Medicine management method and device, computer program product and electronic equipment

Abstract

The invention discloses a medicine management method and device, a computer program product and electronic equipment. The method comprises the following steps: receiving a first management request of a target object for warehouse-out management of a target drug; determining a first hash value of each piece of medicine information in the first multi-dimensional medicine information set, matching the first hash value with a plurality of Merkel trees in a block chain, and determining target storage position information associated with a successfully matched target Merkel tree; acquiring an augmented reality scene graph of a target warehouse where the target drug is located; and determining a target delivery path of the target drug according to the target storage position information and the augmented reality scene graph, and feeding back the target delivery path to the target object. The technical problem of low management precision caused by the fact that related medicine management only depends on manual operation is solved.

Description

Technical Field

[0001] The present application relates to the technical field of drug management, and more specifically, to a drug management method and device, a computer program product, and an electronic device. Background Art

[0002] With the rapid development of the medical industry, drug management plays a vital role in hospital operations. The correct management of drugs is not only related to the treatment effect and life safety of patients, but also directly affects the operational efficiency and service quality of medical institutions. At present, the relevant drug management mainly relies on manual management, such as special personnel responsible for inventorying the storage and outbound information of drugs. On the one hand, these management operations require a lot of time and human resources, especially during periods of large traffic, due to the sharp increase in the workload of receiving, sending and managing drugs, it is easy to lead to low work efficiency and even affect the normal operation of medical services; on the other hand, due to the possibility of errors and negligence in manual operations, the entry and update of drug information are often not accurate enough, which may lead to problems such as wrong issuance and misuse of drugs, which not only wastes medical resources, but also may cause potential harm to patients. In addition, in the production, transportation, storage and use of drugs, if there is a lack of an effective traceability mechanism, once quality problems or safety accidents occur, it is difficult to quickly locate the source of the problem and take remedial measures in time. This not only increases medical risks, but also affects the reputation and image of medical institutions.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a drug management method and apparatus, a computer program product, and an electronic device to at least solve the technical problem that related drug management relies solely on manual operations, resulting in low management accuracy.

[0005] According to one aspect of an embodiment of the present application, a drug management method is provided, including: receiving a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least the outbound quantity of the target drug and a first multidimensional drug information set of the target drug; determining a first hash value of each drug information in the first multidimensional drug information set, and matching the first hash value with a plurality of Merkle trees in a blockchain, and determining target storage location information associated with the successfully matched target Merkle tree, wherein each block storage node in the blockchain stores Merkle trees corresponding to multidimensional drug information sets of different drugs and storage location information of the drugs, respectively, and the Merkle tree is constructed as a leaf node using the hash value of each drug information of the drug, and the storage location information is used to reflect the warehouse identification of the warehouse where the drug is located and the location information in the warehouse; obtaining an augmented reality scene graph of the target warehouse where the target drug is located, wherein the augmented reality scene graph at least includes: the location information of each drug in the target warehouse and the outbound path corresponding to each storage location; determining a target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and feeding back the target outbound path to the target object.

[0006] Optionally, before receiving the first management request from the target object to perform outbound management on the target drug, the method further includes: receiving a second management request from the warehouse manager to perform inbound management on the target drug, and obtaining the target storage location information of the target drug stored by the warehouse manager, wherein the second management request carries at least the inbound quantity of the target drug to be inbound and a second multidimensional drug information set of the target drug, and the second multidimensional drug information set includes at least one of the following: drug transportation information, drug inbound status information, drug production information, and drug attribute information, and the target storage location information is used to reflect the warehouse identification of the target warehouse where the target drug is located and the location information in the target warehouse; performing a hash operation on each drug information in the second multidimensional drug information set to obtain a corresponding second hash value, and using the second hash value of each drug information in the second multidimensional drug information set as a leaf node to construct a target Merkle tree; encrypting the second multidimensional drug information set using a preset encryption algorithm, wherein the type of the encryption algorithm includes at least one of the following: a symmetric encryption algorithm and an asymmetric encryption algorithm; and associating and storing the encrypted second multidimensional drug information set, the target Merkle tree, and the target storage location information in the target block storage node in the blockchain.

[0007] Optionally, after receiving a second management request from the warehouse manager to conduct warehousing management of the target drug, the method further includes: determining a standard parameter range of the storage environment corresponding to the target drug according to a matching result between a preset drug storage rule and the drug attribute information in the second multi-dimensional drug information set, wherein the drug storage rule stores drug attribute information of multiple drugs and a standard parameter range of the corresponding storage environment, and the environmental parameters include at least one of the following: temperature parameters, humidity parameters, and air pressure parameters; collecting real-time environmental parameters of the target warehouse, and determining whether the real-time environmental parameter information is within the standard parameter range; if the real-time environmental parameter information is not within the standard parameter range, adjusting the real-time environmental parameter information of the target warehouse to within the standard parameter range according to a preset adjustment strategy.

