A remote medical data management method based on blockchain

Abstract

The present invention discloses a remote medical data management method based on blockchain, which relates to the technical field of data risk management. The method comprises: encrypting the medical data set of the target patient and storing it in the blockchain; performing risk identification on the K user terminals of the K consulting doctors, segmenting and optimizing the decryption key, performing staggered step segmentation extraction and integration of the decryption key, obtaining Q integrated decryption keys, decrypting the encrypted medical data set, and synchronously distributing the decrypted medical data set to the K consulting doctors for Q remote consultations. The present invention solves the technical problems in the prior art that the remote medical data management involves the patient's personal privacy and sensitive medical data, and the security and reliability of the consultation process have hidden dangers, and achieves the technical effect of storing and encrypting remote medical data based on blockchain technology to improve the security and reliability of the remote consultation process.

Description

Technical Field

[0001] The present invention relates to the technical field of data risk management, and in particular to a remote medical data management method based on blockchain. Background Art

[0002] The core of telemedicine data management lies in the use of modern information technology and communication technology, such as the Internet, 5G, and the Internet of Things, to achieve remote transmission, storage, processing, and analysis of medical data. These technologies provide an efficient and convenient data transmission channel for telemedicine, enabling doctors and patients to communicate and share medical information in real time across geographical boundaries.

[0003] However, since telemedicine data management involves a large amount of patients' personal privacy and sensitive medical data, how to ensure the security and privacy of this data during transmission, storage and processing is an important problem. Summary of the invention

[0004] The present application provides a blockchain-based telemedicine data management method, which is used to solve the technical problems in the prior art that telemedicine data management involves patient personal privacy and sensitive medical data, and there are hidden dangers in the security and reliability of the consultation process.

[0005] The present application provides a remote medical data management method based on blockchain, the method comprising: obtaining a remote consultation plan for a target patient, wherein the remote consultation plan includes K consulting doctors and Q consultation times; calling the RSA algorithm on the medical data set of the target patient to generate a set of encryption keys and decryption keys; encrypting the medical data set using the encryption key, and storing the encrypted medical data set in the blockchain to obtain an encrypted medical data set; performing risk identification on K user terminals of the K consulting doctors to determine K risk coefficients; segmenting and optimizing the decryption key according to the K risk coefficients to obtain a decryption key repository, and segmenting the windows for the Q keys Decryption keys are extracted in a staggered step-by-step manner in the decryption key storage repository to obtain Q decryption key sets, wherein the Q key segmentation windows are Q time nodes in a preset time period before Q consultation times, and each decryption key set includes K decryption keys; the Q decryption key sets are respectively distributed to the K consulting doctors at the Q consultation times, and the K consulting doctors integrate the Q decryption key sets to obtain Q integrated decryption keys; the encrypted medical data sets are respectively decrypted using the Q integrated decryption keys, and if the decryption is successful, the decrypted medical data sets are synchronously distributed to the K consulting doctors for Q remote consultations.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The present application provides a blockchain-based remote medical data management method, which relates to the field of data risk management technology. The medical data set of the target patient is encrypted and stored in the blockchain, risk identification is performed on K user terminals of K consulting doctors, and the decryption keys are segmented and optimized to obtain Q integrated decryption keys to decrypt the encrypted medical data set, and the decrypted medical data set is synchronously distributed to K consulting doctors for Q remote consultations. This solves the technical problems in the prior art that remote medical data management involves patient personal privacy and sensitive medical data, and the security and reliability of the consultation process have hidden dangers, and realizes the technical effect of storing and encrypting remote medical data based on blockchain technology to improve the security and reliability of the remote consultation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A flowchart of a remote medical data management method based on blockchain provided in an embodiment of the present application;

[0010] Figure 2 A schematic diagram of a process for determining K risk coefficients in a blockchain-based telemedicine data management method provided in an embodiment of the present application;

[0011] Figure 3 A schematic diagram of a process for obtaining a decryption key repository in a blockchain-based remote medical data management method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a blockchain-based telemedicine data management method, which is used to solve the technical problems in the prior art that telemedicine data management involves patient personal privacy and sensitive medical data, and there are hidden dangers in the security and reliability of the consultation process.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "first", "second", etc. in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices.

[0015] Embodiment 1

[0016] like Figure 1 As shown, the present application provides a remote medical data management method based on blockchain, the method comprising:

[0017] P10: Obtain a remote consultation plan for the target patient, wherein the remote consultation plan includes K consulting doctors and Q consultation times.

