A medicine data analysis method and system based on traceability identification

Abstract

The present application relates to the technical field of data tracing and processing, and particularly relates to a medicine data analysis method and system based on tracing identification. The method of the present application takes medicine tracing identification as an index, and comprises: collecting and standardizing the flow node data of each tracing identification in a historical period; constructing a preset flow path sequence in chronological order; obtaining and standardizing the flow node data of a target medicine; determining whether there is a new flow node in a preset time window T, so as to store in the original flow node database, or determining whether the flow path terminal is reached; if the terminal is reached, determining that it meets the flow path sequence from the production end to the terminal and checking the flow direction. The present application also provides a corresponding data analysis system, solves the problems of difficult unified construction of flow paths and easy misjudgment of terminal state of multi-source heterogeneous data, and improves the accuracy and reliability of medicine flow analysis.

Description

Technical Field

[0001] This invention relates to the field of data traceability and processing technology, and in particular to a method and system for analyzing drug data based on traceability identifiers. Background Technology

[0002] The pharmaceutical distribution industry has gradually promoted information management. The National Medical Products Administration has successively issued standards such as the "Basic Technical Requirements for Drug Traceability Systems" and the "Specifications for Drug Traceability Code Identification," requiring drug marketing authorization holders, manufacturers, distributors, and users to achieve full-chain traceability of drugs through drug traceability codes. However, in actual implementation, the distribution process is complex and data sources are diverse, and existing solutions still have the following problems.

[0003] Data lag and supply chain disruptions: Drugs typically undergo a distribution cycle of several months from manufacturer to end consumer. Existing traceability data collection methods rely primarily on manual entry or batch uploads, lacking a real-time update mechanism. Regarding flow analysis, the granularity of existing traceability tags usually stops at the warehousing stage of chain pharmacies or hospitals; this results in low accuracy of drug traceability when pharmaceutical companies conduct flow analysis.

[0004] Chinese patent publication CN121810312A discloses a drug data management method based on drug traceability codes. This method uses traceability codes as indexes to link drug attribute data and transaction scenario data, and determines whether transaction behavior is abnormal through anomaly detection analysis. The solution focuses on anomaly detection in medical insurance risk control scenarios and does not address the construction of links with diverse data sources, final state determination, or channel flow analysis. Patent document CN114202348A discloses a medical insurance drug traceability supervision method based on identifier resolution and blockchain. It utilizes the industrial internet identifier resolution system to assign unique identifiers to drugs and encrypts and uploads data via blockchain. This solution emphasizes using blockchain technology to ensure the credibility of traceability data but does not address multi-channel flow analysis. Patent document CN117668118A discloses a drug data traceability method that receives drug data sent from multiple clients, verifies its validity, and then encrypts and stores it via blockchain. This solution solves the data reliability problem but also does not address the determination of link final states or channel flow analysis.

[0005] Existing technologies still have the following problems: how to construct a complete drug circulation path in an environment with complex circulation links and diverse data sources; how to determine that the circulation link has reached the final state when there is a time delay in circulation; and how to compare the actual generated circulation node data with the preset path sequence to verify the integrity and reliability of the link flow. Summary of the Invention

[0006] To address this, the present invention provides a drug data analysis method and system based on traceability identifiers, which overcomes the problems in the prior art where the data links of different drug traceability identifiers are incomplete and the flow of different links varies greatly, resulting in low accuracy of drug traceability identifier flow direction identification.

[0007] To achieve the above objectives, this invention provides a drug data analysis method based on traceability identification, comprising: Collect data on the flow nodes of different drug traceability identifiers within a historical period, and perform data cleaning and standardization processing; The data of each drug traceability identifier corresponding to the circulation node are sorted in chronological order to construct the corresponding preset circulation path sequence of the drug; Acquire the flow node data of the target drug within the predetermined time period for actual verification, and perform data cleaning and standardization processing; Based on the data cleaning and standardization of the transfer node data, it is determined whether a new transfer node has been added within a preset time window T. If a new transfer node is added within the preset time window T, then the data of the new transfer node will be added to the original transfer node database. If no new transfer node is added within the preset time window T, it is determined whether the transfer path sequence terminal has been reached. If it is determined that the terminal of the circulation path sequence has been reached, then it is confirmed that the circulation node data of the target drug conforms to the circulation path sequence of the target drug from the production end to the terminal. Obtain the link data of the flow path sequence of the terminal and verify the corresponding link flow direction of the target drug.

