Tracing method and system based on drug consumable traceability code

Abstract

The invention relates to the technical field of medicine information management, in particular to a traceability method and system based on a medicine consumable traceability code. The method comprises the following steps: acquiring medicine purchasing information data; generating a unique code for the number of medicine purchasing information so as to generate a medicine warehousing traceability code; performing drug information association on the drug purchase information data according to the drug warehousing traceability code to generate a drug warehousing associated traceability code; storing the medicine warehousing associated traceability code in an information base to generate a medicine warehousing traceability code information base; performing scanning access mode division on the medicine warehousing traceability code, and generating a medicine dispensing scanning request mode and a medicine returning scanning request mode; and performing drug splitting tracing on the drug warehousing tracing code in a drug dispensing scanning request mode to generate drug splitting tracing data. According to the invention, through full-process closed-loop management, data association enhancement, intelligent scheduling optimization and drug return tracing improvement, the efficiency and precision of the drug consumable traceability code tracing method are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drug information management, and in particular to a tracing method and system based on drug consumables traceability codes. Background Art

[0002] The earliest drug traceability mainly relied on paper records and manual management, which was difficult to cope with the traceability needs in large-scale circulation, and had problems of low efficiency and high error rate. With the development of information technology, barcode and two-dimensional code technology have been gradually introduced to identify the production, circulation and use information of drugs and consumables. The basic traceability function is realized through scanning equipment, which greatly improves the accuracy and efficiency of data collection. After entering the 21st century, the rise of the Internet of Things (IoT) and big data technology has brought revolutionary changes to the traceability of drugs and consumables. The traceability system based on RFID (radio frequency identification) technology has been applied, which records the full life cycle information of drugs and consumables through wireless signals, and realizes real-time and contactless traceability. At the same time, the introduction of blockchain technology further improves the transparency and immutability of data, and solves the problem of data tampering in traditional traceability systems. The combination of blockchain and cloud computing enables the traceability system of drugs and consumables to have stronger scalability and coordination capabilities. However, current existing technologies usually focus on traceability in a certain link, such as warehousing or drug distribution, and it is difficult to achieve closed-loop management from procurement to drug return. At the same time, there is a lack of intelligent support for drug splitting and dispensing scheduling, resulting in low efficiency, which in turn leads to low efficiency and accuracy of the drug consumables traceability code traceability method. Summary of the invention

[0003] Based on this, it is necessary to provide a traceability method and system based on the traceability code of pharmaceutical consumables to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a tracing method based on a drug consumables traceability code is provided, the method comprising the following steps:

[0005] Step S1: Obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database;

[0006] Step S2: divide the drug entry traceability code into scanning access modes, generate a drug dispensing scanning request mode and a drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data;

[0007] Step S3: Acquire the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data;

[0008] Step S4: restore the drug refund information traceability code according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information library for closed-loop traceability control to generate returned drug restoration traceability data; perform drug traceability feedback on the returned drug restoration traceability data through the drug dispensing intelligent scheduling data, thereby generating drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

[0009] The present invention constructs a closed-loop control system for drug consumables traceability through the traceability code management of the entire process of drug storage, splitting, drug delivery, and drug return. Each link from procurement to use can be traced, ensuring the transparency and standardization of drug circulation. The drug storage traceability code is generated by using a unique code, and more detailed traceability data is generated by association and splitting, thereby ensuring the accuracy of the traceability information, reducing manual intervention, and improving the traceability efficiency. Through the drug delivery scanning request mode and the drug dispensing intelligent scheduling function, the drug inventory is monitored and dynamically adjusted in real time, the inventory management is optimized, the risk of drug expiration and waste is reduced, and the response speed of drug delivery and return is improved. Through the drug return scanning request mode, the drug refund information traceability code is dynamically compared with the sales record in combination with the HIS system, ensuring the authenticity and accuracy of the refund process, and effectively reducing the possibility of erroneous refunds or false refunds. Through the traceability code restoration function, the drug return data is fed back to the drug storage traceability code information library, forming a closed-loop traceability control process. This closed-loop management method improves the traceability and consistency of data, and facilitates subsequent tracking and auditing. Through the traceability feedback function, the use of medicines and consumables can be accurately grasped, providing data support for drug demand forecasting, replenishment decisions and scheduling optimization, avoiding the waste of medical resources due to insufficient inventory or unreasonable allocation. By using the information database and traceability code association technology, the data of multiple links such as procurement, inventory, drug distribution, and drug return are integrated, and data sharing and collaborative operation between different systems are realized, which improves the level of hospital management informationization. Therefore, the present invention improves the efficiency and accuracy of the drug consumables traceability code tracing method through full-process closed-loop management, data association enhancement, intelligent scheduling optimization, and drug return traceability improvement.

[0010] Preferably, step S1 comprises the following steps:

[0011] Step S11: Obtaining drug purchasing information data;

[0012] Step S12: preprocessing the drug procurement information data to generate standard drug procurement information data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0013] Step S13: defining a traceability code generation rule for the standard drug purchase information data, and generating a dynamic traceability code generation rule; generating a unique code for the standard drug purchase information data based on the dynamic traceability code generation rule, thereby generating a drug storage traceability code;

[0014] Step S14: associate the drug purchase information data with the drug information according to the drug entry traceability code to generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database to generate a drug entry traceability code information database.

[0015] The present invention ensures the accuracy and consistency of drug procurement information through data preprocessing steps (data cleaning, denoising, missing value filling and standardization), and provides reliable basic data support for subsequent tracing and management. By defining dynamic traceability code generation rules and generating a unique warehousing traceability code for each drug, the whole process of drug procurement to warehousing can be accurately traced, which enhances the transparency and safety of drug circulation. The generation and information database storage of drug warehousing-associated traceability codes enable drug information to be systematically stored and retrieved, optimizes inventory management and supply chain monitoring, and reduces management costs and operational complexity. This systematic traceability code management system helps to meet regulatory requirements and can provide reliable information basis in drug safety inspections, quality traceability and other links. Based on standardized data processing and traceability rules, it is possible to quickly respond to drug quality problems or supply chain interruptions and enhance the emergency response capabilities of the overall supply chain. Through the establishment of a drug warehousing traceability code information database, the procurement, warehousing, storage and circulation of each batch of drugs can be effectively recorded, thereby enhancing the transparency and safety management of drug circulation links.

[0016] Preferably, step S13 includes the following steps:

[0017] Step S131: parsing the core feature fields of the standard drug procurement information data to generate drug procurement core field data; performing feature field decomposition on the drug procurement core field data to generate drug procurement core feature vectors; performing standardized structural space mapping on the drug procurement core feature vectors to generate drug feature mapping data sets;

[0018] Step S132: Adaptively generate traceability rules for the drug feature mapping data set to obtain dynamic traceability code generation rules, where the formula of the traceability code rule is as follows:

[0019] Y = f(T, B, D, R);

[0020] In the formula, Y represents the traceability code rule, T represents the drug type code, B represents the batch feature code, D represents the production date weight, and R represents the random identification generation algorithm;

[0021] Step S133: generating a non-repetitive multimodal identification for the drug feature mapping data set according to the dynamic traceability code generation rule to generate drug unique identification data; performing bidirectional verification on the drug unique identification data to generate drug identification verification data;

[0022] Step S134: hierarchically encode and integrate the standard drug procurement information data through dynamic traceability code generation rules and drug unique identification data to generate a drug inventory traceability code.

[0023] The present invention can accurately extract the key features of drugs by parsing the core feature fields and decomposing the feature fields of drug procurement information data, and further convert these features into structured core feature vectors. This method effectively improves the accuracy of data processing, making the traceability of drug information clearer and more controllable. The dynamic traceability code generation rule (formula Y=f(T, B, D, R)) is based on the drug type code, batch feature code, production date weight and random identification generation algorithm to assign a unique and dynamic traceability code to each batch of drugs. This traceability code not only supports the traceability function, but also can cope with the changing requirements for traceability code rules in different environments, ensuring that the traceability code can still be accurately generated under various conditions. Through non-repetitive multimodal identification generation, it is ensured that each drug has a unique and non-repeatable identification, avoiding data duplication and conflict. At the same time, the accuracy and consistency of drug identification data are ensured through two-way verification, which increases the reliability of data and improves the security of drug information management. The hierarchical coding integration method is adopted to disassemble and recode the traceability information of drugs layer by layer, which not only enhances the hierarchy of information storage, but also ensures the flexibility and extensibility of data in the traceability system, which helps to improve management efficiency and reduce the occurrence of errors. The adaptive generation of dynamic traceability code generation rules supports the system to adjust the traceability rules according to actual conditions, further enhancing the flexibility of the traceability code. Whether it is the type of drug, production batch or other related factors, it can be generated according to the rules, which improves the system's adaptability in a changing environment. Through this dynamic and accurate traceability code generation process, the entire process of drug procurement to warehousing can be clearly recorded and quickly retrieved, meeting the regulatory authorities' requirements for transparency in the drug circulation process. At the same time, the systematic traceability mechanism helps to meet national and industry drug regulatory requirements and ensure drug quality and safety.

[0024] Preferably, step S2 comprises the following steps:

[0025] Step S21: using a personal digital assistant device to scan and request the drug entry traceability code to obtain drug entry traceability code scanning request data; dividing the drug entry traceability code scanning request data into access modes to generate a drug dispensing scanning request mode and a drug return scanning request mode;

[0026] Step S22: performing drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode to generate drug splitting and tracing data; performing drug inventory monitoring on the drug entry traceability code information database based on the drug splitting and tracing data to generate drug inventory status data;

[0027] Step S23: Based on the drug inventory status data, a real-time warning of inventory shortage is issued for the drug corresponding to the drug entry traceability code, thereby generating drug traceability warning data;

[0028] Step S24: Based on the drug traceability warning data, the drug inventory status data is intelligently scheduled for drug dispensing to generate drug dispensing intelligent scheduling data.

