A supply chain scheduling method and system based on the Internet of Things

Abstract

The application relates to the technical field of supply chains, in particular to a supply chain scheduling method and system based on the Internet of Things, which comprises an information acquisition module, a recognition module, a division unit and a selection unit. The information acquisition module acquires the demand of each commodity according to orders and the staying time length of each commodity in a warehouse. The recognition module acquires a staying time length fluctuation value according to the staying time length of each commodity in the warehouse, determines whether the commodity supply is in a stable state, and divides the commodity supply chain category. The selection unit selects a scheduling strategy according to the commodity supply chain category, which comprises determining the warehouse-in quantity in each time period in the next cycle according to a change curve, or acquiring the time length of the commodity at each link of the supply chain, recognizing an abnormal link, and adjusting the abnormal link. The application identifies the reasons for the stability and low efficiency of the supply chain, adopts different supply chain scheduling strategies for commodities with different reasons, and thus accurately and reliably improves the stability and efficiency of the supply chain.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of supply chain, and in particular to a supply chain scheduling method and system based on Internet of Things. BACKGROUND

[0002] As a link connecting raw material suppliers, manufacturers, distributors and end consumers, the supply chain is crucial to modern business operations. It not only affects the production efficiency and cost of products, but also directly relates to the market response speed and customer satisfaction of enterprises. However, in the current supply chain management, there are many challenges, such as information silos, slow response speed, unreasonable resource allocation and other problems, which often lead to low efficiency of supply chain scheduling and difficulty in adapting to the rapid changes of the market. Therefore, the research on supply chain scheduling is very important.

[0003] In the prior art, application number: CN202410716934.9 discloses a bread distribution scheduling method and system based on supply chain analysis and a storage medium, relating to the technical field of bread distribution, comprising the following steps: obtaining scheduling information of the requestor, combining and analyzing the scheduling information with the supply chain information, and determining the planned route; obtaining the scheduling information of the requestor includes obtaining the scheduling route of the total distribution party to other parties, the required time for scheduling, the initial inventory quantity and the scheduling quantity; the acquisition of the scheduling information of the requestor will first acquire the scheduling information of the total distribution party and then acquire the scheduling information of other parties when receiving the scheduling information of any one requestor; obtaining the business information of the request scheduling party before and after scheduling. The present application determines the planned route of each party requesting scheduling by comparing the supply chain information of each party of the bread distribution with the real-time scheduling urgency, and repeatedly adjusts the distribution route in order to ensure the timely distribution of bread, thereby improving the overall distribution scheduling efficiency.

[0004] However, in the prior art, the reasons for poor stability and low efficiency of the supply chain are not further divided, resulting in low precision of the supply chain scheduling strategy and inability to effectively improve the stability and efficiency of the supply chain. SUMMARY

[0005] The present application aims to provide a supply chain scheduling method and system based on Internet of Things, which can further divide the reasons for poor stability and low efficiency of the supply chain, thereby effectively improving the stability and efficiency of the supply chain.

[0006] In one aspect, the present application provides a supply chain scheduling system based on Internet of Things, comprising:

[0007] The information acquisition module is used to obtain the demand quantity of each commodity according to the order and obtain the residence time of each commodity in the warehouse;

[0008] An identification module, connected with the information acquisition module, is configured to acquire a fluctuation value of the staying time length according to the staying time length of each commodity in the warehouse, and determine whether the commodity supply is in a stable state according to the fluctuation value of the staying time length.

[0009] A scheduling module, connected with each information acquisition module and the identification module, includes a division unit and a selection unit.

[0010] The division unit is configured to divide the commodity supply chain category according to the fluctuation value of the staying time length of the commodity in the warehouse and the commodity demand variation in a case where the commodity supply is in an unstable state.

