Medical supply chain-oriented on-chain and off-chain data efficient processing and safe tracing method

Abstract

The invention relates to the technical field of supply chain data processing, and particularly discloses an on-chain and off-chain data efficient processing and safe tracing method oriented to a medical supply chain. Verification parameters which the medical information belongs to and feature information of a network which the under-chain data verification platform belongs to are acquired and analyzed to obtain medical information verification compliance, so that whether the medical information is directly uploaded to a data processing platform which the block chain belongs to is judged; meanwhile, the medical information of the data processing platform to which the block chain belongs is queried, so that the query parameters to which the medical information belongs are obtained, the medical information query correctness is evaluated, whether the medical information is safely traced or not is judged, if the behavior that the query correctness result is low occurs, a problem node can be quickly positioned, efficient safe traceability is achieved, and the safety of the medical information is improved. Therefore, the accuracy and credibility of medical supply chain data processing are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chain data processing, and specifically to a method for efficient processing and secure tracing of on-chain and off-chain data for a medical supply chain. Background Art

[0002] Supply chain management in the medical industry is crucial to ensuring the safe, effective and timely supply of medical supplies. Due to the complexity of the circulation of medical supplies and the involvement of multiple participants and systems, it is difficult to achieve full traceability. Data processing methods for the medical supply chain have emerged. This method ensures that each link can be accurately recorded, truly realizes the traceability of information, and provides technical support for the efficient processing of on-chain and off-chain data.

[0003] For example, the invention patent with publication number CN117194570A discloses a bidirectional driven synchronization method, system and medium for medical supply chain data, the method includes: a data driven module extracts business data changes and adds a data task queue to the change task SQL; uses a structured data module to extract business system change data; drives an unstructured copy module through a structured data module to analyze whether each thread or each process contains unstructured data; packages and compresses structured data and unstructured data into binary files respectively; loops through a data driven module to extract business data changes; a network gate device asynchronously transfers across networks to synchronously package and compress binary files and sends them to a target server; a forward driven adaptive method creates a structured data storage space and decompresses structured and unstructured data copies to complete the generation of structured data synchronization; loops through the steps of the forward driven adaptive method to complete bidirectional synchronization.

[0004] For example, the invention patent with announcement number CN114066365B announces a cloud-based digital supply chain service management system, including a digital cloud platform, a logistics resource management module, a distribution task allocation module and a monitoring module; the digital cloud platform includes a pharmaceutical supplier integration module and a department request integration module, the pharmaceutical supplier integration module is connected to the ordering intranet of several pharmaceutical suppliers through TCP / IP communication, the department request integration module is connected to the digital cloud platform through an internal LAN, and 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 at the output end of the digital cloud platform, and integrated development is carried out using the cloud management system, and the order flow is optimized in parallel using the parallel processing framework.

[0005] Combined with the above technical solutions, it is found that in the current process of data processing of the medical supply chain, the supply chain is managed directly through medical data. This process will lead to inefficient supply chain management and low management efficiency. If the above process continues, it will further affect the effectiveness of medical data processing and reduce the accuracy of data processing. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a method for efficient processing and secure tracing of on-chain and off-chain data for the medical supply chain, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an efficient on-chain and off-chain data processing and secure tracing method for the medical supply chain, including: S1. The off-chain data verification platform to which the supply chain belongs verifies the medical information, thereby obtaining the verification parameters of the medical information and the network characteristic information of the off-chain data verification platform, and comprehensively analyzes to obtain the medical information verification compliance; S2. The medical information verification compliance is compared with the medical information verification compliance threshold preset in the medical supply chain management library to obtain the medical information verification compliance comparison result, and according to the medical information verification compliance comparison result, it is determined whether to upload the medical information directly to the data processing platform to which the blockchain belongs; S3. The on-chain query platform to which the supply chain belongs receives the medical information query signal, and at the same time queries the medical information of the data processing platform to which the blockchain belongs, thereby obtaining the query parameters of the medical information, and evaluating the medical information query accuracy, and comparing it with the medical information query accuracy threshold preset in the medical supply chain management library to obtain the medical information query accuracy comparison result, and determine whether to securely trace the medical information.

[0008] As a further method, the medical information verification compliance, the specific analysis process is:

[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 compared and verified one by one 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 verification accuracy of the number of the production date of each medical product, the verification accuracy of the byte of the production place of each medical product, and the verification accuracy of the byte of the product name of each medical product.

[0011] The network characteristic information of the off-chain data verification platform specifically includes the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle.

