Efficient processing and secure traceability method of on-chain and off-chain data for medical supply chain

Abstract

The application relates to the technical field of supply chain data processing, and particularly discloses a high-efficiency processing and safe tracing method for on-chain and off-chain data of a medical supply chain. The method verifies medical information through a supply chain off-chain data verification platform, obtains verification parameters of the medical information and network characteristic information of the off-chain data verification platform, analyzes the medical information verification compliance degree, and determines whether the medical information is directly uploaded to a data processing platform of a block chain. A supply chain on-chain query platform receives a medical information query signal, simultaneously queries medical information of the data processing platform of the block chain, obtains query parameters of the medical information, evaluates the medical information query correctness, determines whether to perform safe tracing on the medical information, and if the query correctness is low, the problem node can be quickly located, efficient safe tracing is realized, and the accuracy and credibility of medical supply chain data processing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain data processing, specifically to an efficient processing and secure traceability method for on-chain and off-chain data of a medical supply chain. BACKGROUND

[0002] Supply chain management in the medical industry is crucial for ensuring the safe, effective, and timely supply of medical supplies. Due to the complexity of the circulation of medical supplies, involving multiple participants and systems, it is difficult to achieve full traceability. A data processing method for medical supply chains has emerged to ensure that each link can be accurately recorded and truly achieve information traceability, providing technical support for efficient processing of on-chain and off-chain data.

[0003] For example, the invention patent with publication number CN117194570A discloses a medical supply chain data bidirectional driving synchronization method and system, and a medium. The method includes: a data driving module extracts the business data variation, and adds the data task queue to the change task SQL; a structured data module extracts the change data of the business system; a non-structured copying module is driven by the structured data module to analyze whether each thread or each process contains non-structured data; the structured data and the non-structured data are packaged and compressed into binary files respectively; the data driving module extracts the business data variation; the net-gate device asynchronously transfers the packaged and compressed binary files to the target server; a forward driving adaptive method creates a structured data storage space and decompresses the structured and non-structured data copies to complete the generation of structured data synchronization; the forward driving adaptive method is cycled to complete bidirectional synchronization.

[0004] For example, the invention patent with publication number CN114066365B discloses a cloud-based digital supply chain service management system, which includes a digital cloud platform, a logistics resource management module, a distribution task allocation module, and a monitoring module. The digital cloud platform includes a medical supplier integration module and a department request integration module. The medical supplier integration module is connected to the ordering intranet of several medical suppliers through TCP / IP communication. The department request integration module is connected to the digital cloud platform through an internal LAN. Each department needs to establish a connection with the digital cloud platform through a request. The API interfaces of the logistics resource management module and the distribution task allocation module are connected in parallel to the output end of the digital cloud platform. The cloud management system is used for integrated development, and the order flow is optimized and processed in parallel using a parallel processing framework.

[0005] In combination with the above technical solutions, it is found that in the process of data processing of the medical supply chain, the management of the supply chain is directly performed through medical data, which results in inefficient management of the supply chain and low management efficiency. If the above process is continuously performed, the effectiveness of medical data processing will be further affected, and the accuracy of data processing will be reduced. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a medical supply chain-oriented on-chain and off-chain data efficient processing and secure traceability method, which can effectively solve the problems involved in the above background art.

[0007] To achieve the above purpose, the present application is implemented by the following technical solutions: a medical supply chain-oriented on-chain and off-chain data efficient processing and secure traceability method, comprising: S1. verifying the medical information by the off-chain data verification platform of the supply chain, thereby obtaining the verification parameters of the medical information and the network feature information of the off-chain data verification platform, and comprehensively analyzing to obtain the medical information verification compliance degree; S2. comparing the medical information verification compliance degree with the preset medical information verification compliance threshold in the medical supply chain management library to obtain the medical information verification compliance degree comparison result, and determining whether to directly upload the medical information to the data processing platform of the blockchain according to the medical information verification compliance degree comparison result; S3. receiving the medical information query signal by the on-chain query platform of the supply chain, and querying the medical information of the data processing platform of the blockchain, thereby obtaining the query parameters of the medical information, and evaluating the medical information query accuracy, comparing the medical information query accuracy with the preset medical information query accuracy threshold in the medical supply chain management library to obtain the medical information query accuracy comparison result, and determining whether to perform secure traceability on the medical information.

[0008] As a further method, the medical information verification compliance degree is specifically analyzed as follows:

[0009] The verification parameters of the medical information specifically include the verification number of the production date of each medical product, the verification bytes of the production place of each medical product, and the verification bytes of the product name of each medical product.

[0010] The initial number of the production date of each medical product, the initial bytes of the production place of each medical product, and the initial bytes of the product name of each medical product are obtained, and are respectively compared and verified with the verification number of the production date of each medical product, the verification bytes of the production place of each medical product, and the verification bytes of the product name of each medical product, thereby obtaining the number verification accuracy of the production date of each medical product, the byte verification accuracy of the production place of each medical product, and the byte verification accuracy of the product name of each medical product.

[0011] The network characteristic information of the off-chain data verification platform, specifically including network bandwidth usage rate, node response time, network jitter times and network throughput of the off-chain data verification platform in a data verification period.

[0012] According to the network bandwidth usage rate, node response time, network jitter times and network throughput of the off-chain data verification platform in a data verification period, the network performance index of the off-chain data verification platform is obtained by data processing.

[0013] Through the production date number verification accuracy of each medical product, the production location byte verification accuracy of each medical product, the product name byte verification accuracy of each medical product, and the network performance index of the off-chain data verification platform, the medical information verification compliance degree is obtained by comprehensive processing.

