Method and Device for Internet of Things Trade Inventory Management

Abstract

The present invention provides a method and device for Internet of Things trade inventory management. The method includes a replenishment prediction operation. The replenishment prediction operation includes: predicting the daily sales volume in the next L days based on the daily sales volume in the most recent L days, where L is a preset number of days; for each day in the next L days, taking the daily sales volume in the consecutive L days up to that day and calculating the safety inventory for that day; according to the current actual inventory, the daily sales volume in the next L days, and the replenishment nodes set within the next L days, successively calculating the daily inventory in the next L days. When the daily inventory on a certain day is less than its safety inventory, taking that day as the first replenishment node, and thus performing a replenishment operation on the day of the first replenishment node.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and device for managing trade inventory in the Internet of Things. Background Art

[0002] With the rapid development of automated management, the requirements for computer data evaluation and prediction capabilities are becoming increasingly higher, especially in sales forecasting.

[0003] For the entire region, sales forecasts can largely show the market popularity of a product. Therefore, sales forecasts can be used to analyze the development status of products, which is of great significance to the company's product analysis. In related technologies, future sales are often estimated only through historical order data information, but there is a lack of a complete replenishment recommendation algorithm. Most replenishment decisions are made manually, making the entire system not highly integrated and automated. Summary of the invention

[0004] In order to solve the problems of insufficient inventory, excessive storage, warehouse explosion, etc. caused by the difference between warehousing and actual sales volume of existing Internet of Things trading companies, the present invention provides an Internet of Things trading inventory management method, which includes a replenishment forecasting operation; the replenishment forecasting operation includes:

[0005] Predict the daily sales volume for the next L days based on the daily sales volume for the last L days, where L is the preset number of days;

[0006] For each of the future L days, take the daily sales volume of the L consecutive days ending on the current day and calculate the safety stock volume for the current day;

[0007] According to the current actual inventory, the daily sales volume of the next L days and the replenishment nodes set in the next L days, the daily inventory of the next L days is calculated in sequence. When the daily inventory of a certain day is less than its safety inventory, this day is used as the first replenishment node, and the replenishment operation is performed on the day of the first replenishment node.

[0008] Furthermore, a grayscale model is used to predict the daily sales volume for the next L days based on the daily sales volume for the most recent L days.

[0009] Furthermore, the calculation formula of the safety stock is:

[0010] SS=Z*σ*STD

[0011]

[0012] Where SS represents the safety stock, Z represents the safety factor corresponding to the customer service level, and σ represents the standard deviation calculated based on the daily sales volume for L consecutive days up to the current day.

[0013] Furthermore, a second replenishment node with a fixed period is preset, so that the replenishment operation is performed on the day of the second replenishment node.

[0014] Furthermore, the replenishment quantity of the first replenishment node is the sum of daily sales from the current replenishment node to the next replenishment node, or the sum of daily sales from the current replenishment node to the future L days; the replenishment quantity of the second replenishment node is the sum of daily sales from the current replenishment node to the next replenishment node, or the sum of daily sales from the current replenishment node to the future L days.

[0015] Furthermore, within the future L days, the first replenishment node is set only before the last second replenishment node.

[0016] Furthermore, for any replenishment node, a corresponding replenishment operation reminder is sent t days in advance, where t is a preset number of days.

[0017] Furthermore, the replenishment forecasting operation is performed every day.

[0018] The present invention also provides a device for Internet of Things trade inventory management, the device comprising:

[0019] Processor; and

[0020] A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the operations of the above-described method.

[0021] The present invention also provides a computer readable medium storing instructions, which, when executed, cause the system to perform the operations of the above method.

[0022] The method and device for managing the inventory of Internet of Things trade of the present invention aims at the pain point that the Internet of Things trade company does not accurately control the actual sales volume, resulting in insufficient inventory and overstock. The method and device analyze the historical sales orders, build a gray model, give the sales forecast results, and provide a decision-making basis for upstream production and downstream sales. At the same time, based on customer satisfaction and window distribution, the safety inventory is evaluated for each SKU, and combined with the real-time inventory situation of the warehouse, multi-dimensional analysis is performed to give replenishment suggestions, thereby continuously optimizing the inventory structure and reducing the problems of overstock and insufficient inventory. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0024] Figure 1 A schematic diagram showing a flow chart of a method for managing Internet of Things trade inventory according to an embodiment of the present invention;

[0025] Figure 2 A comparison table showing customer service level and safety factor in one embodiment of the present invention;

[0026] Figure 3 An exemplary forecast inventory trend diagram of the present invention is shown;

[0027] Figure 4 The functional modules of an exemplary system that can be used in various embodiments of the present invention are shown.