[0012] According to the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle, the network performance indicators of the off-chain data verification platform are obtained through data processing.

[0013] The compliance of medical information verification is determined through comprehensive processing of the verification accuracy of the number of each medical product's production date, the verification accuracy of the byte of each medical product's production location, the verification accuracy of the byte of each medical product's product name, and the network performance indicators of the off-chain data verification platform.

[0014] As a further method, the off-chain data verification platform's network performance indicators are obtained by:

[0015]

[0016] 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 during the data verification cycle, and DNB Δu It represents the reference utilization rate of the network bandwidth preset by the medical supply chain management library, NRT represents the node response time of the off-chain data verification platform within the data verification cycle, JQ represents the number of network jitters of the off-chain data verification platform within the data verification cycle, NT represents the network throughput of the off-chain data verification platform within the data verification cycle, e is a natural constant, α1 represents the influence weight of the network bandwidth utilization rate preset by the medical supply chain management library, α2 represents the influence weight of the node response time preset by the medical supply chain management library, α3 represents the influence weight of the number of network jitters preset by the medical supply chain management library, and α4 represents the influence weight of the network throughput preset by the medical supply chain management library.

[0017] As a further method, the determination of whether to upload the medical information directly to the data processing platform to which the blockchain belongs is made based on the medical information verification compliance comparison result. The specific process is as follows:

[0018] If the medical information verification compliance comparison result is that the medical information verification compliance is less than the medical information verification compliance threshold, the network to which the off-chain data verification platform belongs is optimized, and the medical information is re-acquired and verified again. The medical information verification compliance is analyzed and obtained to be greater than or equal to the medical information verification compliance threshold, and the medical information is uploaded to the data processing platform to which the blockchain belongs.

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

[0020] As a further method, the medical information query accuracy, the specific analysis process is:

[0021] The query numbers belonging to the production dates of each medical product, the query bytes belonging to the production places of each medical product, and the query bytes belonging to the product names of each medical product are compared and verified one by one with the initial numbers belonging to the production dates of each medical product, the initial bytes belonging to the production places of each medical product, and the initial bytes belonging to the product names of each medical product, thereby obtaining the query accuracy rates of the numbers belonging to the production dates of each medical product, the bytes belonging to the production places of each medical product, and the bytes belonging to the product names of each medical product.

[0022] Based on the query accuracy of the number of each medical product's production date, the query accuracy of the byte of each medical product's production location, and the query accuracy of the byte of each medical product's product name, and combined with the compliance of medical information verification, the accuracy of medical information query is comprehensively obtained.

[0023] As a further method, the determination of whether to safely trace the medical information is as follows:

[0024] If the medical information query accuracy comparison result is less than the medical information query accuracy threshold, the medical information is securely traced.

[0025] If the medical information query accuracy comparison result is that the medical information query accuracy is greater than or equal to the medical information query accuracy threshold, there is no need to conduct secure tracing of the medical information.

[0026] As a further method, the secure tracing of medical information is specifically a process in which the off-chain data verification platform to which the traceability supply chain belongs verifies the medical information, the traceability medical information is uploaded to the data processing platform to which the blockchain belongs, and the on-chain query platform to which the traceability supply chain belongs receives the medical information query signal and simultaneously queries the medical information on the data processing platform to which the blockchain belongs.

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

[0028] (1) The present invention provides an efficient on-chain and off-chain data processing and secure tracing method for the medical supply chain. First, the off-chain data verification platform of the supply chain verifies the medical information, thereby obtaining the verification parameters of the medical information and the network feature information of the off-chain data verification platform, and comprehensively analyzes to obtain the medical information verification compliance, and compares the medical information verification compliance with the medical information verification compliance threshold preset in the medical supply chain management library to obtain the medical information verification compliance comparison result, and determines whether to upload the medical information directly to the data processing platform of the blockchain according to the medical information verification compliance comparison result. The on-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 at the same time, thereby obtaining the query parameters of the medical information, and evaluating the medical information query accuracy, and comparing it with the medical information query accuracy threshold preset in the medical supply chain management library to obtain the medical information query accuracy comparison result, and determines whether to securely trace the medical information. If a behavior with a low query accuracy result occurs, the problem node can be quickly located to achieve efficient and secure tracing, thereby improving the accuracy and credibility of medical supply chain data processing.

[0029] (2) The present invention verifies medical information through the off-chain data verification platform of the supply chain, thereby obtaining verification parameters of the medical information and network feature information of the off-chain data verification platform, and comprehensively analyzes to obtain the compliance of medical information verification, so that the off-chain data verification platform can accurately verify the medical information, thereby improving the accuracy of information upload and the service quality of the off-chain data verification platform.