[0011] The selection unit is configured to select a scheduling strategy according to the commodity supply chain category, including,

[0012] acquiring a variation curve of the demand over time in a current period, determining the number of sub-periods in a next period according to the fluctuation value of the staying time length in the period, and determining the warehouse entry quantity in each sub-period in the next period according to the variation curve.

[0013] Alternatively, the time length of the commodity in each link of the supply chain is acquired, an abnormal link is identified, and the abnormal link is adjusted.

[0014] The identification module acquires the fluctuation value of the staying time length,

[0015] The staying time length of each commodity in the warehouse is acquired.

[0016] The average value of the staying time length of commodities in the same category in the warehouse is calculated.

[0017] The average value is acquired every preset time length.

[0018] The standard deviation of a plurality of average values is calculated and recorded as the fluctuation value of the staying time length.

[0019] The identification module determines whether the commodity supply is in a stable state according to the fluctuation value of the staying time length,

[0020] The identification module compares the fluctuation value of the staying time length of the commodity with a preset fluctuation value range of the staying time length,

[0021] If the fluctuation value of the staying time length of the commodity exceeds the preset fluctuation value range of the staying time length, it is determined that the commodity supply is in an unstable state.

[0022] The division unit is further configured to acquire a commodity demand variation, which is the difference between the current commodity demand and the commodity demand in historical data in the same time period.

[0023] The division unit divides the commodity supply chain category,

[0024] If the preset condition is met, the commodity supply chain category is divided into a weak response tendency category;

[0025] If the preset condition is not met, the commodity supply chain category is divided into a weak stability tendency category;

[0026] The preset condition is that the fluctuation value of the residence time of the commodity exceeds the preset fluctuation value range of the residence time, and the commodity demand quantity change amount is greater than the preset commodity demand quantity change amount comparison threshold.

[0027] The selecting unit selects a scheduling strategy according to the commodity supply chain category, comprising,

[0028] If the commodity supply chain category is a weak response tendency category, a demand quantity change curve over time in the current period is obtained, the number of sub-periods in the next period is determined according to the fluctuation value of the residence time in the period, and the inventory quantity in each sub-period in the next period is determined according to the change curve.

[0029] If the commodity supply chain category is a weak stability tendency category, the time of the commodity at each link of the supply chain is obtained, the abnormal link is obtained, and the abnormal link is adjusted.

[0030] The selecting unit determines the number of sub-periods in the next period according to the fluctuation value of the residence time in the current period, wherein the fluctuation value of the residence time and the number of sub-periods are in a positive correlation.

[0031] The selecting unit determines the inventory quantity in each sub-period in the next period according to the change curve,

[0032] The current period is divided into a plurality of sub-periods;

[0033] The average demand quantity of each sub-period is obtained;

[0034] The inventory quantity in each sub-period in the next period is the same as the average demand quantity of the corresponding sub-period in the current period.

[0035] The selecting unit identifies an abnormal link,

[0036] The time of the commodity at the production link and the transportation link is obtained respectively;

[0037] The time of the commodity at the production link is compared with a preset production link time comparison threshold, and the time of the commodity at the transportation link is compared with a preset transportation link time comparison threshold,

[0038] If the first preset condition is met, the production link is identified as an abnormal link;

[0039] If the second preset condition is met, the transportation link is identified as an abnormal link;

[0040] The first preset condition is that the length of time of the commodity in the production link is greater than a preset production link time length comparison threshold, and the second preset condition is that the length of time of the commodity in the transportation link is greater than a preset transportation link time length comparison threshold.

