formed and input into the next processing unit.

[0023] S202: Calculate the data encryption complexity level based on the number of fields and the encryption strength parameter, and output the encapsulated encrypted data structure.

[0024] The system performs the following operations on each field: Corresponding strategy candidate set We perform a quantitative evaluation of the complexity, introduce factors such as encryption algorithm strength, key length, execution rounds, and data retention weight, and define the encryption complexity scoring function as follows: ,in, For collection The number of elements in ; For strategy The encryption algorithm strength coefficient reflects the security level of the algorithm itself; is the quantized value after normalization of the key length; For strategy The standard number of execution rounds; To retain the complexity adjustment coefficient; For strategy Retention rate of field information (0~1); is the maximum retention rate under the current strategy set, used for normalization; is a logarithmic function with base 2, which strengthens the complexity difference between strategies.

[0025] After completing the complexity assessment of each field, the system will and encryption complexity , adaptively select the optimal desensitization strategy set that takes into account both security and performance Encryption policy set , encapsulate and generate the final ciphertext data structure: ,in, For fields The processed ciphertext value; 、 The final selected desensitization and encryption strategy; is the encryption complexity score of the corresponding field; Z is the encapsulated encrypted data structure set, which will be used as input data for the subsequent dynamic encryption control strategy construction.

[0026] S30. Extract metadata and permission structure from the encrypted encapsulated data, build an access index path, and register it in the alliance ledger to achieve traceable management.

[0027] Based on the encapsulated and encrypted data content, the system extracts key metadata fields and access permission structures, integrates multi-level feature information to generate a unique access index path identifier, and registers the path and permission relationship in the alliance chain ledger to achieve full-process traceability management of medical data access behavior. The specific steps include the following: S301, extracting the meta information field and permission structure content in the encrypted encapsulated data, and constructing a task access index path; The system extracts the metadata field set from the encrypted encapsulated data structure Z With the authority structure , construct a unique task access index path through feature transformation, weight encoding and encryption splicing , and its calculation formula is: ,in, Identifies the access index path after construction; is the total number of meta-information fields; It is a multi-element hash function used to generate irreversible index digests; is the jth meta information field; For fields The weight coefficient of Encoding functions for field features; Identify the i-th role; is the access operation corresponding to the i-th role; It is a permission structure splicing function that returns an identification string; is the weight parameter of the permission structure pair; is a symmetric encryption function using a secret key; Indicates a string concatenation operation. The access index path I output by this step will serve as the unique index identifier for subsequent alliance ledger registration and channel scheduling module calls.

[0028] S302: Register the access index path and the associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

[0029] System construction includes access index path , authority structure P , registration timestamp and a complete registration record of multi-node signature sets , and synchronously write it into each consensus node in the alliance ledger. The record expression is as follows: ,in, It is a registration record in the alliance ledger; Identifies the access index path; Access permission structure, including roles, scope of operations, and sensitivity levels; The standard timestamp for the registration action; Digital signature of index information for the qth node in the alliance chain; The total number of nodes registered in the alliance chain; Indicates taking the set of signatures of all nodes to ensure multi-node consensus; || indicates the splicing operation.

[0030] Final output registration record It is distributed and written into multiple ledger nodes to ensure the security, integrity and full-process traceability of the medical data index path, serving as the basic support for subsequent transmission control and behavior auditing.

[0031] S40: The index path drives the channel scheduling module to select a transmission mode and control the edge node to complete relay binding and encryption instruction issuance.

[0032] The system uses access index path I as a 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 encryption relay method and sends encryption control instructions to edge nodes to build a complete secure communication link. Specifically, it includes the following sub-steps: S401, matching available transmission channels based on index path content, and scheduling the channel scheduling module to select the optimal transmission mode; System parsing index path The set of fields extracted from , extract the sensitivity level of each field based on the label function , and set the comprehensive sensitivity level of the field in combination with the field weight: ,in, is the jth field in the index path; Representation field The sensitivity level ranges from , the larger the value, the more sensitive; Indicates the weight coefficient of the field, which is set according to the field type and frequency of use; The number of fields involved in the evaluation.