[0030] (3) The present invention compares the medical information verification compliance with the preset medical information verification compliance threshold in the medical supply chain management library to obtain the medical information verification compliance comparison result, and determines whether to upload the medical information directly to the data processing platform to which the blockchain belongs based on the medical information verification compliance comparison result. This can accurately and comprehensively verify the medical information belonging to the supply chain, help reduce data errors and abnormal problems, and provide accurate data basis for subsequent users to query medical information.

[0031] (4) The present invention receives medical information query signals through the on-chain query platform to which the supply chain belongs, and queries the medical information of the data processing platform to which the blockchain belongs, thereby obtaining the query parameters belonging to the medical information, and evaluating the accuracy of the medical information query, and comparing it 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 determine whether to safely trace the medical information. If it is necessary to safely trace the medical information, it can be comprehensively traced through the above steps to quickly and accurately obtain the influencing factors of medical information errors, and realize efficient data processing of the medical supply chain through corresponding solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0033] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0035] Reference Figure 1 As shown, the present invention provides an efficient on-chain and off-chain data processing and secure tracing method for the medical supply chain, including: S1. The off-chain data verification platform of the supply chain verifies the medical information, thereby obtaining the verification parameters of the medical information and the network characteristic information of the off-chain data verification platform, and comprehensively analyzes to obtain the compliance of the medical information verification.

[0036] In this embodiment, the off-chain data verification platform to which the above-mentioned supply chain belongs refers to a platform for performing data verification on medical information. This 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 a data processing technology algorithm, of course, it also includes multiple data monitoring technologies such as a SIEM (security information and event management) system and a network performance monitoring module. Therefore, the off-chain data verification platform first verifies the medical information, obtains the network characteristic information of the off-chain data verification platform through a built-in network performance monitoring tool, analyzes the network characteristic information of the off-chain data verification platform through a built-in data processing technology, obtains the 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 a built-in data processing technology to obtain the compliance of the medical information verification.

[0037] Specifically, the compliance of the medical information verification is analyzed in the following steps:

[0038] The verification parameters to which the medical information belongs specifically include the verification numbers of the production dates of each medical product, each verification byte of the production locations of each medical product, and each verification byte of the product names of each medical product. Here, each medical product refers to multiple medical products produced in the same batch. For example, granules and capsules. The production date number of each medical product specifically refers to the number corresponding to the production date of the medical product. For example, if the production date of a certain capsule is "November 6, 2024", then its production date number is 20241106. The production location byte of each medical product specifically refers to the UTF-8 (Unicode variable-length character encoding) encoded value of the Chinese characters of the production location corresponding to the medical product. For example, if the production location of a certain capsule is "Hefei", the UTF-8 encoding of "合" is E59088, and the UTF-8 encoding of "肥" is E882A5, then its production location byte is E59088E882A5. The product name byte of each medical product specifically refers to the UTF-8 (Unicode variable-length character encoding) encoded value of the Chinese characters of the product name corresponding to the medical product. For example, if the product name of a certain medical product is "Banlangen", the UTF-8 encoding of "板" is E69DBF, the UTF-8 encoding of "蓝" is E8939D, and the UTF-8 encoding of "根" is E6A0B9, then its product name byte is E69DBFE8939DE6A0B9.

[0039] It should be elaborated that the verification numbers of the production dates of the above-mentioned medical products, each verification byte of the production locations of the medical products, and each verification byte of the product names of the medical products can all be obtained through the off-chain data verification platform.

[0040] Obtain 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, and compare and verify them one by one 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 verification accuracy of the number of the production date of each medical product, the verification accuracy of the byte of the production place of each medical product, and the verification accuracy of the byte of the product name of each medical product. For example, if the production date of a capsule is "November 6, 2024", then the initial number of its production date is 20241106. If the verification number of its production date is 20241116, then the verification accuracy of the number of the production date of the medical product is 87.5%; for example, if the production place of a capsule is "Hefei", then the initial byte of its production place is E59088E882 A5. If the verification byte of its production place is E59088E882 A6, then the verification accuracy of the bytes of the medical product's production location is 93.75%; for example, the product name of a medical product is "Isatis Root", then the initial bytes of its product name are E69D BFE8939DE6 A0 B9, if the verification bytes of its product name are E69D BFE8939DE6 A1 A8, then the verification accuracy of the bytes of the medical product's product name is 87.5%.