[0008] Furthermore, the process of acquiring the circulation node data of the drug traceability mark includes: acquiring the circulation node data generated by the traceability mark in the circulation process through an interface collector, an automated collector, a page parsing unit, and a file import unit; the circulation node data includes circulation node identifier, circulation timestamp, and node type field.

[0009] Furthermore, the process of constructing the corresponding preset circulation path sequence of drugs includes: arranging all circulation nodes corresponding to the same traceability identifier in ascending order of circulation time, and constructing the preset circulation path sequence from the initial production node to the final node for the corresponding traceability identifier.

[0010] Furthermore, the process of data cleaning and standardization includes converting data from different sources into unified structured data containing traceability identifiers, transfer nodes, transfer times, node types, and data sources according to preset field mapping rules.

[0011] Furthermore, the process of determining whether a new transfer node has been added within the preset time window T includes: continuously acquiring the transfer node data of the traceability identifier in a dynamic polling manner within the preset time window T, and comparing it with the original transfer node data to verify whether they are the same.

[0012] Furthermore, the process of adding new circulation node data to the original circulation node database includes: after confirming that the newly obtained circulation node data is different from the original circulation node data, all circulation nodes corresponding to the same traceability identifier are arranged in ascending order of circulation time, and a circulation path sequence from the initial production node to the final node corresponding to the traceability identifier is constructed.

[0013] Further, the process of determining whether the terminal of the circulation path sequence has been reached includes: continuously acquiring incremental circulation node data of the traceability identifier in a dynamic polling manner, and marking the corresponding traceability identifier as the final state when any one of the first, second, and third conditions is met: the first condition is that no new circulation node has been added in the circulation path sequence within a preset time window; the second condition is that the current terminal node of the circulation path sequence belongs to a preset set of terminal node types; and the third condition is that the sales behavior record corresponding to the traceability identifier has been found.

[0014] Furthermore, the process of verifying the flow of the target drug includes comparing the preset flow path sequence with the newly generated flow node data to verify the integrity and reliability of the path.

[0015] This invention also provides a drug data analysis system based on traceability identification, comprising: The multi-source data acquisition module is used to acquire data from the flow nodes through the acquisition channel; The data cleaning and standardization module is used to receive the collected data and perform data cleaning and standardization processing. The data storage and modeling module is used to store data after data cleaning and standardization, and to perform modeling on the data. The link construction module is used to read standardized flow node data with a specified traceability identifier and sort it in ascending order by flow time; The drug circulation path pre-construction module is used to read historical circulation node data, sort it in ascending order by timestamp, and construct a circulation path sequence from the initial production node to the final node; The final state determination module is used to obtain incremental flow node data through a configurable polling interval and determine whether the incremental flow node data is final state node data. The data verification module is used to compare and verify the flow node data of the preset flow path sequence and the corresponding target drug's flow path sequence from the production end to the terminal. The analysis and display module is used to verify the corresponding link flow direction of the target drug based on the link data of the terminal's flow path sequence.

[0016] Compared with existing technologies, this invention uses drug traceability identifiers as indexes and employs multi-source data collection, data cleaning and standardization, pre-defined path construction, dynamic determination of newly added nodes, final state determination, and link verification to model and analyze the data of all nodes in the entire drug flow chain from production to the end consumer. This solves the problems of inaccurate flow path identification and unreliable drug flow traceability caused by the difficulty in unifying heterogeneous multi-source data, the susceptibility to misjudgment of final state under circulation delays, and the lack of link flow direction verification in existing technologies. This improves the completeness and reliability of drug flow analysis. This invention also provides a corresponding drug data analysis system based on traceability identifiers. Through the coordinated operation of modules such as multi-source data collection, data cleaning and standardization, data storage and modeling, link construction, pre-construction of flow paths, final state determination, data verification, and analysis and display, it achieves automated and systematic monitoring and analysis of the entire drug flow chain.

[0017] In particular, this invention addresses the challenge of constructing a unified and complete drug distribution path from diverse, heterogeneous data sources. In existing technologies, drug traceability data is often scattered across various sources such as interfaces, automated systems, pages, and files. These sources exhibit significant differences in data format, field definitions, and reliability. Using a single acquisition method or a uniform retention strategy can easily lead to broken links or field conflicts, making it impossible to accurately reconstruct the true distribution path of drugs from production to the end consumer. This invention acquires distribution node data from multiple sources through an interface collector, an automated collector, a page parsing unit, and a file import unit. It then performs data cleaning and standardization based on preset field mapping rules, converting data from different sources into a unified, structured data format. This overcomes the link gaps caused by a single data source and achieves efficient integration and complete construction of drug distribution node data across the entire supply chain.

