Medicinal material replenishment method and device, electronic equipment and computer readable medium

Abstract

The embodiment of the invention discloses a medicinal material replenishment method and device, electronic equipment and a computer readable medium. A specific embodiment of the method comprises the following steps: for each medicinal material in a target clinic, executing the following processing steps: acquiring a medicinal material ex-warehouse information sequence of the medicinal material in a preset time period; inputting the medicinal material ex-warehouse information sequence into a medicinal material replenishment information prediction model to obtain medicinal material replenishment predicted quantity information corresponding to a preset future time period; obtaining current stock value information of the medicinal materials and a preset warehousing replenishment total value; according to the inventory value information, generating current medicinal material unit value information of the medicinal material; determining actual medicinal material replenishment information corresponding to the medicinal materials according to the medicinal material unit value information, the medicinal material replenishment predicted quantity information and the warehousing replenishment total value; and according to the actual medicinal material replenishment information, the associated medicinal material transport vehicle is controlled to execute replenishment operation. According to the embodiment, the replenishment quantity of the medicinal materials can be accurately determined, repeated vehicle dispatching for medicinal material replenishment is not needed, and waste of transportation resources is avoided.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the computer field, and in particular to a medicinal material replenishment method, device, electronic device, and computer-readable medium. Background Art

[0002] Predicting the replenishment of medicinal materials is of great significance for analyzing the real-time cost of storing medicinal materials in clinics. At present, the cost of medicinal materials varies greatly, and there are many categories in clinics. Clinics have a poor grasp of the real-time cost of medicinal materials, and cannot intuitively view the inventory and inventory cost of medicinal materials in the clinic. It is impossible to accurately determine the replenishment quantity, and it is necessary to repeatedly dispatch vehicles to replenish medicinal materials, which wastes transportation resources.

[0003] The above information disclosed in this Background section is only for enhancement of understanding of the background of the inventive concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0004] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0005] Some embodiments of the present disclosure provide a method, device, electronic device, and computer-readable medium for replenishing medicinal materials to solve one or more of the technical problems mentioned in the above background technology section.

[0006] In a first aspect, some embodiments of the present disclosure provide a method for replenishing medicinal materials, the method comprising: for each medicinal material in a target clinic, executing the following processing steps: obtaining a sequence of medicinal material outbound information of the medicinal material within a preset time period, wherein the medicinal material outbound information in the sequence of medicinal material outbound information comprises: medicinal material outbound value and medicinal material outbound quantity; inputting the sequence of medicinal material outbound information into a pre-trained medicinal material replenishment information prediction model to obtain medicinal material replenishment prediction quantity information corresponding to a preset future time period, wherein the medicinal material replenishment information prediction model comprises: an extreme learning machine model model and moving average model; obtain the current inventory value information of the above medicinal materials and the preset total value of incoming replenishment, wherein the above inventory value information includes: inventory value, current medicinal material incoming value, inventory quantity, current medicinal material incoming quantity; generate the current medicinal material unit value information of the above medicinal materials according to the above inventory value information; determine the actual medicinal material replenishment information corresponding to the above medicinal materials according to the above medicinal material unit value information, the above medicinal material replenishment forecast quantity information and the above incoming replenishment total value; control the associated medicinal material transport vehicles to perform replenishment operations according to the above actual medicinal material replenishment information.

[0007] In a second aspect, some embodiments of the present disclosure provide a medicinal material replenishment device, the device comprising: a replenishment unit, configured to perform the following processing steps for each medicinal material in a target clinic: obtaining a medicinal material outbound information sequence of the above medicinal material within a preset time period, wherein the medicinal material outbound information in the above medicinal material outbound information sequence includes: medicinal material outbound value and medicinal material outbound quantity; inputting the above medicinal material outbound information sequence into a pre-trained medicinal material replenishment information prediction model to obtain medicinal material replenishment prediction quantity information corresponding to a preset future time period, wherein the above medicinal material replenishment information prediction model includes: Limited learning machine model and moving average model; obtain the current inventory value information of the above-mentioned medicinal materials and the preset total value of incoming replenishment, wherein the above-mentioned inventory value information includes: inventory value, current medicinal materials incoming value, inventory quantity, current medicinal materials incoming quantity; based on the above-mentioned inventory value information, generate the current medicinal materials unit value information of the above-mentioned medicinal materials; based on the above-mentioned medicinal materials unit value information, the above-mentioned medicinal materials replenishment forecast quantity information and the above-mentioned total value of incoming replenishment, determine the actual medicinal materials replenishment information corresponding to the above-mentioned medicinal materials; based on the above-mentioned actual medicinal materials replenishment information, control the associated medicinal materials transportation vehicles to perform replenishment operations.