[0008] Optionally, the second multidimensional drug information set also includes: drug storage status information. After the target storage path is fed back to the target object, the method also includes: after the target drug is completed, updating the drug storage status information in the second multidimensional drug information set of the target drug, and performing a hash operation on each drug information in the updated second multidimensional drug information set to obtain the corresponding second hash value, and using the second hash value of each drug information in the updated second multidimensional drug information set as a leaf node to obtain an updated target Merkel tree.

[0009] Optionally, the method also includes: matching the first hash value with multiple Merkle trees in the blockchain, and when the obtained matching result is empty, determining that the second multi-dimensional drug information set stored on the blockchain for the target drug is abnormal, and feeding back corresponding abnormal alarm information to the target object.

[0010] Optionally, the method also includes: obtaining drug inventory data of the target drug within a first time period and disease epidemic trend information within the first time period, wherein the drug inventory data is used to reflect the inventory information and multi-dimensional drug information of the target drug, and the inventory information includes at least one of the following: outbound time, inbound time, outbound quantity, inbound quantity; using a pre-trained first neural network model to analyze the drug inventory data and disease epidemic trend information, predicting the shortage probability of the target drug within a second time period, and when the shortage probability is greater than a preset probability threshold, providing feedback to the target object with replenishment reminder information, wherein the second time period is the time period after the first time period.

[0011] Optionally, replenishment prompt information is fed back to the target object, including: obtaining the total quantity of target drugs stored in the target warehouse, and determining the difference between the total quantity and the quantity shipped out; when the difference is lower than a preset threshold value, obtaining the available storage space in the target warehouse, and determining the difference between the total quantity and the quantity shipped out; constructing a procurement optimization function with the goal of minimizing replenishment cost, and using the augmented Lagrange multiplier algorithm to solve the procurement optimization function, and determining a replenishment strategy that meets preset constraints, wherein the constraints include at least one of the following: the storage space required for storing purchased drugs is not higher than the available storage space in the target warehouse, the purchase amount is not higher than a preset limit, and the replenishment time does not exceed a preset time limit; and feeding back the replenishment prompt information carrying the replenishment strategy to the target object.

[0012] According to another aspect of an embodiment of the present application, a drug management device is also provided, including: a receiving module, used to receive a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least the outbound quantity of the target drug and a first multidimensional drug information set of the target drug; a determining module, used to determine a first hash value of each drug information in the first multidimensional drug information set, and match the first hash value with multiple Merkle trees in a blockchain, and determine target storage location information associated with a successfully matched target Merkle tree, wherein each block storage node in the blockchain stores multidimensional drug information of different drugs respectively. A Merkle tree corresponding to a drug information set and storage location information of the drugs, wherein the Merkle tree is constructed with hash values ​​of each drug information of the drugs as leaf nodes, and the storage location information is used to reflect the warehouse identification of the warehouse where the drugs are located and the location information in the warehouse; an acquisition module is used to acquire an augmented reality scene graph of a target warehouse where the target drugs are located, wherein the augmented reality scene graph at least includes: location information of each drug in the target warehouse and an outbound path corresponding to each storage location; a feedback module is used to determine a target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and feed back the target outbound path to the target object.

[0013] According to another aspect of an embodiment of the present application, a computer program product is also provided, the computer program product comprising: a computer program, wherein the computer program implements the above-mentioned drug management method when executed by a processor.

[0014] According to another aspect of an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned drug management method through the computer program.

[0015] In an embodiment of the present application, a first management request for outbound management of a target drug is received by a target object, wherein the first management request carries at least the outbound quantity of the target drug and the first multi-dimensional drug information set of the target drug; the first hash value of each drug information in the first multi-dimensional drug information set is determined, and the first hash value is matched with multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree; an augmented reality scene graph of the target warehouse where the target drug is located is obtained, wherein the augmented reality scene graph at least includes: the location information of each drug in the target warehouse and the outbound path corresponding to each storage location; the target outbound path of the target drug is determined based on the target storage location information and the augmented reality scene graph, and the target outbound path is fed back to the target object. Thereby, automatic outbound is realized, the dependence on human resources is reduced, a large amount of manual participation is not required, the labor cost and management difficulty are reduced, and work efficiency is significantly improved. This solves the technical problem that the management of related drugs relies only on manual operation, resulting in low management accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flowchart of an optional drug management method according to an embodiment of the present application;

[0018] Figure 2 is a schematic diagram of the structure of an optional drug management device according to an embodiment of the present application;

[0019] Figure 3 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0020] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0021] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0022] Example 1

[0023] According to an embodiment of the present application, a drug management method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0024] Figure 1 is a flow chart of a drug management method provided according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0025] Step S102, responding to a first management request from a target object to perform outbound management on a target drug.

[0026] In the technical solution provided in the above step S102, the above first management request can be understood as a doctor, nurse, pharmacy staff or other authorized personnel submitting a request for the target drug through the hospital's drug management mobile application or system interface, and the request carries at least the target drug's outbound quantity (that is, the required number of drugs) and a first multi-dimensional drug information set of the target drug, wherein the first multi-dimensional drug information set refers to the drug information of the target drug in different dimensions, such as drug name, drug type, drug use, etc.

