Medical device storage management method, device, terminal equipment and storage medium

Abstract

The present application discloses a medical device storage management method, apparatus, terminal device, and storage medium. The present application determines the maximum storage quantity of each medical device by obtaining the historical outbound data and order data of the medical device; determines the first storage area corresponding to each medical device based on the material properties and maximum storage quantity of each medical device; based on the first storage area, determines the second storage area corresponding to each medical device based on the use properties and maximum storage quantity of each medical device in combination with a genetic algorithm, and determines the first storage position of each medical device in the corresponding second storage area in combination with the historical outbound data of each medical device. Through the present application, the utilization rate of warehouse space and the storage safety of medical devices under dynamic warehouse conditions can be improved, while reducing the management efficiency of warehouse managers.

Description

Technical Field

[0001] The present application relates to the field of warehouse management, and in particular to a medical device storage management method, apparatus, terminal equipment and storage medium. Background Art

[0002] With the widespread use of medical devices, the types and quantities of medical devices in warehouses are increasing. Traditional medical device storage management methods often rely on manual operations or simple material classification systems, resulting in inefficient management. This is especially true in warehouse environments where storage conditions are subject to large-scale dynamic changes. Reasonable planning of storage locations and rapid access become increasingly difficult. Such problems not only increase the cost of medical device management but also delay the use of medical supplies in emergency situations. In addition, due to the wide variety of medical devices and significant differences in their material properties, different medical devices require dedicated storage environments to prevent contamination. Furthermore, different medical devices have different uses, resulting in different access frequencies for different medical devices.

[0003] Existing technologies often ignore future medical device consumption and storage conditions when addressing these issues. Instead, they re-plan the placement of medical devices within the warehouse based solely on current storage conditions through mathematical programming. These devices are then adjusted based on the latest planning methods. This approach not only results in a high labor load but also inefficient warehouse management in the face of dynamically changing storage conditions. Furthermore, existing technologies often overlook the impact of the storage environment on medical devices, exposing them to the risk of contamination and compromising their storage safety.

[0004] Therefore, in actual operations, how to reasonably plan storage locations based on the properties of medical devices and dynamic consumption and storage conditions has become an important challenge to improve warehouse operation efficiency and ensure the storage safety of medical devices. Summary of the Invention

[0005] The present application provides a medical device storage management method, apparatus, terminal device and storage medium to solve the technical problems of low warehouse operation efficiency and low medical device storage safety under dynamically changing storage conditions.

[0006] In order to solve the above technical problems, in a first aspect, an embodiment of the present application provides a medical device storage management method, comprising:

[0007] Obtaining historical shipment data for each medical device in the warehouse and order data for each medical device within a first preset time in the future, and determining a maximum storage quantity for each medical device based on the historical shipment data and the order data;

[0008] Clustering the medical devices according to material properties of the medical devices to obtain a plurality of first clusters, and determining a first storage area corresponding to each first cluster according to a maximum storage quantity of each medical device; wherein each first cluster includes a plurality of the medical devices;

[0009] According to the usage attributes of each medical device, combined with a genetic algorithm, all medical devices in each first cluster are clustered in turn to obtain corresponding second clusters; wherein each second cluster includes several medical devices;

[0010] According to the maximum storage quantity of each medical device, the corresponding first storage area is divided, the second storage area corresponding to each second cluster is obtained, and the first storage position of each medical device in the corresponding second storage area is determined in combination with the historical outbound data of each medical device.

[0011] Compared to the existing technology, the embodiments of the present application have the following beneficial effects: by collecting historical outbound data and future order data of medical devices, the fluctuation of the storage quantity of various types of medical devices in the warehouse over a period of time in the future is accurately assessed, providing effective space constraints for subsequent space planning, thereby ensuring that the storage space is effectively utilized; in addition, by clustering the attributes of medical devices multiple times, hierarchical planning of storage space is achieved, effectively improving the computational efficiency of the space planning process. Moreover, clustering medical devices by material attributes ensures that medical devices can be stored in a suitable storage environment, thereby improving the storage security of medical devices; further clustering medical devices by use attributes provides a basis for the subsequent detailed arrangement of storage locations, effectively improving the work efficiency of warehouse managers.