[0014] As a further method, the network performance index of the off-chain data verification platform is specifically obtained by the following method:

[0015]

[0016] In the formula, NSNI represents the network performance index of the off-chain data verification platform, DNB represents the network bandwidth usage rate of the off-chain data verification platform in a data verification period, DNB Δu represents the network bandwidth reference usage rate preset by the medical supply chain management library, NRT represents the node response time of the off-chain data verification platform in a data verification period, JQ represents the network jitter times of the off-chain data verification platform in a data verification period, NT represents the network throughput of the off-chain data verification platform in a data verification period, e is a natural constant, α1 represents the network bandwidth usage rate influence weight preset by the medical supply chain management library, α2 represents the node response time influence weight preset by the medical supply chain management library, α3 represents the network jitter times influence weight preset by the medical supply chain management library, and α4 represents the network throughput influence weight preset by the medical supply chain management library.

[0017] As a further method, the medical information verification compliance degree comparison result is used to determine whether the medical information is directly uploaded to the data processing platform of the blockchain, and the specific process is as follows:

[0018] If the medical information verification compliance degree comparison result is that the medical information verification compliance degree is less than the medical information verification compliance threshold, the network of the off-chain data verification platform is optimized, the medical information is reacquired and verified again, and the medical information verification compliance degree is analyzed and obtained to be greater than or equal to the medical information verification compliance threshold, thereby uploading the medical information to the data processing platform of the blockchain.

[0019] If the comparison result of the medical information verification compliance degree is that the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold, the medical information is directly uploaded to the data processing platform to which the blockchain belongs.

[0020] As a further method, the medical information query accuracy is specifically analyzed as follows:

[0021] The query number corresponding to the production date of each medical product, the query byte corresponding to the production place of each medical product and the query byte corresponding to the product name of each medical product are respectively compared with the initial number corresponding to the production date of each medical product, the initial byte corresponding to the production place of each medical product and the initial byte corresponding to the product name of each medical product, thereby obtaining the number query accuracy corresponding to the production date of each medical product, the byte query accuracy corresponding to the production place of each medical product and the byte query accuracy corresponding to the product name of each medical product.

[0022] According to the number query accuracy corresponding to the production date of each medical product, the byte query accuracy corresponding to the production place of each medical product and the byte query accuracy corresponding to the product name of each medical product, and in combination with the medical information verification compliance degree, the medical information query accuracy is comprehensively obtained.

[0023] As a further method, the determination of whether to perform safe traceability on the medical information is specifically determined as follows:

[0024] If the comparison result of the medical information query accuracy is that the medical information query accuracy comparison result is less than the medical information query accuracy threshold, the medical information is subjected to safe traceability.

[0025] If the comparison result of the medical information query accuracy is that the medical information query accuracy is greater than or equal to the medical information query accuracy threshold, the medical information does not need to be subjected to safe traceability.

[0026] As a further method, the safe traceability of the medical information is specifically the process of verifying the medical information by the off-chain data verification platform of the supply chain, the process of uploading the medical information to the data processing platform to which the blockchain belongs, and the process of receiving the medical information query signal and simultaneously querying the medical information of the data processing platform to which the blockchain belongs by the on-chain query platform of the supply chain.

[0027] Compared with the prior art, the embodiments of the present application have at least the following advantages or beneficial effects:

[0028] (1) The present application provides an efficient processing and secure traceability method for medical supply chain on-chain and off-chain data, which first verifies the medical information by the off-chain data verification platform of the supply chain, obtains the verification parameters of the medical information and the network characteristic information of the off-chain data verification platform, and comprehensively analyzes the medical information verification compliance degree, compares the medical information verification compliance degree with the preset medical information verification compliance threshold in the medical supply chain management library, obtains the medical information verification compliance degree comparison result, and determines whether to directly upload the medical information to the data processing platform of the blockchain according to the medical information verification compliance degree comparison result, the off-chain query platform of the supply chain receives the medical information query signal, and queries the medical information of the data processing platform of the blockchain, thereby obtaining the query parameters of the medical information, and evaluating the medical information query accuracy, comparing the medical information query accuracy with the preset medical information query accuracy threshold in the medical supply chain management library, obtaining the medical information query accuracy comparison result, and determining whether to perform secure traceability on the medical information, if the query correct result is low, the problem node can be quickly located, efficient secure traceability is realized, and the accuracy and credibility of the medical supply chain data processing are improved.

[0029] (2) The present application verifies the medical information by the off-chain data verification platform of the supply chain, thereby obtaining the verification parameters of the medical information and the network characteristic information of the off-chain data verification platform, and comprehensively analyzing the medical information verification compliance degree, so that the off-chain data verification platform accurately verifies the medical information, improves the accuracy of information uploading and the service quality of the off-chain data verification platform.

[0030] (3) The present application compares the medical information verification compliance degree with the preset medical information verification compliance threshold in the medical supply chain management library, obtains the medical information verification compliance degree comparison result, and determines whether to directly upload the medical information to the data processing platform of the blockchain according to the medical information verification compliance degree comparison result, which can accurately and comprehensively verify the medical information of the supply chain, help to reduce data errors and abnormal problems, and provide accurate data basis for subsequent user query of medical information.

[0031] (4) The present application receives the medical information query signal by the off-chain query platform of the supply chain, queries the medical information of the data processing platform of the blockchain, thereby obtains the query parameters of the medical information, and evaluates the medical information query accuracy, compares the medical information query accuracy with the preset medical information query accuracy threshold in the medical supply chain management library, obtains the medical information query accuracy comparison result, and determines whether to perform secure traceability on the medical information, if the medical information needs to be securely traced, the above steps can be used for comprehensive traceability to quickly and accurately obtain the influencing factors of the medical information error, and the corresponding solving measures are realized to realize the efficient data processing of the medical supply chain. BRIEF DESCRIPTION OF DRAWINGS

[0032] The application will be further described with the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the application, and other embodiments can be obtained by those skilled in the art without creative labor on the basis of the following drawings.