[0027] As an optional implementation, in the technical solution provided in the above step S102, before receiving the first management request from the target object to perform outbound management on the target drug, the system may first perform an inbound operation on the target drug according to the following process, and the specific process includes:

[0028] The first step: receiving a second management request from a warehouse manager to manage the warehousing of a target drug, and obtaining target storage location information where the warehouse manager stores the target drug.

[0029] Among them, the first management request carries at least the inventory quantity of the target drug to be stored and the second multi-dimensional drug information set of the target drug, and the second multi-dimensional drug information set includes but is not limited to: drug transportation information (including but not limited to transportation method, transportation time, transportation conditions, transportation personnel or carriers, tracking number, etc.), drug storage status information (including but not limited to storage time, storage quantity, drug batch number, drug expiration date, storage condition inspection, storage operator information, etc.), drug production information (including but not limited to manufacturer, production date, production batch number, manufacturing process parameters, etc.), drug attribute information (including but not limited to drug name and generic name, drug ingredients, medicinal indications, usage and dosage, drug packaging specifications, storage conditions, warnings and contraindications when using drugs, etc.), etc.

[0030] That is to say, after the warehouse manager receives the target drug to be put into the warehouse, since the drug production information, such as production date, expiration date, batch number, etc., is usually provided on the drug packaging or accompanying documents, the warehouse manager can first use the IoT-based image acquisition device to analyze the microtext, anti-counterfeiting logo and special patterns on the target drug packaging to identify the drug production information of the target drug. Next, a multi-dimensional sensor array based on the Internet of Things is used to obtain the physical properties (such as temperature, humidity, weight, etc.), chemical properties (i.e., the chemical composition of the drug and its stability) or biological property information (i.e., the active ingredients and biological effects of specific types of drugs such as biological agents). At the same time, a card reader device based on the Internet of Things is used to read the detailed information of the drug (such as production batch, expiration date, manufacturer, etc.), key data in the production process (such as the source of raw materials, production process parameters, etc.), and possible logistics information (such as transportation conditions, etc.) in the smart label built into the target drug. The smart label can be an RFID (Radio Frequency Identification) tag, an NFC (Near Field Communication) tag, or a QR code tag, etc.; then, the above-collected drug information is checked, including but not limited to: checking whether the drug name, specification, batch number, and quantity are consistent with the purchase order; checking the appearance of the drug, including the integrity of the packaging and the state of the drug (such as whether the liquid drug is leaking); checking the expiration date of the drug to ensure that it has not expired; for drugs with special requirements, such as cold chain drugs, it is also necessary to check whether the transportation conditions meet the requirements. If the target drugs collected by the above-mentioned IoT devices and technologies pass the verification, the second multi-dimensional drug information set of the target drugs will be entered into the drug management system to ensure the accuracy and completeness of the entered drug information.

[0031] After completing the above verification operation, the warehouse manager can store the target drug in an empty location in the target warehouse to complete the storage operation of the target drug. Therefore, the system can obtain the target storage location information where the warehouse manager stores the target drug.

[0032] It should be noted that when receiving the second management request from the warehouse manager to manage the warehousing of the target drug, if the second multi-dimensional drug information in the second management request fails to pass the verification, it means that the drug is an unqualified drug. For unqualified drugs, the system will automatically trigger the alarm mechanism and notify relevant personnel to handle it (such as return, destruction, etc.), thereby significantly reducing manual intervention and error rate, and improving the efficiency and accuracy of the drug warehousing process.

[0033] Step 2: Perform hash operation on each drug information in the second multi-dimensional drug information set to obtain the corresponding second hash value, and use the second hash value of each drug information in the second multi-dimensional drug information set as a leaf node to construct a target Merkle tree.

[0034] Among them, the above second step can be understood as: a process of generating a multi-dimensional coding identifier based on a multi-level index structure of a hash pointer and a Merkle tree algorithm. Among them, a hash pointer is a pointer to data, which stores the hash value of the data so that the data can be safely referenced and verified on the blockchain without worrying about data tampering or loss; and by constructing a multi-level index structure, rapid positioning and query of drug information can be achieved, wherein each level in the index structure can be constructed based on key information of different dimensions of the target drug, such as key information such as name, production batch number, and expiration date. In addition, a Merkle tree is a tree-shaped data structure for efficiently verifying the integrity and consistency of a data block. Therefore, the embodiment of the present application divides the second multi-dimensional drug information set into multiple data blocks (i.e., drug information), calculates the hash value of each data block, and then combines these hash values ​​into a Merkle tree according to a preset hierarchical structure, so that the second hash value corresponding to the unique root node of the Merkle tree can be used as the multi-dimensional coding identifier corresponding to the second multi-dimensional drug information set, and this identifier can be used to quickly verify the integrity and authenticity of the drug information of the target drug.