[0018] Specifically, obtain the remote consultation plan for the target patient, that is, a detailed consultation plan developed to conduct a comprehensive and accurate assessment and diagnosis of the target patient's condition, including the number of doctors participating in the consultation and the time schedule for each consultation. This application assumes that there are K consulting doctors and Q consultation times. The K consulting doctors may come from different medical institutions, professional fields or geographical locations, but they all have the professional ability to diagnose and recommend treatment for the target patient's condition. Q consultation times refer to the arrangement of Q specific consultation times during the entire remote consultation process based on the patient's condition needs, the doctor's schedule, and the consultation process. With clear consulting doctors and consultation times, the system can process and transmit telemedicine data in a more orderly and efficient manner, and provide more accurate and timely support for the patient's diagnosis and treatment.

[0019] P20: The medical data set of the target patient is invoked with the RSA algorithm to generate a set of encryption keys and decryption keys.

[0020] Furthermore, step P20 of the embodiment of the present application also includes: P21: obtaining a preset encryption key index and a preset key length; P22: calling the key generation function in the RSA algorithm to generate the decryption key; P23: calling the encryption key extraction function to analyze the decryption key, and generating the encryption key in combination with the preset encryption key index and the preset key length.

[0021] Optionally, the RSA algorithm is used to generate a set of encryption keys and decryption keys for the target patient's medical data set to ensure the security of the data during transmission and storage. Specifically, first, the encryption key exponent (usually a large prime number e, used for public key encryption) and the key length (i.e., the number of bits of the key, which determines the complexity and security of the key) are pre-set according to the system's security requirements, performance requirements, and industry standards. The selection of the encryption key exponent and key length is crucial to the security and efficiency of the RSA algorithm.

[0022] Furthermore, the key generation function in the RSA algorithm is called to generate the decryption key. In the RSA algorithm, the decryption key is usually the private key part of the product n of a pair of large prime numbers p and q (including d, d is the modular inverse of e with respect to φ(n), where φ is the Euler function). These values ​​are generated through complex mathematical operations and have a high degree of security and complexity.

[0023] After the decryption key is generated, the corresponding encryption key needs to be generated. Because the encryption key and decryption key are generated independently in the RSA algorithm, but there is a mathematical relationship between them. Therefore, the encryption key extraction function is called to analyze the decryption key. The encryption key extraction function is actually a generation function that combines the preset encryption key exponent and key length, as well as certain information in the decryption key (such as n and e) to generate the encryption key. And in the RSA algorithm, the encryption key is usually a public key, which consists of (e, n), where e is the preset encryption key exponent and n is the product of p and q.

[0024] By setting encryption keys and decryption keys, the confidentiality of data is ensured so that only authorized users with the correct decryption keys can access and decrypt the data.

[0025] P30: Encrypt the medical data set using the encryption key, and store the encrypted medical data set in the blockchain to obtain an encrypted medical data set.

[0026] It should be understood that, first, the target patient's medical data set is encrypted using the previously generated encryption key. Encryption is an important means to ensure data confidentiality. In this step, the system uses the encryption key (usually the public key) of the RSA algorithm (or other applicable encryption algorithm) to encrypt the medical data set. The encryption process involves converting the original data into a form that can only be interpreted by users with the corresponding decryption key (usually the private key) to prevent unauthorized users from accessing and tampering with medical data, ensuring the security of data during transmission and storage.

[0027] Furthermore, the encrypted medical data set is stored in a blockchain. Blockchain is a decentralized, distributed ledger technology that has the characteristics of being tamper-proof, highly transparent, and traceable. By setting up multiple storage nodes based on the blockchain and storing the encrypted medical data set on multiple storage nodes on the blockchain, the integrity and credibility of the data can be ensured, the data can be prevented from being maliciously tampered with or deleted, and the reliability and availability of the data can be improved.

[0028] After encryption and blockchain storage, the original medical data set is transformed into an encrypted medical data set. This set contains the encrypted medical information of the target patient, and only authorized users can access and interpret this data through the decryption key. The generation of encrypted medical data sets provides a safe and reliable data foundation for subsequent remote consultations, data sharing, and data analysis.

[0029] P40: Perform risk identification on the K user terminals of the K consulting doctors and determine K risk coefficients.