[0033] The channel scheduling module is based on the sensitivity level , Task data range , for the channel set Filter and calculate the scheduling score of each available channel to select the best channel , and its decision function is defined as follows: ,in, For channel At the sensitivity level The following security transmission score; For channel The transmission delay of the signal, the smaller the value, the faster the speed; For channel For task data range Capacity adaptability; It is the weighted coefficient under the multi-dimensional scheduling strategy, which is preset according to task requirements.

[0034] S402: The system controls the edge node to complete the corresponding relay binding task and simultaneously issues the encryption transmission instruction.

[0035] The system selects the optimal channel Corresponding communication protocols and relay configuration rules, build relay binding instructions Encryption Control Command Set , and sent to the edge node The instruction set structure is shown below: ,in, Contains the binding configuration information of the relay node, such as the node address, port and authentication method; Indicates channel-level encryption control parameters, including protocol type, key index, key rotation strategy, etc. For the task path and transmission channels The jointly generated context binding summary is used for subsequent node identity verification and task tracking.

[0036] Control instructions Edge Node After execution, the relay channel initialization and encrypted transmission control process will be completed, and a path-driven secure data path will be built.

[0037] S50: After the recipient initiates the request, the system activates the federated authentication mechanism to parse the identity token and task parameters, and completes the authority verification and context confirmation.

[0038] After receiving a data access request, the system first parses the identity token and task parameters submitted by the recipient, extracting their role information and context tags. The system then invokes the federated authentication mechanism to perform 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 pass flag. This includes the following sub-steps: S501: The recipient submits the identity token and task parameters, and the system parses and extracts the role information and access scope; The system extracts the identity token from the access request submitted by the recipient and a set of task parameters ,in Contains role credentials and timestamp identification, The request task context and target data identification index The system uses the token decoding function Parse the identity token to get the recipient role set: ,in, Indicates the multiple business roles to which the recipient belongs, serving as the basis for access control comparison.

[0039] S502: Call the federated authentication mechanism to complete the authority comparison and context confirmation process, and output a verification pass mark.

[0040] The system is based on structured request information , call the federated authentication mechanism and encrypt and encapsulate the data structure The permission structure bound in and contextual tags Perform joint verification. Index k represents the request task The corresponding data structure number. The verification logic is as follows: ,in, The set of permissions submitted by the requester; Contextual tags submitted for the requester; Encapsulate the data structure for the kth encryption; The permission structure registered in the data structure; Context information registered in the data structure; Verification is passed or not, 1 for passed and 0 for rejected. This logic ensures that the requester's permissions and context information must simultaneously meet the registered access control requirements, ensuring task-level data access security.

[0041] S60. The audit module combines the authorization results with the behavior logs to score the transmission behavior and rule matching and calculate the compliance risk indicators.

[0042] After completing the permission verification and context check 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, extracts a set of behavioral features for risk assessment, and matches them with the rule set and historical tags to ultimately output the compliance risk level of the task. This process specifically includes the following sub-steps: S601. Extract system authorization results, behavior logs, and transmission session records to construct an evaluation sample set; The system first retrieves the permission verification result corresponding to the current task request , Behavior log during request execution , and transfer session records , the three together constitute the multi-dimensional audit evaluation sample set: ,in, Indicates the verification result; Represents a sequence of behavioral events recorded by the system; ,in, Indicates the content of the data segment transmitted for the i-th time, represents the time window of the i-th transmission, The system preprocesses the above samples, unifying the encoding format, removing redundant behavioral events, and reconstructing the transmission trajectory to provide structured input for subsequent risk scoring.

[0043] S602. Calculate compliance scoring indicators based on the rule matching degree and historical risk tags, and output compliance risk level assessment results.

[0044] The system is based on a sample set , combined with the rule base and historical risk label matrix , build 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: ,in, , indicating behavior logs and rules degree of matching; represents the historical risk weight with time decay; Representation Rules Correlation coefficient with label dimension i; Representation Rules Under the i-th risk dimension, the corresponding historical risk label value; n and k represent the total number of rules in the rule base and the total number of risk dimension labels, respectively; is the nonlinear transformation function of risk label; is the normalization factor, ensuring .

