Data exchange system for multi-channel drug sales

Abstract

The invention relates to the technical field of medical information, and particularly discloses a data exchange system for multi-channel drug sales, which comprises a data acquisition module, a route selection module, a real-time monitoring module, a data auditing module and a data inspection module. According to the scheme, in the data exchange process, the link quality is calculated, the transmission strategy is adaptively optimized, the distribution proportion of the data flow is adjusted through error feedback, overload of a single link is avoided, and the overall bandwidth utilization rate is increased; through encryption, block chain storage, hierarchical verification and exception handling, the security, integrity and traceability of drug sales data are enhanced, the legality of transaction data is efficiently verified, the data query and auditing speed is improved, system maintenance is facilitated, and the data security is further guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of medical information technology, and specifically refers to a data exchange system for multi-channel drug sales. Background Art

[0002] With the diversified development of drug sales models, the data exchange ability between drug sales channels becomes particularly important. Traditional data transmission schemes are difficult to adapt to the dynamically changing network environment among various drug sales channels, and do not distinguish different types of data streams, resulting in delays in critical tasks due to network congestion, affecting the normal operation of the drug supply chain; traditional transaction data management methods cannot effectively guarantee the integrity of data, have a high computational complexity, and are inefficient in processing large-scale transaction data, with the problem of easy data loss. Summary of the Invention

[0003] In view of the above situation, to overcome the defects of the prior art, the present invention provides a data exchange system for multi-channel drug sales. Aiming at the problem that traditional data transmission schemes are difficult to adapt to the dynamically changing network environment among various drug sales channels, do not distinguish different types of data streams, resulting in delays in critical tasks due to network congestion, affecting the normal operation of the drug supply chain, this solution calculates the link quality during the data exchange process, adaptively optimizes the transmission strategy, adjusts the allocation ratio of data streams through error feedback to avoid overloading of a single link and improve the overall bandwidth utilization rate; aiming at the problem that traditional transaction data management methods cannot effectively guarantee the integrity of data, have a high computational complexity, and are inefficient in processing large-scale transaction data, with the problem of easy data loss, this solution enhances the security, integrity, and traceability of drug sales data through encryption, blockchain storage, hierarchical verification, and exception handling, efficiently verifies the legality of transaction data, improves the speed of data query and auditing, helps system maintenance, and further ensures data security.

[0004] A data exchange system for multi-channel drug sales provided by the present invention includes a data acquisition module, a routing selection module, a real-time monitoring module, a data auditing module, and a data verification module;

[0005] The data acquisition module obtains the drug sales data from multiple sales channels in real time, preprocesses the drug sales data and sends it to the data auditing module;

[0006] The routing selection module optimizes the transmission paths between each sales channel, combines the load balancing strategy to allocate data streams to different data links for transmission, and sends the transmission path selection result to the real-time monitoring module;

[0007] The real-time monitoring module monitors the data transmission status, predicts potential transmission failures through time series analysis and issues early warnings;

[0008] The data audit module stores the drug sales data in the blockchain and uses the hash tree structure to perform hierarchical verification on the drug sales data during transmission;

[0009] The data verification module verifies the consistency and atomicity of the data in the blockchain and continuously monitors the data status.

[0010] Furthermore, the routing module includes a link scanning unit, a path selection unit, a policy adjustment unit, and a load balancing unit;

[0011] The link scanning unit scans all available data links between each sales channel, collects the bandwidth, delay, and packet loss rate of each link, and calculates the link quality index. The formula used is as follows:

[0012]

[0013] In the formula, LQI i represents the link quality index, i represents the link index, P i represents the link packet loss rate, B i represents the current available bandwidth of the link, D i represents the transmission delay of the link, and w1, w2, and w3 are weight coefficients;

[0014] The path selection unit selects the primary transmission path and the backup transmission path according to the link quality index and automatically switches to the backup transmission path when the primary transmission path is unavailable;

[0015] The policy adjustment unit adopts a dynamic adjustment policy based on quality of service, sets the priority of the transmission task, and dynamically adjusts the path selection policy in combination with the real-time network condition;

[0016] The load balancing unit distributes the data flow to different data links and dynamically adjusts the data distribution ratio of each data link through an intelligent scheduling algorithm. The formula used is as follows:

[0017]

[0018] In the formula, F represents the load balancing adjustment amount, k p represents the influence coefficient of the error on the adjustment amount, k i represents the influence coefficient of the error accumulation on the adjustment amount, k d represents the influence coefficient of the error change rate on the adjustment amount, n represents the moment, E n and E n-1 represent the load errors at the current moment and the previous moment respectively, represents the error accumulation value from the initial moment to the current moment.