[0008] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0009] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0010] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the medicinal material replenishment method of some embodiments of the present disclosure, the replenishment quantity of medicinal materials can be accurately determined, so that there is no need to repeatedly dispatch vehicles to replenish medicinal materials, avoiding the waste of transportation resources. At the same time, it also avoids the waste of medicinal materials caused by excessive replenishment. Based on this, the medicinal material replenishment method of some embodiments of the present disclosure performs the following processing steps for each medicinal material in the target clinic: first, obtain the medicinal material outbound information sequence of the above-mentioned medicinal materials within a preset time period. Among them, the medicinal material outbound information in the above-mentioned medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity. As a result, the remaining amount and remaining cost of the medicinal materials in the clinic can be intuitively understood, so as to more accurately determine the replenishment time. Secondly, the above-mentioned medicinal material outbound information sequence is input into the pre-trained medicinal material replenishment information prediction model to obtain the medicinal material replenishment prediction quantity information corresponding to the preset future time period. Among them, the above-mentioned medicinal material replenishment information prediction model includes: an extreme learning machine model and a moving average model. Therefore, the medicinal material replenishment information prediction model includes an extreme learning machine type and a moving average type, which can avoid directly using a simple model to predict the replenishment quantity, and the extreme learning machine model can effectively learn the information contained in the time series data, thereby improving the accuracy of the predicted replenishment quantity. Also because the time complexity of the extreme learning machine is relatively low compared to the time series or machine learning model, the time complexity can be reduced, and the number of times the replenishment quantity prediction task fails can be reduced. Next, the current inventory value information of the above-mentioned medicinal materials and the preset total value of the incoming replenishment are obtained. Among them, the above-mentioned inventory value information includes: inventory value, current medicinal material incoming value, inventory quantity, and current medicinal material incoming quantity. Thus, it is convenient to determine the current unit price of the medicinal material in the clinic to avoid the cost pressure caused by excessive replenishment. Then, based on the above-mentioned inventory value information, the current medicinal material unit value information of the above-mentioned medicinal material is generated. Afterwards, the actual medicinal material replenishment information corresponding to the above-mentioned medicinal material is determined according to the above-mentioned medicinal material unit value information, the above-mentioned medicinal material replenishment prediction quantity information and the above-mentioned incoming replenishment total value. Thus, the actual replenishment quantity of the medicinal material can be accurately determined. Finally, according to the above actual medicinal material replenishment information, the associated medicinal material transport vehicles are controlled to perform replenishment operations. As a result, there is no need to repeatedly dispatch vehicles to replenish medicinal materials, avoiding the waste of transportation resources. At the same time, it also avoids the waste of medicinal materials caused by excessive replenishment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar figure labels represent the same or similar elements. It should be understood that the drawings are schematic and that the components and elements are not necessarily drawn to scale.

[0012] Figure 1 is a flow chart of some embodiments of the medicinal material replenishment method according to the present disclosure;

[0013] Figure 2 is a flow chart of some embodiments of the medicinal material replenishment device according to the present disclosure;

[0014] Figure 3 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0015] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0016] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0017] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0018] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0019] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0020] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0021] Figure 1 1 is a flow chart of some embodiments of the medicinal material replenishment method according to the present disclosure. 100 of some embodiments of the medicinal material replenishment method according to the present disclosure is shown. The medicinal material replenishment method comprises the following steps:

[0022] Step 101, for each medicinal material in the target clinic, perform the following processing steps:

[0023] Step 1011, obtaining the medicinal material outbound information sequence of the above medicinal materials within a preset time period.

[0024] In some embodiments, the execution subject (e.g., a computing device) of the medicinal material replenishment method may obtain a sequence of medicinal material outbound information of the above-mentioned medicinal materials within a preset time period. Among them, the medicinal material outbound information in the above-mentioned medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity. The preset time period may refer to a historical time period. For example, the preset time period may be the most recent week. The target clinic may refer to a Chinese medicine pharmacy clinic. The medicinal materials may refer to various medicinal materials required for traditional Chinese medicine. One medicinal material outbound information corresponds to a time granularity. For example, the time granularity may be 1 day. The medicinal material outbound information may include: the name of the medicinal material, the quantity of medicinal material outbound, and the unit value of the medicinal material outbound. For example, the unit value of the medicinal material outbound may refer to the unit price of the medicinal material.