[0030] Further, such as Figure 2 As shown, step P40 of the embodiment of the present application also includes:

[0031] P41: Retrieve the K historical conflict records within the preset monitoring window from the K user terminals of the K consulting doctors; P42: Perform feature extraction on the K historical conflict records according to the preset conflict indicator set to obtain K historical conflict indicator value sets; P43: Use the risk coefficient identification network layer to perform risk identification on the K historical conflict indicator sets to determine the K risk coefficients.

[0032] Optionally, risk identification is performed on the K user terminals of the K consulting doctors participating in the remote consultation to determine the risk factor of each user terminal. First, the historical conflict records of each consulting doctor user terminal within the preset monitoring window are retrieved. These conflict records may include but are not limited to: data leakage incidents, unauthorized access attempts, security vulnerability reports, etc. The preset monitoring window is a time range used to limit the time period for querying historical conflict records so that the system can obtain the latest and most relevant data.

[0033] Furthermore, feature extraction is performed on the historical conflict records of each user terminal according to a preset conflict indicator set. The preset conflict indicator set can be extracted and constructed based on historical conflict data, and may include but is not limited to: the frequency of conflict occurrence, the type of conflict, the time of conflict resolution, etc. Through feature extraction, the original K historical conflict records can be converted into a series of specific and quantifiable indicator values ​​to obtain K historical conflict indicator value sets.

[0034] Furthermore, the risk coefficient identification network layer is used to identify the risks of the extracted K historical conflict indicator sets. The risk coefficient identification network layer is a model based on machine learning or deep learning. By training and learning a large amount of historical data, it can automatically identify and evaluate the risk levels of different user terminals. According to the results of risk identification, a risk coefficient is determined for each consulting doctor's user terminal to obtain K risk coefficients. The K risk coefficients will reflect the size of the security risks that may exist in the K user terminals during the data transmission process, and can be used as an important reference factor in the subsequent key distribution and data transmission process to ensure the security of medical data.

[0035] P50: The decryption keys are segmented and optimized according to the K risk factors to obtain a decryption key repository, and decryption keys are extracted in a staggered manner in the decryption key repository for Q key segmentation windows to obtain Q segmented decryption key sets, wherein the Q key segmentation windows are Q time nodes in a preset time period before the Q consultation times, and each segmented decryption key set includes K segmented decryption keys.

[0036] Specifically, the decryption key is divided according to the user risk coefficients of the K consulting doctors. The purpose of the division is to divide the key into different parts according to the risk levels of different users, so that the access rights can be more finely controlled during the subsequent distribution and use. The optimization operation is performed during the division process, that is, different division schemes are tried and the pros and cons of each scheme are evaluated. The basis for the evaluation may include multiple aspects such as the security, integrity, and recoverability of the key after division. Through the optimization process, one or more optimal division schemes are found as the final decryption key division results. The decryption key division results obtained by the optimization are stored in the database to form a decryption key repository. This repository contains the optimal decryption key division schemes for different consulting doctor users and risk coefficients.

[0037] Furthermore, Q key segmentation windows are set, corresponding to Q time nodes in a preset time period before the Q consultation times. Each window represents a specific time period, which is used to determine the segmented portion of the decryption key that needs to be extracted within the time period. In the Q key segmentation windows, the segmentation and extraction of the decryption key is performed in a staggered step manner. Each extracted segmented portion of the decryption key is not only related to the current consultation time node, but also has a certain correlation with the extraction result of the previous or next time node. By comparing the similarity of the segmented decryption keys of adjacent time nodes, the segmented decryption keys with lower similarity are extracted to obtain Q segmented decryption key sets, each set containing K segmented decryption keys, corresponding to the user terminals of K consulting doctors. These segmented decryption keys will be allocated to the corresponding user terminals in the subsequent consultation process for decrypting and accessing medical data.