[0028] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0029] The present application is described in further detail below in conjunction with the accompanying drawings.

[0030] In a typical configuration of the present invention, the terminal, the device of the service network and the trusted party each include one or more processors (eg, a central processing unit (CPU)), an input / output interface, a network interface and a memory.

[0031] Memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash memory. Memory is an example of a computer-readable medium.

[0032] Computer readable media include permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, Phase-Change Memory (PCM), Programmable Random Access Memory (PRAM), Static Random-Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read-Only Memory (ROM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0033] The device referred to in the present invention includes but is not limited to user equipment, network equipment, or equipment formed by integrating user equipment and network equipment through a network. The user equipment includes but is not limited to any mobile electronic product that can interact with a user (for example, interact with a user through a touchpad), such as a smart phone, a tablet computer, etc. The mobile electronic product can use any operating system, such as an Android operating system, an iOS operating system, etc. Among them, the network device includes an electronic device that can automatically perform numerical calculations and information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic device (Programmable Logic Device, PLD), a field programmable gate array (Field Programmable Gate Array, FPGA), a digital signal processor (Digital Signal Processor, DSP), an embedded device, etc. The network device includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud composed of a plurality of servers; here, the cloud is composed of a large number of computers or network servers based on cloud computing (Cloud Computing), wherein cloud computing is a kind of distributed computing, a virtual supercomputer composed of a group of loosely coupled computer sets. The network includes but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a VPN network, a wireless ad hoc network, etc. Preferably, the device may also be a program running on the user device, the network device, or a device formed by integrating the user device and the network device, the network device, the touch terminal, or the network device and the touch terminal through a network.

[0034] Of course, those skilled in the art should understand that the above-mentioned devices are only examples, and other existing or future devices that are applicable to the present invention should also be included in the scope of protection of the present invention and are included here by reference.

[0035] In the description of the embodiments of the present invention, “plurality” means two or more, unless otherwise clearly and specifically defined.

[0036] Figure 1The flowchart of the method for managing the Internet of Things trade inventory of an embodiment of the present invention is shown. The method for managing the Internet of Things trade inventory of this embodiment first estimates the future sales volume based on the historical sales volume. A grayscale model is used here to achieve the estimation of future sales volume. Compared with the white system and the black system, the grayscale model can not only predict the system with uncertain factors, but also the known internal part of the system. Based on the analysis of statistics and probability, the establishment of a grayscale model will have a better effect on sales volume prediction.

[0037] The process of establishing the grayscale model GM(1,1) in this embodiment is as follows:

[0038] Assume the original sequence is:

[0039] x (0) =(x (0) (1), x (0) (2), ...x (0) (n))

[0040] Generate sequence x (0) The sequence generated by one accumulation is:

[0041]

[0042] x (1) =(x (1) (1), x (1) (2), ...x (1) (n))

[0043] In some other embodiments, the sequence x is used (0) The r-times cumulative generation sequence is:

[0044]

[0045] Establish the grey differential equation model of GM(11):

[0046] d(k)+αz (1) (k) = b or x (0) (k)+αz (1) (k) = b

[0047] Among them, x (0) (k) is the grey derivative, α is the development coefficient, z (1) (k) is the whitening background value, and b is the gray contribution.

[0048] Substituting time k = 2, 3, ... n into the above formula, we have

[0049]

[0050] Transformation model, introducing matrix-vector notation

[0051]

[0052] Therefore, the GM(11) model can be expressed as

[0053] Y=Bu

[0054] Use univariate linear regression, i.e. least squares method, to find the estimated values ​​of a and b

[0055]

[0056] Using the above grayscale model, in this embodiment, the historical daily sales volume of the most recent 30 days is used as raw data to generate a series of predicted sales volumes for the next 30 days, that is, the daily sales volume for the next 30 days.

[0057] Based on the daily sales data of the above 30 days in history and the next 30 days, a total of 60 days, calculate the daily safety stock volume for the next 30 days.

[0058] The formula for calculating safety stock is:

[0059] SS=Z*σ*STD

[0060] In the above safety stock formula, Z represents the safety factor corresponding to the customer service level. Figure 2 A comparison table of customer service level and safety factor is shown. Preferably, it is necessary to ensure that customer satisfaction reaches above 95%. In this embodiment, the safety factor is selected based on 95% customer satisfaction.

[0061] In the above safety stock formula, σ represents the standard deviation calculated based on the daily sales volume for L consecutive days up to the current day. In this embodiment, the standard deviation is calculated using data for 30 consecutive days, so the calculation formula for σ is as follows:

[0062]

[0063] Among them, m n The average of the 30-day sales data used to calculate the safety stock for the next n days.