[0025] Step 1012: input the above medicinal materials outbound information sequence into a pre-trained medicinal materials replenishment information prediction model to obtain the medicinal materials replenishment prediction quantity information corresponding to the preset future time period.

[0026] In some embodiments, the above-mentioned execution entity may input the above-mentioned medicinal materials outbound information sequence into a pre-trained medicinal materials replenishment information prediction model to obtain the medicinal materials replenishment prediction quantity information corresponding to the preset future time period. Among them, the above-mentioned medicinal materials replenishment information prediction model includes: an extreme learning machine model and a moving average model. The medicinal materials replenishment information prediction model may be a pre-trained prediction model that takes the medicinal materials outbound information sequence as input and the medicinal materials replenishment prediction quantity information as output. For example, the medicinal materials replenishment information prediction model may be a moving average model or a learning machine model. The medicinal materials replenishment prediction quantity information may refer to the weighted mean of the output results of each medicinal materials replenishment information prediction basic network in the medicinal materials replenishment information prediction model.

[0027] Continuing, when the medicinal material replenishment method disclosed in the present invention is used to predict the medicinal material replenishment quantity, there is further the following technical problem 2: the time complexity of integrating different types of time series or machine learning models is relatively high.

[0028] The above-mentioned medicinal material replenishment information prediction model can be trained through the following steps:

[0029] The first step is to obtain the sample medicinal material outbound quantity sequence corresponding to the above medicinal materials. The sample medicinal material outbound quantity sequence can be a sequence of each outbound quantity of the medicinal materials in a historical time period.

[0030] In the second step, according to the above-mentioned sample medicinal material outbound quantity sequence, each initial medicinal material replenishment information prediction basic network is trained to obtain each trained medicinal material replenishment information prediction basic network. Among them, the initial medicinal material replenishment information prediction basic network corresponds to the model type and model parameter information, and each model type corresponding to each of the above-mentioned initial medicinal material replenishment information prediction basic networks includes an extreme learning machine type and a moving average type. The initial medicinal material replenishment information prediction basic network can be an untrained model for predicting the replenishment quantity of medicinal materials. The various model types corresponding to each of the above-mentioned initial medicinal material replenishment information prediction basic networks include an extreme learning machine type and a moving average type. For example, the initial medicinal material replenishment information prediction basic network with an extreme learning machine model type can be an online cyclic extreme learning machine model. The initial medicinal material replenishment information prediction basic network with a moving average model type can be a weighted moving average model. The model parameter information of the initial medicinal material replenishment information prediction basic network corresponding to the extreme learning machine can include an activation function type and a memory factor. The activation function type can be sigmoid or tanh. The memory factor can be a value between 0 and 1. The model parameter information of the basic network for predicting the initial medicinal material replenishment information corresponding to the moving average may include each historical time range and a weighted ratio corresponding to each historical time range.

[0031] It should be noted that the method of training each initial medicinal material replenishment information prediction basic network may refer to using the sample medicinal material outbound quantity sequence as a training sample and training each initial medicinal material replenishment information prediction basic network through a deep neural network model training method.

[0032] The third step is to verify each of the above medicinal material replenishment information prediction basic networks according to the verification sample medicinal material outbound quantity sequence corresponding to the above sample medicinal material outbound quantity sequence, and obtain each model verification result. Among them, the model verification results in the above each model verification result correspond to the medicinal material replenishment information prediction basic network in the above each medicinal material replenishment information prediction basic network.

[0033] The third step may include the following sub-steps:

[0034] In the first sub-step, for each medicinal material replenishment information prediction basic network, the inventory replenishment quantity prediction error of the medicinal material replenishment information prediction basic network is generated through the verification sample medicinal material outbound quantity sequence. The inventory replenishment quantity prediction error may be a mean absolute percentage error.

[0035] In the second sub-step, each generated inventory replenishment quantity forecast error is standardized to obtain each model verification result. Each obtained inventory replenishment quantity forecast error can be subjected to z-score standardization to obtain each standardized inventory replenishment quantity forecast error as each model verification result.

