Intelligent medicine storage management method and system based on digital twinning

Abstract

The invention provides an intelligent medicine storage management method and system based on digital twinning. The method comprises the following steps: firstly, generating a virtual storage space, embedding a binding relationship between a medicine batch number and a space coordinate, and then collecting temperature gradient distribution data, humidity fluctuation data and illumination intensity time sequence data of each medicine storage unit in a physical medicine warehouse; the method comprises the following steps: acquiring three kinds of data, mapping the three kinds of data to corresponding coordinate positions in a virtual storage space, generating a three-dimensional thermodynamic map based on temperature gradient distribution data and storage environment standard parameters associated with drug batch numbers, identifying an abnormal area, and reversely positioning a corresponding drug storage unit in a physical medicine warehouse according to the coordinate position of the abnormal area. The directional ventilation device is controlled to adjust the airflow circulation path of the medicine storage unit, a batch medicine migration path planning scheme is generated, the technical scheme provided by the invention realizes fine management and real-time monitoring of the medicine storage environment, and the safety and efficiency of medicine storage are significantly improved.

Description

Technical Field

[0001] The present application relates to the technical field of pharmaceutical warehouse management, and in particular to an intelligent pharmaceutical warehouse management method and system based on digital twins. Background Art

[0002] In modern pharmaceutical warehousing management, it is crucial to ensure the stability and compliance of the drug storage environment, especially in large pharmaceutical warehouses. Different drugs have strict requirements on environmental conditions such as temperature, humidity and light. Any deviation from environmental parameters may cause the drug to deteriorate or become ineffective, thus affecting medical safety.

[0003] Currently, many pharmaceutical warehouse management systems mainly rely on traditional sensor networks to monitor environmental parameters in the warehouse, such as temperature and humidity sensors, light sensors, etc. These sensors transmit the collected data to the central control system for analysis and alarm. In addition, some advanced systems also integrate automated control equipment, such as air-conditioning systems and ventilation equipment that automatically adjust temperature and humidity. However, the implementation of such systems is often limited to single-dimensional data monitoring and simple threshold alarm mechanisms, lacking the ability to comprehensively analyze multi-dimensional data in the entire storage space, and the ability to quickly respond to complex environmental changes. Summary of the invention

[0004] The present application provides an intelligent pharmaceutical warehouse management method and system based on digital twins to solve the problems of insufficient environmental monitoring accuracy, slow response speed, and weak data integration and analysis capabilities in the prior art.

[0005] In the first aspect, the present application provides an intelligent pharmaceutical warehouse management method based on digital twins, comprising: Generate a virtual storage space that is consistent with the geometric structure of a physical pharmaceutical warehouse, and embed a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; Collecting temperature gradient distribution data, humidity fluctuation data, and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and mapping the temperature gradient distribution data, the humidity fluctuation data, and the light intensity time series data to corresponding coordinate positions in the virtual storage space; Generate a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identify abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; The corresponding drug storage unit in the physical pharmaceutical warehouse is reversely located according to the coordinate position of the abnormal area, and the directional ventilation equipment is controlled to adjust the airflow circulation path of the drug storage unit, and a batch drug migration path planning scheme is generated.

[0006] Optionally, based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, a three-dimensional thermal map is generated, and abnormal areas exceeding the storage environment standard parameters are identified in the three-dimensional thermal map, including: Obtaining storage environment standard parameters corresponding to each drug batch number in the virtual storage space, and extracting the upper temperature limit and the temperature change rate limit in the storage environment standard parameters; Decomposing the temperature gradient distribution data into the spatial axial temperature change rate and the time dimension temperature accumulation, and generating an environmental tolerance attenuation coefficient inversely proportional to the remaining validity period of the drug according to the storage duration data associated with the coordinate position of the drug batch number; The temperature change rate limit value and the environmental tolerance attenuation coefficient are integrated to generate a fusion result, and the temperature upper limit value is corrected according to the fusion result to obtain a dynamic determination threshold value for each coordinate position; Based on the spatial axial temperature change rate and the dynamic determination threshold, a three-dimensional thermal map is generated by stacking layer by layer in the virtual storage space; A continuous voxel region whose color saturation exceeds a critical value in the three-dimensional thermal map is identified, and a polyhedron formed by connecting the center points of adjacent voxels in the continuous voxel region is marked as an abnormal region.

[0007] Optionally, the upper temperature limit is corrected according to the fusion result to obtain a dynamic determination threshold value for each coordinate position, including: Performing a convolution operation on the temperature change rate limit and the environmental tolerance attenuation coefficient to generate a fusion weight factor; Based on the fusion weight factor, dynamically adjust the upper temperature limit value to generate a preliminary correction threshold value; According to the spatial position characteristics of the shelf level in the physical pharmaceutical warehouse where the drug storage unit is located, the preliminary correction threshold is corrected by spatial compensation to generate an intermediate correction threshold; Combined with the environmental sensitivity data of other drugs within a preset range around the drug storage unit, the intermediate correction threshold is cross-impact corrected to generate a dynamic determination threshold.

[0008] Optionally, the corresponding drug storage unit in the physical medicine warehouse is reversely located according to the coordinate position of the abnormal area, and the directional ventilation equipment is controlled to adjust the airflow circulation path of the drug storage unit, and a batch drug migration path planning scheme is generated, including: Matching the three-dimensional position information of the corresponding drug storage unit in the physical medicine warehouse according to the geometric center coordinates of the abnormal area; Based on the three-dimensional position information of the drug storage unit, establishing an airflow regulation area model centered on the drug storage unit; Calculate the environmental adjustment parameters required by the airflow adjustment area model according to the temperature gradient distribution data and humidity fluctuation data in the airflow adjustment area model, and generate a control instruction set of the directional ventilation equipment based on the environmental adjustment parameters; Determining a migration priority parameter according to the drug attribute information associated with the drug batch number; Based on the migration priority parameters and the real-time layout status of the physical pharmaceutical warehouse, a path planning algorithm is used to generate a migration path plan for the batch of drugs, and the control instruction set is sent to the corresponding directional ventilation equipment for execution, and the migration path plan is started simultaneously.

[0009] Optionally, the migration priority parameter is determined according to the drug attribute information associated with the drug batch number, including: Extracting the remaining validity period of the drug, the sensitivity level of the drug storage environment, and the drug packaging specification parameters from the drug attribute information; Calculating a time decay coefficient based on the remaining validity period data of the drug, wherein the time decay coefficient is inversely proportional to the number of days of the remaining validity period of the drug, and triggering an exponential growth correction when the remaining validity period of the drug is less than a preset critical number of days; According to the drug storage environment sensitivity level, a preset priority weight table is matched to generate an environmental sensitivity weight value; Analyze the stacking layer limit and seismic resistance level in the drug packaging specification parameters, and calculate the handling complexity index of the packaging unit corresponding to each drug batch number; The time decay coefficient, the environmental sensitivity weight value and the handling complexity index are input into a preset migration priority decision model, and the migration priority parameter is generated using the migration priority decision model.

[0010] Optionally, the stacking layer limit and the seismic resistance level in the drug packaging specification parameters are parsed to calculate the handling complexity index of the packaging unit corresponding to each drug batch number, including: Obtain the maximum allowable number of stacking layers and earthquake resistance level data contained in the drug packaging specification parameters; According to the maximum allowed number of stacked layers, a preset layer complexity mapping table is queried to obtain a basic handling complexity value, wherein, for each additional layer in the layer complexity mapping table, the basic handling complexity value increases by a preset step increment; Calculate the seismic correction coefficient based on the seismic grade data; Obtaining the size data of the packaging unit corresponding to the drug batch number, and calculating the space utilization rate of the packaging unit; The basic handling complexity value, seismic correction coefficient and space utilization rate are input into a preset complexity calculation model to generate a handling complexity index of the packaging unit.

[0011] Optionally, generating a virtual storage space consistent with the geometric structure of a physical pharmaceutical warehouse, and embedding a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space, comprises: Obtain architectural CAD drawings and shelf layout data of physical pharmaceutical warehouses, extract warehouse 3D geometric features and functional area division information; Based on the extraction of warehouse 3D geometric features and functional area division information, a reference 3D model of the virtual storage space is constructed. The reference 3D model includes the following hierarchical structure: the overall space outline of the warehouse, the boundary framework of each functional area, the grid coordinates of the shelf unit, and the precise positioning points of the storage slot openings; The batch number information of the medicines stored in each storage compartment is collected by an RFID scanning device, and a first-level binding relationship is established between the batch number information of the medicines and the corresponding storage compartment positioning point; According to the drug packaging size data, a three-dimensional place-occupying model of the drug packaging unit is established in the virtual storage space, and a second-level binding relationship is established between the three-dimensional place-occupying model and the storage compartment positioning point; Receive drug displacement instructions from the warehouse management system in real time, dynamically update the first-level binding relationship and the second-level binding relationship, and record the change timestamp.