[0041] In another aspect, the present application provides a supply chain scheduling method based on Internet of Things,

[0042] Step S1, obtaining the demand of each commodity according to the order and obtaining the length of time of each commodity in the warehouse;

[0043] Step S2, obtaining the length of time fluctuation value of each commodity in the warehouse according to the length of time of each commodity in the warehouse, and determining whether the commodity supply is in a stable state according to the length of time fluctuation value;

[0044] Step S3, obtaining the length of time fluctuation value of the commodity in the warehouse in the non-stable state of the commodity supply in combination with the commodity demand change amount to divide the commodity supply chain category;

[0045] Step S4, selecting a scheduling strategy according to the commodity supply chain category, including,

[0046] obtaining a change curve of the demand over time in the current period, determining the number of sub-periods in the next period according to the length of time fluctuation value in the period, and determining the warehouse entry amount in each sub-period in the next period according to the change curve;

[0047] Or, obtaining the length of time of the commodity in each link of the supply chain, identifying an abnormal link, and adjusting the abnormal link.

[0048] The present application has the following beneficial effects:

[0049] The present application provides a supply chain scheduling method and system based on Internet of Things, which divides the stable state of commodity supply by the length of time fluctuation value, further divides the commodities in the unstable state, identifies the reasons for the low stability and efficiency of the supply chain, adopts different supply chain scheduling strategies for commodities with different reasons, and accurately and reliably improves the stability and efficiency of the supply chain.

[0050] Further, the present application determines whether the commodity supply is in a stable state according to the length of time fluctuation value, the supply chain has a self-adjusting function, can adapt to external environment changes, and maintains operation through internal adjustment, and the length of time fluctuation value of the commodity in the warehouse is an important parameter representing the self-adjusting ability of the supply chain. When the length of time fluctuation value is large, it means that the self-adjusting ability of the supply chain is poor, and the stability and efficiency of the supply chain are at a low level. The length of time fluctuation value can represent the stability of the supply chain, so as to determine whether to schedule the supply chain, thereby improving the stability and efficiency of the supply chain.

[0051] Further, the application divides the commodity supply chain category according to the commodity supply commodity in the non-steady state in the warehouse according to the commodity demand quantity change value, and the commodity demand quantity change value can be divided into the category which causes the poor stability and low efficiency of the supply chain, when the commodity demand quantity change value is large, and the residence time fluctuation value is large, at this time, the adjustment ability of the supply chain to the external demand change is poor, and the supply chain needs to be adjusted according to the external demand change, when the commodity demand quantity change value is small, and the residence time fluctuation value is large, at this time, the internal operation of the supply chain appears the low efficiency, and the production, transportation and other links of the commodity need to be optimized, so that the stability and efficiency of the supply chain are improved.

[0052] Further, the application determines the warehouse entry quantity in each sub-period of the next period according to the change curve through the selection unit, because the poor stability of the supply chain is caused by the external environment, the residence time of the commodity in the warehouse is highly related to the commodity demand quantity, when the residence time fluctuation value is large, at this time, the commodity demand quantity fluctuation is large, the warehouse entry quantity of the commodity needs to be adjusted more accurately, the warehouse entry quantity of the commodity in the next period is adjusted through the change of the commodity demand quantity in the current period, which is helpful to improve the stability and efficiency of the supply chain.

[0053] Further, the application obtains the time length of the commodity in each link of the supply chain through the selection unit, identifies the abnormal link, and adjusts the abnormal link, because the reason for the instability of the supply chain is caused by the internal reason, the internal abnormal link is identified, the internal is optimized through the identification of the internal abnormal link, so that the stability and efficiency of the supply chain are improved. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 It is a structure diagram of the supply chain scheduling system based on the Internet of Things of the embodiment of the application;

[0055] Figure 2 It is a structure diagram of the scheduling module of the embodiment of the application;

[0056] Figure 3 It is a logic determination diagram of the identification module of the embodiment of the application for determining whether the commodity supply is in a steady state;

[0057] Figure 4 It is a step diagram of the supply chain scheduling method based on the Internet of Things of the embodiment of the application. DETAILED DESCRIPTION

[0058] In order to make the purpose and advantages of the application more clear and obvious, the application is further described below in combination with the embodiments; it should be understood that the specific embodiments described herein are only used to explain the application, and do not limit the application.

