Supply chain scheduling method and system based on Internet of Things

Abstract

The invention relates to the technical field of supply chains, in particular to a supply chain scheduling method and system based on the Internet of Things, and the system comprises an information obtaining module which obtains the demand of each commodity according to an order and obtains the stay duration of each commodity in a warehouse, and an identification module which obtains a stay duration fluctuation value according to the stay duration of each commodity in the warehouse, the method comprises the steps that whether commodity supply is in a stable state or not is judged, a dividing unit divides commodity supply chain categories, and a selecting unit selects a scheduling strategy according to the commodity supply chain categories and comprises the steps of determining the warehousing amount in each sub-time period of the next period according to a change curve, or obtaining the duration of commodities in each link of the supply chain, recognizing abnormal links and adjusting the abnormal links. According to the invention, reasons for low stability and efficiency of the supply chain are identified, and different supply chain scheduling strategies are adopted for commodities with different reasons, so that the stability and efficiency of the supply chain are accurately and reliably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of supply chains, and particularly to a supply chain scheduling method and system based on the Internet of Things. Background Art

[0002] As a link connecting raw material suppliers, manufacturers, distributors, and end consumers, the supply chain is crucial for 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 current supply chain management, there are many challenges, such as information silos, slow response speed, unreasonable resource allocation, etc. These problems often lead to low supply chain scheduling efficiency and difficulty in adapting to the rapid changes in the market. Therefore, the research on supply chain scheduling is of great importance.

[0003] In the prior art, application number: CN202410716934.9 discloses a bread distribution scheduling method, system, and storage medium based on supply chain analysis, which relates to the technical field of bread distribution, and includes the following steps: obtaining the scheduling information of the requesting party, combining and analyzing the scheduling information with the supply chain information, and determining the planned route; obtaining the scheduling information of the requesting party includes obtaining the scheduling route of the total distributor allocated to other parties, the required scheduling time, the initial inventory, and the scheduling quantity; the obtaining of the scheduling information of the requesting party will, when receiving the scheduling information of any requesting party, first obtain the scheduling information of the total distributor and then obtain the scheduling information of other parties; obtaining the business information of the requesting party before and after scheduling. The present invention determines the planned routes of the parties requesting scheduling by comparing the supply chain information of all parties involved in bread distribution and the real-time scheduling urgency, and in order to ensure the timely bread distribution scheduling, repeatedly adjusts the distribution route to improve the overall distribution scheduling efficiency.

[0004] However, in the prior art, the reasons for poor supply chain stability and low supply chain efficiency are not further classified, resulting in low accuracy of supply chain scheduling strategies and inability to effectively improve supply chain stability and efficiency. Summary of the Invention

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

[0006] On the one hand, the present invention provides a supply chain scheduling system based on the Internet of Things, including:

[0007] An information acquisition module for obtaining the demand quantity of each commodity according to the order and obtaining the residence time of each commodity in the warehouse;

[0008] An identification module, connected to the information acquisition module, is used to obtain the residence time fluctuation value of each commodity according to the residence time of the commodity in the warehouse, and determine whether the commodity supply is in a stable state according to the residence time fluctuation value;

[0009] A scheduling module, connected to each of the information acquisition modules and the identification module, includes a division unit and a selection unit;

[0010] The division unit is used to obtain the residence time fluctuation value of the commodity with unstable supply in the warehouse, and divide the commodity supply chain category in combination with the change amount of commodity demand;

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

[0012] Obtain the change curve of the demand over time in the current period, determine the number of sub-periods in the next period according to the residence time fluctuation value in the period, and determine the inbound quantity in each sub-period of the next period according to the change curve;

[0013] Or, obtain the time length of the commodity in each link of the supply chain, identify abnormal links, and adjust the abnormal links.

[0014] The identification module obtains the residence time fluctuation value,

[0015] Obtain the residence time of each commodity in the warehouse;

[0016] Calculate the average value of the residence time of the same category of commodities in the warehouse;

[0017] Obtain the average value every preset time length;

[0018] Calculate the standard deviation of several of the average values and record it as the residence time fluctuation value.