[0012] In the second aspect, the present application provides an intelligent pharmaceutical warehouse management system based on digital twins, including: A generation module, used to generate a virtual storage space consistent with the geometric structure of a physical pharmaceutical warehouse, and embed a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; A collection module, used to collect temperature gradient distribution data, humidity fluctuation data and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and map the temperature gradient distribution data, the humidity fluctuation data and the light intensity time series data to corresponding coordinate positions in the virtual storage space; An identification module, for generating a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identifying abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; The control module is used to reversely locate the corresponding drug storage unit in the physical pharmaceutical warehouse according to the coordinate position of the abnormal area, control the directional ventilation equipment to adjust the airflow circulation path of the drug storage unit, and generate a batch drug migration path planning plan.

[0013] In the third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent pharmaceutical warehouse management method based on digital twins as described in the first aspect above.

[0014] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements an intelligent pharmaceutical warehouse management method based on digital twins as described in the first aspect.

[0015] The embodiment of the present application realizes accurate monitoring of the environmental conditions of each drug storage unit by generating a virtual storage space that is consistent with the geometric structure of the physical pharmaceutical warehouse and embedding the binding relationship between the drug batch number and the spatial coordinates therein. This method can not only collect and map temperature gradient distribution data, humidity fluctuation data, and light intensity time series data to the corresponding coordinate positions in the virtual storage space in real time, but also generate three-dimensional thermal maps based on these data and identify abnormal areas, thereby realizing all-round monitoring and rapid response to the drug storage environment. Finally, the specific drug storage unit in the physical pharmaceutical warehouse is reversely located based on the location information of the abnormal area, and the environmental deviation is quickly corrected by adjusting the directional ventilation equipment and planning the batch drug migration path, thereby greatly improving the safety and efficiency of pharmaceutical warehouse management.

[0016] Furthermore, by obtaining the storage environment standard parameters corresponding to each drug batch number and extracting the temperature upper limit, and combining the spatial axial temperature change rate and the time-dimensional temperature accumulation of the temperature gradient distribution data, an environmental tolerance attenuation coefficient inversely proportional to the remaining validity period of the drug is generated, so as to dynamically correct the temperature upper limit and obtain a dynamic judgment threshold suitable for different coordinate positions. This process enables the three-dimensional thermal map to not only reflect the current temperature conditions, but also take into account the increased sensitivity of drugs to environmental changes over time, and more accurately identify potential risk areas. By identifying continuous voxel areas in the three-dimensional thermal map whose color saturation exceeds the critical value, this method can accurately mark abnormal areas that need attention, providing managers with more scientific and detailed decision-making support, effectively preventing drug damage or failure due to excessive environmental parameters, and improving the intelligence level and reliability of the pharmaceutical warehouse management system.

[0017] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flowchart of a smart pharmaceutical warehouse management method based on digital twins provided by the present application is shown; Figure 2 A schematic diagram of the structure of an intelligent pharmaceutical warehouse management system based on digital twins provided by the present application is shown; Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. 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.

[0021] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0022] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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 those skilled in the art without creative work are within the scope of protection of this application.

[0023] Figure 1 A flowchart of an intelligent pharmaceutical warehouse management method based on digital twins is provided for the embodiment of the present application, such as Figure 1 As shown, the method includes: Step 101, generating a virtual storage space consistent with the geometric structure of a physical pharmaceutical warehouse, and embedding a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; In this step, the physical pharmaceutical warehouse geometry refers to the three-dimensional spatial layout of the actual pharmaceutical warehouse and its internal structural characteristics, including the location and size of the shelves and the specific distribution of each storage unit; the virtual storage space is a digital model created based on digital twin technology that is completely consistent with the physical pharmaceutical warehouse. It not only reflects the geometry and layout of the physical pharmaceutical warehouse, but also simulates the changes in its internal environmental parameters; the drug batch number refers to the unique identifier assigned to each batch of drugs when it is produced, which is used to distinguish different batches of drugs and track their source and quality; the spatial coordinates are the three-dimensional coordinate values ​​assigned to each specific storage location in the virtual storage space, which are used to accurately locate the location of each drug batch number; the binding relationship refers to the association of the drug batch number with its spatial coordinates, so that the system can quickly locate the information of the specific drug batch number through the coordinates.

[0024] In this embodiment, first, the entire pharmaceutical warehouse is scanned with high precision by using a laser scanner or a drone to obtain detailed architectural CAD drawings and shelf layout data. Then, an accurate three-dimensional model is constructed using these data and imported into a specialized warehouse management software to generate a virtual storage space. On this basis, each storage location is marked using RFID tags and reading devices to ensure that each drug batch number can be accurately identified and recorded. Next, the drug batch number is manually or automatically bound to the corresponding storage location coordinates through the configuration tool in the warehouse management system. Finally, the system will dynamically adjust the coordinate position corresponding to each drug batch number based on the real-time updated inventory information to ensure data consistency and accuracy.

[0025] For example, suppose a pharmaceutical company owns a large warehouse with an area of ​​5,000 square meters. In order to achieve intelligent management, it decides to use digital twin technology to optimize the warehousing process. First, the technicians use a laser scanner to conduct a comprehensive scan of the warehouse and obtain the precise size and layout information of the warehouse. Then, they use these data to build a highly simulated virtual storage space in the warehouse management software. When a new batch of drugs is put into storage, the staff attaches an RFID tag containing the batch number information of the drugs to each box of drugs. Then, through the RFID reading device at the entrance of the warehouse, the system automatically records the specific time when this batch of drugs enters and allocates a suitable storage location for it. The system binds the drug batch number to the specific location according to the three-dimensional coordinates of the storage location. In this way, whether it is daily inquiries or drug allocation in emergency situations, managers can quickly and accurately find the required drugs, greatly improving work efficiency and management level.

[0026] Step 102, collecting temperature gradient distribution data, humidity fluctuation data, and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and mapping the temperature gradient distribution data, the humidity fluctuation data, and the light intensity time series data to corresponding coordinate positions in the virtual storage space; In this step, the drug storage unit refers to the specific location or container in the warehouse for storing drugs. It can be a grid on the shelf, a specific area in the refrigerator, etc.; the temperature gradient distribution data refers to the temperature changes at different locations in the entire drug storage space, which reflects the change pattern of temperature with spatial position; the humidity fluctuation data refers to the change of relative humidity in each storage unit within a certain period of time, which helps to understand the impact of environmental humidity on drug preservation; the light intensity time series data records the change of light intensity over time in a specific area, which is especially important for drugs that need to be stored away from light; mapping these data to the corresponding coordinate positions in the virtual storage space means matching the actually measured environmental parameters with the corresponding positions in the virtual model for real-time monitoring and analysis.

[0027] In this embodiment, first, multiple sensors are deployed in each drug storage unit, including temperature sensors, humidity sensors, and light sensors, to ensure that the required environmental parameters can be fully collected. Then, the data collected by these sensors are transmitted to the central control system in real time via a wireless network. Then, using specially developed data processing software, the received temperature gradient distribution data, humidity fluctuation data, and light intensity time series data are mapped to the corresponding coordinates of the virtual storage space according to their physical locations. This process involves complex algorithms to ensure the accuracy and consistency of the data, and the system will automatically update the environmental parameters in the virtual storage space so that managers can view the latest environmental conditions at any time.

[0028] For example, continuing with the previous example, suppose that a pharmaceutical company's warehouse has completed the construction of a virtual storage space and assigned a precise location to each box of medicine. In order to further improve management accuracy, the company decided to install highly sensitive sensors in each drug storage unit. For example, temperature and humidity sensors are set up at key locations in the cold storage area, and light sensors are installed in areas where medicines need to be kept away from light. These sensors automatically collect data at regular intervals and send it to the central control system via a wireless network. The system then uses customized data processing software to match this information with the coordinate positions in the previously established virtual storage space. In this way, when the warehouse manager views the virtual storage space, he can intuitively see the current temperature, humidity and light conditions of each storage unit. For example, if the temperature in a certain area suddenly rises, the system will immediately mark the abnormal area in the virtual space and remind the administrator to check the corresponding physical location, so that measures can be taken quickly to avoid damage to the medicine. This not only improves the controllability of the warehouse environment, but also provides an important basis for subsequent optimization of inventory management and logistics scheduling.

[0029] Step 103, generating a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identifying abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; In this step, the storage environment standard parameters refer to the optimal storage conditions specified for different drug batches, including temperature upper limits, humidity ranges, light intensity and other indicators, to ensure that the drugs can maintain their stability and effectiveness under these conditions; the three-dimensional thermal map is a visualization tool that displays the temperature distribution of each location in the virtual storage space in a color gradient, allowing managers to intuitively see which areas may be at risk of overtemperature; abnormal areas refer to locations where the actual temperature exceeds the safety range set by the storage environment standard parameters, which are usually shown as particularly bright or dark areas on the three-dimensional thermal map, indicating that immediate measures need to be taken to make adjustments.

[0030] In this embodiment, first, the system obtains the storage environment standard parameters corresponding to each drug batch number from the database, especially the upper temperature limit. Then, using the temperature gradient distribution data previously collected and mapped to the virtual storage space, an algorithm is used to generate a three-dimensional thermal map covering the entire virtual storage space. In this process, the system calculates whether the temperature at each coordinate point meets the storage requirements of the corresponding drug batch number, and marks the locations that do not meet the standards. Next, the system automatically identifies continuous voxel areas whose color saturation exceeds the critical value (i.e., the temperature is too high or too low), and defines these areas as abnormal areas. In order to facilitate rapid response by operators, the system will mark specific abnormal areas in the virtual storage space and provide corresponding processing suggestions.