[0035] Step 3: Encrypt the second multi-dimensional drug information set using a preset encryption algorithm.

[0036] Specifically, the types of the above encryption algorithms include but are not limited to: symmetric encryption algorithms, such as AES (Advanced Encryption Standard), DES (Data Encryption Standard), etc., and / or asymmetric encryption algorithms, such as ECC (Elliptic Curve Cryptography), etc. In the embodiment of the present application, it is preferred to use a hybrid symmetric encryption algorithm and an asymmetric encryption algorithm, wherein the symmetric encryption algorithm is often used to encrypt the actual data content, because its high efficiency can ensure that it will not cause excessive performance burden when encrypting a large amount of data; the asymmetric encryption algorithm is mainly used to encrypt symmetric keys and perform digital signatures and other operations. It should be noted that for sensitive information in drug information, the embodiment of the present application can select a higher level encryption algorithm for protection.

[0037] For example, when two parties communicate for the first time, the receiver can send his public key to the sender. The sender uses the receiver's public key to encrypt the symmetric key and then sends the encrypted symmetric key to the receiver. The receiver then uses his private key to decrypt the symmetric key. After that, both parties can use this symmetric key for fast symmetric encryption communication.

[0038] Step 4: Associate and store the encrypted second multi-dimensional drug information set, the target Merkle tree, and the target storage location information in the target block storage node in the blockchain.

[0039] Among them, since the second multi-dimensional drug information set includes the commercial information and drug information of the target drug; the Merkle tree contains the hash value of each drug information in the second multi-dimensional drug information set, therefore, when it is necessary to verify a transaction or drug information, it is only necessary to check whether the hash value of the information matches the corresponding hash value in the Merkle tree, without verifying the data of the entire block; the target storage location information provides the location information of the target drug in the target warehouse, so that the system can monitor the physical location of the drug in real time, which is very important for large warehouses or hospital pharmacies, and can ensure the efficient, orderly storage and timely delivery of drugs. In addition, blockchain is a decentralized distributed ledger technology with the characteristics of non-tamperability and transparency. Therefore, the embodiment of the present application associates and stores the above information in the target block storage node in the blockchain, which can ensure the non-tamperability, rapid verification, unique traceability and intelligent tracking of drug information. It should be noted that different block storage nodes or chains in the blockchain store drug information of different types and importance levels.

[0040] After completing the target drug warehousing and drug information storage on the blockchain through the first and fourth steps above. The system can use the blockchain's smart contract technology to automatically trigger information verification and association operations, and strongly associate the label information with the drug information on the blockchain. In other words, when a drug enters a new life cycle stage (such as production completion, warehousing, delivery, sales, etc.), the system can automatically trigger the information verification process through a smart contract, which includes verifying whether the information on the smart label built into the drug is consistent with the information stored on the blockchain, and verifying the integrity and authenticity of the information; after verifying that the information is correct, the smart contract can automatically strongly associate the information on the smart label with the drug information on the blockchain, which can be achieved by binding the smart label's unique identifier (such as the RFID chip's ID or the QR code) to the drug information record on the blockchain.

[0041] In addition, the present application embodiment can also represent the information of each stage of the drug's attributes, production, logistics, sales, etc. as the association or attribute between entities. Therefore, as the drug moves through various stages in the life cycle, the smart contract continuously adds new information to the information map, including drug status information, stored environmental parameter data, drug full chain information recorded on the blockchain, and key data such as drug basic information (such as production batch, expiration date, etc.) stored in the smart tag built into the target drug, as well as association information with other entities (such as raw material suppliers, manufacturers, distributors, etc.), thereby obtaining a traceability information network for the entire life cycle of the drug, thereby visually displaying the entire chain of drugs from production to sales in a graphical manner, and therefore, the full traceability of drugs can be achieved through the network, improving the transparency and efficiency of drug management, and at the same time, it can also provide strong data support for drug supervision, recall, quality control, etc.

[0042] Optionally, after receiving the second management request from the warehouse manager to manage the storage of the target drug, the system may also determine whether the target drug can be stored in the target warehouse according to the following method, including:

[0043] First, according to the preset drug storage rules and the matching results of the drug attribute information in the second multi-dimensional drug information set, the standard parameter range of the storage environment corresponding to the target drug is determined.

[0044] The drug storage rules store drug attribute information of multiple drugs and standard parameter ranges of corresponding storage environments, and the environmental parameters include at least one of the following: temperature parameters, humidity parameters, and air pressure parameters;

[0045] Then, the preset sensors are used to collect the real-time environmental parameters of the target warehouse (such as temperature parameters, humidity parameters, etc.), and it is determined whether the real-time environmental parameter information is within the standard parameter range.

[0046] Finally, if the real-time environmental parameter information is not within the standard parameter range, the real-time environmental parameter information of the target warehouse is adjusted to be within the standard parameter range according to the preset adjustment strategy.