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

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

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

[0022] The division unit is further used to obtain the change amount of commodity demand, and the change amount of commodity demand is the difference between the current commodity demand and the commodity demand in the historical data in the same time period.

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

[0024] If the preset conditions are met, the commodity supply chain category is classified as a weak response tendency category;

[0025] If the preset conditions are not met, the commodity supply chain category is classified as a weak stability tendency category;

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

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

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

[0029] If the commodity supply chain category is a weak stability tendency category, obtain the time of the commodity in each link of the supply chain, obtain the abnormal link, and adjust the abnormal link.

[0030] The selected unit determines the number of sub-periods in the next period according to the residence time fluctuation value in the current period, where the residence time fluctuation value is positively correlated with the number of divisions.

[0031] The selected unit determines the inbound quantity in each sub-period of the next period according to the change curve,

[0032] Divide the current period into several sub-periods;

[0033] Obtain the average demand quantity of each sub-period;

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

[0035] The selected unit identifies the abnormal link,

[0036] Obtain the duration of the commodity in the production link and the transportation link respectively;

[0037] Compare the duration of the commodity in the production link with the preset production link duration comparison threshold, and compare the duration of the commodity in the transportation link with the preset transportation link duration comparison threshold,

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

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

[0040] 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.

[0041] On the other hand, the present invention provides a supply chain scheduling method based on the Internet of Things.

[0042] Step S1: Obtain the demand quantity of each commodity according to the order and obtain the residence duration of each commodity in the warehouse.

[0043] Step S2: Obtain the residence duration fluctuation value according to the residence duration of each commodity in the warehouse, and determine whether the commodity supply is in a stable state according to the residence duration fluctuation value.

[0044] Step S3: Obtain the residence duration fluctuation value of the commodity whose supply is in an unstable state in the warehouse, and divide the commodity supply chain category in combination with the change amount of the commodity demand quantity.

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

[0046] Obtain the change curve of the demand quantity over time in the current period, determine the number of sub-periods in the next period according to the residence duration fluctuation value in the period, and determine the inbound quantity in each sub-period of the next period according to the change curve.

[0047] Or, obtain the duration of the commodity in each link of the supply chain, identify the abnormal link, and adjust the abnormal link.

[0048] The beneficial effects of the present invention are as follows:

[0049] The present invention provides a supply chain scheduling method and system based on the Internet of Things. By dividing the stable state of commodity supply through the residence duration fluctuation value and further dividing the commodities in the unstable state, the reasons for the low stability and efficiency of the supply chain can be identified, and different supply chain scheduling strategies can be adopted for commodities with different reasons, so as to accurately and reliably improve the stability and efficiency of the supply chain.

[0050] Furthermore, the present invention determines whether the commodity supply is in a stable state according to the residence duration fluctuation value. The supply chain has a self-regulating function, can adapt to external environmental changes, and maintains operation through internal adjustment. The residence duration fluctuation value of the commodity in the warehouse is an important parameter representing the self-regulating ability of the supply chain. When the residence duration fluctuation value is large, it means that the self-regulating ability of the supply chain is poor, and the stability and efficiency of the supply chain are at a low level. By obtaining the residence duration fluctuation value, the stability of the supply chain can be characterized, so as to judge whether to schedule the supply chain, thereby improving the stability and efficiency of the supply chain.

[0051] Furthermore, the present invention divides the commodity supply chain categories by the division unit according to the fluctuation value of the residence time of commodities in the warehouse with unstable commodity supply combined with the change amount of commodity demand. By obtaining the change amount of commodity demand, categories that lead to poor supply chain stability and low efficiency can be divided. When the change amount of commodity demand is large and the fluctuation value of the residence time is large at the same time, the adjustment ability of the supply chain to external demand changes is poor at this time, and the supply chain needs to be adjusted according to external demand changes. When the change amount of commodity demand is small and the fluctuation value of the residence time is large at the same time, the internal operation of the supply chain is less efficient at this time, and links such as commodity production and commodity transportation need to be optimized to improve the stability and efficiency of the supply chain.