[0059] The preferred embodiments of the present application will be described below with reference to the drawings. It should be understood that these embodiments are only used to explain the technical principles of the present application, and are not intended to limit the protection scope of the present application.

[0060] It should be noted that, in the description of the present application, the terms indicating the direction or positional relationship of "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or positional relationship shown in the drawings, which is only for the convenience of description, and does not indicate or imply that the device or element must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application.

[0061] In addition, it should be further noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0062] As shown in Figures 1-3 The embodiment provides a supply chain scheduling system based on Internet of Things, which comprises:

[0063] An information acquisition module is configured to acquire the demand of each commodity and the residence time of each commodity in the warehouse according to an order;

[0064] An identification module is connected with the information acquisition module and configured to acquire a residence time fluctuation value according to the residence time of each commodity in the warehouse, and determine whether the commodity supply is in a stable state according to the residence time fluctuation value;

[0065] A scheduling module is connected with each information acquisition module and identification module, and comprises a division unit and a selection unit;

[0066] The division unit is configured to divide the commodity supply chain category according to the residence time fluctuation value of the commodity supply in a non-stable state in the warehouse and the demand variation of the commodity;

[0067] The selection unit is configured to select a scheduling strategy according to the commodity supply chain category, comprising,

[0068] acquiring a change curve of the demand with time in a current period, determining the number of sub-periods in a next period according to the residence time fluctuation value in the period, and determining the warehouse entry quantity in each sub-period in the next period according to the change curve;

[0069] Or, the time length of the commodity at each link of the supply chain is obtained, an abnormal link is identified, and the abnormal link is adjusted.

[0070] Specifically, the Internet of Things technology can obtain the time length of each commodity at each link of the supply chain through automatic identification and tracking technology.

[0071] Specifically, the identification module obtains a time length fluctuation value,

[0072] The time length of each commodity in the warehouse is obtained.

[0073] The average value of the time length of the same category of commodities in the warehouse is calculated.

[0074] The average value is obtained every predetermined time length.

[0075] The standard deviation of a plurality of average values is calculated and is denoted as a time length fluctuation value.

[0076] In this embodiment, the predetermined time length is selected within the range of [1 day, 5 days].

[0077] It can be understood that there may be certain differences in the time length of the same category of commodities in the warehouse, and the average value of the time length of the same category of commodities in the warehouse can represent the time length of the same category of commodities in the warehouse. In the calculation of the time length fluctuation value, the average value of a plurality of average values of the time length of the same category of commodities in the warehouse is the mean of a plurality of data, for example, the time length of the current same category of commodities in the warehouse is obtained every 2 days, and the time length fluctuation value is calculated according to the data obtained for the last 5 times using the standard deviation formula.

[0078] Specifically, the present application determines whether the commodity supply is in a stable state according to the time length fluctuation value, the supply chain has a self-adjusting function, can adapt to external environmental changes, and maintains operation through internal adjustment. The time length fluctuation value of the commodity in the warehouse is an important parameter representing the self-adjusting ability of the supply chain. When the time length fluctuation value is large, it means that the self-adjusting ability of the supply chain is poor, and the stability and efficiency of the supply chain are at a low level. The time length fluctuation value can represent the stability of the supply chain, so as to determine whether to dispatch the supply chain, thereby improving the stability and efficiency of the supply chain.

[0079] Specifically, the identification module determines whether the commodity supply is in a stable state according to the time length fluctuation value,

[0080] The identification module compares the time length fluctuation value of the commodity with a predetermined time length fluctuation value range,

[0081] If the time length fluctuation value of the commodity exceeds the predetermined time length fluctuation value range, it is determined that the commodity supply is in an unstable state.

[0082] In the embodiment, the preset fluctuation range of the staying time is obtained through historical data. A plurality of staying time fluctuation values are obtained in the historical data, an average value Be of the staying time fluctuation values is calculated, and the preset fluctuation range of the staying time is [0.8Be, 1.2Be].