[0047] For example, if the current real-time temperature of the target warehouse exceeds the additional temperature range required for the storage of the target drugs, directly storing the target drugs in the target warehouse may cause the target drugs to become ineffective or spoiled. Therefore, the embodiment of the present application will increase or decrease the real-time temperature of the target warehouse to ensure that the real-time temperature is within the additional temperature range required for the storage of the target drugs.

[0048] Step S104, determining the first hash value of each drug information in the first multi-dimensional drug information set, and matching the first hash value with multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree.

[0049] In the technical solution provided in the above step S104, when each drug is put into storage, the system will associate and store the Merkle tree corresponding to the multi-dimensional drug information set of the drug and the storage location information of the drug in different block storage nodes in the blockchain, and the Merkle tree is constructed by the hash value of each drug information in the multi-dimensional drug information set of the drug as a leaf node. In addition, the storage location information of the drug reflects the warehouse identification of the warehouse where the drug is located and the location information in the warehouse. Therefore, the system calculates the first hash value of each drug information in the first multi-dimensional drug information set through a preset hash operation, and then matches the calculated first hash value with the multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree. Therefore, the warehouse identification of the target warehouse where the target drug is located and the location information in the target warehouse can be determined through the target storage location information.

[0050] It should be noted that if the system matches the first hash value with multiple Merkle trees in the blockchain and the matching result is empty, it is determined that the second multi-dimensional drug information set stored on the blockchain for the target drug is abnormal. In this case, the system can feedback the corresponding abnormal alarm information to the target object, and this abnormal alarm information is used to indicate that the second multi-dimensional drug information set has been tampered with or data damaged during the on-chain storage process.

[0051] Step S106, obtaining an augmented reality scene map of the target warehouse where the target drug is located.

[0052] In the technical solution provided in the above step S106, the above-mentioned augmented reality scene graph is pre-scanned using a three-dimensional scanning device (such as a laser scanner, a 3D camera) to scan the entire warehouse and capture the three-dimensional structure of the target warehouse; then, the scanning data is converted into a three-dimensional model of the warehouse using 3D modeling software; finally, the location information, item status, in-and-out warehouse paths and other information of each drug in the warehouse management system are integrated with the three-dimensional model to obtain the corresponding augmented reality scene graph.

[0053] It should be noted that in order to ensure that the augmented reality scene diagram of the target warehouse is consistent with the actual situation of the warehouse, the system can establish a real-time update mechanism. That is, when the status, location or quantity of the medicine in the warehouse management system changes, the information on the augmented reality scene diagram should also be updated synchronously.

[0054] Step S108, determining the target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and feeding back the target outbound path to the target object.

[0055] In the technical solution provided in the above step S108, the system can determine the target outbound path of the target drug through the target storage location information and the augmented reality scene graph, so it can feed back the target outbound path to the target object, so that the target object can quickly and accurately locate the target drug according to the target outbound path, reducing the time and energy consumption of searching for drugs, and at the same time avoiding erroneous outbound delivery due to human negligence, ensuring that each outbound delivery operation is accurate, which is particularly important in the medical field, because the accurate distribution of drugs is directly related to the treatment effect and life safety of patients.

[0056] Furthermore, after the target object receives the target outbound path fed back by the system, it can extract the target drug from the target warehouse according to the target outbound path, thereby completing the outbound delivery of the target drug. At this time, the system will synchronously update the drug inbound and outbound status information in the second multidimensional drug information set. At the same time, the system can also perform hash operations on each drug information in the updated second multidimensional drug information set to obtain the corresponding second hash value, and use the second hash value of each drug information in the updated second multidimensional drug information set as a leaf node to obtain an updated target Merkle tree, and then update the encrypted second multidimensional drug information set and target Merkle tree stored in the blockchain, and update the target storage location information of the target drug to empty.

[0057] As another optional implementation, the embodiment of the present application can also analyze drug inventory information to predict whether there will be a drug shortage, thereby managing the drug inventory. The specific process is as follows:

[0058] First, obtain the drug in-and-out data of the target drug in the first time period and the disease epidemic trend information in the first time period. The drug in-and-out data is used to reflect the in-and-out information of the target drug and multi-dimensional drug information, and the in-and-out information includes at least one of the following: out-of-stock time, in-stock time, out-of-stock quantity, in-stock quantity; and the disease epidemic trend information includes at least one of the following: disease type, number of infected people, geographical distribution, etc.

[0059] Next, the acquired drug inventory data and disease epidemic trend information are preprocessed, including but not limited to: cleaning, denoising, filling missing values, etc.; features related to drug shortage prediction are extracted from the preprocessed drug inventory data and disease epidemic trend information, such as the peak time of drug demand, the correlation between disease prevalence and drug demand, and drug inventory time series characteristics.