[0018] In particular, this invention addresses the problem of misjudgment in final state determination under drug distribution delays. In actual drug distribution, factors such as data transmission delays, early closure of nodes, or delayed terminal sales behavior make it difficult to accurately distinguish between a naturally terminated distribution path and a state caused by early closure, easily leading to biased final state determination. This invention continuously acquires distribution node data of the target drug through dynamic polling within a preset time window T. It determines whether to add a new node by comparing whether the data of the new node is the same as that of the original distribution nodes. Simultaneously, it employs a combined determination mechanism that marks the final state by satisfying any one of the three conditions: time window silence, terminal node matching, and sales behavior matching. This effectively covers two typical misjudgment scenarios: data delays and early closure of nodes, significantly improving the accuracy of final state determination.

[0019] In particular, this invention addresses the problem of uncontrolled cross-selling and supply chain disruption risks caused by missing link flow verification. Existing technologies, after constructing the distribution path, often lack a systematic verification of the consistency between the actual generated link data and the preset path sequence, leading to the inability to promptly detect issues such as abnormal flow, missing nodes, or broken sequences. This invention verifies the completeness and reliability of the path by comparing the actual generated distribution node data with the corresponding preset distribution path sequence of the target drug from the production end to the terminal item by item. This enables the identification of risks such as missing nodes, disordered sequences, and abnormal flow, ensuring the traceability and verifiability of drug flow between various distribution nodes and improving the credibility of drug flow analysis. Attached Figure Description

[0020] Figure 1 This is a structural block diagram of a drug data analysis system based on traceability identification, as described in an embodiment of the present invention. Figure 2 This is a flowchart illustrating the steps of the drug data analysis method based on traceability identification in an embodiment of the present invention. Figure 3 This is a flowchart of the data acquisition process for the data transfer nodes in an embodiment of the present invention; Figure 4 The flowchart shows the steps of constructing the preset flow path sequence in an embodiment of the present invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] This embodiment describes the system's modular architecture and the data interaction relationships between the modules.

[0024] Please see Figure 1 The diagram shown is a structural schematic of a drug data analysis system based on traceability identification according to an embodiment of the present invention; the system includes: Multi-source data acquisition module: It uses drug traceability identifiers as indexes to acquire data from circulation nodes through the acquisition channel; The acquisition channels include: one acquisition channel corresponding to each acquisition adapter: The interface collector is responsible for calling the drug traceability platform interface through the API collection adapter channel. The automated collector is responsible for simulating manual operation to extract data from the ERP system through the RPA collection adapter channel. The page parsing unit is responsible for parsing data from web pages through the crawler collection adapter channel. The file import unit is responsible for parsing offline data files in formats such as Excel / CSV through the file import adapter channel.

[0025] Data cleaning and standardization module: It is connected to the multi-source data acquisition module and is used to receive the raw flow node data output by the multi-source data acquisition module and perform data cleaning and standardization processing; wherein, data cleaning and standardization processing includes performing field mapping and format unification. The data cleaning and standardization module has a built-in source system field mapping table. For each record from each source system, it looks up the corresponding unified field name in the mapping table and converts the original field value into a unified format.

[0026] The data storage and modeling module is connected to the data cleaning and standardization module and stores the data output by the data cleaning and standardization module in JSON format. It adopts a four-layer architecture: ODS operational data storage layer, DW data warehouse layer, DWN detailed data layer, and ADS application data service layer. The ODS layer stores the cleaned raw data at the same granularity as the source system. The DW layer stores standardized data after unified modeling, organized using a star schema. The DWN layer stores fine-grained data for detailed queries. The ADS layer stores summary results for analytical applications and BI visualization. Data storage simultaneously uses a relational database (MySQL) to store structured data and a non-relational database (MongoDB) to store semi-structured raw data collection logs.

[0027] Link construction module: It is connected to the data storage and modeling module and is used to read standardized flow node data of specified traceability identifiers through the DW layer of the data storage and modeling module and arrange them in ascending order of flow time. When multiple conflicting records from the same time interval are detected, the order of priority of credibility is selected as follows: API acquisition adapter acquisition channel, RPA acquisition adapter acquisition channel, crawler acquisition adapter acquisition channel, and file import adapter acquisition channel. After the arrangement is completed, the flow path sequence of the corresponding traceability identifier is output and written to the ADS layer.