[0083] Specifically, the division unit is also used to obtain a commodity demand quantity change, which is a difference between a current commodity demand quantity and a commodity demand quantity in historical data in the same time period.

[0084] In the embodiment, the time period can be selected in a range of [1 day, 5 days], for example, the commodity demand quantity in the past 5 days and the commodity demand quantity in the same period of last year are obtained, and a difference between the two values is the commodity demand quantity change.

[0085] Specifically, the application divides the commodity supply chain category according to the staying time fluctuation value of the commodity supply in the non-stable state in the warehouse and the commodity demand quantity change. By obtaining the commodity demand quantity change, the category causing poor supply chain stability and low efficiency can be divided. When the commodity demand quantity change is large and the staying time fluctuation value is large, the adjustment ability of the supply chain to external demand change is poor at this time, and the supply chain needs to be adjusted according to the external demand change. When the commodity demand quantity change is small and the staying time fluctuation value is large, the internal operation of the supply chain appears low efficiency at this time, and the production of the commodity, the transportation of the commodity and other links need to be optimized, so as to improve the stability and efficiency of the supply chain.

[0086] Specifically, the division unit divides the commodity supply chain category,

[0087] If the preset condition is met, the commodity supply chain category is divided into a weak response tendency category;

[0088] If the preset condition is not met, the commodity supply chain category is divided into a weak stability tendency category.

[0089] The preset condition is that the staying time fluctuation value of the commodity exceeds the preset staying time fluctuation value range, and the commodity demand quantity change is greater than a preset commodity demand quantity change comparison threshold.

[0090] In the embodiment, the preset commodity demand quantity change comparison threshold Qb0 is obtained through historical data. A plurality of commodity demand quantity data are obtained, a plurality of commodity demand quantity changes are obtained, an average value Qbe of the commodity demand quantity changes is calculated, and Qb0=a*Qbe, a is a commodity demand quantity change coefficient, and 1.1

[0091] Specifically, the selection unit selects a scheduling strategy according to the commodity supply chain category, including,

[0092] If the commodity supply chain category is a weak response tendency category, a demand amount change curve in a current period is obtained, the number of sub-periods in a next period is determined according to a stay duration fluctuation value in the period, and the warehousing amount in each sub-period in the next period is determined according to the change curve.

[0093] If the commodity supply chain category is a weak stability tendency category, the time of the commodity at each link of the supply chain is obtained, an abnormal link is obtained, and the abnormal link is adjusted.

[0094] Specifically, the selecting unit determines the number of sub-periods in a next period according to a stay duration fluctuation value in a current period, wherein the stay duration fluctuation value and the number of sub-periods are in a positive correlation.

[0095] In the embodiment, the period is selected in the range of [15 days, 30 days].

[0096] It can be understood that when the stay duration fluctuation value is larger, the warehousing amount of the commodity should be adjusted more accurately, and at this time, the number of sub-periods divided in the period is larger.

[0097] Specifically, the selecting unit determines the warehousing amount in each sub-period in the next period according to the change curve,

[0098] The current period is divided into a plurality of sub-periods.

[0099] The average demand amount of each sub-period is obtained.

[0100] The warehousing amount in each sub-period in the next period is the same as the average demand amount of the corresponding sub-period in the current period.

[0101] Specifically, the warehousing amount in each sub-period in the next period is determined by the selecting unit according to the change curve, because the poor stability of the supply chain is caused by the external environment, the stay duration of the commodity in the warehouse is highly related to the demand amount of the commodity, when the fluctuation of the stay duration is larger, the fluctuation of the demand amount of the commodity is larger, the warehousing amount of the commodity should be adjusted more accurately, the warehousing amount of the commodity in the next period is adjusted according to the change of the demand amount of the commodity in the current period, which helps to improve the stability and efficiency of the supply chain.

[0102] Specifically, the selecting unit identifies an abnormal link,

[0103] The time of the commodity in the production link and the transportation link is obtained respectively.