[0038] Further, such as Figure 3 As shown, step P50 of the embodiment of the present application also includes:

[0039] P51: Calculate the ratio of the K risk coefficients to the sum of the K risk coefficients respectively to obtain K splitting factors; P52: Randomly split the decryption key into K parts multiple times to obtain multiple initial split decryption key sets, wherein each initial split decryption key set includes K initial split decryption keys; P53: Use the fitness function and the K splitting factors to identify the fitness of the multiple initial split decryption key sets respectively to obtain multiple initial fitnesses; P54: Take the initial split decryption key set corresponding to the maximum value of the multiple initial fitnesses as the main particle, and take the remaining multiple initial split decryption key sets as multiple shadow particles, wherein the initial fitness corresponding to the main particle is taken as the main fitness, and the multiple initial fitnesses corresponding to the multiple shadow particles are taken as multiple shadow fitnesses; P55: With the main particle as the direction, follow and adjust the multiple shadow particles to find the best until the preset number of adjustments is met, and obtain the adjusted multiple target split decryption key sets; P56: Store the multiple target split decryption key sets in a database to obtain the decryption key storage library.

[0040] Wherein, the fitness function is: ;

[0041] in, For fitness, is the i-th initial segmentation decryption key in the initial segmentation decryption key set, X is the key length of the decryption key corresponding to the initial segmentation decryption key set, is the split factor of the i-th client corresponding to the i-th initial split decryption key in the initial split decryption key set, and k is the number of initial split decryption keys in the initial split decryption key set.

[0042] It should be understood that the decryption key is subjected to complex segmentation optimization according to the risk coefficients of the K consulting doctors' user terminals. The specific process may be that the ratio of the K risk coefficients to the sum of the K risk coefficients is calculated respectively, thereby obtaining K segmentation factors. These segmentation factors reflect the relative importance of each consulting doctor's user terminal in the key segmentation process, that is, the higher the risk coefficient of the user terminal, the larger the corresponding segmentation factor, indicating that the length of the segmented decryption key obtained is longer, which means that more attention and protection should be given when the key is segmented.

[0043] Furthermore, the decryption key is randomly split into K parts multiple times to generate multiple initial segmentation decryption key sets. Each set contains K initial segmentation decryption keys, corresponding to K consulting doctors' user terminals. And the fitness function is used to identify the fitness of each initial segmentation decryption key set. The fitness function is: ;

[0044] in, For fitness, is the i-th initial segmentation decryption key in the initial segmentation decryption key set, X is the key length of the decryption key corresponding to the initial segmentation decryption key set, is the split factor of the i-th user terminal corresponding to the i-th initial split decryption key in the initial split decryption key set, and k is the number of initial split decryption keys in the initial split decryption key set. The fitness function comprehensively considers factors such as key length, split factor, and specific content of the initial split decryption key to evaluate the pros and cons of each set, that is, by judging whether the proportion of the lengths of K split decryption keys in the decryption key in the multiple initial split decryption key sets is consistent with the K split factors. The higher the consistency, the more it conforms to the risk situation of each user terminal, and the greater the fitness. Multiple initial fitness of the multiple initial split decryption key sets are obtained by calculation.

[0045] Furthermore, the initial segmentation decryption key set corresponding to the maximum value among the multiple initial fitnesses is taken as the main particle, that is, the optimal solution, and the remaining multiple initial segmentation decryption key sets are taken as multiple shadow particles, that is, the solution to be optimized. At the same time, the initial fitness corresponding to the main particle is taken as the main fitness, and the initial fitness corresponding to the shadow particle is taken as the shadow fitness. Further, with the main particle as the direction, the multiple shadow particles are followed and adjusted to search for the best. In the particle swarm optimization algorithm, each particle will update its position according to its own historical optimal position and the global optimal position of the entire particle swarm. Through multiple iterative adjustments, the global optimal solution is gradually approached, that is, a decryption key segmentation scheme that meets the requirements is obtained. When the preset number of adjustments is met, the optimization process ends, and multiple adjusted target segmentation decryption key sets are obtained.

[0046] Finally, the multiple target segmentation decryption key sets are stored in a database to form a decryption key repository. The decryption key repository contains the optimal decryption key segmentation scheme for different consulting doctor user terminals and risk factors, providing reliable protection for subsequent key distribution and use.

[0047] Furthermore, step P55 of the embodiment of the present application also includes:

[0048] P55-1: Taking the main particle as the direction, by following and increasing or following and decreasing the lengths of the K initial split decryption keys in the multiple shadow particles according to the preset adjustment step length, a plurality of adjusted shadow particles are obtained; P55-2: Using the fitness function and the K split factors to perform fitness identification on the multiple adjusted shadow particles, a plurality of adjusted shadow fitnesses are obtained; P55-3: Determine whether the maximum value among the multiple adjusted shadow fitnesses is greater than or equal to the main fitness. If so, update the adjusted shadow particle corresponding to the maximum value among the multiple adjusted shadow fitnesses to the main particle, and continue to follow the adjustment and optimization until the preset number of adjustments is met, stop the optimization, and use the main particle and the multiple adjusted shadow particles obtained in the last optimization as the multiple target split decryption key sets.