[0029] The present invention can realize accurate splitting, tracing and inventory management of drugs by using a personal digital assistant device to scan the drug entry traceability code. By dividing the access mode of the scan request data, it is possible to clearly distinguish the drug dispensing and return scenarios, ensure that drug information can be correctly processed in different operation modes, and thus improve the accuracy and transparency of drug management. The generation of drug splitting and tracing data and drug inventory status data enables real-time monitoring of drug inventory status and supports dynamic query and update. This real-time feedback of information can ensure that managers can grasp inventory dynamics in a timely manner, optimize inventory utilization efficiency, and reduce the occurrence of drug expiration or out-of-stock phenomena. The real-time warning of inventory shortage generated according to the drug inventory status data effectively avoids the problem of drug shortage. The drug traceability warning data automatically generated by the system can warn of potential inventory problems in advance, help managers to respond quickly, and take necessary measures to ensure the stability of the drug supply chain. Through the drug traceability warning data for intelligent scheduling of drug dispensing, the system can automatically adjust the dispensing plan based on real-time inventory and demand conditions, reduce manual intervention, and improve the efficiency and accuracy of drug dispensing. This helps to reduce the error rate in the drug dispensing process and improve the efficiency of overall medical services. The fully intelligent traceability and inventory monitoring system reduces manual operation links, reduces the possibility of human error, and improves the efficiency and reliability of drug management. This is crucial for the operation of drug management departments and medical institutions, and can better ensure the smoothness of the drug supply chain and the quality and safety of drugs. The real-time monitoring and early warning mechanism of drug inventory status enhances the transparency of drug circulation links, meets the requirements of drug supervision, and can promptly discover and correct potential inventory problems, which helps to meet the compliance requirements of drug management regulations. Through accurate drug inventory status data and intelligent scheduling mechanisms, the management of the supply chain has been optimized. It can be scheduled in time when inventory is close to shortage, which improves the turnover rate of drugs and the response speed of the supply chain, and ensures the continuity and stability of medical needs.

[0030] Preferably, performing drug splitting and tracing of drug entry traceability codes through the drug dispensing scanning request mode includes:

[0031] Extract multi-dimensional information of the drug entering the warehouse from the drug entry traceability code through the drug dispensing scanning request mode to obtain multi-dimensional information data of the drug entering the warehouse, wherein the multi-dimensional information data of the drug entering the warehouse includes the batch number of the drug, the location of the drug entering the warehouse, and the time of drug entering the warehouse;

[0032] Construct a drug entry traceability chain based on the drug batch number, drug entry warehouse location, and drug entry time to generate drug entry traceability chain data; use blockchain technology to extract entry information traceability nodes from drug entry traceability chain data to generate drug entry traceability nodes;

[0033] The drug entry traceability code is split into small packaging units to obtain drug small packaging unit data, wherein the drug small packaging unit data includes the split batch number and the split time; the drug batch number is associated with the parent batch number according to the split batch number of the drug small packaging unit data to generate batch association data; the drug split traceability chain is constructed based on the batch association data and the split time to generate the drug split traceability chain; the drug split traceability node is extracted from the drug split traceability chain to generate the drug split traceability node;

[0034] Analyze the drug distribution data of the drug storage traceability code to generate drug distribution data, where the drug distribution data includes patient collection information and drug distribution time; construct a drug distribution traceability chain based on the patient collection information and drug distribution time to generate a drug distribution traceability chain; extract drug distribution traceability nodes from the drug distribution traceability chain to obtain drug distribution traceability nodes;

[0035] The drug storage traceability node, drug splitting traceability node and drug dispensing traceability node are integrated into multi-dimensional chain nodes to generate a splitting traceability chain network; the splitting traceability chain network is used to perform drug splitting traceability on the drug storage traceability code to generate drug splitting traceability data.

[0036] The present invention extracts multi-dimensional information from the drug storage traceability code through the drug dispensing scanning request mode, and can fully obtain key information such as the batch number, warehouse location and storage time of the drug. This information provides basic data for the subsequent traceability chain construction, ensuring that the entire process of the drug from storage to splitting and dispensing can be accurately recorded and traced. Blockchain technology is used to extract nodes of the drug storage traceability chain data, ensuring the data is tamper-proof and transparent. The use of blockchain makes the traceability of drug information safer and more reliable, preventing the risk of data loss, tampering or forgery, thereby enhancing the credibility of drug management and supervision. Through the function of associating the splitting of small packaging units with the splitting batch number, the splitting process of each drug can be traced, and an independent traceability chain can be generated for each split unit. This provides strong support for the precise management and precise dispensing of drugs, avoiding mismatching or misuse in the process of drug splitting. By analyzing the drug distribution data, the drug dispensing process can be traced, including patient collection information and drug dispensing time. The drug dispensing traceability chain constructed based on these data can help drug management personnel to grasp the distribution of drugs in real time, thereby ensuring the safety and circulation compliance of drugs. Through the multi-dimensional integration of the warehousing traceability nodes, splitting traceability nodes and drug delivery traceability nodes, a splitting traceability chain network is generated, achieving full coverage of the drug traceability process. Through this integration, every link of the drug can be accurately tracked, which improves the efficiency of drug management and supervision and reduces the risk of manual operation and missed links. Based on the generated splitting traceability chain network, the flow and status of drugs can be traced in real time, making supply chain management more transparent. This process can timely discover potential problems, such as poor inventory management and untimely drug distribution, thereby optimizing the operation of the drug supply chain and improving response efficiency and inventory utilization efficiency. Through the establishment of the traceability chain of each link (warehousing, splitting, drug delivery), the safety and quality of drugs are monitored throughout the process. This not only improves the safety of drug management, but also can quickly respond to drug quality issues and ensure the safety of drug use for end consumers.

[0037] Preferably, step S23 includes the following steps:

[0038] Step S231: performing a drug inventory change trend analysis on the drug inventory status data to generate drug inventory change trend data; performing a drug inventory change warning analysis on the drug inventory change trend data based on a preset inventory warning threshold to generate drug inventory change warning data;

[0039] Step S232: performing drug association analysis on drug entry traceability codes according to drug inventory change warning data to generate drug traceability associated inventory data; performing inventory shortage risk assessment on drug traceability associated inventory data using Bayesian network analysis to generate drug inventory shortage risk data;

[0040] Step S233: Generate a real-time inventory shortage warning for the drug corresponding to the drug entry traceability code based on the drug inventory shortage risk data and the preset inventory warning threshold, thereby obtaining drug traceability warning data.

[0041] The present invention can deeply understand the dynamic changes of inventory by analyzing the change trend of drug inventory status data, and provide data support for predicting future inventory status. Based on the preset inventory warning threshold, the inventory change warning analysis can timely discover potential inventory risks and avoid supply problems caused by insufficient or excessive inventory. Combined with Bayesian network analysis, the risk assessment of drug inventory shortage can make inventory management more intelligent and precise. The use of Bayesian network can take into account the correlation between multiple factors, improve the accuracy of inventory risk assessment, and thus provide a scientific basis for drug management. By performing association analysis on the drug entry traceability code, drug traceability-related inventory data is generated, and the full-process traceability capability of drugs from entry to inventory management is further enhanced. This not only improves the transparency of inventory management, but also ensures that the information of drugs in different links is clear and traceable. Based on drug inventory shortage risk data and inventory warning threshold, real-time warning of drug inventory shortage can be achieved. This real-time warning mechanism helps drug managers to identify drugs with tight inventory in a timely manner, reducing drug supply interruptions or delays caused by inventory shortages. By identifying inventory shortage risks in advance and issuing warnings in time, the management efficiency of the drug supply chain has been significantly improved. The drug management department can take replenishment or adjustment measures more quickly, thereby avoiding supply interruptions caused by inventory shortages, optimizing inventory costs, and ensuring the continuity and stability of drug supply. With data-driven Bayesian network analysis and inventory early warning mechanisms, drug inventory management is more intelligent. Through real-time inventory monitoring and dynamic early warnings, managers can make more accurate replenishment decisions and inventory adjustments based on analysis results, improving the efficiency of overall drug management.

[0042] Preferably, step S24 includes the following steps:

[0043] Step S241: performing drug demand analysis on the drug inventory status data based on the drug traceability warning data to generate drug demand data; screening the drug inventory status data for scarce drug information based on the drug demand data to obtain scarce drug information screening data;

[0044] Step S242: performing drug supply time series analysis on the scarce drug information screening data to generate scarce drug supply time series data; performing multi-location collaborative scheduling on the scarce drug information screening data according to the scarce drug supply time series data to generate scarce drug collaborative scheduling data;

[0045] Step S243: Intelligently dispatch drug dispensing by using the scarce drug collaborative dispatch data to the drug inventory status data, and generate intelligent drug dispensing dispatch data.

[0046] The present invention can grasp the demand trend of drugs in real time by analyzing drug demand based on drug traceability warning data, and provide a scientific basis for drug procurement and supply. Accurate demand forecasting helps drug management departments make timely and effective decisions, thereby reducing drug shortages or waste. By combining drug demand data and inventory status data, scarce drug information can be effectively screened out to ensure that scarce drugs can receive priority attention. This screening mechanism helps drug management departments to timely identify drugs with high demand and tight supply, which helps to optimize inventory allocation and reduce supply and demand imbalances. Based on the timing analysis of scarce drug supply, the supply time nodes of drugs can be accurately grasped to help reasonably arrange drug supply plans. Through timing analysis, drug management departments can predict the supply of drugs and ensure that drugs are replenished at the right time, thereby avoiding supply interruptions or surpluses. By performing multi-location collaborative scheduling of scarce drug supply timing data, resource sharing and optimized scheduling between multiple locations can be achieved, avoiding shortages caused by insufficient drug supply at a certain location. This improves the efficiency of drug supply, ensures that drugs can be quickly circulated to the required locations, and reduces delivery delays. Based on the collaborative scheduling data of scarce drugs, intelligent scheduling of drug dispensing can be performed. Intelligent scheduling can automatically optimize the dispensing plan according to changes in drug inventory and demand, improve the efficiency and accuracy of drug distribution, and ensure that patients can get the drugs they need in a timely manner. The implementation of the entire process can significantly optimize the supply chain and inventory management of drugs. Through accurate demand forecasting, time series analysis, and multi-location scheduling, drug management can respond to changes in market demand in real time, reduce inventory backlogs or drug shortages, and improve overall supply chain efficiency. The combination of intelligent scheduling and demand analysis helps to avoid waste of drug resources. The procurement and distribution of drugs are adjusted according to demand to avoid over-purchasing or excess inventory, thereby reducing costs and improving the economic benefits of overall drug supply.