[0031] For example, continuing with the previous example, suppose that a pharmaceutical company's warehouse has deployed a sensor network and successfully built a virtual storage space. Now, the system begins to generate a three-dimensional thermal map based on the existing temperature gradient distribution data and the storage environment standard parameters associated with each drug batch number. When the administrator views the virtual storage space, he can see a colorful image in which green represents areas with suitable temperatures and red represents dangerous areas where the temperature exceeds the standard. For example, a red area is found in the cold storage area, indicating that the temperature here is higher than the specified standard. The system quickly locates the specific physical location of this abnormal area and notifies the warehouse manager. The manager then goes to the site to check and finds that the local temperature rise is caused by a failure of the refrigeration equipment. Subsequently, they start the backup refrigeration unit and temporarily move the affected drugs to other safe areas, avoiding the risk of drugs becoming ineffective due to excessive temperatures. Through this real-time monitoring and early warning mechanism, the company not only improves the safety of drug storage, but also optimizes the emergency response process to ensure that every box of drugs is in the best storage environment.

[0032] Step 104, reversely locate the corresponding drug storage unit in the physical medicine warehouse according to the coordinate position of the abnormal area, control the directional ventilation equipment to adjust the airflow circulation path of the drug storage unit, and generate a batch drug migration path planning scheme; In this step, directional ventilation equipment refers to devices specially designed to accurately control the direction and speed of airflow in a specific area, usually including components such as fans, air ducts and control systems. The airflow circulation path refers to the air flow route adjusted by these devices as needed to optimize the temperature and humidity distribution in different locations in the warehouse. The batch drug migration path planning plan refers to a set of detailed operation plans automatically generated by the system when it is found that the environmental conditions of a storage unit are not suitable for the storage of drugs, guiding staff on how to efficiently migrate the affected drugs to a more suitable location to ensure the safety and effectiveness of the drugs.

[0033] In this embodiment, first, the system determines the specific three-dimensional position information of the corresponding drug storage unit in the physical pharmaceutical warehouse through a matching algorithm based on the coordinate position of the abnormal area. Then, based on these position information, an airflow adjustment area model centered on the affected storage unit is established, and the required environmental adjustment parameters are calculated in combination with the temperature gradient distribution data and humidity fluctuation data in the area. Then, the system generates a series of control instruction sets and sends them to the directional ventilation equipment to adjust its airflow circulation path to quickly improve the environmental conditions in the area. At the same time, the system will also determine the migration priority parameters based on the attribute information associated with the drug batch number (such as the remaining validity period, sensitivity level, etc.), and use the path planning algorithm to generate the optimal migration path plan for the affected drugs. Finally, the system sends these instructions to the corresponding equipment and personnel for execution to ensure the efficiency and accuracy of the operation.

[0034] For example, continuing with the previous example, suppose that a pharmaceutical company's virtual storage space has identified a red abnormal area in the cold storage area, that is, the temperature exceeds the standard. The system reversely locates the specific drug storage unit in the physical pharmaceutical warehouse based on the coordinate position of the abnormal area - a shelf located in the corner of the cold storage area. In order to quickly reduce the temperature in this area, the system automatically controls the directional ventilation equipment installed nearby and adjusts its air circulation path so that the cold air blows directly to the hot spot area. At the same time, the system analyzes the attribute information of all drugs stored in the area and finds that there is a batch of vaccines that are extremely sensitive to temperature and are about to expire. Based on this, the system generates a detailed batch drug migration path planning plan, suggesting that this batch of vaccines be transferred to the other side of the cold storage area where the temperature is more stable. Subsequently, the warehouse managers acted quickly according to the instructions provided by the system and successfully avoided the risk of drugs becoming ineffective due to high temperature.

[0035] In order to further refine the dynamic adjustment mechanism of the upper temperature limit to improve the adaptability to the changes in environmental sensitivity of different drugs in different time periods, in some embodiments, according to step 103, based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, a three-dimensional thermal map is generated, and abnormal areas exceeding the storage environment standard parameters are identified in the three-dimensional thermal map, including: Step 201, obtaining storage environment standard parameters corresponding to each drug batch number in the virtual storage space, and extracting the temperature upper limit value and the temperature change rate limit value in the storage environment standard parameters; In this step, the storage environment standard parameters refer to the specific storage conditions set according to the characteristics of the drug and the guidance information provided by the manufacturer. These conditions include requirements in temperature, humidity, light intensity and other aspects to ensure that the drug maintains its stability and effectiveness during the entire storage period; the upper temperature limit is a key indicator in the storage environment standard parameters. It defines the maximum temperature limit at which the drug can be safely stored. Exceeding this temperature limit may cause the drug to deteriorate or become ineffective, thereby affecting the safety and efficacy of the drug.

[0036] In this embodiment, when a new batch of drugs is put into storage, the staff enters the drug batch number and its related storage environment standard parameters (including the upper temperature limit) into the system through the RFID scanning device. The system automatically matches these data with the corresponding coordinate positions in the virtual storage space and stores them in a special database table. Whenever the storage environment standard parameters of a drug batch number need to be updated or queried, the system will extract relevant information from the database table; for example, a batch of vaccines requires that the storage temperature must not exceed 8 degrees Celsius. This temperature limit will be recorded and used in subsequent environmental monitoring and abnormality detection processes. In this way, the system can ensure that the storage conditions of each drug batch number are accurately controlled to avoid damage to drugs due to excessive environment.

[0037] Step 202, decomposing the temperature gradient distribution data into the spatial axial temperature change rate and the time dimension temperature accumulation, and generating an environmental tolerance attenuation coefficient inversely proportional to the remaining validity period of the drug according to the storage duration data associated with the coordinate position of the drug batch number; In this step, the spatial axial temperature change rate refers to the rate of change of temperature in different directions (such as horizontal or vertical directions) in the warehouse, which reflects the gradient of temperature in a specific spatial dimension. The time dimension temperature accumulation refers to the cumulative effect of temperature in a certain period of time, which is used to evaluate the impact of temperature fluctuations over a long period of time on drugs. The storage duration data records the storage time of each drug batch at its coordinate position, which is an important basis for calculating the remaining shelf life and environmental tolerance attenuation coefficient. The environmental tolerance attenuation coefficient is a factor calculated based on the remaining shelf life of the drug, indicating that as the drug approaches the end of its shelf life, its tolerance to changes in environmental conditions gradually decreases. This coefficient is usually inversely proportional to the remaining shelf life, that is, the shorter the remaining shelf life, the larger the environmental tolerance attenuation coefficient, indicating greater sensitivity to environmental changes.

[0038] In this embodiment, first, the system obtains the temperature gradient distribution data in the virtual storage space from the sensor network, and decomposes it into the spatial axial temperature change rate and the time dimension temperature accumulation through mathematical algorithms. Specifically, the system will perform multi-dimensional analysis on the collected temperature data, extract the temperature change trend in different spatial directions and the temperature accumulation over time. Then, the system calculates the remaining validity period ratio of each batch of drugs based on the storage duration data associated with the coordinate position of the drug batch number. Based on this, the system inputs the remaining validity period ratio into the preset nonlinear attenuation function to generate the environmental tolerance attenuation coefficient.

[0039] Step 203, fusing the temperature change rate limit and the environmental tolerance attenuation coefficient to generate a fusion result, and correcting the temperature upper limit according to the fusion result to obtain a dynamic determination threshold value for each coordinate position; In this step, the dynamic judgment threshold refers to the upper temperature limit value that is dynamically adjusted according to the real-time conditions in the drug storage environment (such as the temperature change rate and the remaining validity period). It not only takes into account the fixed temperature upper limit under standard storage conditions, but also combines the current environmental change rate and the drug's tolerance to environmental changes, thereby generating a more accurate and flexible temperature control standard for each coordinate position. This threshold can better adapt to the changes in environmental sensitivity of different drugs in different time periods, ensuring that the drugs are in the best storage conditions throughout their storage cycle.

[0040] In this embodiment, first, the system obtains the temperature change rate limit corresponding to each drug batch number and the previously calculated environmental tolerance attenuation coefficient. The temperature change rate limit defines the maximum allowable temperature change within a specific time interval, while the environmental tolerance attenuation coefficient reflects the increased sensitivity of the drug to environmental changes as it approaches the end of its shelf life. Then, the system performs a convolution operation on these two parameters to generate a fusion weight factor.

[0041] Step 204, based on the spatial axial temperature change rate and the dynamic determination threshold, a three-dimensional thermal map is generated by stacking layer by layer in the virtual storage space; In this step, layer-by-layer superposition means partitioning the virtual storage space according to certain levels (for example, by shelf number or height), and generating corresponding thermal map fragments at each level according to its specific spatial axial temperature change rate and dynamic judgment threshold. These fragments are then integrated together to form a three-dimensional thermal map covering the entire virtual storage space.