[0064] In the above safety stock formula, STD represents the amount of lead time, and the calculation formula is as follows

[0065]

[0066] Wherein, L is the number of days in advance, and in this embodiment, L is 30.

[0067] Therefore, based on the above safety stock calculation formula, the safety stock for each day in the next 30 days can be obtained.

[0068] The replenishment system can calculate the inventory status for the next 30 days based on the current actual inventory, combined with the daily sales volume and the safety inventory for the next 30 days, and find out the time nodes where there are potential safety hazards, that is, the time point when the future inventory is lower than the future safety inventory. According to the time nodes where there are potential safety hazards, the replenishment node is set and the replenishment operation is performed on the day of the node.

[0069] A simple algorithm that can be used for replenishment is to check the consumption and increase of inventory for each day in the future. If it is not enough, replenish it by △. The replenishment reminder can be sent out t days in advance. The following is a schematic code of the above algorithm logic:

[0070]

[0071]

[0072] The algorithm logic adopted in this embodiment is: subtract the sales volume of the day from the inventory volume of the day to obtain the predicted inventory volume of the day. Then compare it with the safety inventory volume of the day to determine whether the predicted inventory volume will be lower than the safety inventory volume on that day. If the predicted inventory volume is lower than the safety inventory volume, then set the day as a replenishment node, and the replenishment volume of the day is the sum of all sales volumes from the current replenishment node to the next replenishment node. In theory, on the day when the inventory volume is lower than the safety inventory volume, goods will arrive at the warehouse, and there will be no shortage of storage. At the same time, the replenishment volume will also meet all needs within a relatively long period of time. Similarly, a replenishment reminder can be issued t days in advance.

[0073] The method of Internet of Things trade inventory management of the present invention executes the above-mentioned replenishment strategy every day, and once replenishment is found to be needed, a corresponding replenishment node will be set. However, in the above-mentioned replenishment strategy, it is possible that the set replenishment node is the last replenishment node, so that the replenishment quantity of the current replenishment node cannot be determined due to the absence of the next replenishment node. At this time, the replenishment quantity of the above-mentioned replenishment node can be set to the sum of all sales from the current replenishment node to the last day of the calculation period. For example, in this embodiment, the replenishment quantity is set to the sum of sales from the current replenishment node to the next 30 days. As the replenishment strategy is executed every day, a corresponding replenishment node will be generated to ensure effective replenishment operations in the future.

[0074] In a further configuration of this embodiment, a replenishment node with a fixed period is also preset. This periodic replenishment node appears at least once in a future calculation period. For example, in this embodiment, when predicting the inventory status for the next 30 days, if four weeks are used as a period, the periodic replenishment node will appear once or twice in each future 30-day period. If two weeks are used as a period, the periodic replenishment node will appear twice or three times in each future 30-day period. The replenishment quantity of any periodic replenishment node is also set to the sum of its sales volume to the next replenishment node. Setting a periodic replenishment node can make the replenishment node determined by the above algorithm logic (hereinafter referred to as a non-periodic replenishment node) always find the next replenishment node. However, in this process, there will still be a situation where the non-periodic replenishment node or the periodic replenishment node is the last replenishment node in the calculation period. For example, in this embodiment, the next replenishment node of the current replenishment node is after the next 30 days, so the replenishment quantity of the current replenishment node cannot be determined due to the lack of predicted daily sales after the next 30 days. At this time, the replenishment quantity of the above replenishment node can also be set to the sum of all sales from the current replenishment node to the last day of the calculation period. For example, in this embodiment, the replenishment quantity is set to the sum of sales from the current replenishment node to the next 30 days.

[0075] In another embodiment, a replenishment node with a fixed period is also preset, but the setting of the non-periodic replenishment node every day is limited to before the last periodic replenishment node in the future calculation period. For example, in the prediction of the next 30 days, the period of the periodic replenishment node is set to two weeks, and there will be two or three periodic replenishment nodes in the next 30 days. Here, the non-periodic replenishment node is only set for the period before the last periodic replenishment node. For example, within the next 30 days, the first periodic replenishment node is on the third day of the future, and the second periodic replenishment node is on the 17th day of the future. Then, the non-periodic replenishment node is only set within the next 17 days, and the replenishment status of the 18th to 30th days in the future does not need to be considered. With the replenishment strategy being executed again on the next day, the last replenishment node of the next 30 days is the periodic replenishment node on the 30th day of the future. Therefore, overall, even if the replenishment status of the 18th to 30th days in the future is not considered on the previous day, it will not affect the setting of the non-periodic replenishment node and the replenishment operation in this period. For the implementation of the above method, one of the key points is to select a suitable fixed period of the periodic replenishment node. The preferred method is to have at least two periodic replenishment nodes in one calculation period. At the same time, when the replenishment reminder is issued t days in advance, t needs to be less than the fixed period of the periodic replenishment node. Preferably, t is less than half of the above fixed period.