[0047] Preferably, step S3 comprises the following steps:

[0048] Step S31: Obtain hospital drug sales record data through the HIS system;

[0049] Step S32: tracing the drug refund information of the drug storage traceability code through the drug return scanning request mode to obtain the drug refund information traceability code;

[0050] Step S33: Transmitting the drug refund information traceability code to the HIS system and performing data comparison with the hospital drug sales record data. When the drug refund information traceability code and the hospital drug sales record data are successfully compared, drug refund success result data is generated; when the drug refund information traceability code and the hospital drug sales record data are unsuccessful, drug refund failure result data is generated;

[0051] Step S34: verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data.

[0052] The present invention can realize the full traceability of the entire refund process and ensure the transparency of the refund operation by comparing the drug refund information traceability code with the hospital drug sales record data. This not only improves the efficiency of the refund process, but also enhances the system's ability to automatically process refund requests. By automatically comparing the refund information traceability code with the drug sales record, it can be quickly confirmed whether the refund complies with the specification. When the comparison is successful, the compliance and accuracy of the refund operation are ensured; when the comparison fails, the problem can be discovered in time, non-compliant refund behavior can be avoided, and the risk of refund errors or fraud is reduced. The automatic comparison of refund requests and the verification mechanism of the refund process realize the intelligent management of drug refund operations. Through the automated verification process, not only manual operation errors are reduced, but also the risks in the refund process can be controlled in real time, and abnormal situations can be discovered and handled in time. Traditional refund audits usually require a lot of manual intervention, while the automatic comparison and verification mechanism of steps S33 and S34 can greatly reduce the burden of manual audits and improve work efficiency. Manual audits only need to deal with those situations where the comparison fails or is abnormal, making the entire refund processing process smoother. Integrating and comparing drug refund traceability codes with hospital drug sales record data can provide more accurate refund processing and data support for hospital drug management and financial systems. Through this process, hospitals can better track drug sales, returns and refunds, ensure the accuracy of financial records, and optimize drug inventory and financial management. Through refund tracing and data comparison based on traceability codes, the operational data of refunds can be ensured to be accurate. This not only improves the transparency of refund operations, but also enhances the overall data reliability of the hospital system and reduces data redundancy and errors.

[0053] Preferably, transmitting the drug refund information traceability code to the HIS system and comparing it with the hospital drug sales record data includes:

[0054] The drug refund information traceability code is transmitted to the HIS system for data format verification to generate drug refund information format verification data; based on the drug refund information format verification data, the drug purchase information data is analyzed for drug sales traceability according to the drug refund information traceability code to generate drug sales traceability data;

[0055] The drug sales traceability data and hospital drug sales record data are queried for key sales records to generate sales record query data, where the key sales records include drug sales date, patient information and sales price; sales traceability codes are generated for drug sales date, patient information and sales price to obtain sales traceability codes;

[0056] The drug refund information traceability code and the sales traceability code are dynamically matched through the traceability code comparison rules predefined in the HIS system to generate sales data matching results, where the sales data matching results include drug refund success result data and drug refund failure result data.

[0057] The present invention ensures that the data transmitted to the HIS system conforms to the prescribed format by format verification of the drug refund information traceability code, thereby avoiding data errors or losses caused by format problems. This verification step ensures the standardization of the data and lays a foundation for subsequent data comparison and analysis. By performing drug sales traceability analysis on the drug refund information traceability code, the relevant drug sales records, including the sales date, patient information and sales price, can be accurately traced back. This process can ensure the accurate matching of refund information with sales records, thereby ensuring the authenticity and compliance of the refund operation. Through key sales record query, the drug sales records related to the refund can be quickly and accurately identified, reducing the time and workload of manual search. In particular, the query of drug sales date, patient information and sales price ensures the accuracy of the query results and helps to avoid the occurrence of erroneous refunds. By generating a sales traceability code and performing dynamic matching, it can ensure that the drug refund information is completely consistent with the sales record. The predefined traceability code comparison rule ensures the consistency between the refund information and the sales record, improves the accuracy of the refund operation, and reduces the occurrence of manual intervention and errors. Through the generation of sales data comparison results, it can accurately determine whether the drug refund is successful. When the comparison is successful, the drug refund success result data is generated, otherwise the failure result data is generated, providing a complete refund operation record. This process makes the refund operation more transparent and traceable, which helps to enhance the trust of patients and hospitals in the refund process. By automatically comparing the refund information with the sales records, the system can identify the legitimacy of the refund operation in real time, reduce the workload of manual review, and quickly process the refund request. This automated process helps to improve the efficiency of refund review and strengthen the control of refund risks. The accurate comparison of refund information traceability enhances patients' trust in the hospital's refund process. Patients can understand the entire refund process through the traceability code, improve the fairness and transparency of the refund operation, and further enhance the reputation of the hospital. The comparison of drug refund information with sales records not only helps to ensure the accuracy of the refund, but also provides data support for the hospital's inventory management and financial systems. By verifying the refund sales records, it is possible to optimize drug inventory management, ensure the consistency of drug inbound and outbound data, and improve the accuracy of financial accounting. Through the full process of automated format verification, sales traceability analysis and key record query, the efficiency and accuracy of data processing can be significantly improved, and the time and error rate of manual processing can be reduced. The systematic data comparison process ensures that all refund operations can be processed in the shortest time possible, improving overall work efficiency.

[0058] In this specification, a tracing system based on a drug consumables tracing code is provided, which is used to execute the above-mentioned tracing method based on a drug consumables tracing code. The tracing system based on a drug consumables tracing code includes:

[0059] The drug entry module is used to obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database;

[0060] The drug dispensing traceability module is used to divide the scanning access mode of the drug entry traceability code, generate the drug dispensing scanning request mode and the drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data;

[0061] The drug return traceability module is used to obtain the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code based on the drug refund success result data and drug refund failure result data, thereby generating drug refund process traceability data;

[0062] The closed-loop feedback module is used to restore the traceability code of the drug refund information according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information database for closed-loop traceability control to generate returned drug restoration traceability data; the returned drug restoration traceability data is fed back through the drug dispensing intelligent scheduling data to generate drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

[0063] The beneficial effect of the present invention is that by uniquely encoding the drug procurement information data, it is ensured that the storage information of each batch of drugs corresponds to the traceability code one by one to avoid confusion or duplication. The drug procurement data is associated with the storage traceability code to simplify data management, and relevant data can be efficiently stored and extracted in the information library, thereby improving the automation and digitization level of warehouse management. The generation of the drug storage traceability code information library provides a reliable data basis for subsequent drug management, drug dispensing, drug return and other links, ensuring the traceability of the entire drug circulation process. Through centralized storage in the information library, fast query is supported, the traceability and transparency of drug information are ensured, and drug managers are helped to trace and review the circulation of drugs at any time. Through the division of drug dispensing scanning request mode and drug return scanning request mode, different data processing and tracing can be performed according to different drug circulation modes, effectively improving the management efficiency of drug circulation. In the process of drug splitting, splitting traceability data is generated to ensure that the source, use and destination of each split drug are clearly visible, providing detailed traceability information for the dispensing process. According to the drug splitting and tracing data, the system can intelligently dispatch medication, reduce the risk of drug mismatch, improve the accuracy and efficiency of medication, and avoid manual medication errors. The generation of intelligent scheduling data enables the distribution of drugs to be dynamically adjusted based on actual demand and inventory conditions, improving the accuracy of inventory management and inventory turnover efficiency. According to different drug needs and splitting and tracing data, the dispensing and splitting processes can be flexibly arranged to meet the special requirements of different drug management and enhance the personalization and flexibility of hospital drug management. Through the return scan request mode, the refund information of each drug can be accurately traced to ensure that the refund process is transparent and traceable, and prevent malicious drug returns or erroneous refunds. By comparing with the hospital drug sales record data, refund failures or errors can be discovered in a timely manner, reducing disputes or improper refund operations caused by data errors. The system automatically generates the result data of refund success or failure, optimizes the refund verification process, reduces the need for manual intervention, and improves work efficiency and accuracy. The generation of refund process tracing data ensures that the drug return process complies with legal and hospital management requirements, provides clear process records and audit support, and facilitates subsequent review and compliance inspections. Through data comparison and process tracing, hospitals can find weak links or non-compliant operations in the refund process, thereby optimizing the refund management process and improving the management level of drug refunds. Through traceability code restoration, the traceability information after drug return can be restored to the original drug storage traceability code, realizing closed-loop control and effectively avoiding information loss or tracking faults. The restoration of drug return information and the return of traceability data ensure the closed-loop traceability of the entire process of drug purchase, storage, dispensing, use and return, ensuring the transparency and controllability of the entire circulation process. Through closed-loop traceability control, the system can automatically trace back and restore the traceability code information, avoiding human operation errors or interference, and improving the reliability and stability of the system.Through the intelligent scheduling data of drug dispensing, the returned drug restoration traceability data is traced back and feedback is provided, which further optimizes the scheduling mechanism in the drug management process and effectively reduces the waste or unsalable drug phenomenon. The closed-loop control of the traceability code ensures that the drug is traceable throughout the process, thereby improving the compliance of drug management, reducing safety risks, and providing hospitals with a higher level of drug supervision. Therefore, the present invention improves the efficiency and accuracy of the drug consumables traceability code tracing method through full-process closed-loop management, data association enhancement, intelligent scheduling optimization, and returned drug tracing improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A schematic diagram of the steps of a traceability method based on the traceability code of pharmaceutical consumables;

[0065] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0066] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0067] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0068] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0069] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0070] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0071] To achieve this, please refer to Figures 1 to 3 A tracing method based on a drug consumables traceability code, the method comprising the following steps:

[0072] Step S1: Obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database;