[0045] According to the scoring results The system maps the compliance risk level to the range of the interval: , and finally the system will result Logging to an encrypted audit structure , ensuring that the behaviors throughout the entire task process are traceable, risks are traceable, and safety is controllable.

[0046] Example 2 like Figure 2As shown, the second embodiment of the present application provides an intelligent and secure transmission system for medical data, including: The data collection module 21 is used to collect medical data and context information and generate a structured description record containing type, sensitivity level and permission label. It includes the following submodules: The data field extraction submodule 211 receives the raw medical data stream and extracts valid fields and their associated contextual information to construct an initial structured data block. This submodule preprocesses the data through access layer components, identifying key fields (such as patient ID, test results, and diagnosis) and extracting the field's acquisition timestamp and source path (such as HIS, PACS, or interface number). Ultimately, each field is represented as a structured triplet, consisting of the field value, acquisition time, and source identifier, and stored in the initial field set.

[0047] The label generation submodule 212 is responsible for automatically labeling the initial set of fields based on the label rule library, generating field type identifiers, sensitivity level codes, and access permission labels. This submodule uses a field type identification function to determine the field data type (e.g., structured text, image, table, etc.), classifies the importance of the fields based on sensitivity level mapping rules, uses integer codes to represent the strength of the level, and generates a set of role access restrictions for the fields based on the system permission dictionary. The final output is a set of structured description records, recording the original value, source, time, and three-dimensional label features of each field, which serves as the semantic basis for subsequent invocations of the policy engine.

[0048] The policy processing module 22 is used to analyze the structured description record, adaptively match the desensitization and encryption strategies, generate encrypted encapsulated data, and calculate the encryption complexity level. It includes the following submodules: The policy matching submodule 221 performs a policy lookup on each field in the structured description record to determine the set of desensitization and encryption policies applicable to that field. This submodule first loads the policy rule base and extracts information such as the field type, supported sensitivity levels, execution constraints, and expected computational cost for each policy. The system then uses a policy matching function based on the field's type identifier and sensitivity level to filter out the set of policies that meet the requirements. It further eliminates policy entries that do not meet system constraints or conflict with the requirements, generating a candidate subset of policies for that field.

[0049] The encryption encapsulation submodule 222 performs a complexity assessment on a set of candidate strategies and calculates a comprehensive score based on factors such as encryption strength, retention rate, and execution rounds. The module then selects the optimal strategy for data encapsulation. 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 using a weighted function to ensure that the encryption result strikes a balance between security and system load. Ultimately, the selected strategy is applied to the original field, generating an encapsulation structure containing the desensitized result and the encrypted ciphertext. The complexity score corresponding to each field is recorded, providing parameter support for subsequent task level determination and channel selection.

[0050] The index registration module 23 is used to extract the metadata and permission structure from the encapsulated data, construct the access index path, and register it in the alliance ledger. It includes the following submodules: Index construction submodule 231 is responsible for extracting metadata fields and corresponding permission structure pairs from the encrypted encapsulated data structure and constructing a unique access index path identifier based on multi-factor hashing, feature concatenation, and symmetric encryption. This submodule extracts a digest of each field based on the field's weight parameter and feature encoding function, and performs feature concatenation and digest encryption in conjunction with the role operations of the permission structure, ultimately generating a string of irreversible access index path identifiers.

[0051] The consortium registration submodule 232 is responsible for registering the generated access index path and permission structure with multiple consensus nodes in the consortium chain 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 a consensus mechanism. Once registration is complete, the system can quickly locate the corresponding permission rule through the index path and utilize this registration information in subsequent access control and behavior auditing processes, ensuring that all access activities are traceable on the chain, with controllable permissions and traceable accountability.