[0076] The method of IoT trade inventory management in this embodiment aims at the problems of insufficient inventory, excessive storage, warehouse explosion, etc. caused by the difference between storage and actual sales volume of IoT trade companies. By analyzing historical sales volume, combining statistics and probability theory, a gray model is constructed to predict future orders; based on customer satisfaction and window distribution, the safety stock volume is evaluated. Finally, a replenishment strategy is implemented in combination with dynamic monitoring, so that the storage volume is always higher than the safety stock assessment volume, and a theoretical inventory continuous stability algorithm is realized. Figure 3 A predicted inventory trend chart for executing the method for IoT trade inventory management of this embodiment is schematically shown.

[0077] This embodiment further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer code. When the computer code is executed, the method described in any of the preceding items is executed.

[0078] This embodiment further provides a computer program product. When the computer program product is executed by a computer device, the method described in any of the preceding items is executed.

[0079] This embodiment further provides a computer device, the computer device comprising:

[0080] one or more processors;

[0081] a memory for storing one or more computer programs;

[0082] When the one or more computer programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any of the preceding items.

[0083] Figure 4 An exemplary system is shown that can be used to implement the various embodiments described herein.

[0084] like Figure 4 As shown, in some embodiments, the system 1000 can be used as any user terminal device in each of the embodiments. In some embodiments, the system 1000 may include one or more computer-readable media (e.g., system memory or NVM / storage device 1020) having instructions and one or more processors (e.g., (one or more) processors 1005) coupled to the one or more computer-readable media and configured to execute instructions to implement modules to perform the actions described in the present invention.

[0085] For one embodiment, the system control module 1010 may include any suitable interface controller to provide any suitable interface to at least one of the processor(s) 1005 and / or any suitable device or component in communication with the system control module 1010 .

[0086] The system control module 1010 may include a memory controller module 1030 to provide an interface to the system memory 1015. The memory controller module 1030 may be a hardware module, a software module, and / or a firmware module.

[0087] The system memory 1015 may be used, for example, to load and store data and / or instructions for the system 1000. For one embodiment, the system memory 1015 may include any suitable volatile memory, such as a suitable DRAM. In some embodiments, the system memory 1015 may include double data rate type four synchronous dynamic random access memory (DDR4 SDRAM).

[0088] For one embodiment, system control module 1010 may include one or more input / output (I / O) controllers to provide interfaces to NVM / storage device 1020 and communication interface(s) 1025 .

[0089] For example, NVM / storage device 1020 may be used to store data and / or instructions. NVM / storage device 1020 may include any suitable non-volatile memory (e.g., flash memory) and / or may include any suitable non-volatile storage device(s) (e.g., one or more hard disk drives (HDDs), one or more compact disk (CD) drives, and / or one or more digital versatile disk (DVD) drives).

[0090] NVM / storage device 1020 may include storage resources that are physically part of the device on which system 1000 is installed, or it may be accessible to the device without being part of the device. For example, NVM / storage device 1020 may be accessed via communication interface(s) 1025 over a network.

[0091] Communication interface(s) 1025 may provide an interface for system 1000 to communicate over one or more networks and / or with any other suitable devices. System 1000 may wirelessly communicate with one or more components of a wireless network in accordance with any of one or more wireless network standards and / or protocols.

[0092] For one embodiment, at least one of the processor(s) 1005 may be packaged together with the logic of one or more controllers (e.g., memory controller module 1030) of the system control module 1010. For one embodiment, at least one of the processor(s) 1005 may be packaged together with the logic of one or more controllers of the system control module 1010 to form a system-in-package (SiP). For one embodiment, at least one of the processor(s) 1005 may be integrated on the same die with the logic of one or more controllers of the system control module 1010. For one embodiment, at least one of the processor(s) 1005 may be integrated on the same die with the logic of one or more controllers of the system control module 1010 to form a system on chip (SoC).

[0093] In various embodiments, the system 1000 may be, but is not limited to: a server, a workstation, a desktop computing device, or a mobile computing device (e.g., a laptop computing device, a handheld computing device, a tablet computer, a netbook, etc.). In various embodiments, the system 1000 may have more or fewer components and / or a different architecture. For example, in some embodiments, the system 1000 includes one or more cameras, a keyboard, a liquid crystal display (LCD) screen (including a touch screen display), a non-volatile memory port, multiple antennas, a graphics chip, an application specific integrated circuit (ASIC), and a speaker.