[0042] In this embodiment, first, the system divides the virtual storage space into multiple levels, usually based on the actual physical structure of the warehouse, such as the number of shelves. For each level, the system extracts the spatial axial temperature change rate data within the level and the previously calculated dynamic judgment threshold. Next, the system generates a local two-dimensional thermal map fragment based on these data. Specifically, assuming that the height range of a certain shelf is 1.5 meters to 3 meters, the system analyzes the temperature change rate of each coordinate point within this range, and combines the corresponding dynamic judgment threshold to determine the color representation of each point (the darker the color, the higher the temperature or closer to the threshold). Then, the system generates these local two-dimensional thermal map fragments. By stacking layer by layer, a complete three-dimensional thermal map is gradually constructed. For example, during the generation process, the system starts from the ground layer and processes the data of each shelf layer in turn until the height range of the entire warehouse is covered. The final three-dimensional thermal map not only intuitively displays the temperature distribution of each shelf layer, but also helps managers quickly locate abnormal temperature areas and take timely measures to adjust them. In this way, the system realizes the refined monitoring of the temperature environment in the warehouse to ensure that the storage conditions of medicines are always in the best state. In addition, this layer-by-layer stacking method also improves the flexibility and scalability of the system, so that it can quickly adapt and update the thermal map when the warehouse structure changes. Step 205, identifying a continuous voxel region in the three-dimensional thermal map whose color saturation exceeds a critical value, and marking a polyhedron formed by connecting the center points of adjacent voxels in the continuous voxel region as an abnormal region; In this step, the continuous voxel region refers to a connected region consisting of a group of voxels that are adjacent in three-dimensional space and have similar properties (such as color saturation). Color saturation exceeding the critical value means that the color representation of these voxels (usually representing the degree of temperature or other environmental parameters) exceeds the preset safety range, indicating that there may be abnormal conditions in the area and special attention or processing is required. In this case, by identifying the continuous voxel region and marking it as an abnormal area, it can help managers quickly locate the problem and take appropriate measures to make adjustments.

[0043] In this embodiment, the system first analyzes each voxel in the previously generated three-dimensional thermal map to determine whether its color saturation exceeds the set critical value. Once a voxel with a color saturation exceeding the critical value is found, the system further checks the adjacent voxels around it to determine whether they also have the same situation. If a group of adjacent voxels are found to have excessive color saturation, the system defines these voxels as a continuous voxel region. Next, the system calculates the lines between the center points of adjacent voxels in this continuous voxel region, and forms a polyhedral structure based on these lines to clearly identify the specific range of the abnormal area. For example, in a specific implementation, assume that the temperature in a certain area of ​​the warehouse rises abnormally, resulting in an increase in the color saturation of the corresponding voxels. The system can automatically identify this change and accurately outline the specific location and size of the temperature anomaly through the above method, thereby helping managers to respond quickly, such as adjusting ventilation equipment or migrating drug batches to restore normal storage conditions.

[0044] In order to improve the reaction speed to environmental anomalies, optimize the drug migration process, reduce drug losses caused by substandard environments, and improve overall warehouse management efficiency and safety, in some embodiments, according to step 203, the temperature upper limit is corrected according to the fusion result to obtain a dynamic determination threshold value for each coordinate position, including: Step 301, performing a convolution operation on the temperature change rate limit and the environmental tolerance attenuation coefficient to generate a fusion weight factor; In this step, the fusion weight factor refers to a comprehensive parameter generated by mathematical operations, which is used to quantify the interaction between the temperature change rate limit and the environmental tolerance attenuation coefficient, to reflect the impact of environmental conditions on drug storage safety, and to provide an important basis for subsequent abnormal area identification and regulation strategy formulation.

[0045] In this embodiment, the specific values ​​of the temperature change rate limit and the environmental tolerance attenuation coefficient are first obtained, where the temperature change rate limit represents the maximum fluctuation range of the temperature allowed per unit time, and the environmental tolerance attenuation coefficient describes the changing trend of the environmental conditions on the suitability of drug storage over time. Then, these two parameters are regarded as input signals and processed by the convolution operation method, that is, the temperature change rate limit is used as the convolution kernel, and the environmental tolerance attenuation coefficient is multiplied point by point and accumulated to generate a fusion weight factor. This process not only takes into account the independent characteristics of the two parameters, but also fully explores the coupling relationship between them, so that the generated fusion weight factor can more comprehensively reflect the comprehensive impact of the environmental state on the safety of drug storage.

[0046] Step 302, dynamically adjusting the upper temperature limit based on the fusion weight factor to generate a preliminary correction threshold; The preliminary correction threshold refers to a new temperature control standard generated by dynamically adjusting the original temperature upper limit based on the fusion weight factor; this threshold comprehensively considers the influence of the environmental tolerance attenuation coefficient and the temperature change rate limit, and can more accurately reflect the actual needs of drugs under current storage conditions, thereby providing a more accurate basis for subsequent environmental monitoring and anomaly detection.

[0047] In this embodiment, the system first obtains the original temperature upper limit value corresponding to each coordinate position and the previously calculated fusion weight factor. For example, assuming that the original temperature upper limit value of a drug is T_max=8 degrees Celsius, and its corresponding fusion weight factor is W_factor=4; next, the system dynamically adjusts the temperature upper limit value according to the following formula: T_preliminary=T_max-W_factor; in this example, the preliminary correction threshold is T_preliminary=8-4=4 degrees Celsius. This means that the temperature at this location needs to be strictly controlled within 4 degrees Celsius to ensure the safety and effectiveness of the drug. During the specific operation, the system will traverse each coordinate position in the virtual storage space and repeat the above calculation process. For each coordinate position, the system will extract its corresponding fusion weight factor and calculate a new preliminary correction threshold according to the formula; for example, if the original temperature upper limit value of another location is T_max=10 degrees Celsius, and its fusion weight factor is W_factor=3, then the preliminary correction threshold is T_preliminary=10-3=7 degrees Celsius.

[0048] In this way, the system is able to generate a detailed preliminary correction threshold distribution map throughout the virtual warehouse space. These thresholds not only reflect the actual temperature control requirements of each location, but also help managers quickly identify potential risk areas and take corresponding measures to make adjustments. For example, if the preliminary correction threshold of an area is low, it may be necessary to add cooling equipment or optimize ventilation paths to maintain appropriate temperature conditions, which significantly improves the intelligence level of the system and ensures the safety and effectiveness of drugs during the entire storage period.

[0049] Step 303, performing spatial compensation correction on the preliminary correction threshold according to the spatial position characteristics of the shelf level in the physical pharmaceutical warehouse where the drug storage unit is located, to generate an intermediate correction threshold; In this step, the spatial location characteristics of the shelf level refer to the specific spatial distribution characteristics of the drug storage unit in the physical pharmaceutical warehouse, such as the height of the shelf level where it is located, the distance from the ventilation equipment, and whether it is close to the outer wall or corner of the warehouse. These characteristics will affect the distribution of local environmental conditions (such as temperature and humidity), thereby affecting the safety of drug storage; spatial compensation correction is a process of further adjusting the preliminary correction threshold based on these spatial location characteristics, aiming to eliminate environmental parameter deviations caused by spatial location differences.

[0050] In this embodiment, the system first analyzes the impact of the drug storage unit on the environmental parameters based on the spatial location characteristics of the shelf level where the drug storage unit is located; for example, assuming that a drug storage unit is located on the top layer of the shelf, due to the principle that hot air rises, the temperature of this area may be higher than other areas of the warehouse; or if the storage unit is close to the outer wall of the warehouse, it may be more affected by external temperature fluctuations. The system will extract the corresponding space compensation coefficient based on these characteristics; for example, the top shelf may correspond to a positive compensation coefficient C_top=+1, while the storage unit close to the outer wall may correspond to a negative compensation coefficient C_wall=-0.5; Next, the system applies these space compensation coefficients to the preliminary correction threshold to generate an intermediate correction threshold; specifically, assuming that the preliminary correction threshold of a storage unit is T_preliminary=4 degrees Celsius, it is located on the top layer of the shelf and close to the outer wall, then the intermediate correction threshold is calculated by T_intermediate=T_preliminary+C_top+C_wall; in this example, the intermediate correction threshold is T_intermediate =4+1-0.5=4.5 degrees Celsius; in the entire virtual warehouse space, the system will repeat the above calculation process for the location characteristics of each drug storage unit to generate a detailed intermediate correction threshold distribution map; for example, for another storage unit, if its preliminary correction threshold is T_preliminary=7 degrees Celsius, and it is located on the bottom shelf but close to the ventilation equipment, different compensation coefficients may be applied (such as C_bottom=-0.5 and C_vent=-1), and the final intermediate correction threshold is T_intermediate=7-0.5-1=5.5 degrees Celsius.

[0051] In this way, the system can fully consider the specific spatial location characteristics of the drug storage unit in the warehouse, eliminate the environmental parameter deviation caused by location differences, and generate a more accurate intermediate correction threshold. This method not only improves the accuracy of temperature control, but also enhances the system's adaptability, ensuring that drugs are in the best storage environment at different spatial locations.