[0041] It should be explained that the initial number of the production date of each of the above-mentioned medical products, the initial bytes of the production place of each medical product, and the initial bytes of the product name of each medical product can all be obtained through the product inspection report compiled by the medical product inspectors.

[0042] The network characteristic information of the off-chain data verification platform specifically includes the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle.

[0043] According to the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle, the network performance indicators of the off-chain data verification platform are obtained through data processing.

[0044] It should be explained that the network bandwidth utilization rate, node response time, network jitter times and network throughput of the above-mentioned off-chain data verification platform during the data verification cycle can be obtained through the network performance monitoring tool built into the off-chain data verification platform. 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. Therefore, the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle can be queried through the network performance monitoring tool.

[0045] It should be explained that the above-mentioned data verification cycle can be obtained through the SIEM (security information and event management) system built into the off-chain data verification platform. Specifically, it refers to the time interval from the submission of medical information to the off-chain data verification platform to the final acquisition of the verification result, which is recorded as the data verification cycle.

[0046] The medical information verification compliance is obtained through comprehensive processing through the verification accuracy of the number of the production date of each medical product, the verification accuracy of the byte of the production location of each medical product, the verification accuracy of the byte of the product name of each medical product, and the network performance indicators of the off-chain data verification platform. The method for obtaining it 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 analyzing the network bandwidth utilization rate, node response time, network jitter number and network throughput of the off-chain data verification platform during the data verification cycle.

[0049] APD D It indicates the verification accuracy rate of the number belonging to the production date of the D-th medical product, specifically refers to the matching ratio between the verification number belonging to the production date of the D-th medical product and the initial number belonging to the production date of the D-th medical product, which is used to evaluate the effectiveness and accuracy of the data verification process.

[0050] MLB D It indicates the verification accuracy of the bytes belonging to the production location of the D-th medical product, specifically refers to the matching ratio between the verification bytes belonging to the production location of the D-th medical product and the initial bytes belonging to the production location of the D-th medical product.

[0051] APN D It indicates the verification accuracy rate of the bytes belonging to the product name of the D-th medical product, specifically refers to the matching ratio between the verification bytes belonging to the product name of the D-th medical product and the initial bytes belonging to the product name of the D-th medical product.

[0052] D is the serial 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 pieces, D = 1, 2, 3, ..., 200, and M is 200.

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

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

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

[0056] β4 represents the influence weight of the network performance indicators preset in the medical supply chain management library, which represents the numerical value of the influence degree of the network performance indicators preset in the medical supply chain management library on the compliance of medical information verification. When used, the influence weight of the network performance indicators 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 performance indicators and the influence weight of the network performance indicators preset in the medical supply chain management library form a mapping set, and the real-time network performance indicators are input into the mapping set to obtain the influence weight of the network performance indicators. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0057] CDIV represents the compliance of medical information verification. Specifically, in the process of data verification of medical information on the off-chain data verification platform, the verification accuracy of the number belonging to the production date of each medical product, the verification accuracy of the byte belonging to the production location of each medical product, the verification accuracy of the byte belonging to the product name of each medical product, and the network performance indicators of the off-chain data verification platform are used to comprehensively influence the compliance of medical information verification. A decrease in the network performance indicators of the off-chain data verification platform indicates that the network performance of the off-chain data verification platform is unstable, resulting in data verification delays or errors, which reduces the verification accuracy of the number belonging to the production date, the verification accuracy of the byte belonging to the production location, and the verification accuracy of the byte belonging to the product name, ultimately affecting the compliance of medical information verification and reducing the compliance of medical information verification.

[0058] Furthermore, the network performance indicators of the off-chain data verification platform are obtained by:

[0059]

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

[0061] DNB Δu It indicates the reference network bandwidth usage rate preset by the medical supply chain management library, specifically, the optimal adaptation value of the network bandwidth usage rate set during the data verification process.

[0062] NRT refers to the node response time of the off-chain data verification platform within the data verification cycle. Specifically, it refers to the time from when the off-chain data verification request is received by the node to when the node completes the data verification and returns the result.

[0063] JQ represents the number of network jitters of the off-chain data verification platform during the data verification cycle. Specifically, it refers to the number of network jitters caused by network instability during the data verification cycle. The greater the number of network jitters, the worse the network stability.

[0064] NT represents the network throughput of the off-chain data verification platform during the data verification cycle. Specifically, it refers to the ability of information to truly complete verification during the data verification process. It describes the amount of data transmitted through the network per unit time.