[0012] In some embodiments of the first aspect of the present application, determining the maximum storage quantity of each medical device based on the historical shipment data and the order data includes:

[0013] Predicting the consumption rate of each of the medical devices within the first preset time in the future based on all the historical delivery data;

[0014] Predicting, based on all the order data, a warehousing speed of each of the medical devices within the first preset time in the future;

[0015] According to the consumption speed and the warehousing speed, the maximum storage quantity corresponding to the medical device is calculated.

[0016] Compared with the existing technology, the above embodiment has the following beneficial effects: by obtaining historical outbound data, the future consumption rate of medical devices is predicted, and based on future order data, the future warehousing rate of medical devices is predicted, thereby dynamically estimating the storage quantity of medical devices in the warehouse at any time point in the future, accurately evaluating the maximum storage space that may be required for medical devices in the future, and effectively improving the space utilization of the warehouse in a dynamic warehouse state.

[0017] In some embodiments of the first aspect of the present application, determining the first storage location of each medical device in the corresponding second storage area in combination with historical outbound data of each medical device includes:

[0018] Evaluate the unit quantity management load corresponding to each medical device based on the consumption rate and the warehousing rate of each medical device;

[0019] Through a genetic algorithm, the first storage position of each medical device in the second cluster corresponding to the second storage area is optimized to minimize the difference in the total management load between each shelf; wherein the total management load of the shelf is calculated based on the number of medical devices stored on the corresponding shelf and the corresponding unit quantity management load.

[0020] Compared with the existing technology, the above embodiment has the following beneficial effects: the second cluster includes a large number of medical devices with the same purpose but with specific differences, such as brand or medical grade. Medical devices with different differences have different usage frequencies. If medical devices of different medical grades but with high usage frequencies are stored on the same shelf, it may cause the same shelf to have frequent consumption and warehousing operations per unit time, thereby reducing the efficiency of goods retrieval and warehousing in the warehouse. Therefore, by evaluating the frequency of each medical device entering and leaving the warehouse, the frequency of medical device retrieval and warehousing on each shelf is averaged to improve warehouse management efficiency.

[0021] In some embodiments of the first aspect of the present application, calculating the maximum storage quantity of the corresponding medical device according to the consumption rate and the warehousing rate includes:

[0022] The calculation formula of the maximum storage quantity is specifically:

[0023]

[0024] Among them, S i is the maximum storage quantity of medical device i; [T1, T2] is the interval of the first preset time in the future; v in,i (t) and v out,i (t) are the storage speed and consumption speed of medical device i in the first preset time in the future; Represents the maximum value of a function in the interval [T1, T2].

[0025] Compared with the existing technology, the above embodiment has the following beneficial effects: by fitting the warehousing speed and consumption speed of medical devices in the future time period, the accurate storage quantity of medical devices at each time point in the warehouse is obtained, and further by the method of finding the extreme value through integration, the accurate maximum storage quantity is obtained, thereby improving the accuracy of subsequent area division.

[0026] In some embodiments of the first aspect of the present application, clustering all medical devices in each first cluster in sequence according to the usage attributes of each medical device in combination with a genetic algorithm to obtain corresponding second clusters includes:

[0027] Calculating the similarity between the medical devices based on semantic analysis tools and combining the usage attributes of the medical devices;

[0028] Determining idle shelf space based on the maximum storage space of each shelf and the maximum storage quantity of each medical device;

[0029] A fitness function is constructed according to the similarity and the idle space of the shelf, and the fitness function is optimized by the genetic algorithm to obtain a plurality of second clusters.

[0030] Compared with the existing technology, the above embodiment has the following beneficial effects: through semantic analysis tools, the similarity between medical devices is accurately evaluated; based on the maximum storage quantity of medical devices, the storage space required for medical devices is accurately evaluated; combined with the maximum storage space of the shelf, the idle space of the shelf is accurately evaluated; by optimizing similarity and idle space of the shelf, the rationality of the second clustering division result and the storage space utilization rate are dynamically balanced.