[0052] Step 304, combining the environmental sensitivity data of other drugs within a preset range around the drug storage unit, cross-impact correction is performed on the intermediate correction threshold to generate a dynamic determination threshold; In this step, other drugs refer to adjacent drug batches located within the same preset range as the target drug storage unit. These drugs may have a cross-impact on the environmental conditions of the target storage unit; environmental sensitivity data refers to the sensitivity of these adjacent drugs to changes in environmental parameters such as temperature and humidity, usually expressed in numerical form (such as weight values ​​corresponding to high, medium, and low sensitivity levels).

[0053] In this embodiment, the system first determines other drugs within a preset range around the target drug storage unit, and extracts the environmental sensitivity data of these drugs; for example, assuming that the intermediate correction threshold of a drug storage unit is T_intermediate = 4.5 degrees Celsius, there are three adjacent drug batches within the preset range around it, with different environmental sensitivity levels: the sensitivity level of the first drug is "high", and the corresponding weight value is 3; the sensitivity level of the second drug is "medium", and the corresponding weight value is 2; the sensitivity level of the third drug is "low", and the corresponding weight value is 1; next, the system calculates the cross-impact correction coefficient based on these environmental sensitivity data; specifically, the system multiplies the environmental sensitivity weight value of each adjacent drug by the spatial weight factor of its distance from the target storage unit, and accumulates the results; for example, assuming that the first drug is closer to the target storage unit, the spatial weight factor is 0.8; the second drug is slightly farther away, the spatial weight factor is 0.5; the third drug is the farthest away, and the spatial weight factor is 0.3. The cross-impact correction coefficient can be calculated by the formula C_cross=(weight value_1×spatial weight factor_1)+(weight value_2×spatial weight factor_2+(weight value_3×spatial weight factor_3); in this example, the cross-impact correction coefficient is C_cross=(3×0.8)+(2×0.5)+(1×0.3)=2.4+1.0+0.3=3.7; finally, the system applies the cross-impact correction coefficient to the intermediate correction threshold to generate a dynamic determination threshold. Assuming that the cross-impact correction coefficient has a linear decrease in effect on temperature, the dynamic determination threshold can be calculated by the formula T_dynamic=T_intermediate-C_cross×adjustment coefficient; for example, if the adjustment coefficient is 0.1, the dynamic determination threshold is T_dynamic=4.5-3.7×0.1=4.5 -0.37=4.13 degrees Celsius.

[0054] In order to quickly locate and handle the abnormal environment area in the warehouse and ensure the safety of medicines, in some embodiments, according to step 104, the corresponding medicine storage unit in the physical medicine warehouse is reversely located according to the coordinate position of the abnormal area, and the directional ventilation equipment is controlled to adjust the airflow circulation path of the medicine storage unit, and a batch medicine migration path planning scheme is generated, including: Step 401, matching the three-dimensional position information of the corresponding drug storage unit in the physical medicine warehouse according to the geometric center coordinates of the abnormal area; In this step, the geometric center coordinates refer to the average value of the coordinates of all points inside the abnormal area, representing the center position of the abnormal area; the three-dimensional position information refers to the precise spatial coordinates of the specific drug storage unit in the physical pharmaceutical warehouse, including data in dimensions such as height, width, and depth, which are used to accurately locate the position of each storage unit. By matching the geometric center coordinates of the abnormal area with the three-dimensional position information in the physical pharmaceutical warehouse, the specific drug storage unit that needs to be adjusted or processed can be accurately identified.

[0055] In this embodiment, first, the continuous voxel area whose color saturation exceeds the critical value is identified according to the previously generated three-dimensional thermal map, and the geometric center coordinates of the abnormal area are calculated. For example, assuming that a certain abnormal area is composed of multiple voxels, the spatial coordinates of these voxels are x1, y1, z1), (x2, y2, z2), etc., the system averages these coordinates to obtain the geometric center coordinates (xc, yc, zc). Next, the system uses this geometric center coordinate to match the three-dimensional position information of the corresponding drug storage unit in the physical pharmaceutical warehouse. Specifically, the system will query the precise three-dimensional coordinate range of each drug storage unit recorded in the database (such as a specific grid on a shelf) and find the storage unit closest to the geometric center coordinate. For example, assuming that the geometric center coordinates are (3.5, 7.2, 2.0), the system will find the drug storage unit closest to it. If it is found that the coordinates are within the range of the storage unit in the 3rd column of the 5th layer of a shelf, the system confirms that the storage unit is the physical location corresponding to the abnormal area. Subsequently, the system marks the detailed three-dimensional position information of the storage unit (eg, Shelf5, Column3) as an object that requires attention and processing.

[0056] Step 402, based on the three-dimensional position information of the drug storage unit, establishing an airflow adjustment area model centered on the drug storage unit; In this step, the airflow regulation area model refers to a virtual model for optimizing the local airflow circulation path based on the three-dimensional location information of a specific drug storage unit. This model defines the spatial range around the target storage unit and takes into account the temperature gradient distribution data and humidity fluctuation data in the area to determine how to adjust the airflow to improve environmental conditions. The airflow regulation area model not only helps identify local environmental problems that need to be improved, but also provides a specific control instruction set for directional ventilation equipment to ensure that parameters such as temperature and humidity remain within the optimal range.

[0057] In this embodiment, first, based on the three-dimensional position information of the drug storage unit corresponding to the determined abnormal area (such as Shelf5, Column3), an airflow regulation area model centered on the storage unit is established. Specifically, the system extracts the spatial coordinate data of the storage unit and its surroundings within a certain range, and combines the temperature gradient distribution data and humidity fluctuation data provided by sensors in the warehouse to generate a detailed three-dimensional model.

[0058] Step 403, calculating the environmental adjustment parameters required by the airflow adjustment area model according to the temperature gradient distribution data and humidity fluctuation data in the airflow adjustment area model, and generating a control instruction set of the directional ventilation device based on the environmental adjustment parameters; In this step, environmental adjustment parameters refer to specific values ​​calculated based on the temperature gradient distribution data and humidity fluctuation data in the airflow adjustment area model for optimizing local environmental conditions. These parameters include but are not limited to target temperature, humidity setting value, airflow speed and direction, etc., and are intended to restore the environmental conditions in the abnormal area to the standard range required for drug storage; the control instruction set is a set of specific operating instructions generated based on these environmental adjustment parameters, which are used to guide the directional ventilation equipment on how to adjust its working state (such as wind speed, wind direction, etc.) to achieve the best environmental control effect.

[0059] In this embodiment, the required environmental adjustment parameters are first calculated based on the temperature gradient distribution data and humidity fluctuation data in the airflow adjustment area model. For example, in a specific example, assuming that the airflow adjustment area model shows that the temperature of a drug storage unit and its surrounding areas is generally high, and the humidity is also beyond the standard range, the system will analyze the temperature and humidity data of each point in the area, and calculate the ideal temperature and humidity setting values ​​in combination with the storage environment standard parameters corresponding to the drug batch number. Specifically, if the target temperature should be 5 degrees Celsius and the current average temperature is 8 degrees Celsius, the system will determine that the target temperature difference needs to be reduced by 3 degrees Celsius. Next, the system will generate a control instruction set for the directional ventilation equipment based on these environmental adjustment parameters.

[0060] Step 404, determining a migration priority parameter according to the drug attribute information associated with the drug batch number; In this step, the migration priority parameter refers to a value calculated based on the drug attribute information associated with the drug batch number (such as remaining shelf life, storage environment sensitivity level and packaging specification parameters). It is used to determine which drugs need to be migrated first in the event of environmental abnormalities. High-priority drugs are usually those that are about to expire or are particularly sensitive to environmental changes. Ensure that these drugs are processed first to reduce losses and ensure the safety and effectiveness of the drugs.

[0061] In this embodiment, the system first extracts the drug attribute information related to each drug batch from the database. For example, assume that the remaining validity period of a batch of drugs is 30 days, the storage environment sensitivity level is "high", and its packaging specification parameters indicate that the number of stacking layers is limited to 5 layers and the seismic resistance level is "medium". Next, the system calculates the migration priority parameters based on these attribute information. The specific operation process is as follows: the system obtains the relevant attribute information of each drug batch from the central database, such as the remaining validity period of the drug, the storage environment sensitivity level and the packaging specification parameters; then, based on the remaining validity period data of the drug, the system calculates the time decay coefficient, for example, if the remaining validity period of the drug is 30 days, the time decay coefficient may be 1 / 30; then, according to the drug storage environment sensitivity level, the system matches the preset priority weight table and generates the environmental sensitivity weight value, for example, the weight value corresponding to the "high" sensitivity level is 3; finally, the time decay coefficient and the environmental sensitivity weight value are input into the preset migration priority decision model to generate the migration priority parameter. For example, assuming that the time decay coefficient of a drug is 1 / 30 and the environmental sensitivity weight value is 3, the migration priority parameter can be calculated by the formula Priority = (1 / 30) * 3 = 0.1. In this way, the system can calculate a specific migration priority parameter for each drug batch number, and sort them accordingly to determine the drugs that need to be migrated first. For example, in the above example, if the migration priority parameter of another batch of drugs is 0.2, then this batch of drugs has a higher priority and should be migrated first. This method not only improves the intelligence level of the system, but also ensures a rapid response in the event of environmental abnormalities, minimizes drug losses caused by environmental problems, and ensures the safety and stability of the pharmaceutical storage environment.