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

[0067] α3 represents the network jitter impact weight preset by the medical supply chain management library, which represents the numerical value of the impact degree of the network jitter count preset by the medical supply chain management library on the network performance indicators of the off-chain data verification platform. When used, the network jitter impact 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 count and the network jitter impact weight preset by the medical supply chain management library form a mapping set, and the real-time network jitter count is input into the mapping set to obtain the network jitter impact weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0068] α4 represents the network throughput impact weight preset by the medical supply chain management library, which represents the numerical value of the impact degree of the network throughput preset by the medical supply chain management library on the network performance indicators of the off-chain data verification platform. When used, the network throughput impact 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 throughput and the network throughput impact weight preset by 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 impact weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0069] e is a natural constant, NSNI represents the network performance index of the off-chain data verification platform. During the data verification process, when the network bandwidth utilization rate deviates greatly from the network bandwidth reference utilization rate, it will directly lead to a decrease in network throughput, and the network will face congestion, resulting in an increase in the delay in node processing of medical information, thereby increasing the node response time; the increase in node response time increases the waiting time for medical information to be processed at the node, which may lead to an increase in the number of network jitters, thereby reducing the network performance index of the off-chain data verification platform.

[0070] S2. Compare the medical information verification compliance with the preset medical information verification compliance threshold in the medical supply chain management library to obtain the medical information verification compliance comparison result, and determine whether to upload the medical information directly to the data processing platform to which the blockchain belongs based on the medical information verification compliance comparison result.

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

[0072] The first verification compliance comparison result is specifically that the medical information verification compliance is less than the medical information verification compliance threshold, wherein the medical information verification compliance threshold represents the critical value used to determine whether the medical information verification compliance meets the compliance standard when performing medical information verification, and is used to quantitatively evaluate the accuracy of medical verification information, and help the off-chain data verification platform determine whether it is necessary to optimize the network to which the off-chain data verification platform belongs; the above-mentioned medical information verification compliance is less than the medical information verification compliance threshold, indicating that the medical verification information is inaccurate, there will be network problems with 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 comparison result, specifically, the medical information verification compliance is greater than or equal to the medical information verification compliance threshold, indicating that the medical verification information is accurate and there is no need to optimize the network to which the off-chain data verification platform belongs. The medical information can be directly uploaded to the data processing platform to which the blockchain belongs.

[0074] Furthermore, the specific process of determining whether to upload the medical information directly to the data processing platform to which the blockchain belongs is as follows:

[0075] If the medical information verification compliance comparison result is that the medical information verification compliance is less than the medical information verification compliance threshold, the network to which the off-chain data verification platform belongs is optimized, and the medical information is re-acquired and verified again. The medical information verification compliance is analyzed and obtained to be greater than or equal to the medical information verification compliance threshold, and the medical information is uploaded to the data processing platform to which the blockchain belongs.

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

[0077] It needs to be explained that the above-mentioned optimization of the network to which the off-chain data verification platform belongs, thereby the off-chain data verification platform obtains an optimized network, specifically, the medical information verification compliance and the medical information verification compliance threshold are differenced to obtain the medical information verification compliance deviation value, and matched with the network optimization data set corresponding to each medical information verification compliance deviation value interval preset in the medical supply chain management library, and finally the network optimization data set to which the medical information verification compliance belongs is obtained by matching. The medical supply chain management library performs network optimization operations through the network optimization data set, thereby the off-chain data verification platform obtains an optimized network.

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

[0079] The network optimization data set corresponding to the above medical information verification compliance deviation value is developed by an authoritative network optimization agency or standardization organization through data collection, collation, and encryption. Each medical information verification compliance deviation value is carefully divided, and the corresponding network optimization data set is designed for each medical information verification compliance deviation value range based on network optimization standards, threat and risk analysis, and business needs and compliance requirements.

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

[0081] It should be explained that the original 100Mbps bandwidth in the above network optimization data set was upgraded to 1Gbps, and network equipment such as switches and routers were added, RSA key distribution encryption and chameleon hashing were used. The purpose is to increase the network bandwidth of the network to which the off-chain data verification platform belongs, improve network throughput and other factors that are conducive to network performance optimization, and ultimately ensure that the medical information verification compliance evaluated after the optimization is completed is greater than or equal to the medical information verification compliance threshold.

[0082] S3. The on-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 at the same time, thereby obtaining the query parameters of the medical information, and evaluating the accuracy of the medical information query. It is compared with the preset medical information query accuracy threshold in the medical supply chain management library to obtain the comparison result of the medical information query accuracy, and determines whether the medical information should be safely traced.