[0019] Further, the real-time monitoring module monitors the data transmission status, analyzes the historical transmission records based on time series, predicts potential data transmission failures and issues early warnings. The formula used is as follows:

[0020] S t = β·X t +(1 - β)·S t-1 ;

[0021] E t = |X t - S t |;

[0022] T = μ E + λσ E ;

[0023] In the formula, t represents the moment, S t and S t-1 respectively represent the smoothed transmission rates at the current moment and the previous moment, β represents the smoothing coefficient, X t represents the actual transmission rate at the current moment, E t represents the error value between the current transmission rate and the predicted rate, T represents the error threshold, μ E and σ E respectively represent the mean and standard deviation of the errors, and λ represents the alarm threshold coefficient.

[0024] Further, the data audit module includes an encryption unit, a blockchain storage unit, a hierarchical verification unit, and an exception handling unit;

[0025] The encryption unit encrypts all drug sales data and generates a unique hash value;

[0026] The blockchain storage unit constructs a hash tree to hierarchically store the hash values of drug sales data;

[0027] The hierarchical verification unit recalculates the hash value according to the content of the received drug sales data and queries the original hash value in the blockchain for comparison;

[0028] The exception handling unit queries the blockchain, obtains the most recent valid data, resynchronizes the drug sales data with exceptions, and simultaneously triggers an alarm to notify the administrator.

[0029] The beneficial effects achieved by the present invention using the above solution are as follows:

[0030] (1) Aiming at the problem that traditional data transmission schemes are difficult to adapt to the dynamically changing network environment among various drug sales channels, do not distinguish different types of data streams, resulting in key tasks being delayed due to network congestion and affecting the normal operation of the drug supply chain, this solution calculates the link quality during data exchange, adaptively optimizes the transmission strategy, adjusts the allocation ratio of data streams through error feedback, avoids single-link overload, and improves the overall bandwidth utilization rate.

[0031] (2) Aiming at the problems that traditional transaction data management methods cannot effectively guarantee data integrity, have a high computational complexity, low efficiency in processing large-scale transaction data, and are prone to data loss, this solution enhances the security, integrity, and traceability of drug sales data through encryption, blockchain storage, hierarchical verification, and exception handling, efficiently verifies the legality of transaction data, improves the speed of data query and audit, helps system maintenance, and further guarantees data security. Brief Description of the Drawings

[0032] Figure 1 It is a schematic diagram of a data exchange system for multi-channel drug sales proposed by the present invention;

[0033] Figure 2 It is a schematic diagram of the routing selection module;

[0034] Figure 3 It is a schematic diagram of the data audit module.

[0035] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. Detailed Embodiments

[0036] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0037] Embodiment 1, refer to Figure 1 , A data exchange system for multi-channel drug sales provided by the present invention includes a data acquisition module, a routing selection module, a real-time monitoring module, a data audit module, and a data verification module;

[0038] The data acquisition module designs a distributed architecture to connect different sales channels, which include offline pharmacies, e-commerce platforms, and wholesaler systems, and obtains the drug sales data from different sales channels in real time, preprocesses the drug sales data, and sends it to the data audit module;

[0039] The routing selection module optimizes the transmission paths among various sales channels, distributes the data stream to different data links for transmission in combination with the load balancing strategy, and sends the transmission path selection result to the real-time monitoring module;

[0040] The real-time monitoring module monitors the data transmission status, predicts potential transmission failures through time series analysis, and issues early warnings;

[0041] The data audit module stores the drug sales data in the blockchain and uses the hash tree structure to perform hierarchical verification on the drug sales data during transmission;

[0042] The data verification module verifies the consistency and atomicity of the data in the blockchain and continuously monitors the data status.

[0043] Example 2, refer to Figure 1 , based on the above example, the data acquisition module obtains the drug sales data from different sales channels in real time through the API interface and file transfer protocol. The drug sales data includes order information, inventory status, and sales records; preprocesses the drug sales data, including standardization processing, removing redundant information, and filling in missing values, converts all drug sales data into a unified representation form, and sends it to the data audit module.