[0049] Optionally, the embodiment of the present application adopts the idea of ​​particle swarm optimization (PSO) algorithm to optimize the decryption key segmentation scheme. First, with the main particle (i.e., the current optimal solution) as the direction, multiple shadow particles (i.e., the solution to be optimized) are adjusted. Specifically, with the segmentation decryption key scheme of the main particle as the adjustment direction, the length of the K initial segmentation decryption keys in each shadow particle is increased or decreased according to the preset adjustment step size to be closer to the main particle. Among them, the preset adjustment step size can be set by professionals based on experience according to the actual data volume.

[0050] Furthermore, the fitness function and K segmentation factors are used to perform fitness identification on the multiple adjusted shadow particles, and multiple adjusted shadow fitness values ​​are obtained through calculation. These fitness values ​​reflect the fitness of the adjusted shadow particles relative to the original problem.

[0051] Furthermore, it is determined whether the maximum value among the multiple adjusted shadow fitness is greater than or equal to the fitness of the current main particle. If so, it means that there is at least one adjusted shadow particle whose fitness is better than or equal to the current optimal solution. Therefore, the adjusted shadow particle corresponding to the maximum value is updated to the main particle, and the new main particle is continued to be followed and adjusted for optimization. This process will be repeated until the preset number of adjustments is met. The preset number of adjustments is a stop condition, which is used to control the number of iterations of the optimization process to prevent the algorithm from falling into an infinite loop. When the preset number of adjustments is reached, the system stops the optimization and uses the main particle and multiple adjusted shadow particles obtained in the last optimization process as the final target segmentation decryption key set.

[0052] Furthermore, step P55 of the embodiment of the present application also includes:

[0053] P55-3a: Determine whether the maximum value among the multiple adjusted shadow fitnesses is greater than or equal to the main fitness. If not, obtain a first random value; P55-4a: Determine whether the first random value is greater than or equal to a preset random value threshold. If so, accept the adjustment shadow particle corresponding to the maximum value among the multiple adjusted shadow fitnesses to be updated as the main particle; P55-5a: If not, do not accept the adjustment shadow particle corresponding to the maximum value among the multiple adjusted shadow fitnesses to be updated as the main particle, and disable the adjustment method for obtaining the multiple adjusted shadow particles, continue to follow the adjustment and optimization according to the main particle, and obtain the multiple target segmentation decryption key sets.

[0054] In a possible embodiment of the present application, the embodiment of the present application introduces a certain degree of randomness to enhance the search capability of the particle swarm optimization (PSO) algorithm to avoid falling into a local optimal solution. First, determine whether the maximum value of the multiple adjusted shadow fitness obtained after fitness identification is greater than or equal to the fitness of the current main particle. If not, that is, no adjusted shadow particle is better than the current main particle in fitness, then the system needs to decide whether to accept the suboptimal solution or continue to explore other possibilities.

[0055] In order to increase the search breadth of the algorithm, a first random value is generated according to the rand function and compared with a preset random value threshold, which can be set by professionals based on experience. If the first random value is greater than or equal to the preset random value threshold, the adjusted shadow particle corresponding to the maximum value of multiple adjusted shadow fitness is accepted as the new main particle, even if its fitness is not strictly better than the original main particle. The introduction of this step allows the algorithm to jump out of the local optimum with a certain probability and explore new solution space areas.

[0056] Furthermore, if the first random value is less than the preset random value threshold, the adjusted shadow particle corresponding to the maximum value among the multiple adjusted shadow fitness values ​​is not accepted as the new main particle. At this time, in order to avoid repeated invalid exploration, the adjustment method of obtaining these adjusted shadow particles is disabled (for example, the same adjustment step or direction is no longer used in subsequent iterations). Then, the current main particle is continued to be followed and adjusted for optimization until the preset number of adjustments is met, and finally multiple target segmentation decryption key sets are obtained.

[0057] Through the above mechanism, the particle swarm optimization algorithm of the embodiment of the present application increases the randomness and diversity of the search while maintaining a certain search direction, which helps to find the global optimal solution or a solution close to the global optimal solution in complex optimization problems. In the process of segmenting and optimizing the decryption key, this algorithm can more effectively balance security and efficiency, and provide more reliable security protection for application scenarios such as remote consultation.