[0083] In this embodiment, the on-chain query platform to which the above-mentioned supply chain belongs refers to a platform for performing information query on medical information. This on-chain query platform can be connected through the Internet of Things, thereby receiving medical information query signals through the Internet of Things, and finally performing information query operations on the medical information. Since the on-chain query platform is internally provided with a data processing technology algorithm, the on-chain query platform is connected to multiple nodes in the blockchain network, which are components of the blockchain network and are responsible for storing and verifying the 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. Therefore, the on-chain query platform first queries the medical information of the data processing platform to which the blockchain belongs, thereby obtaining the query parameters of the medical information, and evaluating the accuracy of the medical information query, and comparing it with the medical information query accuracy threshold preset in the medical supply chain management library, to obtain the medical information query accuracy comparison result, and determine whether to safely trace the medical information.

[0084] Specifically, the query parameters belonging to the medical information include the query number belonging to the production date of each medical product, the query bytes belonging to the production place of each medical product, and the query bytes belonging to the product name of each medical product. For example, the production date of a capsule is "November 6, 2024", then the query number belonging to its production date is 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, then the query bytes belonging to its production place are E59088E882 A5; the product name of a medical product is "Isatis root", 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, then the query bytes belonging to its product name are E69D BFE8939DE6 A0 B9.

[0085] It should be explained that the query numbers belonging to the production dates of the above-mentioned medical products, the query bytes belonging to the production places of the medical products, and the query bytes belonging to the product names of the medical products can all be obtained through the on-chain query platform of the supply chain.

[0086] Furthermore, the specific analysis process of the accuracy of the medical information query is as follows:

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

[0088] The accuracy of medical information query is obtained by combining the accuracy of the query of the number of the production date of each medical product, the accuracy of the query of the byte of the production place of each medical product, and the accuracy of the query of the byte of the product name of each medical product, and the compliance of medical information verification. The specific method of obtaining the accuracy of medical information query is as follows:

[0089]

[0090] Where, PDI D It indicates the query accuracy rate of the number belonging to the production date of the D-th medical product, specifically refers to the matching ratio between the query number belonging to the production date of the D-th medical product and the initial number belonging to the production date of the D-th medical product.

[0091] PLI D It indicates the query accuracy of the bytes belonging to the production location of the D-th medical product, specifically refers to the matching ratio between the query bytes belonging to the production location of the D-th medical product and the initial bytes belonging to the production location of the D-th medical product.

[0092] PNI D It indicates the query accuracy of the bytes belonging to the product name of the D-th medical product, specifically refers to the matching ratio between the query bytes belonging to the product name of the D-th medical product and the initial bytes belonging to the product name of the D-th medical product.

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

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

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

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

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

[0098] ζ4 represents the impact weight of the medical information verification compliance preset by the medical supply chain management library, which represents the numerical value of the impact of the medical information verification compliance preset by the medical supply chain management library on the accuracy of medical information query. When used, the medical information verification compliance impact 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 medical information verification compliance and the medical information verification compliance impact weight preset by the medical supply chain management library form a mapping set, and the real-time medical information verification compliance is input into the mapping set to obtain the medical information verification compliance impact weight. The mapping relationship can be one-to-one or many-to-one. In this example, its value range is [0, 1].

[0099] e is a natural constant, and AMI represents the accuracy of medical information query. Specifically, it refers to the accuracy of medical information query obtained through the query accuracy of the number belonging to the production date of each medical product, the query accuracy of the byte belonging to the production place of each medical product, the query accuracy of the byte belonging to the product name of each medical product, and the compliance of medical information verification during the information query process of the on-chain query platform. If the compliance of medical information verification is low, it means that the medical information is distorted, which may lead to the reduction of the query accuracy of the number belonging to the production date of each medical product and the query accuracy of the byte belonging to the production place of each medical product, which will ultimately affect the accuracy of medical information query and reduce the accuracy of medical information query.

[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 the medical information query accuracy threshold, where the medical information query accuracy threshold represents the critical value used to determine whether the medical information query accuracy meets the compliance standard when conducting medical information query, and is used to quantitatively evaluate the accuracy of medical information queries, and help the on-chain query platform to which the supply chain belongs to determine whether to safely trace the medical information; the above-mentioned medical information query accuracy is less than the medical information query accuracy threshold, indicating that the medical query information is inaccurate and the medical information needs to be safely traced.

[0102] The second query accuracy comparison result, specifically, 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 there is no need to safely trace the medical information.