[0094] It should be noted that the present invention can be implemented in software and / or a combination of software and hardware, for example, can be implemented using an application specific integrated circuit (ASIC), a general purpose computer or any other similar hardware device. In one embodiment, the software program of the present invention can be executed by a processor to implement the steps or functions described above. Similarly, the software program of the present invention (including related data structures) can be stored in a computer readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and the like. In addition, some steps or functions of the present invention can be implemented using hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0095] In addition, a part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of computer program instructions in computer-readable media includes, but is not limited to, source files, executable files, installation package files, etc., and accordingly, the way in which computer program instructions are executed by a computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0096] Communication media include media by which communication signals containing, for example, computer readable instructions, data structures, program modules, or other data are transmitted from one system to another. Communication media may include guided transmission media such as cables and wires (e.g., fiber optic, coaxial, etc.) and wireless (unguided transmission) media that can propagate energy waves, such as acoustic, electromagnetic, RF, microwave, and infrared. Computer readable instructions, data structures, program modules, or other data may be embodied as a modulated data signal in, for example, a wireless medium such as a carrier wave or similar mechanism such as embodied as part of spread spectrum technology. The term "modulated data signal" refers to a signal whose one or more characteristics are changed or set in such a manner as to encode information in the signal. Modulation may be analog, digital, or a hybrid modulation technique.

[0097] By way of example and not limitation, computer-readable storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. For example, computer-readable storage media include, but are not limited to, volatile memory, such as random access memory (RAM, DRAM, SRAM); and non-volatile memory, such as flash memory, various read-only memories (ROM, PROM, EPROM, EEPROM), magnetic and ferromagnetic / ferroelectric memories (MRAM, FeRAM); and magnetic and optical storage devices (hard disks, magnetic tapes, CDs, DVDs); or other media now known or later developed that can store computer-readable information / data for use by a computer system.

[0098] Here, according to one embodiment of the present invention, a device is included, which includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein, when the computer program instructions are executed by the processor, the device is triggered to run the methods and / or technical solutions based on the aforementioned multiple embodiments of the present invention.

[0099] It is obvious to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A method for inventory management in Internet of Things trade, characterized in that, it includes a replenishment prediction operation; the replenishment prediction operation includes: Step 1: Use a gray model to predict the daily sales volume in the next L days based on the daily sales volume in the most recent L days, where L is a preset number of days; Step 2: For each day in the next L days, take the daily sales volume in the consecutive L days up to that day and calculate the safety stock quantity for that day; the calculation formula for the safety stock quantity is: SS = Z * σ * STD where SS represents the safety stock quantity, Z represents the safety factor corresponding to the customer service level, and σ represents the standard deviation calculated based on the daily sales volume in the consecutive L days up to that day; Step 3: According to the current actual inventory quantity, the daily sales volume in the next L days, and the set replenishment nodes within the next L days, calculate the daily inventory quantity in the next L days in sequence. When the daily inventory quantity on a certain day is less than its safety stock quantity, take that day as the first replenishment node, and thus perform a replenishment operation on the day of the first replenishment node.

2. The method according to claim 1, characterized in that, a second replenishment node with a fixed cycle is preset, and thus a replenishment operation is performed on the day of the second replenishment node.

3. The method according to claim 2, characterized in that, the replenishment quantity at the first replenishment node is the sum of the daily sales volume from the current replenishment node to the next replenishment node, or the sum of the daily sales volume from the current replenishment node to the Lth day in the future; the replenishment quantity at the second replenishment node is the sum of the daily sales volume from the current replenishment node to the next replenishment node, or the sum of the daily sales volume from the current replenishment node to the Lth day in the future.

4. The method according to claim 2, characterized in that, within the next L days, the first replenishment node is set only before the last second replenishment node.

5. The method according to claim 1, characterized in that, for any replenishment node, a corresponding replenishment operation reminder is sent t days in advance, where t is a preset number of days.

6. The method according to claim 1, characterized in that, the replenishment prediction operation is performed every day.

7. A device for inventory management in Internet of Things trade, wherein, the device includes: a processor; and a memory arranged to store computer-executable instructions, and when the executable instructions are executed, the processor performs the operations of the method according to any one of claims 1 to 6.

8. A computer-readable medium storing instructions, and when the instructions are executed, the system performs the operations of the method according to any one of claims 1 to 6.

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