[0028] Drug circulation path pre-construction module: It is connected to the link construction module and is used to read the corresponding historical circulation node data, arrange them in ascending order according to the circulation timestamp, construct the circulation path sequence from the initial production node to the last node, and write the circulation path sequence into the ADS layer.

[0029] Final state determination module: It is connected to the data storage and modeling module and the link construction module, and is used to obtain incremental flow node data from the data storage and modeling module through a configurable polling interval.

[0030] In this embodiment, for each traceability identifier not marked as the final state, a three-condition judgment is performed: First condition detection: The number of circulation nodes of the traceability identifier in the most recent T days has not increased; the T value is automatically set according to the historical average circulation cycle of the corresponding drug category of the traceability identifier; Second condition detection: Determine whether the node type field of the end node of the circulation path sequence belongs to the preset terminal node type set; Third condition detection: The identifier record corresponding to the traceability identifier is found to be a preset user terminal node.

[0031] Data verification module: It is connected to the drug circulation path pre-construction module and the link construction module, and is used to compare and verify the preset circulation path sequence and the newly generated circulation node data to verify the integrity and reliability of the path; Analysis and Display Module: It is used to verify the corresponding link flow direction of the target drug based on the link data of the terminal flow path sequence. Based on the verified data, it generates a multi-channel flow analysis report of the drug. In the example, the corresponding link flow channels include: offline store channels, B2C e-commerce channels, and O2O instant delivery channels.

[0032] Please see Figure 2 The diagram shows a flowchart of the steps in a drug data analysis method based on traceability identification according to an embodiment of the present invention; the method described in the embodiment includes: Step S1: Collect data on the flow nodes of different drug traceability identifiers within the historical period; Step S2: Sort the data of each drug traceability identifier and its corresponding circulation node in chronological order to construct the corresponding drug's preset circulation path sequence; Step S3: Obtain the flow node data of the actual target drug within the predetermined time period, and perform data cleaning and standardization processing; Step S4: Based on the data of the flow nodes after data cleaning and standardization, determine whether a new flow node has been added within the preset time window T; Step S5: If a new transfer node is added within the preset time window T, then confirm that the data of the new transfer node is added to the original transfer node database; Step S6: If no new transfer node is added within the preset time window T, determine whether the transfer path sequence terminal has been reached; Step S7: If it is determined that the terminal of the circulation path sequence has been reached, then it is confirmed that the circulation node data of the target drug conforms to the circulation path sequence of the target drug from the production end to the terminal. Step S8: If it is determined that the terminal of the circulation path sequence has not been reached, continue monitoring until the corresponding circulation path sequence of the target drug from the production end to the terminal is reached; Step S9: Obtain the link data of the circulation path sequence that has reached the terminal, and compare it with the preset circulation path to verify the corresponding link flow direction of the target drug.

[0033] Please see Figure 3 The diagram shown is a flowchart of the data acquisition process for the circulation nodes in an embodiment of the present invention. The process for acquiring the circulation node data of the drug traceability identifier in this embodiment includes: The traceability identifier generates circulation node data in the circulation process through an interface collector, an automated collector, a page parsing unit, and a file import unit; the circulation node data includes circulation node identifier, circulation timestamp, and node type field.

[0034] Specifically, the interface collector is responsible for calling the drug traceability platform interface through the API collection adapter channel, the automated collector is responsible for simulating manual operation to extract data from the ERP system through the RPA collection adapter channel, the page parsing unit is responsible for parsing data from web pages through the crawler collection adapter channel, and the file import unit is responsible for parsing offline data files in formats such as Excel / CSV through the file import adapter channel.

[0035] Please see Figure 4 The diagram shown is a flowchart illustrating the process of constructing a preset circulation path sequence according to an embodiment of the present invention. The process of constructing the preset circulation path sequence for the corresponding drug in the embodiment includes: For all circulation nodes corresponding to the same traceability identifier, arrange them in ascending order of circulation time to construct a preset circulation path sequence from the initial production node to the final node for the corresponding traceability identifier.

[0036] Specifically, the drug data analysis system based on traceability identifiers uses drug traceability identifiers as indexes to read historical circulation node data corresponding to the traceability identifiers from the data storage module; it sorts each circulation node in ascending order according to its circulation timestamp to form a complete circulation path sequence from the manufacturer's outbound circulation node, to the wholesaler's inbound circulation node, to the wholesaler's outbound circulation node, to the retail institution's inbound circulation node, to the terminal sales; after sorting, the constructed circulation path sequence is written into the Application Data Service (ADS) layer for subsequent link construction modules to call.