[0036] The fourth step is to generate the model weights corresponding to the basic networks for predicting the replenishment information of medicinal materials according to the verification results of the above models.

[0037] The fourth step may include the following sub-steps:

[0038] The first sub-step is to extract features from the sample herbal medicine outbound quantity sequence to obtain a sample feature vector set. tsfresh can be used to extract features from the sample herbal medicine outbound quantity sequence to obtain a sample feature vector set.

[0039] In the second sub-step, the medicinal material replenishment information prediction basic network whose corresponding model verification result meets the preset conditions is selected from the above-mentioned medicinal material replenishment information prediction basic networks as sample labels. The above-mentioned preset optimization conditions can represent the model effect of the model verification result to be the best. When the model verification result is the accuracy, the largest model verification result can represent the model effect to be the best. When the model verification result is the prediction error, the smallest model verification result can represent the model effect to be the best.

[0040] The third sub-step is to train a multi-classification model based on the sample feature vector set and the sample label. The multi-classification model can be trained with the sample feature vector set as feature data, the sample label as the true label, and the model identifier of each medicinal material replenishment information prediction basic network as an optional label.

[0041] In a fourth sub-step, the sample feature vector set is input into the multi-classification model to obtain each probability value as each model weight. The probability value in each probability value corresponds to the medicinal material replenishment information prediction basic network in each medicinal material replenishment information prediction basic network. Each probability value can represent the probability that the feature data is applicable to the corresponding medicinal material replenishment information prediction basic network.

[0042] The fifth step is to integrate the above-mentioned basic networks for predicting the replenishment information of medicinal materials according to the weights of the above-mentioned models to obtain a prediction model for the replenishment information of medicinal materials. The weights of the above-mentioned models can be used as weighting coefficients to weight and integrate the basic networks for predicting the replenishment information of medicinal materials into a prediction model for the replenishment information of medicinal materials.

[0043] The above-mentioned related contents, as an inventive point of the present disclosure, solve the second technical problem "high time complexity". The factors with high time complexity are often as follows: the way of integrating different types of time series or machine learning models has high time complexity. If the above-mentioned factors are solved, the effect of reducing time complexity can be achieved. In order to achieve this effect, first, the sample medicinal material outbound quantity sequence corresponding to the above-mentioned medicinal materials is obtained. Secondly, according to the above-mentioned sample medicinal material outbound quantity sequence, each initial medicinal material replenishment information prediction basic network is trained to obtain each trained medicinal material replenishment information prediction basic network. Among them, the initial medicinal material replenishment information prediction basic network corresponds to the model type and model parameter information, and the model types corresponding to the above-mentioned initial medicinal material replenishment information prediction basic networks include extreme learning machine type and moving average type. Then, according to the verification sample medicinal material outbound quantity sequence corresponding to the above-mentioned sample medicinal material outbound quantity sequence, the above-mentioned medicinal material replenishment information prediction basic network is verified and processed to obtain each model verification result. Among them, the model verification results in the above-mentioned each model verification result correspond to the medicinal material replenishment information prediction basic network in the above-mentioned each medicinal material replenishment information prediction basic network. Then, according to the verification results of each model, the model weights corresponding to each of the above-mentioned basic networks for predicting the replenishment information of medicinal materials are generated. Finally, according to the weights of each model, the basic networks for predicting the replenishment information of medicinal materials are fused to obtain the prediction model for the replenishment information of medicinal materials. It can avoid directly using a simple model to predict the replenishment quantity, and the extreme learning machine model can effectively learn the information contained in the time series data, thereby improving the accuracy of the predicted replenishment quantity. In addition, because the time complexity of the extreme learning machine is lower than that of the time series or machine learning model, the time complexity is reduced.

[0044] Step 1013, obtaining the current inventory value information of the above medicinal materials and the preset total value of replenishment.

[0045] In some embodiments, the execution entity may obtain the current inventory value information of the medicinal material and the preset total value of the incoming replenishment. The inventory value information includes: inventory value, current medicinal material incoming value, inventory quantity, and current medicinal material incoming quantity. The preset total value of the incoming replenishment may be a preset total value for obtaining the medicinal material (i.e., the total budget for purchasing the medicinal material).