[0052] Furthermore, the present invention determines the incoming quantity in each sub-period of the next cycle by the selection unit according to the change curve. Since the poor stability of the supply chain is caused by the external environment, the residence time of commodities in the warehouse is highly correlated with the commodity demand. When the fluctuation amount of the residence time is larger, the volatility of the commodity demand is larger at this time, and the incoming quantity of commodities should be adjusted more precisely. Adjusting the incoming quantity of commodities in the next cycle through the change of the commodity demand in the current cycle helps to improve the stability and efficiency of the supply chain.

[0053] Furthermore, the present invention obtains the time duration of commodities in each link of the supply chain by the selection unit, identifies abnormal links, and adjusts the abnormal links. Since the instability of the supply chain is caused by internal reasons, identify the links with internal abnormalities, and optimize the internal part by identifying the internal abnormal links, thereby improving the stability and efficiency of the supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a structural diagram of the supply chain scheduling system based on the Internet of Things according to an embodiment of the present invention;

[0055] Figure 2 It is a structural diagram of the scheduling module according to an embodiment of the present invention;

[0056] Figure 3 It is a logical decision diagram for the recognition module to determine whether the commodity supply is in a stable state according to an embodiment of the present invention;

[0057] Figure 4 It is a step diagram of the supply chain scheduling method based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0059] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principle of the present invention and do not limit the protection scope of the present invention.

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

[0061] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0062] As Figures 1 - 3 shown, this embodiment provides an Internet of Things-based supply chain scheduling system. The Internet of Things-based supply chain scheduling system includes:

[0063] An information acquisition module for obtaining the demand quantity of each commodity according to the order and obtaining the residence time of each commodity in the warehouse;

[0064] An identification module, connected to the information acquisition module, for obtaining the residence time fluctuation value 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;

[0065] A scheduling module, connected to each of the information acquisition modules and the identification module, including a division unit and a selection unit;

[0066] The division unit is used to obtain the residence time fluctuation value of the commodity whose supply is in an unstable state in the warehouse and divide the commodity supply chain category in combination with the change amount of the commodity demand quantity;

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

[0068] Obtaining the change curve of the demand quantity over time in the current period, determining the number of sub-periods in the next period according to the residence time fluctuation value in the period, and determining the inbound quantity in each sub-period of the next period according to the change curve;

[0069] Alternatively, obtain the duration of the commodity at each link in the supply chain, identify abnormal links, and adjust the abnormal links.

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

[0071] Specifically, the identification module obtains the residence duration fluctuation value.

[0072] Obtain the residence duration of each commodity in the warehouse.

[0073] Calculate the average residence duration of commodities of the same category in the warehouse.

[0074] Obtain the average value at every preset duration.

[0075] Calculate the standard deviation of several such average values and denote it as the residence duration fluctuation value.

[0076] In this embodiment, the preset duration is selected within the range of [1 day, 5 days].

[0077] It can be understood that there may be certain differences in the residence duration of commodities of the same category in the warehouse. Obtaining the average residence duration of commodities of the same category in the warehouse can represent the residence duration of commodities of the same category in the warehouse. When calculating the residence duration fluctuation value, the average value of the average residence durations of several commodities of the same category in the warehouse is the mean of several data. For example, obtain the residence duration of the current commodities of the same category in the warehouse every 2 days, and calculate the residence duration fluctuation value according to the data obtained in the recent 5 times using the standard deviation formula.

[0078] Specifically, the present invention determines whether the commodity supply is in a stable state according to the residence duration fluctuation value. The supply chain has a self-regulating function, can adapt to external environmental changes, and maintains operation through internal adjustment. The residence duration fluctuation value of commodities in the warehouse is an important parameter representing the self-regulating ability of the supply chain. When the residence duration fluctuation value is large, it means that the self-regulating ability of the supply chain is poor, and the stability and efficiency of the supply chain are at a low level. By obtaining the residence duration fluctuation value, the stability of the supply chain can be characterized, so as to determine whether to schedule 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 residence duration fluctuation value.

[0080] The identification module compares the residence duration fluctuation value of the commodity with the preset residence duration fluctuation value range.

[0081] If the residence duration fluctuation value of the commodity exceeds the preset residence duration fluctuation value range, it is determined that the commodity supply is in an unstable state.