[0062] Step 405, based on the migration priority parameter and the real-time layout status of the physical medicine warehouse, a path planning algorithm is used to generate a migration path plan for the batch of medicines, and the control instruction set is sent to the corresponding directional ventilation equipment for execution, and the migration path plan is started synchronously; In this step, the real-time layout status refers to the actual storage situation of the physical pharmaceutical warehouse, including dynamic information such as the occupancy status of each drug storage unit, whether the channel is unobstructed, and the equipment operation status; the path planning algorithm is a mathematical method for calculating the optimal path. It generates an efficient migration path plan from the abnormal area to the target storage location based on the migration priority parameters and the real-time layout status, ensuring that the drugs can be quickly and safely transferred to a more suitable storage environment.

[0063] In this embodiment, the system first determines the batches of drugs that need to be migrated and their target storage locations by combining the migration priority parameters and the real-time layout status of the physical pharmaceutical warehouse. For example, assuming that a batch of drugs needs to be migrated to the cold storage area due to environmental abnormalities, and some storage units in the cold storage area are occupied, the system will find a target location that is free and meets the storage conditions according to the real-time layout status. Next, the system uses a path planning algorithm (such as an A* algorithm or a Dijkstra algorithm) to generate an optimal migration path plan. Specifically, the algorithm will comprehensively consider factors such as the channel width, obstacle distribution, and moving speed of the handling equipment in the warehouse to calculate the shortest or fastest path from the abnormal area to the target storage location. For example, if the abnormal area is located in the 3rd column of the 5th layer of the shelf and the target storage location is the 1st column of the 2nd layer of the cold storage area, the system will generate a path plan that avoids the congested area and makes full use of the existing channels. Finally, the system sends the control instruction set to the corresponding directional ventilation equipment, adjusts the airflow to optimize the environmental conditions during the migration process, and simultaneously starts the migration path plan to guide the staff or automated equipment to complete the drug migration task.

[0064] In order to arrange the drug migration sequence more efficiently and reduce unnecessary delays and losses in the face of environmental abnormalities or other emergency situations, in some embodiments, according to step 404, the migration priority parameters are determined according to the drug attribute information associated with the drug batch number, including: Step 501, extracting the remaining validity period data of the drug, the sensitivity level of the drug storage environment, and the drug packaging specification parameters from the drug attribute information; In this embodiment, the system first extracts detailed drug attribute information related to each drug batch from the central database. For example, suppose a batch of drugs has a remaining shelf life of 30 days, a storage environment sensitivity level of "high", and its packaging specification parameters indicate that the stacking layer limit is 5 layers and the seismic resistance level is "medium". The system extracts this information as the basis for the subsequent calculation of the migration priority parameters.

[0065] Step 502, calculating a time decay coefficient based on the remaining validity period data of the drug, wherein the time decay coefficient is inversely proportional to the number of days of the remaining validity period of the drug, and triggering an exponential growth correction when the remaining validity period of the drug is less than a preset critical number of days; In this step, the time decay coefficient is a value calculated based on the remaining validity period of the drug, which is used to reflect the impact of the remaining validity period of the drug on the migration priority. The time decay coefficient is inversely proportional to the remaining validity period of the drug, that is, the shorter the remaining validity period, the greater the time decay coefficient. When the remaining validity period of the drug is less than the preset critical number of days, the time decay coefficient will trigger an exponential growth correction to significantly increase the priority of the drug that is about to expire.

[0066] In this embodiment, the system calculates the time decay coefficient based on the remaining validity period of the drug. For example, if the remaining validity period of the drug is 30 days, the time decay coefficient may be 1 / 30. Assuming that the preset critical number of days is 7 days, when the remaining validity period of the drug is less than 7 days, the time decay coefficient will increase significantly, such as 1 / (7+0.01). In this way, the system can more accurately reflect the urgency of drugs that are about to expire and give them a higher weight when calculating the migration priority parameter.

[0067] Step 503, generating an environmental sensitivity weight value according to the drug storage environment sensitivity level matching the preset priority weight table; In this step, the environmental sensitivity weight value is a value generated based on the drug storage environment sensitivity level matching the preset priority weight table. Different environmental sensitivity levels correspond to different weight values, which is intended to ensure that drugs that are particularly sensitive to environmental changes can obtain higher migration priority.

[0068] In this embodiment, the system matches the preset priority weight table according to the drug storage environment sensitivity level to generate the environment sensitivity weight value. For example, the weight value corresponding to the "high" sensitivity level is 3, the weight value corresponding to the "medium" sensitivity level is 2, and the weight value corresponding to the "low" sensitivity level is 1. Assuming that the storage environment sensitivity level of a batch of drugs is "high", its environment sensitivity weight value is 3.

[0069] Step 504, analyzing the stacking layer limit and the earthquake resistance level in the drug packaging specification parameters, and calculating the handling complexity index of the packaging unit corresponding to each drug batch number; In this step, the handling complexity index is a value calculated by analyzing the stacking layer limit and seismic resistance level in the drug packaging specification parameters to evaluate the difficulty of handling drugs during handling. The stacking layer limit determines the maximum number of layers that can be safely stacked in a packaging unit, while the seismic resistance level reflects the ability of the packaging unit to resist vibration during handling.

[0070] In this embodiment, the system analyzes the stacking layer limit and seismic level in the drug packaging specification parameters and calculates the handling complexity index. For example, assuming that the maximum allowable stacking layer is 5 layers, the basic handling complexity value is 2, and the seismic level is "medium", the seismic correction factor is 1.5. Combined with the space utilization of the packaging unit, the final handling complexity index is 2*1.5=3. This index reflects the difficulty of drug handling and helps the system to arrange the migration order reasonably.

[0071] Step 505, inputting the time decay coefficient, the environmental sensitivity weight value and the handling complexity index into a preset migration priority decision model, and generating a migration priority parameter using the migration priority decision model; In this step, the migration priority decision model is a mathematical model that uses the time attenuation coefficient, environmental sensitivity weight value and handling complexity index as input to generate a comprehensive migration priority parameter. This parameter is used to determine which drugs need to be migrated first in the event of environmental abnormalities to ensure that high-priority drugs are processed first.

[0072] In this embodiment, the system inputs the time decay coefficient, the environmental sensitivity weight value and the handling complexity index into the preset migration priority decision model to generate the migration priority parameter. For example, assuming that the time decay coefficient of a drug is 1 / 30, the environmental sensitivity weight value is 3, and the handling complexity index is 3, the migration priority parameter can be calculated by the formula Priority=(1 / 30)*3*3=0.3. The system sorts all batches of drugs according to the calculated migration priority parameters to determine the drugs that need to be migrated first. For example, in the above example, if the migration priority parameter of another batch of drugs is 0.5, the priority of this batch of drugs is higher and should be migrated first. This method not only improves the intelligence level of the system, but also ensures rapid response in abnormal environmental conditions, minimizes the loss of drugs caused by environmental problems, and ensures the safety and stability of the pharmaceutical storage environment.

[0073] In order to more accurately predict the risks and difficulties of drugs during transportation and provide an important reference for migration priority decision-making, in some embodiments, according to step 504, the stacking layer limit and the seismic resistance level in the drug packaging specification parameters are parsed, and the transportation complexity index of the packaging unit corresponding to each drug batch number is calculated, including: Step 601, obtaining the maximum allowable stacking layer number and earthquake resistance level data contained in the drug packaging specification parameters; In this step, the maximum allowable stacking number refers to the maximum number of layers that a single packaging unit can safely stack as specified in the drug packaging specification parameters, while the seismic grade data is an indicator that describes the drug packaging's ability to resist vibration and impact during transportation. These two data are important foundations for calculating the handling complexity index and can reflect the difficulty of handling drugs during transportation.

[0074] In this embodiment, the system extracts the maximum allowable number of stacking layers and seismic resistance level data from the drug packaging specification parameters. For example, the maximum allowable number of stacking layers of a drug is 5 layers, and the seismic resistance level is "medium". These data will serve as the basis for calculating the basic handling complexity value, seismic correction coefficient, and final handling complexity index in subsequent steps.

[0075] Step 602: querying a preset layer complexity mapping table according to the maximum allowed number of stacked layers to obtain a basic handling complexity value, wherein the basic handling complexity value increases by a preset step increment for each additional layer in the layer complexity mapping table; In this step, the layer complexity mapping table is a preset table used to query the corresponding basic handling complexity value according to the maximum allowed number of stacking layers. The table stipulates that when each additional layer of stacking is added, the basic handling complexity value will increase according to the preset step increment. This mechanism ensures that the higher the number of stacking layers, the greater the handling difficulty.

[0076] In this embodiment, the system queries the preset layer complexity mapping table according to the maximum allowed number of stacking layers to obtain the basic handling complexity value. For example, suppose the layer complexity mapping table stipulates that the basic handling complexity value of 1 layer is 1, and the complexity value increases by 0.5 for each additional layer. If the maximum allowed number of stacking layers of a certain drug is 5 layers, its basic handling complexity value is 1+0.5*(5-1)=3. This value reflects the operational difficulty of drug packaging in terms of the number of stacking layers.