[0037] Specifically, the data cleaning and standardization process in this embodiment of the invention includes: Data from different sources of circulation nodes is converted into unified structured data containing traceability identifiers, circulation nodes, circulation time, node types, and data sources according to preset field mapping rules.

[0038] Specifically, the data cleaning and standardization module has a built-in source system field mapping table. For each record from each source system, it looks up the corresponding unified field name in the mapping table and converts the original field value into a unified format. The preset field mapping rules establish a mapping table from source system field names to unified field names to address the differences in field naming for the same flow node in different source systems. The converted unified structured data includes traceability identifier, flow node name, flow time, node type, and data source identifier. The node type values ​​include: production node, wholesale node, retail node, or terminal node. The converted data is output to the data storage and modeling module in JSON format.

[0039] Specifically, the new process for determining the flow node in this embodiment of the invention includes determining whether a new flow node has been added within a preset time window T. This process involves continuously acquiring the flow node data of the traceability identifier in a dynamic polling manner within the preset time window T, and comparing it with the original flow node data to verify whether they are the same.

[0040] Specifically, incremental transfer node data of the traceability identifier within a preset time window T is continuously acquired using a dynamic polling method. For each incremental data record, its transfer node identifier is extracted and compared with the existing set of original transfer node identifiers. If the transfer node identifier does not exist in the original set, it is determined that a new transfer node has been added to the traceability identifier. If the transfer node identifier exists in the original set, it is determined that no new node has been added. The dynamic polling process can be executed at a configurable polling interval to continuously monitor the changes in the transfer status of the target traceability identifier.

[0041] Specifically, the process of adding data storage for transfer nodes and constructing transfer path sequences in this embodiment of the invention includes: The process of adding new transfer node data to the original transfer node database includes: after confirming that the newly obtained transfer node data is different from the original transfer node data, all transfer nodes corresponding to the same traceability identifier are arranged in ascending order of transfer time, and a transfer path sequence from the initial production node to the final node for the corresponding traceability identifier is constructed.

[0042] Specifically, when multiple conflicting records from the same time interval are detected, the implementation selects data according to credibility priority, with the credibility priority order being API acquisition adapter acquisition channel, RPA acquisition adapter acquisition channel, crawler acquisition adapter acquisition channel, and file import adapter acquisition channel; after confirming the addition of new flow node data, the corresponding data is merged into the original flow node database; for all flow nodes corresponding to the same traceability identifier, they are re-sorted in ascending order of flow time, the flow path sequence of the corresponding traceability identifier is updated, and the updated sequence is written to the ADS layer.

[0043] Specifically, this embodiment describes the process of determining whether the flow path sequence terminal has been reached, including: The incremental flow node data of the traceability identifier is continuously obtained in a dynamic polling manner. When any one of the first, second and third conditions is met, the corresponding traceability identifier is marked as the final state: the first condition is that no new flow node is added in the flow path sequence within a preset time window; the second condition is that the current end node of the flow path sequence belongs to a preset set of terminal node types; and the third condition is that the sales behavior record corresponding to the traceability identifier is found.

[0044] Specifically, the first condition check in the embodiment is to query the number of circulation nodes of the corresponding traceability identifier in the most recent T days. If the number of circulation nodes has not increased, the condition is met. The T value is automatically set according to the historical average circulation cycle of the corresponding drug category of the traceability identifier. The second condition check is to determine whether the node type field of the end node of the circulation path sequence belongs to the preset terminal node type set. The terminal node type set includes retail pharmacy nodes and medical institution nodes. If it belongs to the set, the condition is met. The third condition check is to query whether the identifier record corresponding to the traceability identifier is a preset user terminal node. If it is, the condition is met. The three conditions are operated with OR logic. When any one of the conditions is met, the status field of the corresponding traceability identifier is updated from circulation to final state.

[0045] The embodiments described above are for illustrative purposes only and are not intended to limit the scope of this application. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application, and all such modifications or substitutions should be covered within the scope of the claims of this application.