[0082] In this embodiment, the preset range of the residence time fluctuation value is obtained from historical data. A number of residence time fluctuation values are obtained from the historical data, and the average value Be of the residence time fluctuation values is calculated. The preset range of the residence time fluctuation value is [0.8Be, 1.2Be].

[0083] Specifically, the division unit is further configured to obtain the change amount of the commodity demand quantity, where the change amount of the commodity demand quantity is the difference between the current commodity demand quantity and the commodity demand quantity in the historical data within the same time period.

[0084] In this embodiment, the duration range of the time period can be selected within the 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 5 days of the same period last year are obtained, and the difference between the two values is the change amount of the commodity demand quantity.

[0085] Specifically, the present invention divides the commodity supply chain categories by the division unit according to the residence time fluctuation value of the commodity in the warehouse where the commodity supply is in an unstable state in combination with the change amount of the commodity demand quantity. By obtaining the change amount of the commodity demand quantity, categories that lead to poor supply chain stability and low efficiency can be divided. When the change amount of the commodity demand quantity is large and the residence time fluctuation value is large at the same time, the adjustment ability of the supply chain to external demand changes is poor at this time, and the supply chain needs to be adjusted according to external demand changes. When the change amount of the commodity demand quantity is small and the residence time fluctuation value is large at the same time, the internal operation of the supply chain is inefficient at this time, and links such as commodity production and commodity transportation need to be optimized to improve the stability and efficiency of the supply chain.

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

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

[0088] If the preset conditions are not met, the commodity supply chain category is divided into the weak stability tendency category;

[0089] The preset conditions are that the residence time fluctuation value of the commodity exceeds the preset range of the residence time fluctuation value, and the change amount of the commodity demand quantity is greater than the preset comparison threshold of the commodity demand quantity change amount.

[0090] In this embodiment, the preset comparison threshold Qb0 of the commodity demand quantity change amount is obtained from historical data. A number of commodity demand quantity data are obtained, a number of change amounts of the commodity demand quantity are obtained, and the average value Qbe of the change amounts of the commodity demand quantity is calculated. Let Qb0 = a×Qbe, where a is the coefficient of the change amount of the commodity demand quantity, and 1.1 < a < 1.2.

[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 weakly responsive tendency category, obtain the curve of the demand quantity changing with time within the current period, determine the number of sub-periods in the next period according to the fluctuation value of the residence duration within the period, and determine the inbound quantity within each sub-period of the next period according to the said curve.

[0093] If the commodity supply chain category is a weakly stable tendency category, obtain the time of the commodity in each link of the supply chain, identify the abnormal link, and adjust the said abnormal link.

[0094] Specifically, the selection unit determines the number of sub-periods in the next period according to the fluctuation value of the residence duration within the current period, wherein the fluctuation value of the residence duration has a positive correlation with the number of divisions.

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

[0096] It can be understood that when the fluctuation value of the residence duration is large, a more precise adjustment should be made to the inbound quantity of the commodity. At this time, the number of sub-periods into which the period is divided is more.

[0097] Specifically, the selection unit determines the inbound quantity within each sub-period of the next period according to the said curve.

[0098] Divide the current period into several sub-periods.

[0099] Obtain the average demand quantity of each sub-period.

[0100] The inbound quantity within each sub-period of the next period is the same as the average demand quantity of the corresponding sub-period of the current period.

[0101] Specifically, in the present invention, the selection unit determines the inbound quantity within each sub-period of the next period according to the said curve. Since the poor stability of the supply chain is caused by the external environment, the residence duration of the commodity in the warehouse is highly correlated with the demand quantity of the commodity. When the fluctuation quantity of the residence duration is larger, at this time, the fluctuation of the demand quantity of the commodity is larger, and a more precise adjustment should be made to the inbound quantity of the commodity. Adjust the inbound quantity of the commodity in the next period through the change of the demand quantity of the commodity in the current period, which helps to improve the stability and efficiency of the supply chain.

[0102] Specifically, the selection unit identifies the abnormal link.

[0103] Respectively obtain the duration of the commodity in the production link and the transportation link.

[0104] Compare the duration of the commodity in the production link with the preset comparison threshold of the production link duration, and compare the duration of the commodity in the transportation link with the preset comparison threshold of the transportation link duration.