[0033] Figure 1 The method flowchart of the application is shown. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0035] Referring to Figure 1 The application provides a medical supply chain-oriented efficient processing and secure tracing method for on-chain and off-chain data, which comprises: S1. verifying medical information by a supply chain-owned off-chain data verification platform, thereby obtaining verification parameters of the medical information and network characteristic information of the off-chain data verification platform, and comprehensively analyzing to obtain a medical information verification compliance degree.

[0036] In the embodiment, the supply chain-owned off-chain data verification platform refers to a platform for verifying medical information. The off-chain data verification platform can be connected through the Internet of Things, thereby receiving a medical information verification signal through the Internet of Things, and finally performing a data verification operation on the medical information. Since the off-chain data verification platform is internally provided with data processing technology algorithms, of course, including a SIEM (Security Information and Event Management) system and a network performance monitoring module and other data monitoring technologies, the off-chain data verification platform first verifies the medical information, obtains network characteristic information of the off-chain data verification platform through the built-in network performance monitoring tool, analyzes the network characteristic information of the off-chain data verification platform through the built-in data processing technology, obtains network performance indicators of the off-chain data verification platform, and comprehensively analyzes the verification parameters of the medical information and the network characteristic information of the off-chain data verification platform through the built-in data processing technology to obtain a medical information verification compliance degree.

[0037] Specifically, the medical information verification compliance degree has the following specific analysis process:

[0038] The medical information belongs to the verification parameter, specifically including the production date of each medical product belongs to the verification number, the production place of each medical product belongs to each verification byte and the product name of each medical product belongs to each verification byte, wherein each medical product refers to a plurality of medical products produced in the same batch, for example, granules, capsules; wherein the production date number of each medical product specifically refers to the number of the production date corresponding to the medical product, for example, the production date of a certain capsule is "November 6, 2024", then its production date number is 20241106; The production place byte of each medical product specifically refers to the UTF-8 (Unicod variable length character encoding) encoding value of the Chinese character corresponding to the production place of the medical product, for example, the production place of a certain capsule is "Hefei", the UTF-8 encoding of "He" is E59088, and the UTF-8 encoding of "Fei" is E882A5, then its production place byte is E59088E882A5; The product name byte of each medical product specifically refers to the UTF-8 (Unicod variable length character encoding) encoding value of the Chinese character corresponding to the product name of the medical product, for example, the product name of a certain medical product is "Banlangen", the UTF-8 encoding of "Ban" is E69DBF, the UTF-8 encoding of "Lan" is E8939D, and the UTF-8 encoding of "Gen" is E6A0B9, then its product name byte is E69DBFE8939DE6A0B9.

[0039] It should be pointed out that the production date of each medical product belongs to the verification number, the production place of each medical product belongs to each verification byte and the product name of each medical product belongs to each verification byte, which can be obtained through the off-chain data verification platform.

[0040] The initial number of the production date of each medical product, the initial byte of the production place of each medical product, and the initial byte of the product name of each medical product are obtained, and are respectively compared with the verification number of the production date of each medical product, the verification byte of the production place of each medical product, and the verification byte of the product name of each medical product, thereby obtaining the number verification accuracy rate of the production date of each medical product, the byte verification accuracy rate of the production place of each medical product, and the byte verification accuracy rate of the product name of each medical product. For example, the production date of a capsule is "November 6, 2024", and the initial number of the production date thereof is 20241106. If the verification number of the production date thereof is 20241116, then the number verification accuracy rate of the production date of the medical product is 87.5%. For example, the production place of a capsule is "Hefei", and the initial byte of the production place thereof is E59088E882A5. If the verification byte of the production place thereof is E59088E882A6, then the byte verification accuracy rate of the production place of the medical product is 93.75%. For example, the product name of a medical product is "Banlangen", and the initial byte of the product name thereof is E69DBFE8939DE6A0B9. If the verification byte of the product name thereof is E69DBFE8939DE6A1A8, then the byte verification accuracy rate of the product name of the medical product is 87.5%.

[0041] It should be noted that the initial number of the production date of each medical product, the initial byte of the production place of each medical product, and the initial byte of the product name of each medical product can be obtained from the product inspection report summarized by the medical product inspection personnel.

[0042] The network feature information of the off-chain data verification platform, specifically including the network bandwidth utilization rate, node response time, network jitter times, and network throughput of the off-chain data verification platform in the data verification period.

[0043] According to the network bandwidth utilization rate, node response time, network jitter times, and network throughput of the off-chain data verification platform in the data verification period, the network performance index of the off-chain data verification platform is obtained by data processing.

[0044] It should be explained that the network bandwidth usage rate, node response time, network jitter times and network throughput of the off-chain data verification platform in the data verification period can be obtained by the network performance monitoring tool built in the off-chain data verification platform, wherein the network performance monitoring tool can monitor the key performance indicators of the network in real time, record and store the historical performance data of the network, so that the network bandwidth usage rate, node response time, network jitter times and network throughput of the off-chain data verification platform in the data verification period can be queried through the network performance monitoring tool.

[0045] It should be explained that the data verification period can be obtained by the SIEM (Security Information and Event Management) system built in the off-chain data verification platform, which is the time interval from the submission of medical information to the off-chain data verification platform to the final verification result, recorded as the data verification period.

[0046] The medical information verification compliance degree is obtained by comprehensively processing the production date belonging number verification accuracy of each medical product, the production place belonging byte verification accuracy of each medical product, the product name belonging byte verification accuracy of each medical product and the network performance index of the off-chain data verification platform, and the acquisition method is as follows:

[0047]

[0048] In the formula, NSNI represents the network performance index of the off-chain data verification platform, which is obtained by comprehensively considering the network bandwidth usage rate, node response time, network jitter times and network throughput of the off-chain data verification platform in the data verification period.

[0049] APD D APD represents the production date belonging number verification accuracy of the Dth medical product, which is the matching proportion between the production date belonging verification number of the Dth medical product and the initial number of the production date belonging of the Dth medical product, and is used to evaluate the effectiveness and accuracy of the data verification process.