[0031] In some embodiments of the first aspect of the present application, constructing a fitness function based on the similarity and the idle space on the shelf includes:

[0032] The fitness function is specifically:

[0033]

[0034] Among them, ω1 and ω2 are weight coefficients; n k The second cluster G k The number of types of medical devices in the dataset; K is the number of the second cluster; sim(i,j) represents the similarity between medical device i and medical device j; V k The second cluster G k The storage space required for all medical devices in the system; V is the maximum storage space of the shelf; mod is the remainder operator.

[0035] Compared with the existing technology, the above embodiment has the following beneficial effects: by maximizing the similarity between the medical devices in the second cluster and minimizing the total amount of idle space on each shelf, the second cluster can not only reasonably divide medical devices for different purposes, but also improve the utilization rate of shelf space.

[0036] In some embodiments of the first aspect of the present application, after determining the first storage position of each of the medical devices, it also includes: optimizing the first storage position of each medical device based on the current second storage position of each of the medical devices so that the sum of the adjustment distances of all the medical devices is minimized.

[0037] Compared with the existing technology, the above embodiment has the following beneficial effects: the present application obtains the first storage location by estimating the inbound and outbound data of the future time period in advance, so that the warehouse management personnel can adjust the medical equipment stored in the warehouse to the first storage location in advance. However, since some medical equipment has already been stored in the warehouse, there may be a certain difference between the re-planned first storage location and the currently placed second storage location. By optimizing the first storage location again, the sum of the distances between the second storage location and the first storage location is minimized, thereby reducing the workload of the warehouse management personnel.

[0038] In a second aspect, an embodiment of the present application further provides a medical device storage management device, comprising: a first prediction module, a first clustering module, a second clustering module, and a first storage location determination module;

[0039] The first prediction module is configured to obtain historical shipment data of each medical device in the warehouse and order data of each medical device within a first preset time in the future, and determine the maximum storage quantity of each medical device based on the historical shipment data and the order data;

[0040] The first clustering module is configured to cluster the medical devices according to material properties of the medical devices to obtain a plurality of first clusters, and determine a first storage area corresponding to each first cluster according to a maximum storage quantity of each medical device; wherein each first cluster includes a plurality of the medical devices;

[0041] The second clustering module is configured to cluster all medical devices in each of the first clusters in sequence according to the usage attributes of each of the medical devices in combination with a genetic algorithm to obtain a corresponding number of second clusters; wherein each of the second clusters includes a number of the medical devices;

[0042] The first storage location determination module is used to divide the corresponding first storage area according to the maximum storage quantity of each medical device, obtain the second storage area corresponding to each second cluster, and determine the first storage position of each medical device in the corresponding second storage area in combination with the historical outbound data of each medical device.

[0043] In a third aspect, the present application also provides a terminal device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the above-mentioned medical device storage management method when executing the computer program.

[0044] In a fourth aspect, the present application also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned medical device storage management method. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a medical device storage management method provided in some embodiments of the present application;

[0046] Figure 2 This is a structural diagram of a medical device storage management device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0047] Existing warehouse space planning often ignores future medical device consumption and storage requirements. Instead, it re-plans the placement of medical devices within the warehouse based solely on current storage conditions through mathematical programming. This approach then adjusts the placement of medical devices based on the latest planning methods. This approach not only results in a high labor load but also inefficient warehouse management in the face of dynamically changing storage conditions. Furthermore, existing technology often overlooks the impact of the storage environment on medical devices, exposing them to the risk of contamination and compromising storage safety.

[0048] In order to solve the above technical problems, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of this application.

[0049] Example 1

[0050] Please refer to Figure 1, a medical device storage management method provided in an embodiment of the present application, including S10 to S40, specifically:

[0051] S10: Obtain historical shipment data of each medical device in the warehouse, and order data of each medical device within a first preset time in the future, and determine the maximum storage quantity of each medical device based on the historical shipment data and the order data.

[0052] Furthermore, in some embodiments of the present application, determining the maximum storage quantity of each medical device based on the historical delivery data and the order data includes:

[0053] Predicting the consumption rate of each of the medical devices within the first preset time in the future based on all the historical delivery data;

[0054] Predicting, based on all the order data, a warehousing speed of each of the medical devices within the first preset time in the future;

[0055] According to the consumption speed and the warehousing speed, the maximum storage quantity corresponding to the medical device is calculated.