[0044] Example 3, refer to Figure 1 and Figure 2 , based on the above example, the routing selection module includes a link scanning unit, a path selection unit, a policy adjustment unit, and a load balancing unit;

[0045] The link scanning unit scans all available data links among various sales channels, collects the bandwidth, delay, and packet loss rate of each link, and calculates the link quality index. The formula used is as follows:

[0046]

[0047] In the formula, LQI i represents the link quality index, i represents the link index, P i represents the link packet loss rate, B i represents the current available bandwidth of the link, D i represents the transmission delay of the link, and w1, w2, and w3 are weight coefficients;

[0048] The path selection unit selects the optimal data link as the primary transmission path according to the link quality index, selects the backup transmission path by combining the shortest path algorithm, and automatically switches to the backup transmission path when the primary transmission path is unavailable;

[0049] The policy adjustment unit adopts a dynamic adjustment policy based on quality of service, sets the priority of the transmission task, and dynamically adjusts the path selection policy in combination with the real-time network condition: for high-priority tasks, it uses a low-latency and high-bandwidth path; for medium-priority tasks, it uses a balanced path; for low-priority tasks, it uses the lowest-cost path;

[0050] The load balancing unit distributes the data flow to different data links, and dynamically adjusts the data distribution ratio of each data link through an intelligent scheduling algorithm. The formula used is as follows:

[0051]

[0052] In the formula, F represents the load balancing adjustment amount, k p represents the influence coefficient of the error on the adjustment amount, k i represents the influence coefficient of the error accumulation on the adjustment amount, k d represents the influence coefficient of the error change rate on the adjustment amount, n represents the moment, E n 、E n-1 respectively represent the load errors at the current moment and the previous moment, represents the error accumulation value from the initial moment to the current moment.

[0053] By performing the above operations, for the problem that the traditional data transmission scheme is difficult to adapt to the dynamically changing network environment among various drug sales channels, does not distinguish different types of data flows, resulting in key tasks being delayed due to network congestion and affecting the normal operation of the drug supply chain, this scheme calculates the link quality during the data exchange process, adaptively optimizes the transmission strategy, adjusts the data distribution ratio through error feedback, avoids single-link overload, and improves the overall bandwidth utilization rate.

[0054] Example 4, refer to Figure 1 , based on the above example, the real-time monitoring module monitors the data transmission status, regularly collects various indicators during the data exchange process of each channel, including data transmission time, packet loss rate of the transmission path, transmission progress, and historical transmission records, and sets a log management tool to analyze the historical transmission records based on time series, predict potential data transmission failures and issue early warnings. The formula used is as follows:

[0055] S t =β·X t +(1-β)·S t-1 ;

[0056] Et = |X t - S t |;

[0057] T = μ E + λσ E ;

[0058] Wherein, t represents the moment, S t and S t-1 respectively represent the smoothed transmission rates at the current moment and the previous moment, β represents the smoothing coefficient, X t represents the actual transmission rate at the current moment, E t represents the error value between the current transmission rate and the predicted rate, T represents the error threshold, σ E and σ E respectively represent the mean and standard deviation of the error, λ represents the alarm threshold coefficient;

[0059] When the error value exceeds the error threshold, it indicates that the data transmission rate fluctuates abnormally, and at this time, a warning needs to be issued.

[0060] Example Five, refer to Figure 1 and Figure 3 , this example is based on the above example, and the data audit module includes an encryption unit, a blockchain storage unit, a hierarchical verification unit, and an exception handling unit;

[0061] The encryption unit encrypts the drug sales data and generates a corresponding hash value using the SHA-256 hash function;

[0062] The blockchain storage unit constructs a hash tree to hierarchically store the hash values of the drug sales data, and the formula used is as follows:

[0063] H root = H(H left || H right );

[0064] Wherein, H root represents the hash value of the root node in the hash tree, H(·) represents the hash function, H left and H right respectively represent the hash values of the left and right child nodes;

[0065] The hierarchical verification unit recalculates the hash value according to the content of the received drug sales data, queries the original hash value in the blockchain for comparison. If the recalculated hash value is consistent with the original hash value, it indicates that the data is complete and the data transmission is successful; otherwise, exception handling is performed;

[0066] The abnormal handling unit queries the blockchain, obtains the most recent valid data, resynchronizes the drug sales data with exceptions, and simultaneously triggers an alarm to notify the administrator.