[0050] MLB D MLB represents the production place belonging byte verification accuracy of the Dth medical product, which is the matching proportion between the production place belonging verification byte of the Dth medical product and the initial byte of the production place belonging of the Dth medical product.

[0051] APN D APN represents the product name belonging byte verification accuracy of the Dth medical product, which is the matching proportion between the product name belonging verification byte of the Dth medical product and the initial byte of the product name belonging of the Dth medical product.

[0052] D is the number of each medical product, D = 1, 2, 3, …, M, M is the total number of medical products, for example, a batch of medical products has 200, D = 1, 2, 3, …, 200, M is 200.

[0053] β1 represents the production date number verification accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the production date number verification accuracy preset by the medical supply chain management library on the medical information verification compliance degree. When used, the production date number verification accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the production date number verification accuracy and the production date number verification accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time production date number verification accuracy is input into the mapping set to obtain the production date number verification accuracy influence weight. The mapping relationship in it can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0054] β2 represents the production location byte verification accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the production location byte verification accuracy preset by the medical supply chain management library on the medical information verification compliance degree. When used, the production location byte verification accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the production location byte verification accuracy and the production location byte verification accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time production location byte verification accuracy is input into the mapping set to obtain the production location byte verification accuracy influence weight. The mapping relationship in it can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0055] β3 represents the product name byte verification accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the product name byte verification accuracy preset by the medical supply chain management library on the medical information verification compliance degree. When used, the product name byte verification accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the product name byte verification accuracy and the product name byte verification accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time product name byte verification accuracy is input into the mapping set to obtain the product name byte verification accuracy influence weight. The mapping relationship in it can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0056] B4 represents the network performance index influence weight preset by the medical supply chain management library, represents the numerical value of the influence degree of the network performance index preset by the medical supply chain management library on the medical information verification compliance degree, and the network performance index influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the network performance index and the network performance index influence weight preset by the medical supply chain management library form a mapping set. The real-time network performance index is input into the mapping set to obtain the network performance index influence weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0057] CDIV represents the medical information verification compliance degree. Specifically, in the data verification process of the medical information by the off-chain data verification platform, the medical information verification compliance degree is comprehensively influenced by the production date number verification accuracy of each medical product, the production location byte verification accuracy of each medical product, the product name byte verification accuracy of each medical product, and the network performance index of the off-chain data verification platform. The network performance index of the off-chain data verification platform decreases, indicating that the network performance of the off-chain data verification platform is unstable, resulting in data verification delay or error, which reduces the production date number verification accuracy, the production location byte verification accuracy, and the product name byte verification accuracy, and ultimately affects the medical information verification compliance degree, thereby reducing the medical information verification compliance degree.

[0058] Further, the network performance index of the off-chain data verification platform is specifically obtained as follows:

[0059]

[0060] In the formula, DNB represents the network bandwidth utilization rate of the off-chain data verification platform in the data verification period. Specifically, it refers to the proportion of the bandwidth occupied by network transmission to the available network bandwidth during the data verification process.

[0061] DNB Δu represents the network bandwidth reference utilization rate preset by the medical supply chain management library, which is specifically the best adaptive numerical value of the network bandwidth utilization rate during the data verification process.

[0062] NRT represents the node response time of the off-chain data verification platform in the data verification period, which is specifically the time consumed from the reception of the off-chain data verification request by the node to the completion of the data verification by the node and the return of the result.

[0063] JQ represents the network jitter frequency of the off-chain data verification platform in the data verification period, which is specifically the number of network jitter occurrences due to network instability in the data verification period. The greater the network jitter frequency, the poorer the network stability.

[0064] NT represents the network throughput of the off-chain data verification platform in the data verification period, specifically the ability of the information to truly complete verification in the data verification process, which describes the amount of data transmitted through the network per unit of time.

[0065] α1 represents the network bandwidth usage rate influence weight preset by the medical supply chain management library, which represents the numerical value of the influence degree of the network bandwidth usage rate preset by the medical supply chain management library on the network performance index to which the off-chain data verification platform belongs. When used, the network bandwidth usage rate influence weight can be directly obtained from the medical supply chain management library, and the corresponding relationship can be a pre-set mapping relationship. For example, the network bandwidth usage rate and the network bandwidth usage rate influence weight preset by the medical supply chain management library form a mapping set, and the real-time network bandwidth usage rate is input into the mapping set to obtain the network bandwidth usage rate influence weight. The mapping relationship therein can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0066] α2 represents the node response time influence weight preset by the medical supply chain management library, which represents the numerical value of the influence degree of the node response time preset by the medical supply chain management library on the network performance index to which the off-chain data verification platform belongs. When used, the node response time influence weight can be directly obtained from the medical supply chain management library, and the corresponding relationship can be a pre-set mapping relationship. For example, the node response time and the node response time influence weight preset by the medical supply chain management library form a mapping set, and the real-time node response time is input into the mapping set to obtain the node response time influence weight. The mapping relationship therein can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0067] α3 represents the network jitter frequency influence weight preset by the medical supply chain management library, which represents the numerical value of the influence degree of the network jitter frequency preset by the medical supply chain management library on the network performance index to which the off-chain data verification platform belongs. When used, the network jitter frequency influence weight can be directly obtained from the medical supply chain management library, and the corresponding relationship can be a pre-set mapping relationship. For example, the network jitter frequency and the network jitter frequency influence weight preset by the medical supply chain management library form a mapping set, and the real-time network jitter frequency is input into the mapping set to obtain the network jitter frequency influence weight. The mapping relationship therein can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0068] a4 represents the network throughput influence weight preset in the medical supply chain management library, represents the numerical value of the influence degree of the network throughput preset in the medical supply chain management library on the network performance index to which the off-chain data verification platform belongs, and the network throughput influence weight can be directly obtained from the medical supply chain management library when used. The corresponding relationship can be a pre-set mapping relationship, for example, the network throughput and the network throughput influence weight preset in the medical supply chain management library form a mapping set, and the real-time network throughput is input into the mapping set to obtain the network throughput influence weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0069] e is a natural constant, and NSNI represents the network performance index to which the off-chain data verification platform belongs. In the data verification process, when the network bandwidth usage rate deviates greatly from the network bandwidth reference usage rate, the network throughput will directly decrease, the network will face congestion, the delay of the node in processing medical information will increase, and thus the node response time will increase. The increase of the node response time increases the time for which the medical information waits for processing at the node, which can increase the network jitter frequency, thereby reducing the network performance index to which the off-chain data verification platform belongs.