[0104] The time of the commodity in the production link is compared with a preset production link time comparison threshold value, and the time of the commodity in the transportation link is compared with a preset transportation link time comparison threshold value.

[0105] If the first preset condition is met, the production link is identified as an abnormal link;

[0106] If the second preset condition is met, the transportation link is identified as an abnormal link.

[0107] The first preset condition is that the duration of the commodity in the production link is greater than a preset production link duration comparison threshold, and the second preset condition is that the duration of the commodity in the transportation link is greater than a preset transportation link duration comparison threshold.

[0108] In the embodiment, the preset production link duration comparison threshold ts0 and the preset transportation link duration comparison threshold tt0 are obtained through historical data. The duration of producing each commodity and the duration of transporting each commodity in the historical data are obtained, the average value tse of the duration of producing each commodity and the average value tte of the duration of transporting each commodity are calculated, ts0 is set as b*tse, b is a coefficient of the duration of producing each commodity, 0.9

[0109] It can be understood that when the production link is an abnormal link, the production link in the supply chain can be adjusted by updating the production line and the like, and when the transportation link is an abnormal link, the transportation link in the supply chain can be adjusted by replacing the transportation route and the like.

[0110] Specifically, the application obtains the duration of the commodity in each link of the supply chain through the selection unit, identifies an abnormal link, identifies an internal abnormal link when the internal reason causes the instability of the supply chain, optimizes the internal through the identification of the internal abnormal link, and thus improves the stability and efficiency of the supply chain.

[0111] Referring to Figure 4 The application provides a supply chain scheduling method based on the Internet of Things,

[0112] Step S1, obtaining the demand of each commodity according to the order and obtaining the duration of each commodity in the warehouse;

[0113] Step S2, obtaining the duration fluctuation value of each commodity in the warehouse according to the duration of each commodity in the warehouse, and determining whether the commodity supply is in a stable state according to the duration fluctuation value;

[0114] Step S3, obtaining the duration fluctuation value of each commodity in the warehouse in a non-stable state of the commodity supply in combination with the commodity demand change amount to divide the commodity supply chain category;

[0115] Step S4, selecting a scheduling strategy according to the commodity supply chain category, including,

[0116] Obtain a change curve of the demand amount with time in the current period, determine the number of sub-periods in the next period according to the fluctuation value of the stay duration in the period, and determine the warehouse-in amount in each sub-period in the next period according to the change curve.

[0117] Alternatively, the time length of the commodity at each link of the supply chain is obtained, an abnormal link is identified, and the abnormal link is adjusted.

[0118] The modules involved in the embodiments of the present application can be implemented in a software manner or in a hardware manner. The described modules can also be arranged in a processor.

[0119] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the apparatuses, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code containing one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than those noted in the drawings. For example, two blocks represented in succession can actually be executed substantially in parallel, and sometimes they can be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based device performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0120] Obviously, the above-described embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the implementation manners of the present application. Based on the above description, other different forms of changes or variations can be made by those of ordinary skill in the art. Here, all the implementation manners do not need to be exhausted. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the claims of the present application.

Claims

1. An Internet of Things based supply chain scheduling system, characterized in that, The application relates to a method for dynamically adjusting a supply chain of a product, comprising the following steps: An information acquisition module is used to acquire the demand of each product and the residence time of each product in a warehouse according to an order; An identification module is connected with the information acquisition module and is used to acquire a residence time fluctuation value of each product in the warehouse according to the residence time of each product in the warehouse, and to determine whether the product supply is in a stable state according to the residence time fluctuation value; A scheduling module is connected with each information acquisition module and identification module and comprises a division unit and a selection unit; The division unit is used to divide the product supply chain category of a product in a non-stable state according to the residence time fluctuation value of the product in the warehouse and the demand change amount of the product; the division unit divides the product supply chain category, If a preset condition is met, the product supply chain category is divided into a weak response tendency category; If the preset condition is not met, the product supply chain category is divided into a weak stable tendency category; The preset condition is that the residence time fluctuation value of the product exceeds a preset residence time fluctuation value range, and the demand change amount of the product is greater than a preset product demand change amount comparison threshold value; The selection unit is used to select a scheduling strategy according to the product supply chain category, comprising If the product supply chain category is a weak response tendency category, the resource occupied by the product in each link of the supply chain is adjusted according to the residence time of the product in the warehouse; If the product supply chain category is a weak stable tendency category, the time of the product in each link of the supply chain is acquired, an abnormal link is identified, and the abnormal link is adjusted.