[0056] By obtaining historical outbound data, we can predict the future consumption rate of medical devices, and based on future order data, we can predict the future warehousing rate of medical devices, thereby dynamically estimating the storage quantity of medical devices in the warehouse at any point in the future, and accurately assessing the maximum storage space that may be required for medical devices in the future, effectively improving the space utilization of the warehouse in a dynamic warehouse state.

[0057] Preferably, in some embodiments of the present application, the consumption rate of each medical device can be predicted by the following preferred implementations:

[0058] S11: Determine the first preset time in the future; here, a quarter can be preferably selected as the first preset time in the future;

[0059] S12: Obtain historical shipment data for each medical device over several years, and organize the historical shipment data in chronological order; if the first preset time selected in S11 is the third quarter, then obtain the historical shipment data for all third quarters of the previous three years;

[0060] S13: Based on the SARIMA model and combined with the historical outbound data obtained in S12, the parameters of the non-seasonal part and the seasonal part of the SARIMA model corresponding to each medical device are fitted in sequence to obtain the consumption rate of each medical device.

[0061] Preferably, in some embodiments of the present application, the warehousing speed can be fitted directly according to the order data through a linear fitting method; or the real order data can be directly called, and according to the time unit in the order data, the continuous time variables involved in the consumption speed obtained by fitting S11 to S13 are replaced with discrete time variables with the above time units as intervals.

[0062] Preferably, in some embodiments of the present application, the calculating the maximum storage quantity of the corresponding medical device according to the consumption rate and the storage rate includes:

[0063] The calculation formula of the maximum storage quantity is specifically:

[0064]

[0065] Among them, S i is the maximum storage quantity of medical device i; [T1, T2] is the interval of the first preset time in the future; v in,i (t) and v out,i (t) are the storage speed and consumption speed of medical device i in the first preset time in the future; Represents the maximum value of a function in the interval [T1, T2].

[0066] Specifically, when the time variable t in the above formula is a continuous variable, directly use the function v in,i (t)-v out,i (t) is integrated to obtain the function of the change in the storage quantity of medical devices in the warehouse, and the maximum value of the function of the change in the storage quantity within the first preset time in the future is obtained. When the time variable t in the above formula is a discrete variable, the number of medical devices stored in each discrete time period is obtained based on the order data. If the order is in days, the discrete time period is one day; further, by integrating v in,i (t), calculate the consumption of medical devices in the discrete time period; combine the inventory quantity and consumption of medical devices in each discrete time period to calculate the storage quantity of medical devices at the end of each discrete time period; obtain the maximum storage quantity from all discrete time periods.

[0067] S20: Clustering the medical devices according to their material properties to obtain a plurality of first clusters, and determining a first storage area corresponding to each first cluster according to a maximum storage quantity of each medical device; wherein each first cluster includes a plurality of the medical devices.

[0068] Preferably, in some embodiments of the present application,

[0069] S30: Clustering all medical devices in each of the first clusters in turn according to the usage attributes of each of the medical devices in combination with a genetic algorithm to obtain corresponding second clusters; wherein each of the second clusters includes several of the medical devices.

[0070] Furthermore, in some embodiments of the present application, according to the usage attributes of each medical device, combined with a genetic algorithm, all medical devices in each first cluster are clustered in sequence to obtain corresponding second clusters, including:

[0071] Calculating the similarity between the medical devices based on semantic analysis tools and combining the usage attributes of the medical devices;

[0072] Determining idle shelf space based on the maximum storage space of each shelf and the maximum storage quantity of each medical device;

[0073] A fitness function is constructed according to the similarity and the idle space of the shelf, and the fitness function is optimized by the genetic algorithm to obtain a plurality of second clusters.

[0074] Through semantic analysis tools, the similarities between medical devices are accurately evaluated. The storage space required for medical devices is accurately evaluated based on the maximum storage quantity of medical devices. Combined with the maximum storage space of the shelves, the idle space on the shelves is accurately evaluated. By optimizing similarities and idle space on the shelves, the rationality of the second clustering results and the utilization rate of storage space are dynamically balanced.