[0070] S2. Comparing the medical information verification compliance degree with the medical information verification compliance threshold preset in the medical supply chain management library to obtain a medical information verification compliance degree comparison result, and determining whether to directly upload the medical information to the data processing platform of the blockchain according to the medical information verification compliance degree comparison result.

[0071] Specifically, the medical information verification compliance degree comparison result is specifically a first verification compliance degree comparison result and a second verification compliance degree comparison result.

[0072] The first verification compliance degree comparison result is specifically that the medical information verification compliance degree is less than the medical information verification compliance threshold. The medical information verification compliance threshold represents a critical value for judging whether the medical information verification compliance degree meets the compliance standard when verifying the medical information, is used for quantitatively evaluating the accuracy of the medical verification information, and helps the off-chain data verification platform to determine whether the network to which the off-chain data verification platform belongs needs to be optimized. The above medical information verification compliance degree being less than the medical information verification compliance threshold indicates that the medical verification information is inaccurate, and there is a network problem of the off-chain data verification platform, and the network to which the off-chain data verification platform belongs needs to be optimized.

[0073] The second verification compliance degree comparison result is specifically that the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold, indicating that the medical verification information is accurate, and the network to which the off-chain data verification platform belongs does not need to be optimized, and the medical information is directly uploaded to the data processing platform of the blockchain.

[0074] Further, the medical information is directly uploaded to the data processing platform of the blockchain according to the comparison result of the medical information verification compliance degree, and the specific process is as follows:

[0075] If the comparison result of the medical information verification compliance degree is that the medical information verification compliance degree is less than the medical information verification compliance threshold, the network of the off-chain data verification platform is optimized, the medical information is reacquired and verified again, and the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold, and the medical information is uploaded to the data processing platform of the blockchain.

[0076] If the comparison result of the medical information verification compliance degree is that the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold, the medical information is directly uploaded to the data processing platform of the blockchain.

[0077] It should be explained that the above optimization of the network of the off-chain data verification platform is to obtain the optimized network of the off-chain data verification platform, which is to obtain the network optimization data set corresponding to the medical information verification compliance degree deviation value interval in the medical supply chain management library by difference processing of the medical information verification compliance degree and the medical information verification compliance threshold, and finally matching the medical information verification compliance degree to obtain the network optimization data set of the medical information verification compliance degree. The network optimization data set is used for network optimization operation in the medical supply chain management library, so that the off-chain data verification platform obtains the optimized network.

[0078] The above matching of the network optimization data set of the off-chain data verification platform is as follows:

[0079] The above medical information verification compliance degree deviation value corresponds to the network optimization data set which is formulated by the authoritative network optimization organization or standardization organization after data collection, arrangement and encryption processing. Each medical information verification compliance degree deviation value is carefully divided, and the network optimization data set is designed based on the network optimization standard, threat and risk analysis, and business demand and compliance requirements for each medical information verification compliance degree deviation value interval.

[0080] For example, if the medical information verification compliance degree deviation value in this example is 5, it belongs to the medical information verification compliance degree deviation value interval [3, 7], and the network optimization data set of the medical information verification compliance degree deviation value interval [3, 7] in the preset network optimization data set of the medical supply chain management library includes the original 100Mbps bandwidth upgrade to 1Gbps, RSA key distribution encryption and chameleon hash mode, and finally the optimized network is obtained.

[0081] It needs to be stated that the original 100 Mbps bandwidth in the network optimization data set is upgraded to 1 Gbps, network devices such as switches and routers are added, RSA key distribution encryption and chameleon hash are added, the purpose is to increase the network bandwidth of the network to which the off-chain data verification platform belongs, improve the network throughput and other factors conducive to network performance optimization, and ultimately ensure that the evaluation of medical information verification compliance after optimization is greater than or equal to the medical information verification compliance threshold.

[0082] S3. The supply chain belongs to the on-chain query platform to receive the medical information query signal, and queries the medical information of the data processing platform of the blockchain, thereby obtaining the query parameters of the medical information, and evaluating the medical information query correctness, and comparing with the preset medical information query correctness threshold in the medical supply chain management library, obtaining the comparison result of the medical information query correctness, and determining whether to perform safety traceability on the medical information.

[0083] In this embodiment, the supply chain belongs to the on-chain query platform refers to a platform for information query of medical information, and the on-chain query platform can be connected through the Internet of Things, so that the medical information query signal is received through the Internet of Things, and finally the information query operation of the medical information is performed; Because the on-chain query platform is internally provided with a data processing technology algorithm, the on-chain query platform is connected to a plurality of nodes in the blockchain network, and these nodes are components of the blockchain network and are responsible for storing and verifying medical information on the blockchain. By accessing these nodes, the on-chain query platform can obtain the query parameters of the medical information on the blockchain, so the on-chain query platform first queries the medical information of the data processing platform of the blockchain, thereby obtaining the query parameters of the medical information, and evaluating the medical information query correctness, and comparing with the preset medical information query correctness threshold in the medical supply chain management library, obtaining the comparison result of the medical information query correctness, and determining whether to perform safety traceability on the medical information.