[0073] Step S2: divide the drug entry traceability code into scanning access modes, generate a drug dispensing scanning request mode and a drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data;

[0074] Step S3: Acquire the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data;

[0075] Step S4: restore the drug refund information traceability code according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information library for closed-loop traceability control to generate returned drug restoration traceability data; perform drug traceability feedback on the returned drug restoration traceability data through the drug dispensing intelligent scheduling data, thereby generating drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

[0076] The present invention constructs a closed-loop control system for drug consumables traceability through the traceability code management of the entire process of drug storage, splitting, drug delivery, and drug return. Each link from procurement to use can be traced, ensuring the transparency and standardization of drug circulation. The drug storage traceability code is generated by using a unique code, and more detailed traceability data is generated by association and splitting, thereby ensuring the accuracy of the traceability information, reducing manual intervention, and improving the traceability efficiency. Through the drug delivery scanning request mode and the drug dispensing intelligent scheduling function, the drug inventory is monitored and dynamically adjusted in real time, the inventory management is optimized, the risk of drug expiration and waste is reduced, and the response speed of drug delivery and return is improved. Through the drug return scanning request mode, the drug refund information traceability code is dynamically compared with the sales record in combination with the HIS system, ensuring the authenticity and accuracy of the refund process, and effectively reducing the possibility of erroneous refunds or false refunds. Through the traceability code restoration function, the drug return data is fed back to the drug storage traceability code information library, forming a closed-loop traceability control process. This closed-loop management method improves the traceability and consistency of data, and facilitates subsequent tracking and auditing. Through the traceability feedback function, the use of medicines and consumables can be accurately grasped, providing data support for drug demand forecasting, replenishment decisions and scheduling optimization, avoiding the waste of medical resources due to insufficient inventory or unreasonable allocation. By using the information database and traceability code association technology, the data of multiple links such as procurement, inventory, drug distribution, and drug return are integrated, and data sharing and collaborative operation between different systems are realized, which improves the level of hospital management informationization. Therefore, the present invention improves the efficiency and accuracy of the drug consumables traceability code tracing method through full-process closed-loop management, data association enhancement, intelligent scheduling optimization, and drug return traceability improvement.

[0077] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a tracing method based on a drug consumables traceability code of the present invention. In this example, the tracing method based on a drug consumables traceability code includes the following steps:

[0078] Step S1: Obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database;

[0079] In an embodiment of the present invention, the purchase information data of the drug is collected from the drug purchase platform or related system, including but not limited to the drug name, batch number, purchase quantity, purchase date, supplier information, etc. The collected drug purchase information is formatted, the noise or redundant information in the data is cleaned, and a standardized drug purchase information data set is generated. A unique coding rule is generated for each record in the drug purchase information, and each drug purchase record is encoded as a unique drug entry traceability code according to the generated unique coding rule to form a drug traceability initial table. According to the drug entry traceability code, the purchase information (such as batch number and supplier) in the drug purchase information data is associated with the logistics and storage data of the drug. Generate a drug entry associated traceability code to identify a specific entry record, and support multi-dimensional information traceability. For example, the associated traceability code can be in the following format: R_Code=T_Code+P+S; wherein: R_Code represents the drug entry associated traceability code; P represents logistics tracking information; S represents the storage location code. Create a database or distributed storage system for storing drug entry traceability codes and associated traceability code information. The table structure of the information database should include the following fields: drug name, batch number, entry traceability code, associated traceability code, procurement information, storage location, and update time. The generated drug entry traceability code information is stored in the information database. Repeatability checks are performed during storage to ensure the uniqueness and accuracy of the traceability code.

[0080] Step S2: divide the drug entry traceability code into scanning access modes, generate a drug dispensing scanning request mode and a drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data;

[0081] In an embodiment of the present invention, a classification algorithm (such as a decision tree or a support vector machine) is used to automatically divide the scan request into a drug dispensing scan request mode and a drug return scan request mode according to the information of the drug entry traceability code (such as drug type, use, etc.). A specific scan request format is defined for each mode through a rule engine (such as a rule-based decision system), including necessary drug quantity, target location, and other information. The drugs are split according to demand and inventory using an inventory allocation algorithm (such as a greedy algorithm or dynamic programming). By calculating the demand and available inventory, an appropriate split quantity is generated to ensure the traceability consistency of the drugs. The split data is stored in a database, and the split data is associated with the drug entry traceability code to ensure the integrity of the traceability information. An optimization algorithm (such as a linear programming or a heuristic algorithm) is used to schedule drug dispensing, and the optimal scheduling plan is generated by comprehensively considering factors such as inventory, demand priority, and expiration date. The dispensing plan is adjusted in real time through a dynamic scheduling system to cope with demand fluctuations and inventory changes. A data verification algorithm is used to ensure the accuracy of drug splitting and dispensing scheduling, and consistency verification is performed by comparing the original traceability code with the split result. The report generation system outputs detailed reports of the scheduling and splitting process for subsequent auditing and decision support.

[0082] Step S3: Acquire the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data;

[0083] In an embodiment of the present invention, drug sales record data including drug name, sales time, sales quantity, patient information, etc. are obtained from a hospital information system (HIS) through an interface (such as a RESTful API). The sales record data is synchronized regularly using an ETL tool (such as Apache Nifi or Talend) to ensure consistency with the drug storage traceability data. According to the drug return scan request mode, the drug storage traceability code is scanned and the refund information is obtained to generate a drug refund information traceability code. The refund information is tamper-proof traced and recorded through blockchain technology or a distributed ledger to ensure the authenticity and consistency of the refund information. The drug refund information traceability code is compared with the hospital drug sales record data through a data matching algorithm (such as hash matching, data fingerprint technology). Comparison content: drug name, batch number, refund amount, patient information, etc. Refund result data is generated based on the comparison result: Refund success: a refund record that meets the conditions generates a refund success result data. Refund failure: a refund record that does not meet the conditions generates a refund failure result data. According to the refund result data, combined with the hospital refund process and rules, the refund process is verified to ensure the compliance of each step of the operation. Verification content: whether the refund conditions are met, whether the refund amount is correct, whether the patient information matches, etc. Based on the verification results of the refund process, drug refund process traceability data is generated, stored and audited.

[0084] Step S4: restore the drug refund information traceability code according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information library for closed-loop traceability control to generate returned drug restoration traceability data; perform drug traceability feedback on the returned drug restoration traceability data through the drug dispensing intelligent scheduling data, thereby generating drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

[0085] In an embodiment of the present invention, the drug refund process traceability data is restored by using a data decoding algorithm (such as a hash algorithm or encryption decoding technology). The restoration process needs to reconstruct the original traceability code of the drug based on the drug refund information traceability code and the result of the refund process verification. The restored traceability code is transmitted back to the drug entry traceability code information library through the API interface and matched with the original data to ensure that each refund process can be traced back to the original entry record, thereby completing the closed-loop traceability control. The restored traceability code is integrated with the relevant refund data to generate the returned drug restoration traceability data. This data records the entire process of the drug from refund to restoration. The returned drug restoration traceability data is stored in a database or a distributed storage system to ensure that it can be used for subsequent query and analysis. Using the intelligent scheduling data of drug dispensing, combined with the drug inventory and demand information, the returned drug restoration traceability data is traced back. The feedback optimization algorithm (such as regression analysis or machine learning model) is used to optimize the returned drug information to generate drug traceability feedback data. The traceability feedback includes information such as inventory adjustment, drug distribution and remaining quantity after the drug is returned. Integrate drug traceability feedback data with drug consumables management system data (such as drug consumption and usage) to generate drug consumables traceability feedback data. Based on drug consumables traceability feedback data, perform full-process drug consumables traceability operations to ensure that the entire process of drugs from storage to use, and then to return and refund can be accurately traced.

[0086] Preferably, step S1 comprises the following steps:

[0087] Step S11: Obtaining drug purchasing information data;

[0088] Step S12: preprocessing the drug procurement information data to generate standard drug procurement information data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization;

[0089] Step S13: defining a traceability code generation rule for the standard drug purchase information data, and generating a dynamic traceability code generation rule; generating a unique code for the standard drug purchase information data based on the dynamic traceability code generation rule, thereby generating a drug storage traceability code;

[0090] Step S14: associate the drug purchase information data with the drug information according to the drug entry traceability code to generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database to generate a drug entry traceability code information database.

[0091] In an embodiment of the present invention, drug procurement information data is obtained from a drug procurement system or a supply chain management platform. This data includes drug name, specification, batch number, purchase date, supplier information, etc. The data source can be manually input, exported from an ERP system, or automatically collected. Redundant or invalid records in the data are removed, such as invalid drug information or repeated purchase items. De-noising algorithms (such as mean filtering, wavelet transform, etc.) are applied to clean up errors or outliers in the data to ensure the reliability of the data. Appropriate methods are used to fill missing data. For example, the mean, median is used to fill or machine learning methods are used to predict missing values. Data from different sources are uniformly standardized, such as date format unification, amount unit conversion, etc., to ensure the consistency of data in subsequent analysis. Design and define rules for traceability code generation to ensure that each drug procurement record can be uniquely identified. The rules may include: drug number (such as drug ID), purchase date, batch number, supplier ID, special identifier (such as QR code, barcode, etc.). Based on these rules, dynamic traceability code generation rules are generated to ensure that each drug entering the warehouse can generate a unique traceability code according to these rules. Based on the dynamic traceability code generation rules, the standard drug procurement information data is processed to generate a unique drug entry traceability code for each procurement record. The generated traceability code should contain detailed information about the drug and ensure that each traceability code is unique and non-repeatable. The generated drug entry traceability code is associated with the procurement information to ensure that the traceability code can be mapped to specific drug information. By associating the drug entry traceability code, the procurement, entry and related information of the drug are stored to generate a drug entry traceability code information database. The information database should facilitate subsequent query, traceability and report generation, and support querying the supply chain information, storage status, circulation status, etc. of the drug based on the traceability code.