[0075] Preferably, in some embodiments of the present application, the text embedding vector of each medical device usage attribute can be obtained by any semantic analysis method, and the cosine similarity between any two corresponding text embedding vectors of medical devices can be calculated as a measure of similarity.

[0076] Preferably, in some embodiments of the present application, the chromosome of the genetic algorithm can be constructed by the following preferred implementations:

[0077] chromosome=[random[h k ],random[h k ],…,random[h k ]]

[0078] Among them, chromosome is a randomly generated chromosome; random[h k ] is a chromosome gene, and its physical meaning represents which cluster the medical device corresponding to the gene belongs to. The value is in the interval [1,h k ] randomly generated between kis a parameter set artificially, representing the number of the second cluster. Assuming random[h k ]=1, it means that the current corresponding medical device belongs to the first second cluster; a chromosome contains l k Gene random[h k ], l k is the first cluster L k The number of types of medical devices in the industry. Through a chromosome, we can determine the number of types of medical devices in the industry. k The second cluster corresponding to various medical devices.

[0079] Preferably, in some embodiments of the present application, each medical device has a corresponding minimum occupied storage space. By multiplying the maximum storage quantity by the minimum occupied storage space, the maximum storage space required for any medical device can be obtained. After obtaining the division scheme of the second cluster, medical devices with different uses will not be placed on the same shelf. Therefore, after the medical devices with the same use are placed, there will be a part of idle space on the last shelf. The total storage space occupied by all medical devices in each second cluster can be calculated based on the maximum storage space of various medical devices, which serves as the basis for subsequent calculation of the idle space on the shelf. The total storage space occupied by each specific second cluster can be obtained by the following calculation formula:

[0080]

[0081] Among them, S i is the maximum storage quantity of medical device i; V k,i The second cluster G k In the figure, the minimum storage space occupied by each medical device i; V k The second cluster G k The storage space required for all medical devices in the facility.

[0082] Preferably, in some embodiments of the present application, constructing a fitness function according to the similarity and the idle space of the shelf includes:

[0083] The fitness function is specifically:

[0084]

[0085] Among them, ω1 and ω2 are weight coefficients; n k The second cluster G k The number of types of medical devices in the dataset; K is the number of the second cluster; sim(i,j) represents the similarity between medical device i and medical device j; V k The second cluster G kThe storage space required for all medical devices in the system; V is the maximum storage space of the shelf; mod is the remainder operator.

[0086] According to the above preferred embodiment, there is no need to decode the chromosomes. The fitness function is directly optimized by the existing genetic algorithm in combination with the above chromosome generation method to obtain the optimal feasible solution.

[0087] S40: Divide the corresponding first storage area according to the maximum storage quantity of each medical device, obtain the second storage area corresponding to each second cluster, and determine the first storage position of each medical device in the corresponding second storage area in combination with the historical outbound data of each medical device.

[0088] Preferably, in some embodiments of the present application, after determining the first storage area corresponding to each first cluster, the second cluster G is determined to be stored according to the above preferred embodiment. k Storage space required for all medical devices V k , thereby determining the required number of shelves and the corresponding size of each second storage area. The specific corresponding location of each second storage area can be evaluated based on the use of the medical devices corresponding to each second cluster and its priority. This application does not limit the optimization of the specific corresponding location of each second storage area, but it is necessary to ensure the continuity of each second storage area.

[0089] Furthermore, in some embodiments of the present application, determining the first storage location of each medical device in the corresponding second storage area in combination with the historical outbound data of each medical device includes:

[0090] Evaluate the unit quantity management load corresponding to each medical device based on the consumption rate and the warehousing rate of each medical device;

[0091] Through a genetic algorithm, the first storage position of each medical device in the second cluster corresponding to the second storage area is optimized to minimize the difference in the total management load between each shelf; wherein the total management load of the shelf is calculated based on the number of medical devices stored on the corresponding shelf and the corresponding unit quantity management load.