[0058] Furthermore, step P50 of the embodiment of the present application also includes:

[0059] P57: randomly extracting a first split decryption key set from the decryption key repository in the first key split window, and adding the first split decryption key set to a jump set; P58: randomly extracting a second split decryption key set from the decryption key repository in the second key split window, determining the similarity between the second split decryption key set and the jump set, and when the calculation result satisfies the preset jump similarity, adding the second split decryption key set to the jump set; P59: randomly extracting a Qth split decryption key set from the decryption key repository in the Qth key split window, determining the similarity between the Qth split decryption key set and the jump set, and when the calculation result satisfies the preset jump similarity, adding the Qth split decryption key set to the jump set; P510: aggregating the first split decryption key set, the second split decryption key set and the Qth split decryption key set to generate the Q split decryption key sets.

[0060] Specifically, in the first key split window (i.e., the earliest or specified starting time node), the first split decryption key set is randomly extracted from the decryption key storage library and added to the jump set. At the same time, in the second key split window, a second split decryption key set is randomly extracted from the decryption key storage library again. Then, the similarity between the newly extracted second split decryption key set and the existing set in the jump set is calculated. The similarity calculation can be based on a variety of factors, such as key length, content difference, splitting method, etc. If the calculation result meets the preset jump similarity threshold, that is, it is less than the jump similarity threshold, it means that the two sets are less similar to a certain extent, and it is not easy to have a similar key situation, then the second split decryption key set is also added to the jump set. Among them, the jump similarity threshold is a maximum value, and the larger the jump similarity, the worse the security of the decryption key. When the calculation result does not meet the preset jump similarity threshold, that is, it is greater than or equal to the jump similarity threshold, it means that the split decryption key similarity is too high, and it is necessary to re-randomly extract to avoid the decryption key duplication of Q consultations.

[0061] Further, the above process is repeated by analogy until the Qth key split window (i.e., the latest or specified end time node), the Qth split decryption key set is extracted from the decryption key storage library, and its similarity with the existing set in the jump set is determined again. If the preset jump similarity threshold is met, that is, the similarity is low, the set will also be added to the jump set.

[0062] Finally, all the split decryption key sets in the jump set (including the first, second, and Qth split decryption key sets) are summarized to generate the final Q split decryption key sets. This set contains all the split decryption keys extracted from the Q key split windows and screened by similarity judgment. They maintain a certain degree of difference, which helps to improve security in the subsequent decryption process.

[0063] P60: Distribute the Q segmented decryption key sets to the K consulting doctors during the Q consultation times respectively. The K consulting doctors integrate the Q segmented decryption key sets to obtain Q integrated decryption keys.

[0064] Optionally, according to the schedule of Q consultations, the Q split decryption key sets are distributed to the K consulting doctors participating in the consultation. After receiving their own split decryption key sets, each consulting doctor integrates these sets, including recombining the split decryption keys at different time nodes according to a certain rule or order to restore the complete decryption key. After the integration is completed, each consulting doctor will obtain Q integrated decryption keys. These integrated decryption keys correspond one-to-one to the Q consultation times and can be used to decrypt encrypted data or files generated at the corresponding time nodes. In this way, it is ensured that each consulting doctor can obtain the correct decryption key when needed, thereby ensuring data security and smooth communication during the remote consultation process.

[0065] P70: Decrypt the encrypted medical data sets respectively using the Q integrated decryption keys. If the decryption is successful, the decrypted medical data sets are synchronously distributed to the K consulting doctors for Q remote consultations.

[0066] It should be understood that the encrypted medical data sets are decrypted respectively using the Q integrated decryption keys, and the decryption process involves applying each integrated decryption key to the encrypted medical data set corresponding to it. Since each integrated decryption key is generated for the encrypted data at a specific time node, they can correctly decrypt the medical data at the corresponding time point. If the decryption is successful, the system will verify whether the decrypted data is complete and has not been tampered with to ensure the accuracy and reliability of the data. Once all encrypted medical data sets are successfully decrypted and the data verification is passed, the decrypted medical data sets will be synchronously distributed to the K doctors participating in the consultation. Doctors can access medical data through the remote consultation platform to determine the security and convenience of data access.