[0060] Then, the pre-trained first neural network model is used to analyze the drug in-and-out data and disease epidemic trend information to predict the probability of shortage of the target drug in the second time period. For time series data, the first neural network model can use the recurrent layer to capture the long-term dependencies of the time series; for non-time series features, the first neural network can use the fully connected layer for feature fusion; and then use the multi-head attention mechanism to fuse the prediction results of time series and non-time series features to output the predicted value of the probability of drug shortage.

[0061] Among them, the above-mentioned first neural network model is trained using drug inventory data in different historical time periods and disease epidemic trend information (i.e. season, disease epidemic trend) in the corresponding historical time periods to learn the complex relationship between drug demand and disease trends.

[0062] Finally, the predicted shortage probability is compared with the preset probability threshold. If the shortage probability is greater than the preset probability threshold, it is judged that there is a risk of drug shortage. At this time, replenishment reminder information can be fed back to the target object (such as the drug procurement department of the medical institution, supplier, etc.) to notify them that they need to replenish or adjust the drug inventory in advance.

[0063] The above process can optimize drug inventory management to reduce medical risks caused by shortages.

[0064] Optionally, with respect to the replenishment reminder information feedback in the above process, in order to further optimize the drug inventory management, the embodiment of the present application further proposes the following solution to ensure that the drug management process is optimized under the premise of satisfying various constraints, including:

[0065] Step 1: Obtain the total quantity of target drugs stored in the target warehouse and determine the difference between the total quantity and the quantity shipped out;

[0066] Step 2: When the difference is lower than the preset threshold, obtain the available storage space in the target warehouse and determine the difference between the total quantity and the outbound quantity;

[0067] Step 3: construct a procurement optimization function with the goal of minimizing the cost of replenishment, and use the augmented Lagrange multiplier algorithm to solve the procurement optimization function, and determine a replenishment strategy that meets the preset constraints, where the constraints include at least one of the following: the storage space required to store the purchased drugs is not higher than the available storage space in the target warehouse, the purchase amount is not higher than the preset limit, and the replenishment time does not exceed the preset time limit;

[0068] Step 4: Feedback the target object with the replenishment prompt information containing the replenishment strategy.

[0069] In other words, the system can first determine the optimization goal as the lowest replenishment cost, and the constraints are that the storage space required to store purchased drugs is not higher than the storage space in the target warehouse, the purchase amount is not higher than the preset limit, and the replenishment time does not exceed the preset time limit; then, the Lagrange multiplier is introduced to transform the original constrained optimization problem into an unconstrained problem. In the iterative calculation process, the values ​​of the Lagrange multiplier and decision variables (such as the purchase volume of drugs, replenishment time point, etc.) are continuously adjusted, and the optimal solution is gradually approached by solving a series of sub-problems. In each iteration, the characteristics of the augmented Lagrange multiplier algorithm are used to reduce the amount of calculation and improve the solution efficiency while maintaining the problem constraints until the convergence conditions are met and the optimal drug replenishment strategy that meets the constraints is obtained.

[0070] As another optional implementation, in terms of drug quality, the embodiment of the present application can also use the second neural network model to learn the association pattern between the production process parameters (such as temperature, pressure, reaction time, etc.) in the drug production process, the source of raw materials and the final quality test results of the drug, so as to use the pre-trained second neural network model to predict the expiration probability of the drug; in terms of drug use, the embodiment of the present application can also use the third neural network model to learn the relationship pattern between the dosage, usage time and treatment effect and adverse reactions of various drugs used by different departments and patients with different diseases, so as to use the pre-trained third neural network model to predict the abuse probability of the drug.

[0071] Through the above steps, the embodiment of the present application can achieve the following technical effects:

[0072] (1) The embodiments of the present application use intelligent management methods to automatically complete operations such as the warehousing, outbound storage, and inventory of drugs, thereby reducing dependence on human resources, eliminating the need for a large amount of manual participation, reducing labor costs and management difficulties, and thus significantly improving work efficiency. During peak periods, the system can respond quickly to ensure the timely receipt, delivery, and management of drugs, ensure the normal operation of medical services, and improve overall operational efficiency.

[0073] (2) The embodiments of the present application automatically read and record drug information, thereby avoiding errors and negligence that may occur during manual entry and updating, ensuring the accuracy and reliability of drug information, avoiding problems such as wrong issuance and misuse of drugs, reducing medical risks, and ensuring the safety of patients' medication.

[0074] (3) The embodiment of the present application uses blockchain technology to achieve full traceability of drugs from production to use. Once a quality problem or safety accident occurs, the system can quickly locate the source of the problem, providing important support for taking remedial measures, thereby reducing medical risks, reducing potential harm to patients and medical institutions, and maintaining the reputation and image of medical institutions.