Claims

1. A drug data analysis method based on traceability identification, characterized in that, include: Collect data on the flow nodes of different drug traceability identifiers within a historical period, and perform data cleaning and standardization processing; The data of each drug traceability identifier corresponding to the circulation node are sorted in chronological order to construct the corresponding preset circulation path sequence of the drug; Acquire the flow node data of the target drug within the predetermined time period for actual verification, and perform data cleaning and standardization processing; Based on the data cleaning and standardization of the transfer node data, it is determined whether a new transfer node has been added within a preset time window T. If a new transfer node is added within the preset time window T, then the data of the new transfer node will be added to the original transfer node database to determine whether the transfer path sequence terminal has been reached. If it is determined that the terminal of the circulation path sequence has been reached, then it is confirmed that the circulation node data of the target drug conforms to the circulation path sequence of the target drug from the production end to the terminal. Obtain the link data of the flow path sequence of the terminal and verify the corresponding link flow direction of the target drug.

2. The drug data analysis method based on traceability identification according to claim 1, characterized in that, The process of acquiring data on the circulation nodes of drug traceability labels includes: The traceability identifier generates circulation node data in the circulation process through an interface collector, an automated collector, a page parsing unit, and a file import unit. The data of the transfer nodes includes a transfer node identifier, a transfer timestamp, and a node type field.

3. The drug data analysis method based on traceability identification according to claim 2, characterized in that, The process of data cleaning and standardization includes: Data from different sources of circulation nodes is converted into unified structured data containing traceability identifiers, circulation nodes, circulation time, node types, and data sources according to preset field mapping rules.

4. The drug data analysis method based on traceability identification according to claim 3, characterized in that, The process of constructing the corresponding pre-defined drug circulation path sequence includes: All circulation node identifiers corresponding to the same traceability identifier; All circulation node identifiers are sorted in ascending order according to circulation time, and a preset circulation path sequence from the initial production node to the final node is constructed with corresponding traceability identifiers.

5. The drug data analysis method based on traceability identification according to claim 4, characterized in that, The process of determining whether a new flow node has been added within the preset time window T includes: Within a preset time window T, continuously acquire the flow node data of the traceability identifier in a dynamic polling manner; The traceability identifier is compared with the original traceability node identifier to verify whether they are the same.

6. The drug data analysis method based on traceability identification according to claim 5, characterized in that, The process of adding storage for newly added transfer node data to the original transfer node database includes: Extract the newly acquired flow node data and the original flow node data; If the newly obtained circulation node data is different from the original circulation node data, then for all circulation nodes corresponding to the same traceability identifier, they are arranged in ascending order of circulation time, and a circulation path sequence from the initial production node to the final node for the corresponding traceability identifier is constructed.

7. The drug data analysis method based on traceability identification according to claim 6, characterized in that, The process of determining whether the flow path sequence terminal has been reached includes: The incremental flow node data of the traceability identifier is continuously acquired through dynamic polling. When the predetermined conditions are met, it is determined that the flow path sequence terminal has been reached.

8. The drug data analysis method based on traceability identification according to claim 7, characterized in that, The process for determining whether the predetermined conditions are met includes: Obtain the first, second, and third conditions within the predetermined time; When any one of the first, second, and third conditions is met, the corresponding traceability identifier is marked as the final state: The first condition is that no new flow node is added in the flow path sequence within the preset time window; the second condition is that the current end node of the flow path sequence belongs to a preset set of terminal node types; and the third condition is that the identifier record corresponding to the traceability identifier is a preset user terminal node.

9. The drug data analysis method based on traceability identification according to claim 8, characterized in that, The process of verifying the flow of the target drug through the supply chain includes: Obtain the preset flow path sequence and newly generated flow node data; The integrity and reliability of the path are verified by comparing the results of the preset flow path sequence and the newly generated flow node data.

10. A drug data analysis system based on traceability identification, applied to the drug data analysis method based on traceability identification as described in any one of claims 1 to 9, characterized in that, include: The multi-source data acquisition module is used to acquire data from the flow nodes through the acquisition channel; The data cleaning and standardization module is used to receive the collected data and perform data cleaning and standardization processing. The data storage and modeling module is used to store data after data cleaning and standardization, and to perform modeling on the data. The link construction module is used to read standardized flow node data with a specified traceability identifier and sort it in ascending order by flow time; The drug circulation path pre-construction module is used to read historical circulation node data, sort it in ascending order by timestamp, and construct a circulation path sequence from the initial production node to the final node; The final state determination module is used to obtain incremental flow node data through a configurable polling interval and determine whether the incremental flow node data is final state node data. The data verification module is used to compare and verify the flow node data of the preset flow path sequence and the corresponding target drug's flow path sequence from the production end to the terminal. The analysis and display module is used to verify the corresponding link flow direction of the target drug based on the link data of the terminal's flow path sequence.

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