[0052] The channel scheduling module 24 is used to select the transmission mode according to the index path and control the edge node to complete the relay binding and instruction issuance. It includes the following submodules: The channel matching submodule 241 parses the set of fields in the access index path, calculates the field's comprehensive sensitivity level, and screens and scores available transmission channels based on sensitivity, task level, and network status, selecting the optimal transmission path with the highest scheduling score. This submodule calculates a comprehensive score based on the sensitivity level in the field tag and its assigned weight. It then uses a weighted strategy function to perform a multi-dimensional evaluation based on each channel's security capabilities, transmission delay, and capacity adaptability, ultimately determining the communication path that best matches the task.

[0053] The relay control submodule 242 is responsible for generating the corresponding relay binding instructions and channel-level encryption control command set based on the selected optimal transmission channel and sending this instruction set to the edge node. 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). It also appends a context binding summary generated by combining the task path and channel for subsequent node identity verification and task tracking. After the command is executed, the communication channel is initialized and secure encryption is set up, ensuring the security and consistency of medical data during transmission.

[0054] The identity authentication module 25 is used to parse the identity token and task parameters submitted by the recipient to complete the authority verification and context confirmation. It includes the following submodules: 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 tag, and constructing a structured authentication request. This submodule invokes the token decoding function in the system to perform format identification and content decryption on the identity credential, obtaining the requester's business role set. It also identifies the context of the target task, including the request time, data type, and access scope, providing semantic input for subsequent permissions comparison.

[0055] The federated authentication submodule 252 is used to invoke the federated authentication mechanism and 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 implements strict consistency verification logic to ensure that the permission set is fully contained within the authorized role set and that the context parameters are fully consistent with the registered content. If verification passes, a "1" is output as a flag; if verification fails, a "0" is output, terminating the access process. This mechanism ensures that access actions must meet both permission control and context consistency requirements, strengthening security boundary control during data access.

[0056] The audit assessment module 26 is used to analyze authorization results and behavior logs, score the compliance of transmission behavior, and output the compliance risk level. It includes the following submodules: Sample Construction Submodule 261 extracts system authorization results, behavior logs, and data transmission records from the current task execution process to construct a multi-dimensional audit assessment sample set. This submodule retrieves the permission determination results output from the task verification phase and simultaneously loads the behavior log sequence and the transmission session information for each data segment, including the transmission time window, data rate, and content summary. The system performs standardized preprocessing on this sample data, including log structure regularization, invalid item removal, and behavior sequence reconstruction, to generate a unified input structure for compliance analysis, providing basic data support for rule matching and risk calculation.

[0057] The risk scoring submodule 262 combines the degree of rule matching with historical risk tags to execute a compliance scoring function and calculate the risk level assessment result for the current task. This submodule performs a similarity analysis on each behavior log entry against the entries in the rule base, overlays historical risk tags with time-decaying weights, constructs a compliance scoring expression, and outputs a standardized score. The system maps the scoring result to a discrete risk level identifier, such as "Low," "Medium," or "High," based on the range within which it falls. This result is then recorded in an encrypted audit structure, enabling task-level audit traceability and behavioral risk traceability.

[0058] Corresponding to the above embodiment, an embodiment of the present invention provides a computer storage medium, comprising: at least one memory and at least one processor; The memory is used to store one or more program instructions; The processor is used to run one or more program instructions to execute an intelligent and secure transmission method for medical data.

[0059] Corresponding to the above embodiment, an embodiment of the present invention provides a computer-readable storage medium, which contains one or more program instructions, and the one or more program instructions are used by a processor to execute an intelligent and secure transmission method for medical data.

[0060] The embodiments disclosed in the present invention provide a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed on a computer, the computer executes the above-mentioned intelligent and secure transmission method for medical data.

[0061] In the embodiments of the present invention, the processor may be an integrated circuit chip having signal processing capabilities. The processor may 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.

[0062] The methods, steps, and logic diagrams disclosed in the embodiments of the present invention can be implemented or executed. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor, or by a combination of hardware and software modules within the decoding processor. The software modules can be located in a storage medium well-established in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. The processor reads the information from the storage medium and, in conjunction with its hardware, completes the steps of the aforementioned methods.

[0063] The storage medium may be a memory and may be, for example, a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memory.