[0092] The second cluster includes a large number of medical devices with the same purpose but with specific differences, such as brand or medical grade. Different medical devices have different usage frequencies. If medical devices of different medical grades but with high usage frequencies are stored on the same shelf, it may cause the same shelf to have frequent consumption and warehousing operations per unit time, thereby reducing the efficiency of goods retrieval and warehousing in the warehouse. Therefore, by evaluating the frequency of each medical device entering and leaving the warehouse, based on this, the frequency of medical device retrieval and warehousing on each shelf is averaged to improve warehouse management efficiency.

[0093] Preferably, in some embodiments of the present application, the unit quantity management load corresponding to each medical device can be directly calculated as the sum of the consumption rate and the storage speed, as the unit quantity management load of the corresponding medical device; or the consumption rate and the storage speed can be used to calculate the average daily storage and retrieval volume of the corresponding medical device in the first preset time in the future, as the unit quantity management load of the corresponding medical device; or the medical devices can be sorted according to the sum of the consumption rate and the storage speed, and the corresponding weights are assigned according to the order of priority, and the weights are used as the unit quantity management load of the medical device. The above methods are only examples, and the present application does not limit how to evaluate the unit quantity management load of the corresponding medical device based on the consumption rate and the storage speed.

[0094] Preferably, in some embodiments of the present application, when optimizing the first storage location by a genetic algorithm, a corresponding number may be set for each storage location, and a chromosome may be generated according to the following example:

[0095] chromosome=[random[G k ],random[G k ],…,random[G k ]]

[0096] Assuming that each row of each shelf is a storage location, the length of each chromosome (also the number of genes) is the total number of storage locations in the corresponding second region, and random[G k ] represents the second cluster G k Randomly select a medical device, if random[G k ] is the i-th gene in the chromosome, which means that the i-th number corresponds to the storage location that can be stored as random[G k ]A medical device is randomly selected. At the same time, when optimizing the difference in the total management load between shelves through genetic algorithms, it is also necessary to set constraints so that the storage space allocated to each medical device can meet its maximum storage quantity requirements.

[0097] Furthermore, in some embodiments of the present application, after determining the first storage position of each of the medical devices, it also includes: optimizing the first storage position of each medical device based on the current second storage position of each of the medical devices so that the sum of the adjustment distances of all the medical devices is minimized.

[0098] This application obtains the first storage location by estimating the inbound and outbound data of the future time period in advance, so that the warehouse management personnel can adjust the medical equipment stored in the warehouse to the first storage location in advance. However, since some medical devices have already been stored in the warehouse, there may be certain differences between the re-planned first storage location and the currently placed second storage location. By optimizing the first storage location again, the sum of the distances between the second storage location and the first storage location is minimized, thereby reducing the workload of the warehouse management personnel.

[0099] Preferably, in some embodiments of the present application, when adjusting the second storage location, it is necessary to ensure that the second storage location cannot be adjusted from the second area to which it currently belongs to other second areas.

[0100] In summary, it can be seen that the medical device storage management method provided by the embodiment of the present application has the following beneficial effects: by collecting historical outbound data and future order data of medical devices, the fluctuation of the storage quantity of various types of medical devices in the warehouse over a period of time in the future is accurately assessed, providing effective space constraints for subsequent space planning, thereby ensuring that the storage space is effectively utilized; in addition, by clustering the attributes of medical devices multiple times, hierarchical planning of storage space is achieved, effectively improving the computational efficiency of the space planning process. Moreover, clustering medical devices by material attributes ensures that medical devices can be stored in a suitable storage environment, thereby improving the storage security of medical devices; further clustering medical devices by use attributes provides a basis for the subsequent detailed arrangement of storage locations, effectively improving the work efficiency of warehouse managers.

[0101] Example 2

[0102] refer to Figure 2 , a medical device storage management device provided in an embodiment of the present application, includes: a first prediction module 11, a first clustering module 12, a second clustering module 13 and a first storage location determination module 14.