[0092] Preferably, step S13 includes the following steps:

[0093] Step S131: parsing the core feature fields of the standard drug procurement information data to generate drug procurement core field data; performing feature field decomposition on the drug procurement core field data to generate drug procurement core feature vectors; performing standardized structural space mapping on the drug procurement core feature vectors to generate drug feature mapping data sets;

[0094] Step S132: Adaptively generate traceability rules for the drug feature mapping data set to obtain dynamic traceability code generation rules, where the formula of the traceability code rule is as follows:

[0095] Y = f(T, B, D, R);

[0096] In the formula, Y represents the traceability code rule, T represents the drug type code, B represents the batch feature code, D represents the production date weight, and R represents the random identification generation algorithm;

[0097] Step S133: generating a non-repetitive multimodal identification for the drug feature mapping data set according to the dynamic traceability code generation rule to generate drug unique identification data; performing bidirectional verification on the drug unique identification data to generate drug identification verification data;

[0098] Step S134: hierarchically encode and integrate the standard drug procurement information data through dynamic traceability code generation rules and drug unique identification data to generate a drug inventory traceability code.

[0099] In an embodiment of the present invention, the core feature fields in the drug procurement information are extracted. These fields generally include the type, batch number, production date, supplier information, etc. of the drug. The standard drug procurement information data is structured and parsed to identify and extract key fields. For example, drug type (T), batch number (B), production date (D), etc. Generate drug procurement core field data, which is a set of original field data containing key drug information. The drug procurement core field data is further decomposed into fine-grained features. The type, batch, production date, etc. of the drug are attributed to generate more representative feature data. For example, the batch number can be split by year and manufacturer, and the drug type can be further classified (such as antibiotics, over-the-counter drugs, etc.). Generate drug procurement core feature vectors, each of which represents the multi-dimensional feature information of the drug. Through a standardization method, the drug feature vectors are mapped to a unified structured space for subsequent analysis and processing. The drug procurement core feature vectors are standardized and mapped to a standard structured space (such as vectorization and normalization) to ensure that feature data of different dimensions can be compared and fused. Generate a drug feature mapping data set, which contains standardized drug feature data. Generate a dynamic traceability code generation rule for each piece of drug data so that the generation of traceability codes can adapt to the characteristics of different drugs. By analyzing the core feature data of the drug, a dynamic rule is defined to generate a unique traceability code based on factors such as drug type, batch number, production date, and randomly generated identifiers. The traceability code generation rule formula is: Y = f (T, B, D, R); where Y represents the traceability code rule, T represents the drug type code, B represents the batch feature code, D represents the production date weight, and R represents the random identifier generation algorithm; a dynamic traceability code generation rule is generated, which can adaptively generate a unique traceability code according to the characteristics of different drugs. Generate a unique identifier for the drug based on the dynamic traceability code generation rule to ensure that each drug has a unique identifier. According to the traceability code generation rule, a unique identifier is generated through a multimodal method (such as a combination of drug type, batch number, production date, random identifier, etc.). For example, identification can be achieved using QR code, barcode, RFID, etc. Generate drug unique identification data, which contains the multimodal unique identifier of the drug. Perform two-way verification by verifying the uniqueness of the drug unique identification data. On the one hand, verify whether the generated identifier is unique in the system; on the other hand, confirm whether the drug information corresponding to the identifier is accurate, generate drug identification verification data, and confirm the validity and accuracy of each identifier through the verification results. According to the dynamic traceability code generation rules and the drug unique identification data, hierarchical coding integration of drug entry information is carried out to finally generate a drug entry traceability code. Combine the various characteristics of the drug (such as drug type, batch number, production date, etc.) with the unique identifier to form a complete drug entry traceability code.The traceability code not only uniquely identifies the drug, but also traces the drug's warehousing and supply chain information. Generate a drug warehousing traceability code for subsequent tracking and management of the drug.

[0100] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0101] Step S21: using a personal digital assistant device to scan and request the drug entry traceability code to obtain drug entry traceability code scanning request data; dividing the drug entry traceability code scanning request data into access modes to generate a drug dispensing scanning request mode and a drug return scanning request mode;

[0102] Step S22: performing drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode to generate drug splitting and tracing data; performing drug inventory monitoring on the drug entry traceability code information database based on the drug splitting and tracing data to generate drug inventory status data;

[0103] Step S23: Based on the drug inventory status data, a real-time warning of inventory shortage is issued for the drug corresponding to the drug entry traceability code, thereby generating drug traceability warning data;

[0104] Step S24: Based on the drug traceability warning data, the drug inventory status data is intelligently scheduled for drug dispensing to generate drug dispensing intelligent scheduling data.

[0105] In an embodiment of the present invention, a drug entry traceability code is scanned by a personal digital assistant device (such as a smart phone, a tablet, etc.) to obtain drug-related information. The user uses a personal digital assistant device to scan the drug entry traceability code, sends a scan request through the scanning software in the device (such as a barcode / QR code scanning application), and obtains drug entry traceability code related data. The scanned data generally includes basic information of the drug, entry time, batch number, etc. Generate drug entry traceability code scanning request data, which contains the scanned drug traceability code information. The scan request data is divided into modes to distinguish between two different operation modes of drug dispensing and drug return. According to the context information contained in the scan request data (such as scanning time, operation purpose, etc.), the system automatically identifies whether it is a drug dispensing request or a drug return request. The drug dispensing scanning request mode is applicable to the drug dispensing process, and the drug return scanning request mode is applicable to the drug return process. Generate a drug dispensing scanning request mode and a drug return scanning request mode for subsequent processing and data management. According to the drug dispensing request mode, the drug entry traceability code is traced to ensure the accuracy of the drug dispensing process. The system obtains the drug entry traceability code according to the drug dispensing scanning request mode, and traces the drug entry information, batch number, production date and other detailed data. According to the drug splitting rules, the system splits the drugs into dispensing units according to batches or specifications to ensure that the traceability information of each dispensing unit is complete. Generate drug splitting traceability data, including the traceability code of each split unit and its related information. According to the drug splitting traceability data, the drug inventory is monitored in real time to ensure the reasonable management of the drug inventory. The system compares the drug splitting traceability data with the drug entry traceability code information database to determine the quantity and status of the drugs in the current inventory. Monitor the entry and exit of drugs and record each drug dispensing or return operation in time. Generate drug inventory status data, which includes the real-time status of drug inventory, remaining quantity, out-of-stock information, etc. According to the drug inventory status data, monitor inventory changes in real time and warn of drug inventory shortages in time. The system analyzes the drug inventory status data (such as inventory quantity, dispensing quantity, etc.) to determine whether there is a risk of inventory shortage. When the inventory quantity is lower than the set threshold, the system will trigger a shortage warning. Generate drug traceability warning data, which reminds managers to replenish inventory in time to prevent medical accidents caused by drug shortages. Based on drug traceability warning data, intelligently schedule drug dispensing to ensure timely supply of drugs. Based on drug traceability warning data, the system analyzes drugs with inventory shortages and automatically generates a replenishment plan. The system considers factors such as drug circulation demand, inventory, and supplier information to perform intelligent scheduling and allocation. The dispensing process is automated, and drug dispatchers perform replenishment or allocation according to the scheduling plan recommended by the system. Generate intelligent scheduling data for drug dispensing, including drug replenishment plans, scheduling strategies, and execution steps.

[0106] Preferably, performing drug splitting and tracing of drug entry traceability codes through the drug dispensing scanning request mode includes:

[0107] Extract multi-dimensional information of the drug entering the warehouse from the drug entry traceability code through the drug dispensing scanning request mode to obtain multi-dimensional information data of the drug entering the warehouse, wherein the multi-dimensional information data of the drug entering the warehouse includes the batch number of the drug, the location of the drug entering the warehouse, and the time of drug entering the warehouse;

[0108] Construct a drug entry traceability chain based on the drug batch number, drug entry warehouse location, and drug entry time to generate drug entry traceability chain data; use blockchain technology to extract entry information traceability nodes from drug entry traceability chain data to generate drug entry traceability nodes;

[0109] The drug entry traceability code is split into small packaging units to obtain drug small packaging unit data, wherein the drug small packaging unit data includes the split batch number and the split time; the drug batch number is associated with the parent batch number according to the split batch number of the drug small packaging unit data to generate batch association data; the drug split traceability chain is constructed based on the batch association data and the split time to generate the drug split traceability chain; the drug split traceability node is extracted from the drug split traceability chain to generate the drug split traceability node;

[0110] Analyze the drug distribution data of the drug storage traceability code to generate drug distribution data, where the drug distribution data includes patient collection information and drug distribution time; construct a drug distribution traceability chain based on the patient collection information and drug distribution time to generate a drug distribution traceability chain; extract drug distribution traceability nodes from the drug distribution traceability chain to obtain drug distribution traceability nodes;

[0111] The drug storage traceability node, drug splitting traceability node and drug dispensing traceability node are integrated into multi-dimensional chain nodes to generate a splitting traceability chain network; the splitting traceability chain network is used to perform drug splitting traceability on the drug storage traceability code to generate drug splitting traceability data.