[0067] By performing the above operations, for the traditional transaction data management method, it is unable to effectively guarantee the integrity of data, has a relatively high computational complexity, and is inefficient in processing large-scale transaction data, with the problem of easy data loss. This solution enhances the security, integrity, and traceability of drug sales data through encryption, blockchain storage, hierarchical verification, and abnormal handling, efficiently verifies the legality of transaction data, improves the speed of data query and audit, helps system maintenance, and further ensures data security.

[0068] Example Six, refer to Figure 1 , based on the above example, the data verification module includes a consistency verification unit, a transaction management unit, and a data monitoring unit;

[0069] The consistency verification unit comprehensively verifies the drug sales data before writing to ensure the integrity and consistency of the data: checks whether the order information contains necessary fields, including customer ID, drug code, and quantity. If data anomalies are found, it triggers an alarm mechanism and writes the anomaly records to a log file;

[0070] The transaction management unit manages the atomicity of the data writing process: when a batch of data writing fails, it automatically rolls back the written part of the data to avoid data inconsistency;

[0071] The data monitoring unit records the writing time, data source, and operation result of the data after the data writing is completed, and continuously monitors the data status.

[0072] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article, or device.

[0073] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

[0074] The above describes the present invention and its embodiments. Such description is not restrictive. What is shown in the drawings is only one of the embodiments of the present invention, and the actual structure is not limited thereto. In general, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural modes and embodiments to this technical solution without creative efforts, they shall fall within the protection scope of the present invention.

Claims

1. A data exchange system for multi-channel drug sales, characterized in that: It includes a data acquisition module, a routing selection module, a data audit module, a real-time monitoring module, and a central database module; The data acquisition module obtains the drug sales data from multiple sales channels in real time, preprocesses the drug sales data and sends it to the data audit module; The routing selection module optimizes the transmission paths between each sales channel, distributes the data flow to different data links for transmission in combination with the load balancing strategy, and sends the transmission path selection result to the real-time monitoring module; The real-time monitoring module monitors the data transmission status, predicts potential transmission failures through time series analysis and issues early warnings; The data audit module stores the drug sales data in the blockchain and performs hierarchical verification on the drug sales data during transmission using a hash tree structure; The data verification module verifies the consistency and atomicity of the data in the blockchain and continuously monitors the data status.

2. The data exchange system for multi-channel drug sales according to claim 1, wherein: The routing selection module includes a link scanning unit, a path selection unit, a policy adjustment unit, and a load balancing unit; The link scanning unit scans all available data links between each sales channel, collects the bandwidth, delay, and packet loss rate of each link, and calculates the link quality index; The path selection unit selects the primary transmission path and the backup transmission path according to the link quality index, and automatically switches to the backup transmission path when the primary transmission path is unavailable; The policy adjustment unit adopts a dynamic adjustment policy based on service quality, sets the priority of the transmission task, and dynamically adjusts the path selection policy in combination with the real-time network condition; The load balancing unit distributes the data flow to different data links and dynamically adjusts the data distribution ratio of each data link.

3. The data exchange system for multi-channel drug sales according to claim 1, characterized in that: The data audit module includes an encryption unit, a blockchain storage unit, a hierarchical verification unit, and an exception handling unit; The encryption unit encrypts all the drug sales data and generates a unique hash value; The blockchain storage unit constructs a hash tree to hierarchically store the hash values of the drug sales data; The hierarchical verification unit recalculates the hash value according to the content of the received drug sales data, and queries the original hash value in the blockchain for comparison; The exception handling unit queries the blockchain, obtains the latest valid data, resynchronizes the drug sales data with exceptions, and at the same time triggers an alarm to notify the administrator.

4. A data exchange system for multi-channel drug sales according to claim 1, characterized in that: The real-time monitoring module predicts potential data transmission failures based on time series analysis of historical transmission records, and the formula used is as follows: S t = β·X t + (1 - β)·S t-1 ; E t = |X t - S t |; T = μ E + λσ E ; where t represents the time, S t and S t-1 represent the smoothed transmission rates at the current time and the previous time respectively, β represents the smoothing coefficient, X t represents the actual transmission rate at the current time, E t represents the error value between the current transmission rate and the predicted rate, T represents the error threshold, μ E and σ E represent the mean and standard deviation of the error respectively, and λ represents the alarm threshold coefficient.

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