[0084] Specifically, the medical information belongs to the query parameter, specifically including the production date of each medical product belongs to the query number, the production place of each medical product belongs to each query byte, and the product name of each medical product belongs to each query byte. For example, the production date of a capsule is "November 6, 2024", and its production date belongs to the query number 20241106; the production place of a capsule is "Hefei", the UTF-8 encoding of "He" is E59088, and the UTF-8 encoding of "Fei" is E882 A5, so its production place belongs to each query byte E59088E882 A5; the product name of a medical product is "Banlangen", the UTF-8 encoding of "Ban" is E69D BF, the UTF-8 encoding of "Lan" is E8939D, and the UTF-8 encoding of "Gen" is E6 A0 B9, so its product name belongs to each query byte E69D BFE8939DE6 A0 B9.

[0085] It should be noted that the production date of each medical product belongs to the query number, the production place of each medical product belongs to each query byte, and the product name of each medical product belongs to each query byte can be obtained through the supply chain belongs to the chain query platform.

[0086] Further, the medical information query accuracy is specifically analyzed as follows:

[0087] The production date of each medical product belongs to the query number, the production place of each medical product belongs to each query byte, and the product name of each medical product belongs to each query byte are respectively compared and verified with the production date of each medical product belongs to the initial number, the production place of each medical product belongs to each initial byte, and the product name of each medical product belongs to each initial byte, thereby obtaining the production date of each medical product belongs to the number query accuracy, the production place of each medical product belongs to the byte query accuracy, and the product name of each medical product belongs to the byte query accuracy. For example, the production date of a capsule is "November 6, 2024", and its production date belongs to the initial number 20241106, and if its production date belongs to the query number 20241116, then the production date of the medical product belongs to the number query accuracy is 87.5%; For example, the production place of a capsule is "Hefei", and its production place belongs to the initial byte E59088E882 A5, and if its production place belongs to the query byte E59088E882 A6, then the production place of the medical product belongs to the byte query accuracy is 93.75%; For example, the product name of a medical product is "Banlangen", and its product name belongs to the initial byte E69DBFE8939DE6A0 B9, and if its product name belongs to the query byte E69D BFE8939DE6 A1 A8, then the product name of the medical product belongs to the byte query accuracy is 87.5%.

[0088] According to the production date number query accuracy of each medical product, the production place byte query accuracy of each medical product, and the product name byte query accuracy of each medical product, and combined with the medical information verification compliance degree, the medical information query accuracy is obtained, and the specific obtaining method is:

[0089]

[0090] In the formula, PDI D PDI represents the production date number query accuracy of the Dth medical product, specifically the matching proportion between the production date number query of the Dth medical product and the initial production date number of the Dth medical product.

[0091] PLI D PLI represents the production place byte query accuracy of the Dth medical product, specifically the matching proportion between the production place byte query of the Dth medical product and the initial production place byte of the Dth medical product.

[0092] PNI D PNI represents the product name byte query accuracy of the Dth medical product, specifically the matching proportion between the product name byte query of the Dth medical product and the initial product name byte of the Dth medical product.

[0093] CDIV represents the medical information verification compliance degree, which is obtained by comprehensively considering the production date number verification accuracy of each medical product, the production place byte verification accuracy of each medical product, the product name byte verification accuracy of each medical product, and the network performance index of the off-chain data verification platform. The medical information verification compliance degree here is the medical information verification compliance degree corresponding to the case where the medical information verification compliance degree is greater than or equal to the preset medical information verification compliance threshold.

[0094] D is the number of each medical product, D=1, 2, 3,..., M, and M is the total number of medical products.

[0095] ζ1 represents the production date belonging number query accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the production date belonging number query accuracy preset by the medical supply chain management library on the medical information query correctness. When used, the production date belonging number query accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the production date belonging number query accuracy and the production date belonging number query accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time production date belonging number query accuracy is input into the mapping set to obtain the production date belonging number query accuracy influence weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0096] ζ2 represents the production location belonging byte query accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the production location belonging byte query accuracy preset by the medical supply chain management library on the medical information query correctness. When used, the production location belonging byte query accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the production location belonging byte query accuracy and the production location belonging byte query accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time production location belonging byte query accuracy is input into the mapping set to obtain the production location belonging byte query accuracy influence weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0097] ζ3 represents the product name belonging byte query accuracy influence weight preset by the medical supply chain management library, and represents the numerical value of the influence degree of the product name belonging byte query accuracy preset by the medical supply chain management library on the medical information query correctness. When used, the product name belonging byte query accuracy influence weight can be directly obtained from the medical supply chain management library. The corresponding relationship can be a pre-set mapping relationship. For example, the product name belonging byte query accuracy and the product name belonging byte query accuracy influence weight preset by the medical supply chain management library form a mapping set. The real-time product name belonging byte query accuracy is input into the mapping set to obtain the product name belonging byte query accuracy influence weight. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0098] ζ4 represents the medical information verification compliance degree influence weight preset in the medical supply chain management library, represents the numerical value of the influence degree of the medical information verification compliance degree preset in the medical supply chain management library on the medical information query accuracy, and the medical information verification compliance degree influence weight can be directly obtained from the medical supply chain management library when used. The corresponding relationship can be a pre-set mapping relationship. For example, the medical information verification compliance degree and the medical information verification compliance degree influence weight preset in the medical supply chain management library form a mapping set. The medical information verification compliance degree influence weight is obtained by inputting the real-time medical information verification compliance degree into the mapping set. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range is [0, 1].

[0099] e is a natural constant, and AMI represents the medical information query accuracy. Specifically, in the information query process of the medical information on the chain query platform, the medical information query accuracy is obtained by comprehensively considering the production date belonging number query accuracy of each medical product, the production place belonging byte query accuracy of each medical product, the product name belonging byte query accuracy of each medical product, and the medical information verification compliance degree. If the medical information verification compliance degree is low, it indicates that the medical information is distorted, which may reduce the production date belonging number query accuracy of each medical product, the production place belonging byte query accuracy of each medical product, and finally affect the medical information query accuracy, thereby reducing the medical information query accuracy.