[0112] In the embodiment of the present invention, the drug storage traceability code is multi-dimensionally extracted through the drug dispensing scanning request mode to understand the core information such as the batch, warehouse location, and storage time of the drug. After the system receives the drug dispensing scanning request data, it extracts the multi-dimensional information of the stored drug according to the drug storage traceability code. Including: drug batch number: identifying the production batch of the drug. Drug storage warehouse location: extracting the specific warehouse location where the drug is stored. Drug storage time: recording the specific time of drug storage. Generate multi-dimensional information data of stored drugs, which contains key information such as the batch number, storage warehouse location, and storage time of the drug. Based on the extracted drug information, a drug storage traceability chain is constructed to track the drug storage process. The drug batch number, storage warehouse location, and storage time are used as core information to construct the traceability chain, and these information are connected into a chain form to form a full-process traceability path from drug storage to inventory storage. Generate drug storage traceability chain data, which contains all information about the drug from production to storage. Blockchain technology is used to extract the traceability node of the drug storage information to ensure that the data cannot be tampered with and can be verified. Blockchain technology is used to process the drug entry traceability chain data, and each traceability event (such as drug entry, warehouse location change, etc.) is recorded as a traceability node on the blockchain. Each node contains detailed information about the drug (batch number, warehouse location, entry time, etc.) and ensures that the data cannot be tampered with. Generate drug entry traceability nodes, which are data protected by blockchain, ensuring the security and integrity of the information. Split the drugs into small packaging units, track the batch and split time of each split unit, and establish an association with the parent batch number. During the drug delivery process, the system splits the drugs into small packaging units according to the drug entry traceability code. Generate a unique split batch number and split time for each split small packaging unit, and associate it with the original batch number (parent batch number). Generate association data between the batch number of each split unit and the original batch number to form a parent-child batch relationship. Generate batch association data, including the split batch number and split time of each split unit. Based on batch association data and split time, build a drug split traceability chain to track the flow of drugs after splitting. Based on the data of the small packaging unit of the drug (split batch number and split time), a drug splitting traceability chain is constructed. The chain connects the various nodes of drug splitting to ensure that the entire process from warehousing to splitting can be traced. Generate a drug splitting traceability chain to record each split, batch number changes and time information. Extract the traceability nodes of each splitting link from the drug splitting traceability chain. Analyze each link in the drug splitting traceability chain and extract the traceability nodes of each split unit. These nodes contain detailed information at the time of splitting, such as batch number, splitting time, splitting location, etc. Generate drug splitting traceability nodes, which help managers track every detail of the drug splitting process. Extract relevant data on drug distribution, including patient collection information and drug distribution time.According to the patient's collection information and the time of drug delivery, a traceability chain from drug storage to drug delivery is constructed to ensure that the flow of drugs can be traced. Generate a drug delivery traceability chain to record the drug delivery process and related information. Extract the information of each node from the drug delivery traceability chain, including the time of drug delivery, the location of drug delivery, patient information, etc. Generate a drug delivery traceability node for subsequent drug traceability and management. Integrate the drug storage traceability node, the drug splitting traceability node and the drug delivery traceability node to generate a complete splitting traceability chain network. This chain network can reflect the entire process of drugs from storage to splitting and then to drug delivery, ensuring the full traceability of data. Generate a splitting traceability chain network, which can be used to trace each link of the drug flow and ensure the traceability of drugs. Use the integrated splitting traceability chain network to conduct the final drug splitting traceability of the drug storage traceability code. Generate drug splitting traceability data to provide support for the safety management, supervision and circulation of drugs.

[0113] Preferably, step S23 includes the following steps:

[0114] Step S231: performing a drug inventory change trend analysis on the drug inventory status data to generate drug inventory change trend data; performing a drug inventory change warning analysis on the drug inventory change trend data based on a preset inventory warning threshold to generate drug inventory change warning data;

[0115] Step S232: performing drug association analysis on drug entry traceability codes according to drug inventory change warning data to generate drug traceability associated inventory data; performing inventory shortage risk assessment on drug traceability associated inventory data using Bayesian network analysis to generate drug inventory shortage risk data;

[0116] Step S233: Generate a real-time inventory shortage warning for the drug corresponding to the drug entry traceability code based on the drug inventory shortage risk data and the preset inventory warning threshold, thereby obtaining drug traceability warning data.

[0117] In the embodiment of the present invention, the inventory status data of the medicines are collected, including the historical inventory quantity, storage and outbound conditions, etc. By performing time series analysis on these data, the drug inventory change trend data is generated to reflect the increase and decrease trend of the inventory. According to the set inventory warning threshold, the early warning analysis of inventory changes is performed. The inventory warning threshold is usually based on the average demand and supply cycle of the medicine to ensure that an early warning is issued in time when the inventory is insufficient. The system compares and analyzes the drug inventory change trend data, detects whether the inventory is close to or below the preset warning line, and generates drug inventory change early warning data. According to the drug entry traceability code, the system performs drug association analysis on the inventory data. The association analysis is mainly based on the type of drug, demand pattern and supply chain link, and associates the same type of drugs or similar drugs to form drug traceability associated inventory data. The Bayesian network model is used to evaluate the inventory shortage risk of drug traceability associated inventory data. Bayesian network is a probabilistic reasoning model that can comprehensively consider the dependency relationship between multiple variables (such as demand changes, supply time, inventory, etc.) to generate drug inventory shortage risk assessment data. Combining the drug inventory shortage risk data with the set inventory warning threshold, the system will monitor the inventory status of the drug in real time. Once the inventory shortage risk data exceeds the warning threshold, the system will automatically trigger a real-time inventory shortage warning. The warning notifies managers to take corresponding measures, such as speeding up procurement or adjusting distribution plans. After the inventory shortage warning is generated, the system will also generate detailed drug traceability warning data based on the drug's entry traceability code. The traceability warning data can track the source, distribution and inventory information of the drug, making it easier to trace the cause of the shortage.

[0118] Preferably, step S24 includes the following steps:

[0119] Step S241: performing drug demand analysis on the drug inventory status data based on the drug traceability warning data to generate drug demand data; screening the drug inventory status data for scarce drug information based on the drug demand data to obtain scarce drug information screening data;

[0120] Step S242: performing drug supply time series analysis on the scarce drug information screening data to generate scarce drug supply time series data; performing multi-location collaborative scheduling on the scarce drug information screening data according to the scarce drug supply time series data to generate scarce drug collaborative scheduling data;

[0121] Step S243: Intelligently dispatch drug dispensing by using the scarce drug collaborative dispatch data to the drug inventory status data, and generate intelligent drug dispensing dispatch data.

[0122] In an embodiment of the present invention, historical sales data, drug consumption data, inventory data, expired drug information, drug recovery data, regional demand data, etc. are obtained from a drug traceability system. Data sources include hospitals, pharmacies, drug distribution centers, and online sales platforms. Time series analysis methods (such as ARIMA and exponential smoothing) are used to predict the trend of historical sales data, taking into account the influence of seasonality, holidays, and other factors. Machine learning models (such as support vector machines and random forests) are used to predict future demand based on characteristics such as sales, inventory, and regional demand. The prediction results are converted into drug demand data, and the expected demand for each drug in different time periods is output. Consider multi-dimensional information such as drug categories, dosage forms, and units to ensure the accuracy and refinement of demand data. Compare drug demand data with inventory data to identify drugs whose demand exceeds the current inventory. For example, the difference between the remaining inventory and the demand is used to calculate the inventory shortage of each drug and generate inventory difference data. According to the set threshold (such as the inventory difference is greater than a certain ratio), screen out drugs whose demand is greater than the inventory and mark them as scarce drugs. This process can use the scarcity index model (Scarcity Index) to dynamically screen out scarce drugs by comprehensively evaluating drug demand fluctuations, inventory conditions, and supply capabilities. All information on scarce drugs (including drug name, demand, inventory, out-of-stock period, etc.) is organized into scarce drug information screening data for subsequent analysis. Based on historical drug supply records, the supply of scarce drugs is modeled using time series prediction algorithms (such as ARIMA, LSTM neural network) to identify drug supply cycles, supply peaks, and shortage trends. For example, use the sliding average or weighted average method to predict the supply of drugs in future time periods and predict when the drugs will reach the lowest inventory point. Combine the supply cycle and drug demand to generate supply time series data for each scarce drug and predict its supply situation in the future time period (such as replenishment date, supply, etc.). Based on drug inventory, demand, supply time series and other data from different locations (such as hospitals, drug distribution centers, and pharmacies), a multi-location collaborative scheduling model is constructed. Use the shortest path algorithm or distributed optimization algorithm (such as particle swarm optimization PSO) to determine the optimal distribution route and scheduling plan for drugs. Make dynamic adjustments based on real-time data (such as inventory changes, sudden growth in demand, etc.) to ensure that drugs can quickly flow from all links of the supply chain to the demand location, reducing drug supply shortages. Generate scheduling plans and timetables for scarce drugs through collaborative scheduling algorithms, and determine scheduling plans for each drug at each location. Design an intelligent drug dispensing scheduling system based on factors such as drug demand, inventory status, and transportation timeliness. This system can use genetic algorithms or ant colony algorithms to optimize scheduling plans. For example, a constrained optimization algorithm is used to consider resource constraints during scheduling, such as transportation capacity, time windows, etc. The system will dynamically adjust the dispensing priority and scheduling path based on the collaborative scheduling data of scarce drugs and combined with real-time drug inventory data.The system automatically generates drug distribution and replenishment plans based on drug demand priority, delivery distance, inventory status and other factors. The scheduling data for each drug includes information such as the target location, delivery time, priority, etc. The intelligent scheduling platform monitors drug inventory in real time, automatically updates the dispensing plan based on changes in drug demand, and optimizes drug distribution strategies.

[0123] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0124] Step S31: Obtain hospital drug sales record data through the HIS system;

[0125] Step S32: tracing the drug refund information of the drug storage traceability code through the drug return scanning request mode to obtain the drug refund information traceability code;

[0126] Step S33: Transmitting the drug refund information traceability code to the HIS system and performing data comparison with the hospital drug sales record data. When the drug refund information traceability code and the hospital drug sales record data are successfully compared, drug refund success result data is generated; when the drug refund information traceability code and the hospital drug sales record data are unsuccessful, drug refund failure result data is generated;

[0127] Step S34: verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data.