[0077] Step 603, calculating the seismic correction coefficient based on the seismic grade data; In this step, the seismic correction coefficient is a value calculated based on the seismic grade data, which is used to reflect the adaptability of the drug packaging to vibration and impact during transportation. The higher the seismic grade, the smaller the seismic correction coefficient, indicating that the handling difficulty is relatively low; conversely, the lower the seismic grade, the larger the seismic correction coefficient, indicating that the handling difficulty is relatively high.

[0078] In this embodiment, the system calculates the seismic correction coefficient based on the seismic grade data. For example, the seismic grades are divided into "high", "medium" and "low", and the corresponding seismic correction coefficients are 1.0, 1.5 and 2.0 respectively. Assuming that the seismic grade of a certain drug is "medium", its seismic correction coefficient is 1.5.

[0079] Step 604, obtaining the size data of the packaging unit corresponding to the drug batch number, and calculating the space utilization rate of the packaging unit; In this step, the space utilization rate refers to the ratio of the actual occupied volume of the packaging unit corresponding to the drug batch number to the available storage space, which is used to evaluate the efficiency of the packaging unit during storage and handling. The higher the space utilization rate, the more compact the design of the packaging unit, and the easier it may be to handle.

[0080] In this embodiment, the system obtains the size data of the packaging unit corresponding to the drug batch number, and calculates the space utilization rate by combining the actual occupied volume and available storage space. For example, assuming that the size of a packaging unit is 1mx1mx1m and the actual occupied volume is 0.8m³, then its space utilization rate is 0.8 / 1=0.8.

[0081] Step 605, inputting the basic handling complexity value, seismic correction coefficient and space utilization into a preset complexity calculation model to generate a handling complexity index of the packaging unit; In this step, the complexity calculation model is a mathematical model that takes the basic handling complexity value, seismic correction coefficient and space utilization as input to generate a comprehensive handling complexity index. The handling complexity index is a comprehensive indicator to measure the difficulty of operating drugs during the handling process, helping the system to arrange the migration order reasonably.

[0082] In this embodiment, the system inputs the basic handling complexity value, seismic correction coefficient and space utilization into a preset complexity calculation model to generate a handling complexity index. For example, assuming that the basic handling complexity value of a certain drug is 3, the seismic correction coefficient is 1.5, and the space utilization is 0.8, the handling complexity index can be calculated by the formula Complexity = basic handling complexity value * seismic correction coefficient / space utilization, that is, Complexity = 3 * 1.5 / 0.8 = 5.625.

[0083] In order to accurately reflect the virtual storage space of the physical pharmaceutical warehouse structure and ensure the consistency and timeliness of data, in some embodiments, according to step 101, a virtual storage space consistent with the geometric structure of the physical pharmaceutical warehouse is generated, and a binding relationship between the drug batch number and the spatial coordinates is embedded in the virtual storage space, including: Step 701, obtaining the architectural CAD drawings and shelf layout data of the physical pharmaceutical warehouse, and extracting the warehouse's three-dimensional geometric features and functional area division information; Architectural CAD drawings refer to architectural design drawings of physical pharmaceutical warehouses drawn by computer-aided design (CAD) software, including detailed information such as the warehouse structure, size, and layout; shelf layout data refers to the specific location and arrangement of shelves in the warehouse and their relationship with functional areas. By extracting the warehouse's three-dimensional geometric features and functional area division information from these data, a basis can be provided for the subsequent construction of a benchmark three-dimensional model of a virtual storage space.

[0084] In this embodiment, the system first obtains the architectural CAD drawings of the physical pharmaceutical warehouse from the architectural design department, and extracts the three-dimensional geometric features (such as length, width, and height) and functional area division information (such as cold storage area, normal temperature area, channel area, etc.) of the warehouse in combination with the actual shelf layout data. For example, assume that the total length of the warehouse is 50 meters, the width is 30 meters, and the height is 10 meters, where the cold storage area is located on the north side of the warehouse, the normal temperature area is located on the south side, and the channel area runs through the center. The system organizes and stores this information in the database as the basic data for building a virtual storage space.

[0085] Step 702, based on the extracted warehouse 3D geometric features and functional area division information, construct a reference 3D model of the virtual storage space, wherein the reference 3D model includes the following hierarchical structure: the overall space outline of the warehouse, the boundary frame of each functional area, the grid coordinates of the shelf unit, and the precise positioning points of the storage slot openings; In this step, the benchmark three-dimensional model is a virtual storage space model constructed based on the three-dimensional geometric features of the warehouse and the functional area division information, which is used to accurately represent the spatial structure of the physical pharmaceutical warehouse. The model contains multiple hierarchical structures, including the overall spatial outline of the warehouse, the boundary framework of each functional area, the grid coordinates of the shelf unit, and the precise positioning points of the storage space grid, which can realize the refined management of the warehouse space.

[0086] In this embodiment, the system constructs a reference three-dimensional model of the virtual storage space based on the warehouse's three-dimensional geometric features and functional area division information extracted in the previous step. Specifically, the system first generates the overall spatial outline of the warehouse and determines the range of its length, width, and height; then, based on the functional area division information, the boundary frames of functional areas such as the refrigerated area and the normal temperature area are marked in the model; then, the system further refines the location of the shelf unit and divides it into grid coordinates, each grid coordinate corresponding to a specific shelf unit; finally, the system accurately locates the storage slot opening on each shelf unit and records its three-dimensional coordinates. For example, the coordinates of a storage slot opening may be (x=10, y=5, z=2), indicating that it is located at a specific location in the warehouse.

[0087] Step 703, collecting the batch number information of the medicines stored in each storage compartment through the RFID scanning device, and establishing a first-level binding relationship between the batch number information of the medicines and the corresponding storage compartment positioning point; In this step, the first-level binding relationship refers to collecting the batch number information of the drugs stored in each storage compartment through the RFID scanning device, and binding it with the corresponding storage compartment positioning point. This binding relationship establishes a direct association between the drug batch number and its physical storage location, which facilitates subsequent inventory management and environmental monitoring.

[0088] In this embodiment, the system collects the batch number information of the drugs stored in each storage compartment through the RFID scanning equipment deployed in the warehouse. For example, suppose a storage compartment stores a batch of drugs numbered Batch_001, and its corresponding storage compartment positioning point is (x=10, y=5, z=2). The system binds the drug batch number Batch_001 to the positioning point to form a first-level binding relationship, and stores these data in the central database. This method not only realizes the digital management of drug storage locations, but also provides a basis for subsequent dynamic updates and exception handling.

[0089] Step 704: establishing a three-dimensional place-occupying model of the drug packaging unit in the virtual storage space according to the drug packaging size data, and establishing a second-level binding relationship between the three-dimensional place-occupying model and the storage compartment positioning point; In this step, the second-level binding relationship refers to building a three-dimensional occupancy model of the drug packaging unit in the virtual storage space based on the drug packaging size data, and binding it to the storage grid opening positioning point. This binding relationship not only clarifies the specific occupancy of the drug in the warehouse, but also helps optimize space utilization and transportation path planning.

[0090] In this embodiment, the system generates a three-dimensional placeholder model of the drug packaging unit in the virtual storage space based on the drug packaging size data. For example, assuming that the packaging size of a drug is 1mx0.5mx0.5m, the system will create a corresponding three-dimensional placeholder model in the virtual storage space, and bind it to the storage compartment opening positioning point (x=10, y=5, z=2) to form a second-level binding relationship. In this way, the system can not only intuitively see the specific location of the drug in the warehouse, but also optimize the shelf layout and transportation path according to the placeholder model. For example, if the space of a storage compartment opening is not enough to accommodate a drug packaging unit, the system will automatically prompt and recommend other suitable storage locations.

[0091] Step 705, receiving the drug displacement instruction from the warehouse management system in real time, dynamically updating the first-level binding relationship and the second-level binding relationship, and recording the change timestamp; In this step, drug displacement instructions refer to instructions issued by the warehouse management system regarding the movement or adjustment of drugs, which are usually triggered by inventory allocation, exception handling or order requirements; the change timestamp records the time when each drug displacement operation occurs, which is used to track the historical storage trajectory and status changes of drugs.

[0092] In this embodiment, the system receives the drug displacement instructions from the warehouse management system in real time, and dynamically updates the first-level binding relationship and the second-level binding relationship according to the instructions. For example, assuming that a drug batch Batch_001 needs to be moved from a storage slot (x=10, y=5, z=2) to a new storage slot (x=15, y=8, z=3), the system will update the first-level binding relationship between the drug batch number and the new storage slot location point according to the displacement instruction, and update the second-level binding relationship between the three-dimensional placeholder model of the drug packaging unit and the new storage slot location point. In addition, the system will also record the timestamp of this change for subsequent query and tracing of the historical storage trajectory of the drug. This method ensures the real-time and accuracy of the warehouse management system, and also provides reliable data support for the full life cycle management of drugs.