[0100] Specifically, the medical information query accuracy comparison result is specifically a first query accuracy comparison result and a second query accuracy comparison result.

[0101] The first query accuracy comparison result is specifically that the medical information query accuracy is less than a medical information query accuracy threshold. The medical information query accuracy threshold represents a critical value for judging whether the medical information query accuracy meets the compliance standard when the medical information is queried. It is used to quantitatively evaluate the accuracy of the medical information query and help the supply chain to determine whether to perform a safety trace on the medical information. The above medical information query accuracy less than the medical information query accuracy threshold indicates that the medical query information is inaccurate, and the medical information needs to be traced for safety.

[0102] The second query accuracy comparison result is specifically that the medical information query accuracy is greater than or equal to the medical information query accuracy threshold, indicating that the medical query information is accurate, and the medical information does not need to be traced for safety.

[0103] Further, the determination of whether to perform a safety trace on the medical information includes the following specific determination process:

[0104] If the medical information query accuracy comparison result is less than the medical information query accuracy threshold, the medical information is subjected to security traceability.

[0105] If the medical information query accuracy comparison result is greater than or equal to the medical information query accuracy threshold, the medical information does not need to be subjected to security traceability.

[0106] Specifically, the security traceability of the medical information specifically refers to tracing the process of verifying the medical information by the off-chain data verification platform of the supply chain, the process of uploading the medical information to the data processing platform of the blockchain, and the process of receiving the medical information query signal by the on-chain query platform of the supply chain while querying the medical information of the data processing platform of the blockchain.

[0107] It needs to be explained that the security traceability of the medical information described above, from which the on-chain query platform obtains the correct medical information query result, specifically refers to the difference processing of the medical information query accuracy of the on-chain query platform and the medical information query accuracy threshold, from which the medical information query accuracy deviation value is obtained, and is matched with the security traceability data set corresponding to each medical information query accuracy deviation value interval in the medical supply chain management library, and finally the security traceability data set to which the medical information query accuracy belongs is matched. The medical supply chain management library performs security traceability operation through the security traceability data set, from which the on-chain query platform obtains the correct medical information query result.

[0108] The matching of the security traceability data set to which the medical information query accuracy belongs is specifically as follows:

[0109] The medical information query accuracy deviation value described above corresponds to the security traceability data set formulated by the authoritative network security agency or standardization organization after data collection, sorting, and encryption processing. Each medical information query accuracy deviation value is carefully divided, and for each medical information query accuracy deviation value interval range, the corresponding security traceability data set is designed based on network security standards, threat and risk analysis, and business demand and compliance requirements.

[0110] For example, if the medical information query correctness deviation value in the present example is 6, it belongs to the medical information query correctness deviation value interval [3, 7], and the medical information query correctness deviation value interval corresponding to the security traceability data set in the medical supply chain management library is included in the process of verifying the medical information by the off-chain data verification platform. The security traceability data set includes identifying and coding the medical products by RFID (radio frequency identification) technology in the process of verifying the medical information by the off-chain data verification platform, using Internet of Things technology to monitor and track in real time and using blockchain technology to encrypt the medical information in the process of uploading the medical information to the data processing platform belonging to the blockchain and receiving the medical information query signal by the on-chain query platform, recording all access and operation logs of the medical information in the process of querying the medical information of the data processing platform belonging to the blockchain, obtaining the complete information and digital archives of the products, tracing the products from the production source to the final use link, understanding the whole life cycle information of the products, verifying the authenticity and source of the products, and finally obtaining the correct medical information query result.

[0111] It should be noted that the above-mentioned process of verifying the medical information by the off-chain data verification platform includes identifying and coding the medical products by RFID (radio frequency identification) technology, using Internet of Things technology to monitor and track in real time and using blockchain technology to encrypt the medical information in the process of uploading the medical information to the data processing platform belonging to the blockchain and receiving the medical information query signal by the on-chain query platform, recording all access and operation logs of the medical information in the process of querying the medical information of the data processing platform belonging to the blockchain, obtaining the complete information and digital archives of the products, tracing the products from the production source to the final use link, understanding the whole life cycle information of the products, verifying the authenticity and source of the products, and finally obtaining the correct medical information query result.

[0112] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the application or exceed the scope defined by the application, and should belong to the protection scope of the present application.