[0075] Example 2

[0076] According to an embodiment of the present application, a drug management device for implementing the drug management method in embodiment 1 is also provided. Figure 2 As shown, the drug management device at least includes: a receiving module 22, a determining module 24, an acquiring module 26 and a feedback module 28, wherein:

[0077] The receiving module 22 is used to receive a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least the outbound quantity of the target drug and a first multi-dimensional drug information set of the target drug;

[0078] A determination module 24 is used to determine a first hash value of each drug information in the first multi-dimensional drug information set, and match the first hash value with multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree, wherein each block storage node in the blockchain stores Merkle trees corresponding to multi-dimensional drug information sets of different drugs and storage location information of the drugs, and the Merkle tree is constructed with the hash value of each drug information of the drug as a leaf node, and the storage location information is used to reflect the warehouse identification of the warehouse where the drug is located and the location information in the warehouse;

[0079] The acquisition module 26 is used to acquire an augmented reality scene diagram of the target warehouse where the target drug is located, wherein the augmented reality scene diagram at least includes: location information of each drug in the target warehouse and a delivery path corresponding to each storage location;

[0080] The feedback module 28 is used to determine the target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and feed back the target outbound path to the target object.

[0081] It should be noted that each module in the drug management device in the embodiment of the present application corresponds one by one to each implementation step of the drug management method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be elaborated here.

[0082] Example 3

[0083] According to an embodiment of the present application, a computer program product is also provided, which includes a computer program, wherein when the computer program is executed by a processor, the drug management method in Example 1 is implemented.

[0084] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the drug management method in Example 1 by running the computer program.

[0085] According to an embodiment of the present application, a processor is also provided, which is used to run a computer program, wherein the drug management method in Example 1 is executed when the computer program is running.

[0086] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the drug management method in Example 1 through the computer program.

[0087] Specifically, when the computer program is running, the following steps are executed: receiving a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least the outbound quantity of the target drug and a first multidimensional drug information set of the target drug; determining a first hash value of each drug information in the first multidimensional drug information set, and matching the first hash value with a plurality of Merkle trees in the blockchain, and determining the target storage location information associated with the successfully matched target Merkle tree, wherein each block storage node in the blockchain stores Merkle trees corresponding to multidimensional drug information sets of different drugs and the storage location information of the drugs, respectively, and the Merkle tree is constructed as a leaf node using the hash value of each drug information of the drug, and the storage location information is used to reflect the warehouse identification of the warehouse where the drug is located and the location information in the warehouse; obtaining an augmented reality scene graph of the target warehouse where the target drug is located, wherein the augmented reality scene graph at least includes: the location information of each drug in the target warehouse and the outbound path corresponding to each storage location; determining a target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and feeding back the target outbound path to the target object.

[0088] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 3 FIG. 1 shows a hardware structure block diagram of an electronic device for implementing a drug management method. Figure 3 As shown, the electronic device 30 may include one or more (302a, 302b, ..., 302n are used to illustrate) processors 302 (the processor 302 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 304 for storing data, and a transmission device 306 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 3 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 3 More or fewer components as shown, or with Figure 3 Different configurations shown.

[0089] It should be noted that the one or more processors 302 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 30. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0090] The memory 304 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the drug management method in the embodiment of the present application. The processor 302 executes various functional applications and data processing by running the software programs and modules stored in the memory 304, that is, realizing the vulnerability detection method of the above-mentioned application. The memory 304 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 304 may further include a memory remotely arranged relative to the processor 302, and these remote memories may be connected to the electronic device 30 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0091] The transmission device 306 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 30. In one example, the transmission device 306 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 306 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0092] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 30 .

[0093] The serial numbers of the above embodiments are only for description and do not represent the advantages or disadvantages of the embodiments.

[0094] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0096] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0098] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0099] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A drug management method, characterized in that: include: Receive a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least an outbound quantity of the target drug and a first multi-dimensional drug information set of the target drug; Determine a first hash value of each drug information in the first multi-dimensional drug information set, and match the first hash value with multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree, wherein each block storage node in the blockchain stores a Merkle tree corresponding to a multi-dimensional drug information set of different drugs and the storage location information of the drugs, and the Merkle tree is constructed with the hash value of each drug information of the drug as a leaf node, and the storage location information is used to reflect the warehouse identifier of the warehouse where the drug is located and the location information of the drug in the warehouse; Obtaining an augmented reality scene diagram of a target warehouse where the target drug is located, wherein the augmented reality scene diagram at least includes: location information of each drug in the target warehouse and a warehouse-out path corresponding to each storage location; The target outbound path of the target drug is determined based on the target storage location information and the augmented reality scene graph, and the target outbound path is fed back to the target object.