[0064] Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory.

[0065] Volatile memory may be random access memory (RAM), which is used as an external cache memory. By way of example and 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 link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM).

[0066] 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.

[0067] Those skilled in the art will appreciate that in one or more of the above examples, the functions described herein can be implemented using a combination of hardware and software. When software is used, the corresponding functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium. Computer-readable media include computer storage media and communication media, wherein communication media includes any medium that facilitates the transmission of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0068] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for intelligent and secure transmission of medical data, characterized in that: The steps include: S10. Collect medical data and context information through an intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags; S20. Calling the policy matching engine to analyze the description record, adaptively selecting the desensitization and encryption strategies, and generating encrypted encapsulated data, while calculating its encryption complexity level. S30. Extract metadata and permission structure from the encrypted and encapsulated data, construct an access index path, and register it in the alliance ledger to achieve traceable management. S40, the index path drives the channel scheduling module to select a transmission mode and control the edge node to complete the relay binding and encryption instruction issuance; S50: After the recipient initiates the request, the system activates the federated authentication mechanism to parse the identity token and task parameters, completing the authority verification and context confirmation; S60. The audit module combines the authorization results with the behavior logs to score the transmission behavior and rule matching and calculate the compliance risk indicators.

2. The intelligent and secure transmission method for medical data according to claim 1, characterized in that: Medical data and context information are collected through the intelligent tag parsing mechanism to generate a structured description record containing type, sensitivity level, and permission tags, including the following sub-steps: S101, extracting medical data and its associated context information, constructing an initial data block and parsing its semantic fields and source paths; S102: Generate corresponding type identification, sensitivity level code and access permission label according to the preset label rule library, and output structured description record.

3. The intelligent and secure transmission method for medical data according to claim 1, characterized in that: Calling the policy matching engine to analyze the description record, adaptively select the desensitization and encryption strategy and generate encrypted encapsulated data, while calculating its encryption complexity level, including the following sub-steps: S201. Perform policy lookup on the field type and sensitivity level in the structured description record to match the available desensitization and encryption policy sets; S202: Calculate the data encryption complexity level based on the number of fields and the encryption strength parameter, and output the encapsulated encrypted data structure.

4. The intelligent and 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, extracting the meta information field and permission structure content in the encrypted encapsulated data, and constructing a task access index path; S302: Register the access index path and the associated permission structure to the alliance ledger node to achieve traceable management of information access behavior.

5. The intelligent and secure transmission method for medical data according to claim 1, characterized in that: The index path drives the channel scheduling module to select the transmission mode and control the edge node to complete the relay binding and encryption instruction issuance, including the following sub-steps: S401, matching available transmission channels based on index path content, and scheduling the channel scheduling module to select the optimal transmission mode; S402: The system controls the edge node to complete the corresponding relay binding task and simultaneously issues the encryption transmission instruction.

6. The intelligent and secure transmission method for medical data according to claim 1, characterized in that: After the recipient initiates the request, the system activates the federated authentication mechanism to parse the identity token and task parameters, completing permission verification and context confirmation, including the following sub-steps: S501: The recipient submits the identity token and task parameters, and the system parses and extracts the role information and access scope; S502: Call the federated authentication mechanism to complete the authority comparison and context confirmation process, and output a verification pass mark.

7. The intelligent and secure transmission method for medical data according to claim 1, characterized in that: The audit module combines authorization results with behavior logs to score transmission behavior and rule matching 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 the rule matching degree and historical risk tags, and output compliance risk level assessment results.

8. An intelligent and secure transmission system for medical data, characterized in that: include: The data collection module is used to collect medical data and context information and generate structured description records containing type, sensitivity level and permission labels; The policy processing module is used to analyze structured description records, adaptively match desensitization and encryption strategies, generate encrypted encapsulated data, and calculate the encryption complexity level; The index registration module is used to extract metadata and permission structures from encapsulated data, build access index paths, and register them in the alliance ledger; The channel scheduling module is used to select the transmission mode according to the index path and control the edge nodes to complete the relay binding and instruction 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.

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