[0067] Furthermore, the embodiment of the present application further includes step P80, and step P80 further includes:

[0068] P81: If the decryption fails, M abnormal data are obtained; P82: Feature recognition is performed on the M abnormal data, and the M risk coefficients corresponding to the corresponding M user terminals are corrected according to the recognition results to obtain M corrected risk coefficients; P83: The K risk coefficients are updated according to the M corrected risk coefficients.

[0069] Specifically, during the decryption process, if it is found that one or some encrypted medical data sets cannot be successfully decrypted, that is, the decryption operation fails, M abnormal data are screened out from the data that failed to be decrypted. These abnormal data may be unable to be decrypted due to various reasons (such as encryption key error, data damage, encryption algorithm mismatch, etc.). Further, feature recognition is performed on the M abnormal data, including analyzing the specific manifestations and possible causes of the abnormal data, such as checking the integrity of the data, verifying the compatibility of the encryption algorithm, and analyzing possible transmission errors. Through feature recognition, detailed information about the abnormal data is obtained, and the risk coefficients of the corresponding M user terminals (which may be patients, doctors or other data providers) are corrected accordingly. If the abnormal data indicates that there are security risks in the user terminal (such as device intrusion, key leakage, etc.), the risk coefficient may increase. If the abnormal data is caused by non-security factors (such as accidental errors in data transmission), the risk coefficient may remain unchanged or only be slightly adjusted, thereby obtaining M corrected risk coefficients.

[0070] Furthermore, the existing K risk factors are updated according to the M modified risk factors to ensure that the risk factors can accurately reflect the current security status of the consulting doctors and the data they process. By dynamically adjusting the risk factors, the system can more flexibly respond to various security threats and improve the security and reliability of the remote consultation process.

[0071] In summary, the embodiments of the present application have at least the following technical effects:

[0072] This application encrypts the target patient's medical data set and stores it in the blockchain, identifies risks for K user terminals of K consulting doctors, and divides and optimizes the decryption keys to obtain Q integrated decryption keys to decrypt the encrypted medical data set, and synchronously distributes the decrypted medical data set to K consulting doctors for Q remote consultations.

[0073] The technical effect of storing and encrypting remote medical data based on blockchain technology and improving the security and reliability of the remote consultation process has been achieved.

[0074] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0075] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0076] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A remote medical data management method based on blockchain, characterized in that: The method comprises: Obtain a remote consultation plan for a target patient, wherein the remote consultation plan includes K consulting doctors and Q consultation times; Using the RSA algorithm to generate a set of encryption keys and decryption keys for the medical data set of the target patient; Encrypting the medical data set using the encryption key, and storing the encrypted medical data set in a blockchain to obtain an encrypted medical data set; Perform risk identification on the K user terminals of the K consulting doctors to determine K risk coefficients; The decryption key is segmented and optimized according to the K risk coefficients to obtain a decryption key repository, and the decryption key is segmented and extracted in a staggered manner in the decryption key repository for Q key segmentation windows to obtain Q segmented decryption key sets, wherein the Q key segmentation windows are Q time nodes in a preset time period before the Q consultation times, and each segmented decryption key set includes K segmented decryption keys; Distributing the Q segmented decryption key sets to the K consulting doctors at the Q consultation times respectively, and the K consulting doctors integrating the Q segmented decryption key sets to obtain Q integrated decryption keys; Decrypt the encrypted medical data sets respectively using the Q integrated decryption keys, and if the decryption is successful, synchronously distribute the decrypted medical data sets to the K consulting doctors for Q remote consultations; The decryption key is segmented and optimized according to the K risk coefficients to obtain a decryption key repository, including: Calculate the ratio of the K risk coefficients to the sum of the K risk coefficients respectively to obtain K segmentation factors; The decryption key is randomly split into K parts multiple times to obtain multiple initial split decryption key sets, wherein each initial split decryption key set includes K initial split decryption keys; Using the fitness function and combining the K segmentation factors, respectively, the multiple initial segmentation decryption key sets are subjected to fitness identification to obtain multiple initial fitnesses; The initial segmentation decryption key set corresponding to the maximum value among the multiple initial fitnesses is used as the main particle, and the remaining multiple initial segmentation decryption key sets are used as multiple shadow particles, wherein the initial fitness corresponding to the main particle is used as the main fitness, and the multiple initial fitnesses corresponding to the multiple shadow particles are used as multiple shadow fitnesses; Taking the main particle as the direction, the multiple shadow particles are followed and adjusted to optimize until a preset number of adjustments is met, thereby obtaining multiple adjusted target segmentation decryption key sets; Storing the multiple target segmentation decryption key sets in a database to obtain the decryption key repository; The fitness function is: ； in, For fitness, is the i-th initial segmentation decryption key in the initial segmentation decryption key set, X is the key length of the decryption key corresponding to the initial segmentation decryption key set, is the split factor of the i-th client corresponding to the i-th initial split decryption key in the initial split decryption key set, and k is the number of initial split decryption keys in the initial split decryption key set.