[0103] Furthermore, in some embodiments of the present application, the first prediction module 11 is used to obtain historical outbound data of each medical device in the warehouse, as well as order data of each medical device within a first preset time in the future, and determine the maximum storage quantity of each medical device based on the historical outbound data and the order data; the first clustering module 12 is used to cluster the medical devices according to the material properties of each medical device to obtain a plurality of first clusters, and determine a first storage area corresponding to each first cluster based on the maximum storage quantity of each medical device; wherein each first cluster includes a plurality of medical devices; the second clustering module 13 is used to cluster all medical devices in each first cluster in turn based on the use properties of each medical device in combination with a genetic algorithm to obtain a plurality of corresponding second clusters; wherein each second cluster includes a plurality of medical devices; the first storage location determination module 14 is used to divide the corresponding first storage area according to the maximum storage quantity of each medical device, obtain a second storage area corresponding to each second cluster, and determine a first storage location of each medical device in the corresponding second storage area based on the historical outbound data of each medical device.

[0104] It can be understood that the above-mentioned device embodiment corresponds to the method embodiment of the present invention. The medical device storage management device provided by the embodiment of the present invention can implement any method embodiment of the present invention, that is, the medical device storage management method provided in Example 1.

[0105] In summary, it can be seen that the medical device storage management device provided by the embodiment of the present application has the following beneficial effects: by collecting historical outbound data and future order data of medical devices, it accurately assesses the fluctuation of the storage quantity of various types of medical devices in the warehouse over a period of time in the future, providing effective space constraints for subsequent space planning, thereby ensuring that the storage space is effectively utilized; in addition, by clustering the attributes of medical devices multiple times, hierarchical planning of storage space is achieved, effectively improving the computational efficiency of the space planning process. Moreover, clustering medical devices by material attributes ensures that medical devices can be stored in a suitable storage environment, thereby improving the storage security of medical devices; further clustering medical devices by use attributes provides a basis for the subsequent detailed arrangement of storage locations, effectively improving the work efficiency of warehouse managers.

[0106] Example 3

[0107] Based on the above-mentioned embodiment of the medical device storage management method, another embodiment of the present application provides a medical device storage management terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the medical device storage management method of any embodiment of the present application is implemented.

[0108] For example, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to implement the present application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program in the medical device storage management device.

[0109] The medical device storage management device may be a computing device such as a desktop computer, a notebook computer, a palmtop computer, a cloud server, etc. The medical device storage management terminal device may include, but is not limited to, a processor and a memory.

[0110] The processor can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor serves as the control center of the medical device storage and management device, connecting the various components of the device using various interfaces and circuits. The memory can be used to store the computer programs and / or modules. The processor implements the various functions of the medical device storage and management device by running or executing the computer programs and / or modules stored in the memory and accessing data stored in the memory. The memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application required for a function, etc.; the data storage area can store data generated based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0111] Example 4

[0112] Based on the above-mentioned embodiment of the medical device storage management method, another embodiment of the present application provides a storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to execute the medical device storage management method of any embodiment of the present application.

[0113] In this embodiment, the storage medium is a computer-readable storage medium, and the computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0114] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this application by those skilled in the art should be included within the scope of protection of this application.

Claims

1. A medical device storage management method, characterized in that: include: Obtaining historical shipment data for each medical device in the warehouse and order data for each medical device within a first preset time in the future, and determining a maximum storage quantity for each medical device based on the historical shipment data and the order data; Clustering the medical devices according to material properties of the medical devices to obtain a plurality of first clusters, and determining a first storage area corresponding to each first cluster according to a maximum storage quantity of each medical device; wherein each first cluster includes a plurality of the medical devices; According to the usage attributes of each medical device, combined with a genetic algorithm, all medical devices in each first cluster are clustered in turn to obtain corresponding second clusters; wherein each second cluster includes several medical devices; Divide the first storage area corresponding to each medical device according to the maximum storage quantity of each medical device, obtain the second storage area corresponding to each second cluster, and determine the first storage position of each medical device in the corresponding second storage area in combination with the historical outbound data of each medical device; The method of clustering all medical devices in each of the first clusters in accordance with the usage attributes of each of the medical devices in combination with a genetic algorithm to obtain a corresponding number of second clusters includes: Calculating the similarity between the medical devices based on semantic analysis tools and combining the usage attributes of the medical devices; Determining idle shelf space based on the maximum storage space of each shelf and the maximum storage quantity of each medical device; Constructing a fitness function according to the similarity and the idle space of the shelf, and optimizing the fitness function through the genetic algorithm to obtain a plurality of second clusters; The constructing of a fitness function according to the similarity and the idle space of the shelf includes: The fitness function is specifically: in, and is the weight coefficient; The second cluster The number of types of medical devices; is the number of the second cluster; Representative medical devices and medical devices similarities between; The second cluster The storage space required for all medical devices; The maximum storage space for the shelf; is the remainder operator.