[0128] In an embodiment of the present invention, drug sales record data is obtained by docking with a hospital information management system (HIS). These data include information such as the name, specification, batch number, sales time, sales quantity, sales amount, and purchaser of the drug. The HIS system should support an API interface or a data export function, by which drug sales record data is extracted from the HIS system. If the amount of data is large, daily or weekly drug sales data can be exported in batches to a data warehouse through a scheduled task. The extracted data is cleaned to ensure data format consistency and integrity, remove invalid or duplicate records, fill in missing values, and unify the time format. The acquired drug sales record data is stored in a database and indexed by drug category, sales date, sales amount, etc., to facilitate subsequent query and comparison operations. Each drug should generate a unique traceability code when it is put into storage, and the traceability code is associated with the batch number, production date, manufacturer, drug name, specification, and other information of the drug. The traceability code can be generated using a QR code or a barcode and embedded in the drug packaging for easy scanning and tracing. When refunding drugs, use a scanning device (such as a QR code scanner) to scan the traceability code of the returned drugs. The system obtains the traceability code of the drugs by scanning, and automatically queries the database to extract detailed information of the drugs, including sales records, reasons for return, etc. The drug refund request information is associated with the drug traceability code to generate a drug refund information traceability code. The refund information traceability code includes information such as the return date, refund amount, return reason, and return quantity of the drugs. The drug refund information traceability code is transmitted to the HIS system through the interface and compared with the drug sales record data in the HIS system. During the comparison, the system will match the sales record with the fields such as the drug name, specification, return quantity, and return date in the refund information. If the drug information (such as drug name, specification, quantity, and sales time) in the drug refund information traceability code is consistent with the corresponding drug information in the sales record data, the refund is determined to be successful. If there is any field mismatch or inconsistent information, refund failure data is generated. When the refund information traceability code is successfully compared with the sales record data, the system generates drug refund success result data. This data includes detailed information such as refund amount, refund time, and refund reason. When the refund information traceability code fails to match the sales record data, the system generates refund failure result data. This data includes the reason for failure, relevant drug information, and the failed field. For each refund request, the system needs to verify whether the refund process complies with the hospital's refund policy. The verification process includes: whether it meets the refund time limit (for example, returns must be made within 30 days of purchase). Whether it meets the permitted refund reasons (for example, expired, damaged, or wrongly delivered drugs, etc.). Whether the refund quantity is consistent with the quantity in the sales record to avoid excessive refunds. Based on the results of successful and failed refunds, combined with the refund process verification, generate drug refund process traceability data.The data includes: recording the drugs that have been successfully refunded and their detailed information (such as the number of returned products, refund amount, refund time, etc.). Recording the drugs that have failed to be refunded and the reasons for their failure (such as unsuccessful matching, excessive refund, refund process failure, etc.). The refund process traceability data is stored in a dedicated database, which supports historical query and traceability to ensure that the entire refund process is transparent and traceable. The system can generate refund reports regularly for hospital managers to audit the compliance and accuracy of the refund process.

[0129] Preferably, transmitting the drug refund information traceability code to the HIS system and comparing it with the hospital drug sales record data includes:

[0130] The drug refund information traceability code is transmitted to the HIS system for data format verification to generate drug refund information format verification data; based on the drug refund information format verification data, the drug purchase information data is analyzed for drug sales traceability according to the drug refund information traceability code to generate drug sales traceability data;

[0131] The drug sales traceability data and hospital drug sales record data are queried for key sales records to generate sales record query data, where the key sales records include drug sales date, patient information and sales price; sales traceability codes are generated for drug sales date, patient information and sales price to obtain sales traceability codes;

[0132] The drug refund information traceability code and the sales traceability code are dynamically matched through the traceability code comparison rules predefined in the HIS system to generate sales data matching results, where the sales data matching results include drug refund success result data and drug refund failure result data.

[0133] In an embodiment of the present invention, the drug refund information traceability code is transmitted to the HIS system through an API interface or a data transmission protocol. Ensure that the data transmission meets the standards of the hospital information system and can be further processed in the system. After the HIS system receives the drug refund information traceability code, it first performs data format verification to ensure that the refund information traceability code meets the predetermined data format specification. The inspection items include: whether the length and character format of the traceability code are correct. Whether the coding in the traceability code complies with the rules (such as the format of the coding fields such as the drug batch number, manufacturer, and specification). Whether the traceability code contains all necessary verification information (such as timestamp, unique identifier, etc.). If the format verification is successful, generate drug refund information format verification data and record the successful verification information. If the format verification fails, generate format error data, including error code and error description, and feedback to the refund application end for correction. Based on the drug refund information traceability code that has passed the format verification, perform drug sales traceability analysis. At this time, the system will query the purchase and sales record information of the drug according to the traceability code. Query relevant drug procurement information, including data such as supplier, batch number, purchase date, and storage quantity. Through the purchase information, the sales records of the drugs in the hospital are traced back to query the sales date, sales quantity, sales price and other information of the drugs. Generate drug sales traceability data, including key information such as the sales flow, sales records and procurement sources of the drug. According to the drug sales traceability data, match it with the hospital drug sales record data to query the key sales records. These records include: the actual sales time of the drug to ensure the timeliness of the refund. Basic information of the patient who purchased the drug, such as patient ID, name, etc. The sales unit price of the drug to ensure the accuracy of the refund amount. The key sales records queried are sorted to generate sales record query data, including sales date, patient information, sales price and other information. This data serves as the basis for subsequent refund verification. The system will use a specific algorithm to generate a sales traceability code based on the key fields such as drug sales date, patient information, sales price and other key fields in the sales record query data. The traceability code is a unique identifier used to identify and trace the drug sales transaction. Use encryption algorithms such as MD5 or SHA to encrypt the sales date, patient information and sales price to generate a fixed-length sales traceability code. The sales traceability code should be tamper-proof and unique to ensure that every sales record can be accurately traced. The HIS system should predefine a set of traceability code comparison rules to ensure dynamic matching between the refund information traceability code and the sales traceability code. The comparison rules include: the refund information traceability code and the drug information in the sales traceability code (such as drug batch number, specifications, patient information, sales time) must be completely consistent. Through the hash comparison algorithm or database query, a one-to-one match is performed based on the sales traceability code and the refund information traceability code. The system dynamically compares the drug refund information traceability code with the sales traceability code to verify whether the refund request is legal and accurate.The matching process includes: comparing the sales date, patient information, sales price and the corresponding fields in the refund information. If the matching is successful, the system generates the drug refund success result data and records the details of the successful refund. If the matching fails, the drug refund failure result data is generated and the reasons for failure are recorded (such as information mismatch, inconsistent refund amount, etc.). According to the matching results, the sales data matching comparison results are generated. The results include: drug refund success result data: when the refund information traceability code matches the sales traceability code successfully, the detailed information of the successful refund is generated, including the refund amount, refund time, refund drug information, etc. Drug refund failure result data: when the refund information traceability code fails to match the sales traceability code, the refund failure data is generated and the reasons for failure are recorded (such as refund amount exceeded, sales record missing, etc.). The matching comparison result data of the successful and failed refunds are stored in the database, and a refund processing report is generated for auditing and tracking. Hospital managers can view the detailed refund processing through the management background and further analyze the refund reasons and processing results.

[0134] In this specification, a tracing system based on a drug consumables tracing code is provided, which is used to execute the above-mentioned tracing method based on a drug consumables tracing code. The tracing system based on a drug consumables tracing code includes:

[0135] The drug entry module is used to obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database;

[0136] The drug dispensing traceability module is used to divide the scanning access mode of the drug entry traceability code, generate the drug dispensing scanning request mode and the drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data;

[0137] The drug return traceability module is used to obtain the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code based on the drug refund success result data and drug refund failure result data, thereby generating drug refund process traceability data;

[0138] The closed-loop feedback module is used to restore the traceability code of the drug refund information according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information database for closed-loop traceability control to generate returned drug restoration traceability data; the returned drug restoration traceability data is fed back through the drug dispensing intelligent scheduling data to generate drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

[0139] The beneficial effect of the present invention is that by uniquely encoding the drug procurement information data, it is ensured that the storage information of each batch of drugs corresponds to the traceability code one by one to avoid confusion or duplication. The drug procurement data is associated with the storage traceability code to simplify data management, and relevant data can be efficiently stored and extracted in the information library, thereby improving the automation and digitization level of warehouse management. The generation of the drug storage traceability code information library provides a reliable data basis for subsequent drug management, drug dispensing, drug return and other links, ensuring the traceability of the entire drug circulation process. Through centralized storage in the information library, fast query is supported, the traceability and transparency of drug information are ensured, and drug managers are helped to trace and review the circulation of drugs at any time. Through the division of drug dispensing scanning request mode and drug return scanning request mode, different data processing and tracing can be performed according to different drug circulation modes, effectively improving the management efficiency of drug circulation. In the process of drug splitting, splitting traceability data is generated to ensure that the source, use and destination of each split drug are clearly visible, providing detailed traceability information for the dispensing process. According to the drug splitting and tracing data, the system can intelligently dispatch medication, reduce the risk of drug mismatch, improve the accuracy and efficiency of medication, and avoid manual medication errors. The generation of intelligent scheduling data enables the distribution of drugs to be dynamically adjusted based on actual demand and inventory conditions, improving the accuracy of inventory management and inventory turnover efficiency. According to different drug needs and splitting and tracing data, the dispensing and splitting processes can be flexibly arranged to meet the special requirements of different drug management and enhance the personalization and flexibility of hospital drug management. Through the return scan request mode, the refund information of each drug can be accurately traced to ensure that the refund process is transparent and traceable, and prevent malicious drug returns or erroneous refunds. By comparing with the hospital drug sales record data, refund failures or errors can be discovered in a timely manner, reducing disputes or improper refund operations caused by data errors. The system automatically generates the result data of refund success or failure, optimizes the refund verification process, reduces the need for manual intervention, and improves work efficiency and accuracy. The generation of refund process tracing data ensures that the drug return process complies with legal and hospital management requirements, provides clear process records and audit support, and facilitates subsequent review and compliance inspections. Through data comparison and process tracing, hospitals can find weak links or non-compliant operations in the refund process, thereby optimizing the refund management process and improving the management level of drug refunds. Through traceability code restoration, the traceability information after drug return can be restored to the original drug storage traceability code, realizing closed-loop control and effectively avoiding information loss or tracking faults. The restoration of drug return information and the return of traceability data ensure the closed-loop traceability of the entire process of drug purchase, storage, dispensing, use and return, ensuring the transparency and controllability of the entire circulation process. Through closed-loop traceability control, the system can automatically trace back and restore the traceability code information, avoiding human operation errors or interference, and improving the reliability and stability of the system.Through the intelligent scheduling data of drug dispensing, the returned drug restoration traceability data is traced back and feedback is provided, which further optimizes the scheduling mechanism in the drug management process and effectively reduces the waste or unsalable drug phenomenon. The closed-loop control of the traceability code ensures that the drug is traceable throughout the process, thereby improving the compliance of drug management, reducing safety risks, and providing hospitals with a higher level of drug supervision. Therefore, the present invention improves the efficiency and accuracy of the drug consumables traceability code tracing method through full-process closed-loop management, data association enhancement, intelligent scheduling optimization, and returned drug tracing improvement.