2. The Internet of Things based supply chain scheduling system as claimed in claim 1, wherein, The identification module acquires the residence time fluctuation value, The residence time of each product in the warehouse is acquired; The average value of the residence time of products of the same category in the warehouse is calculated; The average value is acquired every preset time; The standard deviation of a plurality of average values is calculated and is recorded as the residence time fluctuation value.

3. The Internet of Things based supply chain scheduling system as claimed in claim 2, wherein, The identification module determines whether the product supply is in a stable state according to the residence time fluctuation value, The identification module compares the residence time fluctuation value of the product with a preset residence time fluctuation value range, If the residence time fluctuation value of the product exceeds the preset residence time fluctuation value range, it is determined that the product supply is in a non-stable state.

4. The Internet of Things based supply chain scheduling system as claimed in claim 1, wherein, The division unit is also used to acquire the demand change amount of the product, which is the difference between the current product demand and the product demand in historical data in the same time period.

5. The Internet of Things based supply chain scheduling system as claimed in claim 1 wherein, The selection unit adjusts the resource occupied by the product in each link of the supply chain according to the residence time of the product in the warehouse, wherein the resource of the production link is a production device, the resource of the transportation link is a transportation tool, and the resource of the storage link is a storage space.

6. The Internet of Things based supply chain scheduling system of claim 1, wherein, The selection unit adjusts the resource occupied by the product in each link of the supply chain according to the residence time of the product in the warehouse, wherein the residence time of the product in the warehouse and the adjustment amount of the resource occupied by the product in each link of the supply chain are in a positive correlation.

7. The Internet of Things based supply chain scheduling system as claimed in claim 1 wherein, The selection unit identifies an abnormal link, The time of the product in the production link and the transportation link is respectively acquired; The time of the product in the production link is compared with a preset production link time comparison threshold value, and the time of the product in the transportation link is compared with a preset transportation link time comparison threshold value, If the first preset condition is met, the production link is identified as an abnormal link; If the second preset condition is met, the transportation link is identified as an abnormal link; The first preset condition is that the duration of the commodity in the production link is greater than the preset production link duration comparison threshold, and the second preset condition is that the duration of the commodity in the transportation link is greater than the preset transportation link duration comparison threshold.

8. An Internet of Things based supply chain scheduling method, characterized in that, The supply chain scheduling system based on Internet of Things according to any one of claims 1-7, the supply chain scheduling method based on Internet of Things comprises: Step S1, obtaining the demand of each commodity according to the order and obtaining the residence time of each commodity in the warehouse; Step S2, obtaining the residence time fluctuation value of each commodity in the warehouse according to the residence time of each commodity in the warehouse, and determining whether the commodity supply is in a stable state according to the residence time fluctuation value; Step S3, obtaining the residence time fluctuation value of the commodity in the warehouse which is in the non-stable state of the commodity supply combined with the commodity demand change to divide the commodity supply chain category; Step S4, selecting a scheduling strategy according to the commodity supply chain category, including, adjusting the resources occupied by the commodity in each link of the supply chain according to the residence time of the commodity in the warehouse; or, obtaining the duration of the commodity in each link of the supply chain, identifying the abnormal link, and adjusting the abnormal link.

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