[0093] Figure 2 A structural diagram of an intelligent pharmaceutical warehouse management system based on digital twins is provided for the embodiment of the present application, such as Figure 2 As shown, the system includes: A generating module 21, for generating a virtual storage space consistent with the geometric structure of a physical pharmaceutical warehouse, and embedding a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; The acquisition module 22 is used to collect the temperature gradient distribution data, humidity fluctuation data and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and map the temperature gradient distribution data, the humidity fluctuation data and the light intensity time series data to the corresponding coordinate position in the virtual storage space; An identification module 23, for generating a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identifying abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; The control module 24 is used to reversely locate the corresponding drug storage unit in the physical pharmaceutical warehouse according to the coordinate position of the abnormal area, control the directional ventilation equipment to adjust the airflow circulation path of the drug storage unit, and generate a batch drug migration path planning plan.

[0094] Figure 2 The intelligent pharmaceutical warehouse management system based on digital twin can be executed Figure 1 The implementation principle and technical effects of the intelligent pharmaceutical warehouse management method based on digital twin described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the intelligent pharmaceutical warehouse management system based on digital twin in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0095] In one possible design, Figure 2 An intelligent pharmaceutical warehouse management system based on digital twins of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0096] The processing component 32 is used for the above Figure 1 The embodiment described is an intelligent pharmaceutical warehouse management method based on digital twins.

[0097] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0098] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0099] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0100] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0101] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0102] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0103] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 An intelligent pharmaceutical warehouse management method based on digital twins is shown in the embodiment.

[0104] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0105] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0106] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent pharmaceutical warehouse management method based on digital twins, characterized in that: include: Generate a virtual storage space that is consistent with the geometric structure of a physical pharmaceutical warehouse, and embed a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; Collecting temperature gradient distribution data, humidity fluctuation data, and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and mapping the temperature gradient distribution data, the humidity fluctuation data, and the light intensity time series data to corresponding coordinate positions in the virtual storage space; Generate a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identify abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; The corresponding drug storage unit in the physical pharmaceutical warehouse is reversely located according to the coordinate position of the abnormal area, and the directional ventilation equipment is controlled to adjust the airflow circulation path of the drug storage unit, and a batch drug migration path planning scheme is generated.

2. The method according to claim 1, characterized in that Based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, a three-dimensional thermal map is generated, and abnormal areas exceeding the storage environment standard parameters are identified in the three-dimensional thermal map, including: Obtaining storage environment standard parameters corresponding to each drug batch number in the virtual storage space, and extracting the upper temperature limit and the temperature change rate limit in the storage environment standard parameters; Decomposing the temperature gradient distribution data into the spatial axial temperature change rate and the time dimension temperature accumulation, and generating an environmental tolerance attenuation coefficient inversely proportional to the remaining validity period of the drug according to the storage duration data associated with the coordinate position of the drug batch number; The temperature change rate limit value and the environmental tolerance attenuation coefficient are integrated to generate a fusion result, and the temperature upper limit value is corrected according to the fusion result to obtain a dynamic determination threshold value for each coordinate position; Based on the spatial axial temperature change rate and the dynamic determination threshold, a three-dimensional thermal map is generated by stacking layer by layer in the virtual storage space; A continuous voxel region whose color saturation exceeds a critical value in the three-dimensional thermal map is identified, and a polyhedron formed by connecting the center points of adjacent voxels in the continuous voxel region is marked as an abnormal region.

3. The method according to claim 2, characterized in that The temperature upper limit is corrected according to the fusion result to obtain the dynamic determination threshold of each coordinate position, including: Performing a convolution operation on the temperature change rate limit and the environmental tolerance attenuation coefficient to generate a fusion weight factor; Based on the fusion weight factor, dynamically adjust the upper temperature limit value to generate a preliminary correction threshold value; According to the spatial position characteristics of the shelf level in the physical pharmaceutical warehouse where the drug storage unit is located, the preliminary correction threshold is corrected by spatial compensation to generate an intermediate correction threshold; Combined with the environmental sensitivity data of other drugs within a preset range around the drug storage unit, the intermediate correction threshold is cross-impact corrected to generate a dynamic determination threshold.

4. The method according to claim 1, characterized in that Reversely locate the corresponding drug storage unit in the physical medicine warehouse according to the coordinate position of the abnormal area, control the directional ventilation equipment to adjust the airflow circulation path of the drug storage unit, and generate a batch drug migration path planning scheme, including: Matching the three-dimensional position information of the corresponding drug storage unit in the physical medicine warehouse according to the geometric center coordinates of the abnormal area; Based on the three-dimensional position information of the drug storage unit, establishing an airflow regulation area model centered on the drug storage unit; Calculate the environmental adjustment parameters required by the airflow adjustment area model according to the temperature gradient distribution data and humidity fluctuation data in the airflow adjustment area model, and generate a control instruction set of the directional ventilation equipment based on the environmental adjustment parameters; Determining a migration priority parameter according to the drug attribute information associated with the drug batch number; Based on the migration priority parameters and the real-time layout status of the physical pharmaceutical warehouse, a path planning algorithm is used to generate a migration path plan for the batch of drugs, and the control instruction set is sent to the corresponding directional ventilation equipment for execution, and the migration path plan is started simultaneously.

5. The method according to claim 4, characterized in that Determine the migration priority parameters according to the drug attribute information associated with the drug batch number, including: Extracting the remaining validity period of the drug, the sensitivity level of the drug storage environment, and the drug packaging specification parameters from the drug attribute information; Calculating a time decay coefficient based on the remaining validity period data of the drug, wherein the time decay coefficient is inversely proportional to the number of days of the remaining validity period of the drug, and triggering an exponential growth correction when the remaining validity period of the drug is less than a preset critical number of days; According to the drug storage environment sensitivity level, a preset priority weight table is matched to generate an environmental sensitivity weight value; Analyze the stacking layer limit and seismic resistance level in the drug packaging specification parameters, and calculate the handling complexity index of the packaging unit corresponding to each drug batch number; The time decay coefficient, the environmental sensitivity weight value and the handling complexity index are input into a preset migration priority decision model, and the migration priority parameter is generated using the migration priority decision model.

6. The method according to claim 1, characterized in that Analyze the stacking layer limit and seismic resistance level in the drug packaging specification parameters, and calculate the handling complexity index of the packaging unit corresponding to each drug batch number, including: Obtain the maximum allowable number of stacking layers and earthquake resistance level data contained in the drug packaging specification parameters; According to the maximum allowed number of stacked layers, a preset layer complexity mapping table is queried to obtain a basic handling complexity value, wherein, for each additional layer in the layer complexity mapping table, the basic handling complexity value increases by a preset step increment; Calculate the seismic correction coefficient based on the seismic grade data; Obtaining the size data of the packaging unit corresponding to the drug batch number, and calculating the space utilization rate of the packaging unit; The basic handling complexity value, seismic correction coefficient and space utilization rate are input into a preset complexity calculation model to generate a handling complexity index of the packaging unit.

7. The method according to claim 1, characterized in that Generate a virtual storage space that is consistent with the geometric structure of a physical pharmaceutical warehouse, and embed a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space, including: Obtain architectural CAD drawings and shelf layout data of physical pharmaceutical warehouses, extract warehouse 3D geometric features and functional area division information; Based on the extraction of warehouse 3D geometric features and functional area division information, a reference 3D model of the virtual storage space is constructed. The reference 3D model includes the following hierarchical structure: the overall space outline of the warehouse, the boundary framework of each functional area, the grid coordinates of the shelf unit, and the precise positioning points of the storage slot openings; The batch number information of the medicines stored in each storage compartment is collected by an RFID scanning device, and a first-level binding relationship is established between the batch number information of the medicines and the corresponding storage compartment positioning point; According to the drug packaging size data, a three-dimensional place-occupying model of the drug packaging unit is established in the virtual storage space, and a second-level binding relationship is established between the three-dimensional place-occupying model and the storage compartment positioning point; Receive drug displacement instructions from the warehouse management system in real time, dynamically update the first-level binding relationship and the second-level binding relationship, and record the change timestamp.

8. An intelligent pharmaceutical warehouse management method based on digital twins, characterized in that: include: A generation module, used to generate a virtual storage space consistent with the geometric structure of a physical pharmaceutical warehouse, and embed a binding relationship between a drug batch number and a spatial coordinate in the virtual storage space; A collection module, used to collect temperature gradient distribution data, humidity fluctuation data and light intensity time series data of each drug storage unit in the physical pharmaceutical warehouse, and map the temperature gradient distribution data, the humidity fluctuation data and the light intensity time series data to corresponding coordinate positions in the virtual storage space; An identification module, for generating a three-dimensional thermal map based on the temperature gradient distribution data and the storage environment standard parameters associated with the drug batch number, and identifying abnormal areas exceeding the storage environment standard parameters in the three-dimensional thermal map; The control module is used to reversely locate the corresponding drug storage unit in the physical pharmaceutical warehouse according to the coordinate position of the abnormal area, control the directional ventilation equipment to adjust the airflow circulation path of the drug storage unit, and generate a batch drug migration path planning plan.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an intelligent pharmaceutical warehouse management method based on digital twins as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an intelligent pharmaceutical warehouse management method based on digital twins as described in any one of claims 1 to 7 is implemented.

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