2. The method according to claim 1, characterized in that Before receiving a first management request from a target object to perform outbound management on a target drug, the method further includes: Receive a second management request from a warehouse manager to perform warehousing management on the target drug, and obtain target storage location information for the target drug to be stored by the warehouse manager, wherein the second management request carries at least the warehousing quantity of the target drug to be stored, and a second multi-dimensional drug information set of the target drug, and the second multi-dimensional drug information set includes at least one of the following: drug transportation information, drug warehousing status information, drug production information, and drug attribute information, and the target storage location information is used to reflect the warehouse identification of the target warehouse where the target drug is located and the location information of the target drug in the target warehouse; Performing a hash operation on each piece of drug information in the second multi-dimensional drug information set to obtain a corresponding second hash value, and using the second hash value of each piece of drug information in the second multi-dimensional drug information set as a leaf node to construct a target Merkle tree; Encrypting the second multi-dimensional drug information set using a preset encryption algorithm, wherein the type of the encryption algorithm includes at least one of the following: a symmetric encryption algorithm and an asymmetric encryption algorithm; The encrypted second multi-dimensional drug information set, the target Merkle tree, and the target storage location information are associated and stored in the target block storage node in the blockchain.

3. The method according to claim 2, characterized in that After receiving a second management request from a warehouse manager to perform storage management on the target drug, the method further includes: According to the matching result of the preset drug storage rule and the drug attribute information in the second multi-dimensional drug information set, determine the standard parameter range of the storage environment corresponding to the target drug, wherein the drug storage rule stores drug attribute information of multiple drugs and the standard parameter range of the corresponding storage environment, and the environmental parameter includes at least one of the following: temperature parameter, humidity parameter, and air pressure parameter; Collecting the real-time environmental parameters of the target warehouse, and determining whether the real-time environmental parameter information is within the standard parameter range; When the real-time environmental parameter information is not within the standard parameter range, the real-time environmental parameter information of the target warehouse is adjusted to be within the standard parameter range according to a preset adjustment strategy.

4. The method according to claim 1, characterized in that: The second multi-dimensional drug information set also includes: drug storage status information. After feeding back the target storage path to the target object, the method further includes: After the target drug is shipped out of the warehouse, the drug in-and-out status information in the second multidimensional drug information set of the target drug is updated, and a hash operation is performed on each drug information in the updated second multidimensional drug information set to obtain the corresponding second hash value, and the second hash value of each drug information in the updated second multidimensional drug information set is used as a leaf node to obtain an updated target Merkle tree.

5. The method according to claim 1, characterized in that The method further comprises: The first hash value is matched with multiple Merkle trees in the blockchain. When the obtained matching result is empty, it is determined that the second multi-dimensional drug information set stored on the blockchain for the target drug is abnormal, and corresponding abnormal alarm information is fed back to the target object.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining drug in-and-out data of the target drug in a first time period and disease epidemic trend information in the first time period, wherein the drug in-and-out data is used to reflect the in-and-out information and multi-dimensional drug information of the target drug, and the in-and-out information includes at least one of the following: out-of-stock time, in-stock time, out-of-stock quantity, and in-stock quantity; The drug in-and-out data and the disease epidemic trend information are analyzed using a pre-trained first neural network model to predict the shortage probability of the target drug in a second time period, and when the shortage probability is greater than a preset probability threshold, replenishment reminder information is fed back to the target object, wherein the second time period is the time period after the first time period.

7. The method according to claim 6, characterized in that Feedback the target object with the margin call reminder information, including: Obtaining the total quantity of the target drug stored in the target warehouse, and determining the difference between the total quantity and the quantity shipped out; When the difference is lower than a preset threshold, the available storage space in the target warehouse is obtained, and the difference between the total quantity and the outbound quantity is determined; A procurement optimization function is constructed with the goal of minimizing the cost of replenishment, and the augmented Lagrange multiplier algorithm is used to solve the procurement optimization function to determine a replenishment strategy that satisfies preset constraints, wherein the constraints include at least one of the following: the storage space required to store the purchased drugs is not higher than the available storage space in the target warehouse, the purchase amount is not higher than the preset limit, and the replenishment time does not exceed the preset time limit; Feedback the target object with the margin call prompt information containing the margin call strategy.

8. A drug management device, characterized in that: include: A receiving module, configured to receive a first management request from a target object to perform outbound management on a target drug, wherein the first management request carries at least an outbound quantity of the target drug and a first multi-dimensional drug information set of the target drug; A determination module, used to determine a first hash value of each drug information in the first multi-dimensional drug information set, and match the first hash value with multiple Merkle trees in the blockchain to determine the target storage location information associated with the successfully matched target Merkle tree, wherein each block storage node in the blockchain stores a Merkle tree corresponding to a multi-dimensional drug information set of different drugs and the storage location information of the drugs, and the Merkle tree is constructed with the hash value of each drug information of the drug as a leaf node, and the storage location information is used to reflect the warehouse identification of the warehouse where the drug is located and the location information of the drug in the warehouse; An acquisition module, used to acquire an augmented reality scene diagram of a target warehouse where the target drug is located, wherein the augmented reality scene diagram at least includes: location information of each drug in the target warehouse and a delivery path corresponding to each storage location; A feedback module is used to determine a target outbound path of the target drug based on the target storage location information and the augmented reality scene graph, and to feed back the target outbound path to the target object.

9. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, it implements the drug management method described in any one of claims 1 to 7.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the drug management method according to any one of claims 1 to 7 through the computer program.

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