2. A blockchain-based remote medical data management method as claimed in claim 1, characterized in that: Taking the main particle as the direction, the multiple shadow particles are followed, adjusted and optimized until the preset adjustment times are met, and multiple adjusted target segmentation decryption key sets are obtained, including: Taking the main particle as the direction, by following and increasing or following and decreasing the lengths of K initial segmentation decryption keys in the multiple shadow particles according to a preset adjustment step length, multiple adjusted shadow particles are obtained; Performing fitness identification on the plurality of adjusted shadow particles using the fitness function and the K segmentation factors to obtain a plurality of adjusted shadow fitnesses; Determine whether the maximum value among the multiple adjusted shadow fitnesses is greater than or equal to the main fitness. If so, update the adjusted shadow particle corresponding to the maximum value among the multiple adjusted shadow fitnesses to the main particle, and continue to follow the adjustment optimization until the preset number of adjustments is met, stop the optimization, and use the main particle and multiple adjusted shadow particles obtained in the last optimization as multiple target segmentation decryption key sets.

3. A blockchain-based remote medical data management method as claimed in claim 2, characterized in that: include: Determine whether the maximum value among the multiple adjusted shadow fitnesses is greater than or equal to the main body fitness, and if not, obtain a first random value; Determine whether the first random value is greater than or equal to a preset random value threshold, and if so, accept the adjusted shadow particle corresponding to the maximum value among the multiple adjusted shadow fitness values ​​and update it as the main particle; If not, the adjusted shadow particle corresponding to the maximum value among the multiple adjusted shadow fitnesses will not be accepted to be updated as the main particle, and the adjustment method for obtaining the multiple adjusted shadow particles will be disabled, and the follow-up adjustment optimization will continue to be performed according to the main particle to obtain the multiple target segmentation decryption key sets.

4. A blockchain-based remote medical data management method as claimed in claim 1, characterized in that: include: randomly extracting a first split decryption key set from the decryption key storage library in a first key split window, and adding the first split decryption key set to a jump set; randomly extracting a second split decryption key set from the decryption key storage library in a second key split window, determining a similarity between the second split decryption key set and the jump-step set, and when a calculation result satisfies a preset jump-step similarity, adding the second split decryption key set to the jump-step set; randomly extracting a Qth split decryption key set from the decryption key storage library in the Qth key split window, determining the similarity between the Qth split decryption key set and the jump-step set, and when the calculation result satisfies a preset jump-step similarity, adding the Qth split decryption key set to the jump-step set; The first split decryption key set, the second split decryption key set and the Qth split decryption key set are aggregated to generate the Q split decryption key sets.

5. A blockchain-based remote medical data management method as claimed in claim 1, characterized in that: include: If the decryption fails, M abnormal data are obtained; Performing feature recognition on the M abnormal data, and correcting the M risk coefficients corresponding to the corresponding M user terminals according to the recognition results to obtain M corrected risk coefficients; The K risk coefficients are updated according to the M modified risk coefficients.

6. A blockchain-based remote medical data management method as claimed in claim 1, characterized in that: include: Obtaining a preset encryption key index and a preset key length; Calling the key generation function in the RSA algorithm to generate the decryption key; The encryption key extraction function is called to analyze the decryption key, and the encryption key is generated by combining the preset encryption key index and the preset key length.

7. A blockchain-based remote medical data management method as claimed in claim 1, characterized in that: include: Retrieving K historical conflict records of K user terminals of the K consulting doctors within a preset monitoring window; Perform feature extraction on the K historical conflict records according to a preset conflict indicator set to obtain K historical conflict indicator value sets; The risk coefficient identification network layer is used to perform risk identification on the K historical conflict indicator sets to determine the K risk coefficients.

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