[0046] Step 1014: Generate the current medicinal material unit value information of the medicinal materials according to the inventory value information.

[0047] In some embodiments, the execution entity may generate the current medicinal material unit value information of the medicinal materials based on the inventory value information.

[0048] The first step is to determine the total inventory value as the sum of the above inventory value and the above current medicinal materials storage value.

[0049] The second step is to determine the total inventory quantity as the sum of the above inventory quantity and the above current medicinal materials in stock quantity.

[0050] The third step is to determine the ratio of the above-mentioned total inventory value to the above-mentioned total inventory quantity as the current medicinal material unit value information of the above-mentioned medicinal materials.

[0051] Step 1015, determining the actual medicinal material replenishment information corresponding to the medicinal materials according to the medicinal material unit value information, the medicinal material replenishment forecast quantity information and the total value of the incoming replenishment.

[0052] In some embodiments, the execution entity may determine the actual medicinal material replenishment information corresponding to the medicinal materials based on the medicinal material unit value information, the medicinal material replenishment forecast quantity information and the total value of the incoming replenishment.

[0053] In practice, the above-mentioned execution entity can determine the actual medicinal material replenishment information corresponding to the above-mentioned medicinal materials through the following steps:

[0054] The first step is to determine the total predicted value of medicinal material replenishment by multiplying the above medicinal material unit value information by the above medicinal material replenishment forecast quantity information.

[0055] In the second step, in response to determining that the total value of the medicinal material replenishment forecast is less than or equal to the total value of the incoming replenishment, the medicinal material replenishment forecast quantity information and the medicinal material name of the medicinal material are determined as actual medicinal material replenishment information.

[0056] In the third step, in response to determining that the total predicted value of the medicinal material replenishment is greater than the total value of the incoming replenishment, the ratio of the total value of the incoming replenishment to the medicinal material unit value information is determined as the actual medicinal material replenishment quantity. For example, the ratio of the total value of the incoming replenishment to the medicinal material unit value information can be rounded down to determine the actual medicinal material replenishment quantity.

[0057] The fourth step is to determine the actual medicinal material replenishment quantity and the medicinal material name of the above medicinal material as the actual medicinal material replenishment information.

[0058] Step 1016: According to the actual medicinal material replenishment information, the associated medicinal material transport vehicles are controlled to perform replenishment operations.

[0059] In some embodiments, the execution entity may control the associated medicinal material transport vehicle to perform a replenishment operation based on the actual medicinal material replenishment information.

[0060] In practice, the above execution entity can control the associated medicinal material transport vehicles to perform replenishment operations through the following steps:

[0061] The first step is to determine the medicinal material attribute information corresponding to the medicinal material name included in the actual medicinal material replenishment information. The medicinal material attribute information may include the storage attributes of the medicinal material, and the medicinal materials excluded by the medicinal material. For example, medicinal material A needs to be stored at a temperature of 10-20 degrees for transportation, and cannot be placed together with medicinal materials B and C.

[0062] The second step is to determine the type of transport vehicle corresponding to the above medicinal material attribute information. For example, the type of transport vehicle may indicate the type of transport vehicle, for example, it may include: refrigerated truck, non-refrigerated truck.

[0063] The third step is to determine whether the target clinic currently has idle medicinal material transport vehicles corresponding to the above transport vehicle types.

[0064] In step 4, in response to determining that there is an idle medicinal material transport vehicle, the occupied space information corresponding to the actual medicinal material replenishment information is determined. The occupied space information may represent the occupied space of the medicinal materials corresponding to the actual medicinal material replenishment information. For example, the occupied space of a medicinal material may be determined and then multiplied by the replenishment quantity to obtain the total occupied space information.

[0065] Step 5: Determine available space information of idle medicinal material transport vehicles. Available space information may refer to maximum transport space information of the vehicle.

[0066] Step 6: In response to determining that the available space information is greater than the occupied space information, and the difference between the available space information and the occupied space information is less than or equal to a preset difference, the idle medicinal material transport vehicle is controlled to perform a replenishment operation. That is, the idle medicinal material transport vehicle can be controlled to go to the warehouse where the medicinal materials are located to perform the medicinal material transport operation.

[0067] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a medicinal material replenishment device. These medicinal material replenishment device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the medicinal material replenishment device can be specifically applied to various electronic devices.