Claims

1. A method for efficient processing and secure traceability of on-chain and off-chain data for medical supply chain, characterized in that, Comprise: S1. The medical information is verified by the chain-off data verification platform of the supply chain, thereby obtaining the verification parameters of the medical information and the network feature information of the chain-off data verification platform, and comprehensively analyzing to obtain the medical information verification compliance degree; S2. The medical information verification compliance degree is compared with the preset medical information verification compliance threshold in the medical supply chain management library to obtain the medical information verification compliance degree comparison result, and whether the medical information is directly uploaded to the data processing platform of the blockchain is determined according to the medical information verification compliance degree comparison result; S3. The medical information query signal is received by the chain-on query platform of the supply chain, and the medical information of the data processing platform of the blockchain is queried, thereby obtaining the query parameters of the medical information, and the medical information query correctness is evaluated, which is compared with the preset medical information query correctness threshold in the medical supply chain management library to obtain the medical information query correctness comparison result, and whether the medical information is safe traceable is determined; The safe traceability of the medical information is specifically tracing the process of verifying the medical information by the chain-off data verification platform of the supply chain, the process of uploading the medical information to the data processing platform of the blockchain, and the process of receiving the medical information query signal by the chain-on query platform of the supply chain and querying the medical information of the data processing platform of the blockchain; The above safe traceability of the medical information, thereby the correct medical information query result of the chain-on query platform, specifically, the medical information query correctness of the chain-on query platform is processed by difference with the medical information query correctness threshold, thereby obtaining the medical information query correctness deviation value, and matching with the safety traceability data set corresponding to each medical information query correctness deviation value interval in the medical supply chain management library, finally matching to obtain the safety traceability data set of the medical information query correctness, the medical supply chain management library performs safety traceability operation through the safety traceability data set, thereby obtaining the correct medical information query result of the chain-on query platform; The safety traceability data set includes identifying and coding the medical products by RFID technology in the process of verifying the medical information by the chain-off data verification platform, using Internet of Things technology to real-time monitor and track and using blockchain technology to encrypt the medical information in the process of uploading the medical information to the data processing platform of the blockchain and the process of receiving the medical information query signal by the chain-on query platform, recording all access and operation logs of the medical information in the process of querying the medical information of the data processing platform of the blockchain, and obtaining the complete information and digital archives of the products; The medical information verification compliance degree is specifically analyzed as follows: The verification parameters of the medical information specifically include the verification number of the production date of each medical product, the verification bytes of the production place of each medical product, and the verification bytes of the product name of each medical product; Obtaining the initial number of the production date of each medical product, the initial byte of the production place of each medical product, and the initial byte of the product name of each medical product, and comparing them with the verification number of the production date of each medical product, the verification byte of the production place of each medical product, and the verification byte of the product name of each medical product respectively, thereby obtaining the number verification accuracy of the production date of each medical product, the byte verification accuracy of the production place of each medical product, and the byte verification accuracy of the product name of each medical product; The network feature information of the off-chain data verification platform, specifically including the network bandwidth utilization, node response time, network jitter times and network throughput of the off-chain data verification platform in the data verification period; According to the network bandwidth utilization, node response time, network jitter times and network throughput of the off-chain data verification platform in the data verification period, the network performance index of the off-chain data verification platform is obtained by data processing; Through the number verification accuracy of the production date of each medical product, the byte verification accuracy of the production place of each medical product, the byte verification accuracy of the product name of each medical product, and the network performance index of the off-chain data verification platform, the medical information verification compliance degree is obtained by comprehensive processing; The network performance index of the off-chain data verification platform, specifically obtained by: ； In the formula, NSNI represents the network performance index of the off-chain data verification platform, DNB represents the network bandwidth utilization rate of the off-chain data verification platform in the data verification period, represents the network bandwidth reference utilization rate preset by the medical supply chain management library, NRT represents the node response time of the off-chain data verification platform in the data verification period, JQ represents the network jitter frequency of the off-chain data verification platform in the data verification period, NT represents the network throughput of the off-chain data verification platform in the data verification period, and e is a natural constant, represents the network bandwidth utilization rate preset by the medical supply chain management library, represents the node response time preset by the medical supply chain management library, represents the network jitter frequency preset by the medical supply chain management library, represents the network throughput preset by the medical supply chain management library. The query parameters of the medical information, specifically including the query number of the production date of each medical product, the query byte of the production place of each medical product, and the query byte of the product name of each medical product; The medical information query accuracy, specifically the analysis process is: The query number of the production date of each medical product, the query byte of the production place of each medical product, and the query byte of the product name of each medical product are compared with the initial number of the production date of each medical product, the initial byte of the production place of each medical product, and the initial byte of the product name of each medical product respectively, thereby obtaining the number query accuracy of the production date of each medical product, the byte query accuracy of the production place of each medical product, and the byte query accuracy of the product name of each medical product; According to the number query accuracy of the production date of each medical product, the byte query accuracy of the production place of each medical product, and the byte query accuracy of the product name of each medical product, and combined with the medical information verification compliance degree, the medical information query accuracy is obtained by comprehensive processing.

2. The method for efficient processing and secure traceability of on-chain and off-chain data for medical supply chain according to claim 1, characterized in that: The medical information verification compliance degree comparison result, specifically the first verification compliance degree comparison result and the second verification compliance degree comparison result; The first verification compliance degree comparison result, specifically the medical information verification compliance degree is less than the medical information verification compliance threshold; The second verification compliance degree comparison result, specifically the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold.

3. The method for efficient processing and secure traceability of on-chain and off-chain data for medical supply chain according to claim 1, characterized in that: The specific process of determining whether to directly upload the medical information to the data processing platform of the blockchain according to the medical information verification compliance degree comparison result is: If the comparison result of the medical information verification compliance degree is that the medical information verification compliance degree is less than the medical information verification compliance threshold, the network to which the off-chain data verification platform belongs is optimized, the medical information is reacquired and verified again, and the medical information verification compliance degree is analyzed and obtained to be greater than or equal to the medical information verification compliance threshold, and thus the medical information is uploaded to the data processing platform to which the blockchain belongs; If the comparison result of the medical information verification compliance degree is that the medical information verification compliance degree is greater than or equal to the medical information verification compliance threshold, the medical information is directly uploaded to the data processing platform to which the blockchain belongs.

4. The method for efficient processing and secure traceability of on-chain and off-chain data for medical supply chain according to claim 1, characterized in that: The comparison result of the medical information query correctness, specifically a first comparison result of the query correctness and a second comparison result of the query correctness; The first comparison result of the query correctness is specifically that the medical information query correctness is less than a medical information query correctness threshold; The second comparison result of the query correctness is specifically that the medical information query correctness is greater than or equal to the medical information query correctness threshold.

5. The method for efficient processing and secure traceability of on-chain and off-chain data for medical supply chain according to claim 1, characterized in that: The specific determination process of determining whether to perform the safety trace on the medical information is as follows: If the comparison result of the medical information query correctness is that the comparison result of the medical information query correctness is less than the medical information query correctness threshold, the safety trace is performed on the medical information; If the comparison result of the medical information query correctness is that the medical information query correctness is greater than or equal to the medical information query correctness threshold, the safety trace does not need to be performed on the medical information.

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