Claims

1. A tracing method based on drug consumables traceability code, characterized in that: The following steps are involved: Step S1: Obtain drug purchasing information data; Generate a unique code for drug purchase information, thereby generating a drug inventory traceability code; According to the drug entry traceability code, the drug purchase information data is associated with the drug information to generate the drug entry associated traceability code; the drug entry associated traceability code is used to store the information database to generate the drug entry traceability code information database; Step S2: divide the drug entry traceability code into scanning access modes, generate a drug dispensing scanning request mode and a drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data; Step S3: Acquire the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data; Step S4: restore the drug refund information traceability code according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information library for closed-loop traceability control to generate returned drug restoration traceability data; perform drug traceability feedback on the returned drug restoration traceability data through the drug dispensing intelligent scheduling data, thereby generating drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

2. The tracing method based on the drug consumables traceability code according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Obtaining drug purchasing information data; Step S12: preprocessing the drug procurement information data to generate standard drug procurement information data, wherein the data preprocessing includes data cleaning, data denoising, data missing value filling and data standardization; Step S13: defining a traceability code generation rule for the standard drug purchase information data, and generating a dynamic traceability code generation rule; generating a unique code for the standard drug purchase information data based on the dynamic traceability code generation rule, thereby generating a drug storage traceability code; Step S14: associate the drug purchase information data with the drug information according to the drug entry traceability code to generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database to generate a drug entry traceability code information database.

3. The tracing method based on the drug consumables traceability code according to claim 2 is characterized in that: Step S13 includes the following steps: Step S131: parsing the core feature fields of the standard drug procurement information data to generate drug procurement core field data; performing feature field decomposition on the drug procurement core field data to generate drug procurement core feature vectors; performing standardized structural space mapping on the drug procurement core feature vectors to generate drug feature mapping data sets; Step S132: Adaptively generate traceability rules for the drug feature mapping data set to obtain dynamic traceability code generation rules, where the formula of the traceability code rule is as follows: Y = f(T, B, D, R); In the formula, Y represents the traceability code rule, T represents the drug type code, B represents the batch feature code, D represents the production date weight, and R represents the random identification generation algorithm; Step S133: generating a non-repetitive multimodal identification for the drug feature mapping data set according to the dynamic traceability code generation rule to generate drug unique identification data; performing bidirectional verification on the drug unique identification data to generate drug identification verification data; Step S134: hierarchically encode and integrate the standard drug procurement information data through dynamic traceability code generation rules and drug unique identification data to generate a drug inventory traceability code.

4. The tracing method based on the drug consumables traceability code according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using a personal digital assistant device to scan and request the drug entry traceability code to obtain drug entry traceability code scanning request data; dividing the drug entry traceability code scanning request data into access modes to generate a drug dispensing scanning request mode and a drug return scanning request mode; Step S22: performing drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode to generate drug splitting and tracing data; performing drug inventory monitoring on the drug entry traceability code information database based on the drug splitting and tracing data to generate drug inventory status data; Step S23: Based on the drug inventory status data, a real-time warning of inventory shortage is issued for the drug corresponding to the drug entry traceability code, thereby generating drug traceability warning data; Step S24: Based on the drug traceability warning data, the drug inventory status data is intelligently scheduled for drug dispensing to generate drug dispensing intelligent scheduling data.

5. The tracing method based on the drug consumables traceability code according to claim 4 is characterized in that: The drug splitting and tracing of drug entry traceability codes through the drug dispensing scanning request mode includes: Extract multi-dimensional information of the drug entering the warehouse from the drug entry traceability code through the drug dispensing scanning request mode to obtain multi-dimensional information data of the drug entering the warehouse, wherein the multi-dimensional information data of the drug entering the warehouse includes the batch number of the drug, the location of the drug entering the warehouse, and the time of drug entering the warehouse; Construct a drug entry traceability chain based on the drug batch number, drug entry warehouse location, and drug entry time to generate drug entry traceability chain data; use blockchain technology to extract entry information traceability nodes from drug entry traceability chain data to generate drug entry traceability nodes; The drug entry traceability code is split into small packaging units to obtain drug small packaging unit data, wherein the drug small packaging unit data includes the split batch number and the split time; the drug batch number is associated with the parent batch number according to the split batch number of the drug small packaging unit data to generate batch association data; the drug split traceability chain is constructed based on the batch association data and the split time to generate the drug split traceability chain; the drug split traceability node is extracted from the drug split traceability chain to generate the drug split traceability node; Analyze the drug distribution data of the drug storage traceability code to generate drug distribution data, where the drug distribution data includes patient collection information and drug distribution time; construct a drug distribution traceability chain based on the patient collection information and drug distribution time to generate a drug distribution traceability chain; extract drug distribution traceability nodes from the drug distribution traceability chain to obtain drug distribution traceability nodes; The drug storage traceability node, drug splitting traceability node and drug dispensing traceability node are integrated into multi-dimensional chain nodes to generate a splitting traceability chain network; the splitting traceability chain network is used to perform drug splitting traceability on the drug storage traceability code to generate drug splitting traceability data.

6. The tracing method based on the drug consumables traceability code according to claim 4 is characterized in that: Step S23 includes the following steps: Step S231: performing a drug inventory change trend analysis on the drug inventory status data to generate drug inventory change trend data; performing a drug inventory change warning analysis on the drug inventory change trend data based on a preset inventory warning threshold to generate drug inventory change warning data; Step S232: performing drug association analysis on drug entry traceability codes according to drug inventory change warning data to generate drug traceability associated inventory data; performing inventory shortage risk assessment on drug traceability associated inventory data using Bayesian network analysis to generate drug inventory shortage risk data; Step S233: Generate a real-time inventory shortage warning for the drug corresponding to the drug entry traceability code based on the drug inventory shortage risk data and the preset inventory warning threshold, thereby obtaining drug traceability warning data.

7. The tracing method based on the drug consumables traceability code according to claim 6 is characterized in that: Step S24 includes the following steps: Step S241: performing drug demand analysis on the drug inventory status data based on the drug traceability warning data to generate drug demand data; screening the drug inventory status data for scarce drug information based on the drug demand data to obtain scarce drug information screening data; Step S242: performing drug supply time series analysis on the scarce drug information screening data to generate scarce drug supply time series data; performing multi-location collaborative scheduling on the scarce drug information screening data according to the scarce drug supply time series data to generate scarce drug collaborative scheduling data; Step S243: Intelligently dispatch drug dispensing by using the scarce drug collaborative dispatch data to the drug inventory status data, and generate intelligent drug dispensing dispatch data.

8. The tracing method based on the drug consumables traceability code according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: Obtain hospital drug sales record data through the HIS system; Step S32: tracing the drug refund information of the drug storage traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; Step S33: Transmitting the drug refund information traceability code to the HIS system and performing data comparison with the hospital drug sales record data. When the drug refund information traceability code and the hospital drug sales record data are successfully compared, drug refund success result data is generated; when the drug refund information traceability code and the hospital drug sales record data are unsuccessful, drug refund failure result data is generated; Step S34: verify the refund process of the drug refund information traceability code according to the drug refund success result data and the drug refund failure result data, thereby generating drug refund process traceability data.

9. The tracing method based on the drug consumables traceability code according to claim 8 is characterized in that: The drug refund information traceability code is transmitted to the HIS system and compared with the hospital drug sales record data, including: The drug refund information traceability code is transmitted to the HIS system for data format verification to generate drug refund information format verification data; based on the drug refund information format verification data, the drug purchase information data is analyzed for drug sales traceability according to the drug refund information traceability code to generate drug sales traceability data; The drug sales traceability data and hospital drug sales record data are queried for key sales records to generate sales record query data, where the key sales records include drug sales date, patient information and sales price; sales traceability codes are generated for drug sales date, patient information and sales price to obtain sales traceability codes; The drug refund information traceability code and the sales traceability code are dynamically matched through the traceability code comparison rules predefined in the HIS system to generate sales data matching results, where the sales data matching results include drug refund success result data and drug refund failure result data.

10. A traceability system based on drug consumables traceability code, characterized in that: Used to execute the tracing method based on the drug and consumables traceability code according to claim 1, the tracing system based on the drug and consumables traceability code comprises: The drug entry module is used to obtain drug purchase information data; generate a unique code for the drug purchase information data, thereby generating a drug entry traceability code; associate the drug purchase information data with the drug information according to the drug entry traceability code, and generate a drug entry associated traceability code; use the drug entry associated traceability code to store the information database, and generate a drug entry traceability code information database; The drug dispensing traceability module is used to divide the scanning access mode of the drug entry traceability code, generate the drug dispensing scanning request mode and the drug return scanning request mode; perform drug splitting and tracing on the drug entry traceability code through the drug dispensing scanning request mode, and generate drug splitting and tracing data; perform drug dispensing intelligent scheduling on the drug entry traceability code information database according to the drug splitting and tracing data, and generate drug dispensing intelligent scheduling data; The drug return traceability module is used to obtain the hospital drug sales record data; trace the drug refund information of the drug entry traceability code through the drug return scanning request mode to obtain the drug refund information traceability code; transmit the drug refund information traceability code to the HIS system and compare it with the hospital drug sales record data to generate drug refund success result data and drug refund failure result data; verify the refund process of the drug refund information traceability code based on the drug refund success result data and drug refund failure result data, thereby generating drug refund process traceability data; The closed-loop feedback module is used to restore the traceability code of the drug refund information according to the drug refund process traceability data, and transmit the restored traceability code back to the drug entry traceability code information database for closed-loop traceability control to generate returned drug restoration traceability data; the returned drug restoration traceability data is fed back through the drug dispensing intelligent scheduling data to generate drug consumables traceability feedback data to perform full-process traceability operations for drugs and consumables.

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