[0068] like Figure 2As shown, the medicinal material replenishment device 200 of some embodiments includes: a replenishment unit 201. The replenishment unit 201 is configured to perform the following processing steps for each medicinal material in the target clinic: obtaining a sequence of medicinal material outbound information of the above medicinal materials within a preset time period, wherein the medicinal material outbound information in the above medicinal material outbound information sequence includes: medicinal material outbound value and medicinal material outbound quantity; inputting the above medicinal material outbound information sequence into a pre-trained medicinal material replenishment information prediction model to obtain medicinal material replenishment prediction quantity information corresponding to a preset future time period, wherein the above medicinal material replenishment information prediction model includes: an extreme learning machine model and a moving average model; obtaining the current inventory value information of the above medicinal materials and the preset total value of incoming replenishment, wherein the above inventory value information includes: inventory value, current medicinal material incoming value, inventory quantity, and current medicinal material incoming quantity; generating the current medicinal material unit value information of the above medicinal materials based on the above inventory value information; determining the actual medicinal material replenishment information corresponding to the above medicinal materials based on the above medicinal material unit value information, the above medicinal material replenishment prediction quantity information and the above incoming replenishment total value; and controlling the associated medicinal material transport vehicle to perform a replenishment operation based on the above actual medicinal material replenishment information.

[0069] It is understood that the units described in the medicinal material replenishment device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the medicinal material replenishment device 200 and the units included therein, and will not be described in detail here.

[0070] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device (e.g., a computing device) 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0071] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0072] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0073] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0074] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0075] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0076] The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist independently without being assembled into the electronic device. The above-mentioned computer-readable medium carries one or more programs. When the above-mentioned one or more programs are executed by the electronic device, the electronic device: for each medicinal material in the target clinic, performs the following processing steps: obtains the medicinal material outbound information sequence of the above-mentioned medicinal material within a preset time period, wherein the medicinal material outbound information in the above-mentioned medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity; inputs the above-mentioned medicinal material outbound information sequence into a pre-trained medicinal material replenishment information prediction model to obtain the medicinal material replenishment prediction quantity information corresponding to the preset future time period, wherein the above-mentioned medicinal material replenishment information prediction model Including: an extreme learning machine model and a moving average model; obtaining the current inventory value information of the above-mentioned medicinal materials and the preset total value of incoming replenishment, wherein the above-mentioned inventory value information includes: inventory value, current medicinal material incoming value, inventory quantity, and current medicinal material incoming quantity; generating the current medicinal material unit value information of the above-mentioned medicinal materials according to the above-mentioned inventory value information; determining the actual medicinal material replenishment information corresponding to the above-mentioned medicinal materials according to the above-mentioned medicinal material unit value information, the above-mentioned medicinal material replenishment forecast quantity information and the above-mentioned total value of incoming replenishment; and controlling the associated medicinal material transportation vehicles to perform replenishment operations according to the above-mentioned actual medicinal material replenishment information.

[0077] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0078] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0079] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be set in a processor, for example, may be described as: a processor including: a replenishment unit. The names of these units do not constitute a limitation on the units themselves in certain cases. For example, the replenishment unit may also be described as "for each medicinal material in the target clinic, perform the following processing steps: obtain the medicinal material outbound information sequence of the above-mentioned medicinal materials within a preset time period, wherein the medicinal material outbound information in the above-mentioned medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity; input the above-mentioned medicinal material outbound information sequence into the pre-trained medicinal material replenishment information prediction model to obtain the medicinal material replenishment prediction quantity information corresponding to the preset future time period, wherein the above-mentioned medicinal material replenishment information prediction model includes: The method comprises the following steps: obtaining the current inventory value information of the above medicinal materials and the preset total value of incoming replenishment, wherein the above inventory value information includes: inventory value, current incoming value of medicinal materials, inventory quantity, and current incoming quantity of medicinal materials; generating the current unit value information of the above medicinal materials according to the above inventory value information; determining the actual replenishment information of the above medicinal materials according to the unit value information of the medicinal materials, the replenishment forecast information of the above medicinal materials, and the total value of incoming replenishment; and controlling the associated medicinal materials transport vehicle to perform replenishment operations according to the actual replenishment information of the medicinal materials.