[0103] Furthermore, the specific process of determining whether to safely trace the medical information is as follows:

[0104] If the medical information query accuracy comparison result is less than the medical information query accuracy threshold, the medical information is securely traced.

[0105] If the medical information query accuracy comparison result is that the medical information query accuracy is greater than or equal to the medical information query accuracy threshold, there is no need to conduct secure tracing of the medical information.

[0106] Specifically, the secure tracing of medical information includes the process of verifying the medical information by the off-chain data verification platform of the traceable supply chain, uploading the traceable medical information to the data processing platform of the blockchain, and receiving the medical information query signal by the on-chain query platform of the traceable supply chain, and querying the medical information of the data processing platform of the blockchain at the same time.

[0107] It needs to be explained that the above-mentioned secure tracing of medical information, whereby the on-chain query platform obtains the correct medical information query results, specifically involves performing a difference processing between the medical information query accuracy of the on-chain query platform and the medical information query correct threshold, thereby obtaining a medical information query accuracy deviation value, and matching it with the secure tracing data sets corresponding to each medical information query accuracy deviation value interval preset in the medical supply chain management library, and finally obtaining the secure tracing data set to which the medical information query accuracy belongs. The medical supply chain management library performs secure tracing operations through the secure tracing data set, whereby the on-chain query platform obtains the correct medical information query results.

[0108] The above matching obtains the safety traceability data set to which the accuracy of medical information query belongs. The specific matching process is as follows:

[0109] The above medical information query accuracy deviation values ​​and corresponding security traceability data sets are developed by authoritative network security institutions or standardization organizations through data collection, collation, and encryption. Each medical information query accuracy deviation value is carefully divided, and for each medical information query accuracy deviation value range, the corresponding security traceability data set is designed based on network security standards, threat and risk analysis, business needs, and compliance requirements.

[0110] For example, if the medical information query accuracy deviation value in this example is 6, it belongs to the medical information query accuracy deviation value interval corresponding to [3, 7]. At the same time, the security traceability data set corresponding to the medical information query accuracy deviation value interval of the security traceability data set [3, 7] preset in the medical supply chain management library includes the use of RFID (radio frequency identification) technology to identify and encode medical products during the verification of medical information on the off-chain data verification platform, the use of Internet of Things technology to monitor and track in real time during the process of uploading medical information to the data processing platform to which the blockchain belongs and the use of blockchain technology to encrypt medical information during the process of receiving medical information query signals on the on-chain query platform, and the recording of all access and operation logs to medical information during the query of medical information on the data processing platform to which the blockchain belongs, obtaining complete information and digital files of the product, starting from the source of product production, all the way to the final use of the product, understanding the full life cycle information of the product, verifying the authenticity and source of the product, and finally obtaining the correct medical information query results.

[0111] It needs to be explained that in the process of verifying medical information by the above-mentioned off-chain data verification platform, medical products are identified and encoded through RFID (radio frequency identification) technology, and in the process of uploading medical information to the data processing platform to which the blockchain belongs and in the process of receiving medical information query signals by the on-chain query platform, the Internet of Things technology is used to monitor and track in real time and blockchain technology is used to encrypt medical information, and in the process of querying medical information on the data processing platform to which the blockchain belongs, all access and operation logs to medical information are recorded to obtain complete information and digital files of the product, starting from the source of product production, all the way to the final use of the product, to understand the full life cycle information of the product, to verify the authenticity and source of the product, and through the safe tracing of each process, its purpose is to make the unique identification code of the on-chain query platform, the decentralization and non-tamperability of blockchain technology and other factors that are conducive to safe tracing, and ultimately ensure that the accuracy of the medical information query evaluated after the completion of the safe tracing is greater than the correct threshold of the medical information query.

[0112] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. An efficient on-chain and off-chain data processing and secure tracing method for the medical supply chain, characterized by: include: S1. The off-chain data verification platform of the supply chain verifies the medical information, thereby obtaining the verification parameters of the medical information and the network feature information of the off-chain data verification platform, and comprehensively analyzes and obtains the compliance of the medical information verification; S2. Compare the medical information verification compliance with the preset medical information verification compliance threshold in the medical supply chain management library to obtain the medical information verification compliance comparison result, and determine whether to upload the medical information directly to the data processing platform to which the blockchain belongs based on the medical information verification compliance comparison result; S3. The on-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 at the same time, thereby obtaining the query parameters of the medical information, and evaluating the accuracy of the medical information query. It is compared with the preset medical information query accuracy threshold in the medical supply chain management library to obtain the comparison result of the medical information query accuracy, and determines whether the medical information should be safely traced.