2. A medical device storage management method according to claim 1, characterized in that: Determining the maximum storage quantity of each medical device based on the historical outbound data and the order data includes: Predicting the consumption rate of each of the medical devices within the first preset time in the future based on all the historical delivery data; Predicting, based on all the order data, a warehousing speed of each of the medical devices within the first preset time in the future; According to the consumption speed and the warehousing speed, the maximum storage quantity corresponding to the medical device is calculated.

3. A medical device storage management method according to claim 2, characterized in that: Determining the first storage location of each medical device in the corresponding second storage area by combining the historical outbound data of each medical device includes: Evaluate the unit quantity management load corresponding to each medical device based on the consumption rate and the warehousing rate of each medical device; Through a genetic algorithm, the first storage position of each medical device in the second cluster corresponding to the second storage area is optimized to minimize the difference in the total management load between each shelf; wherein the total management load of the shelf is calculated based on the number of medical devices stored on the corresponding shelf and the corresponding unit quantity management load.

4. A medical device storage management method according to claim 2, characterized in that: The calculating, based on the consumption rate and the warehousing rate, the maximum storage quantity of the corresponding medical device includes: The calculation formula of the maximum storage quantity is specifically: in, For medical devices Maximum storage quantity; The interval for the first preset time in the future; and Medical devices The speed of inventory entry and consumption within the first preset time in the future; Represents a function in the interval The maximum value of .

5. A medical device storage management method according to claim 1, characterized in that: After determining the first storage position of each medical device, the method further includes: optimizing the first storage position of each medical device according to the current second storage position of each medical device so as to minimize the sum of the adjustment distances of all the medical devices.

6. A medical device storage management device, characterized in that: include: a first prediction module, a first clustering module, a second clustering module, and a first storage location determination module; The first prediction module is configured to obtain historical shipment data of each medical device in the warehouse and order data of each medical device within a first preset time in the future, and determine the maximum storage quantity of each medical device based on the historical shipment data and the order data; The first clustering module is configured to cluster the medical devices according to material properties of the medical devices to obtain a plurality of first clusters, and determine a first storage area corresponding to each first cluster according to a maximum storage quantity of each medical device; wherein each first cluster includes a plurality of the medical devices; The second clustering module is configured to cluster all medical devices in each of the first clusters in sequence according to the usage attributes of each of the medical devices in combination with a genetic algorithm to obtain a corresponding number of second clusters; wherein each of the second clusters includes a number of the medical devices; The first storage location determination module is configured to divide the first storage area corresponding to each medical device according to the maximum storage quantity of each medical device, obtain the second storage area corresponding to each second cluster, and determine the first storage location of each medical device in the corresponding second storage area based on the historical outbound data of each medical device; The method of clustering all medical devices in each of the first clusters in accordance with the usage attributes of each of the medical devices in combination with a genetic algorithm to obtain a corresponding number of second clusters includes: Calculating the similarity between the medical devices based on semantic analysis tools and combining the usage attributes of the medical devices; Determining idle shelf space based on the maximum storage space of each shelf and the maximum storage quantity of each medical device; Constructing a fitness function according to the similarity and the idle space of the shelf, and optimizing the fitness function through the genetic algorithm to obtain a plurality of second clusters; The constructing of a fitness function according to the similarity and the idle space of the shelf includes: The fitness function is specifically: in, and is the weight coefficient; The second cluster The number of types of medical devices; is the number of the second cluster; Representative medical devices and medical devices similarities between; The second cluster The storage space required for all medical devices; The maximum storage space for the shelf; is the remainder operator.

7. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, a medical device storage management method according to any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute a medical device storage management method according to any one of claims 1 to 5.

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