[0080] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0081] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for replenishing medicinal materials, comprising: For each medicinal material in the target clinic, the following processing steps are performed: Acquire a medicinal material outbound information sequence of the medicinal material within a preset time period, wherein the medicinal material outbound information in the medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity; Inputting the medicinal materials outbound information sequence into a pre-trained medicinal materials replenishment information prediction model to obtain medicinal materials replenishment prediction quantity information corresponding to a preset future time period, wherein the medicinal materials replenishment information prediction model includes: an extreme learning machine model and a moving average model; Obtaining the current inventory value information of the medicinal materials and the preset total value of incoming replenishment, wherein the inventory value information includes: inventory value, current medicinal materials incoming value, inventory quantity, and current medicinal materials incoming quantity; Generating current medicinal material unit value information of the medicinal material according to the inventory value information; Determine the actual medicinal material replenishment information corresponding to the medicinal material according to the medicinal material unit value information, the medicinal material replenishment forecast quantity information and the total value of the incoming replenishment; According to the actual medicinal material replenishment information, the associated medicinal material transport vehicles are controlled to perform replenishment operations.

2. The method according to claim 1, wherein: The step of generating the current medicinal material unit value information of the medicinal material according to the inventory value information includes: The sum of the inventory value and the current medicinal material storage value is determined as the total inventory value; The sum of the inventory quantity and the current quantity of medicinal materials in storage is determined as the total inventory quantity; The ratio of the total inventory value to the total inventory quantity is determined as the current medicinal material unit value information of the medicinal material.

3. The method according to claim 1, wherein: The determining of the actual medicinal material replenishment information corresponding to the medicinal material according to the medicinal material unit value information, the medicinal material replenishment forecast quantity information and the total value of the incoming replenishment includes: Determine the product of the medicinal material unit value information and the medicinal material replenishment forecast quantity information as the medicinal material replenishment forecast total value; In response to determining that the total predicted value of medicinal material replenishment is less than or equal to the total value of incoming replenishment, determining the predicted quantity information of medicinal material replenishment and the medicinal material name of the medicinal material as actual medicinal material replenishment information; In response to determining that the total predicted value of the medicinal material replenishment is greater than the total value of the incoming replenishment, determining a ratio of the total value of the incoming replenishment to the medicinal material unit value information as the actual medicinal material replenishment quantity; The actual medicinal material replenishment quantity and the medicinal material name of the medicinal material are determined as actual medicinal material replenishment information.

4. The method according to claim 1, wherein: The step of controlling the associated medicinal material transport vehicle to perform a replenishment operation according to the actual medicinal material replenishment information includes: Determining medicinal material attribute information corresponding to the medicinal material name included in the actual medicinal material replenishment information; Determine the type of transport vehicle corresponding to the medicinal material attribute information; Determine whether the target clinic currently has an idle medicinal material transport vehicle corresponding to the transport vehicle type; In response to determining that there is an idle medicinal material transport vehicle, determining occupied space information corresponding to the actual medicinal material replenishment information; Determine available space information of idle medicinal material transport vehicles; In response to determining that the available space information is greater than the occupied space information, and the difference information between the available space information and the occupied space information is less than or equal to a preset difference, the idle medicinal material transport vehicle is controlled to perform a replenishment operation.

5. A medicinal material replenishment device, comprising: The replenishment unit is configured to perform the following processing steps for each medicinal material in the target clinic: Acquire a medicinal material outbound information sequence of the medicinal material within a preset time period, wherein the medicinal material outbound information in the medicinal material outbound information sequence includes: the medicinal material outbound value and the medicinal material outbound quantity; Inputting the medicinal materials outbound information sequence into a pre-trained medicinal materials replenishment information prediction model to obtain medicinal materials replenishment prediction quantity information corresponding to a preset future time period, wherein the medicinal materials replenishment information prediction model includes: an extreme learning machine model and a moving average model; Obtaining the current inventory value information of the medicinal materials and the preset total value of incoming replenishment, wherein the inventory value information includes: inventory value, current medicinal materials incoming value, inventory quantity, and current medicinal materials incoming quantity; Generating current medicinal material unit value information of the medicinal material according to the inventory value information; Determine the actual medicinal material replenishment information corresponding to the medicinal material according to the medicinal material unit value information, the medicinal material replenishment forecast quantity information and the total value of the incoming replenishment; According to the actual medicinal material replenishment information, the associated medicinal material transport vehicles are controlled to perform replenishment operations.

6. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 4.

7. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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