2. The method for efficient processing and secure tracing of on-chain and off-chain data for the medical supply chain according to claim 1 is characterized by: The specific analysis process of the medical information verification compliance is 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; Obtain 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, and compare and verify them one by one 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 verification accuracy rate of the number of the production date of each medical product, the verification accuracy rate of the byte of the production place of each medical product, and the verification accuracy rate of the byte of the product name of each medical product; The network characteristic information of the off-chain data verification platform includes the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle; According to the network bandwidth utilization rate, node response time, network jitter times and network throughput of the off-chain data verification platform during the data verification cycle, the network performance indicators of the off-chain data verification platform are obtained through data processing; The compliance of medical information verification is determined through comprehensive processing of the verification accuracy of the number of each medical product's production date, the verification accuracy of the byte of each medical product's production location, the verification accuracy of the byte of each medical product's product name, and the network performance indicators of the off-chain data verification platform.

3. The method for efficient processing and secure tracing of on-chain and off-chain data for the medical supply chain according to claim 2 is characterized by: The network performance indicators of the off-chain data verification platform are 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 during the data verification cycle, and DNB Δu It represents the reference utilization rate of the network bandwidth preset by the medical supply chain management library, NRT represents the node response time of the off-chain data verification platform within the data verification cycle, JQ represents the number of network jitters of the off-chain data verification platform within the data verification cycle, NT represents the network throughput of the off-chain data verification platform within the data verification cycle, e is a natural constant, α1 represents the influence weight of the network bandwidth utilization rate preset by the medical supply chain management library, α2 represents the influence weight of the node response time preset by the medical supply chain management library, α3 represents the influence weight of the number of network jitters preset by the medical supply chain management library, and α4 represents the influence weight of the network throughput preset by the medical supply chain management library.

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

5. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 1 is characterized by: The specific process of determining whether to upload the medical information directly to the data processing platform to which the blockchain belongs based on the medical information verification compliance comparison result is as follows: If the medical information verification compliance comparison result is that the medical information verification compliance is less than the medical information verification compliance threshold, the network to which the off-chain data verification platform belongs is optimized, and the medical information is re-acquired and verified again. After analysis and obtaining that the medical information verification compliance is greater than or equal to the medical information verification compliance threshold, the medical information is uploaded to the data processing platform to which the blockchain belongs; If the medical information verification compliance comparison result is that the medical information verification compliance is greater than or equal to the medical information verification compliance threshold, the medical information will be directly uploaded to the data processing platform to which the blockchain belongs.

6. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 1 is characterized by: The query parameters of the medical information specifically include the query number of the production date of each medical product, the query bytes of the production place of each medical product, and the query bytes of the product name of each medical product.

7. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 6 is characterized by: The specific analysis process of the medical information query accuracy is as follows: The query number of the production date of each medical product, the query bytes of the production place of each medical product, and the query bytes of the product name of each medical product are compared and verified one by one with 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, thereby obtaining the query accuracy rate of the number of the production date of each medical product, the query accuracy rate of the byte of the production place of each medical product, and the query accuracy rate of the byte of the product name of each medical product; Based on the query accuracy of the number of each medical product's production date, the query accuracy of the byte of each medical product's production location, and the query accuracy of the byte of each medical product's product name, and combined with the compliance of medical information verification, the accuracy of medical information query is comprehensively obtained.

8. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 1 is characterized by: The medical information query accuracy comparison result is specifically a first query accuracy comparison result and a second query accuracy comparison result; The first query accuracy comparison result is specifically that the medical information query accuracy is less than the medical information query accuracy threshold; 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.

9. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 1 is characterized by: The specific process of determining whether to safely trace the medical information is as follows: If the medical information query accuracy comparison result is that the medical information query accuracy comparison result is less than the medical information query accuracy threshold, the medical information is securely traced; If the medical information query accuracy comparison result is that the medical information query accuracy is greater than or equal to the medical information query accuracy threshold, there is no need to conduct secure tracing of the medical information.

10. The method for efficient on-chain and off-chain data processing and secure tracing for the medical supply chain according to claim 9 is characterized by: The secure tracing of medical information is specifically a process of verifying the medical information by the off-chain data verification platform of the traceability supply chain, uploading the traceability medical information to the data processing platform of the blockchain, and receiving the medical information query signal by the on-chain query platform of the traceability supply chain, while querying the medical information of the data processing platform of the blockchain.

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