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

[0106] If the second preset condition is satisfied, 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 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.

[0108] In this embodiment, the preset production link duration comparison threshold ts0 and the preset transportation link duration comparison threshold tt0 are obtained through historical data. Obtain the duration of producing each commodity and the duration of transporting each commodity in the historical data, calculate the average value tse of the duration of producing each commodity and the average value tte of the duration of transporting each commodity. Let ts0 = b × tse, where b is the coefficient of the duration of producing each commodity, 0.9 < b < 1.1, and let tt0 = c × tte, where c is the coefficient of the duration of transporting each commodity, 0.9 < c < 1.1.

[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, etc. When the transportation link is an abnormal link, the transportation link in the supply chain can be adjusted by changing the transportation route, etc.

[0110] Specifically, the present invention obtains the duration of the commodity in each link of the supply chain through the selection unit, identifies the abnormal link, and adjusts the abnormal link. Since the reason for the instability of the supply chain is caused by internal reasons, the link with internal abnormalities is identified, and the internal is optimized by identifying the internal abnormal link, thereby improving the stability and efficiency of the supply chain.

[0111] Please refer to Figure 4 As shown, the present invention provides a supply chain scheduling method based on the Internet of Things.

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

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

[0114] Step S3, obtain the residence duration fluctuation value of the commodity with the commodity supply in an unstable state in the warehouse and divide the commodity supply chain category in combination with the change amount of the commodity demand quantity;

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

[0116] Obtain the curve of the demand varying with time within the current period, determine the number of sub-period divisions in the next period according to the fluctuation value of the residence duration within the period, and determine the inbound quantity within each sub-period of the next period according to the said curve.

[0117] Or, obtain the duration of the commodity in each link of the supply chain, identify the abnormal links, and adjust the said abnormal links.

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

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

[0120] Obviously, the above embodiments of the present invention are merely examples for clearly explaining the present invention, rather than limiting the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A supply chain scheduling system based on the Internet of Things, characterized in that: include: The information acquisition module is used to obtain the demand for each product according to the order and the length of time each product stays in the warehouse; an identification module connected to the information acquisition module, for obtaining a residence time fluctuation value 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; A scheduling module, connected to each of the information acquisition modules and the identification module, including a division unit and a selection unit; The classification unit is used to obtain the fluctuation value of the length of stay of commodities in the warehouse whose supply is in an unstable state and to classify the commodity supply chain into categories in combination with the change in commodity demand; The selection unit is used to select a scheduling strategy according to the commodity supply chain category, including: Obtain a curve of demand changes over time in the current cycle, determine the number of sub-periods in the next cycle according to the fluctuation value of the stay time in the cycle, and determine the inventory quantity in each sub-period of the next cycle according to the change curve; Or, obtain the time that the goods spend in each link of the supply chain, identify abnormal links, and adjust the abnormal links.

2. The supply chain scheduling system based on the Internet of Things according to claim 1 is characterized in that: The recognition module obtains the residence time fluctuation value, Get the length of time each product stays in the warehouse; Calculate the average length of time that products of the same category stay in the warehouse; Obtaining the average value at preset time intervals; The standard deviation of several of the average values ​​is calculated and recorded as the residence time fluctuation value.

3. The supply chain scheduling system based on the Internet of Things according to claim 2 is characterized in that: The identification module determines whether the commodity supply is in a stable state according to the residence time fluctuation value. The recognition module compares the product's residence time fluctuation value with a preset residence time fluctuation value range. If the residence time fluctuation value of the commodity exceeds a preset residence time fluctuation value range, it is determined that the supply of the commodity is in an unstable state.

4. The supply chain scheduling system based on the Internet of Things according to claim 1 is characterized in that: The division unit is also used to obtain a change in commodity demand, where the change in commodity demand is a difference between the current commodity demand and the commodity demand in historical data within the same time period.

5. The supply chain scheduling system based on the Internet of Things according to claim 1 is characterized in that: The classification unit classifies the commodity supply chain into categories, If the preset conditions are met, the commodity supply chain category is classified as a weak response tendency category; If the preset conditions are not met, the commodity supply chain category is classified as a weakly stable tendency category; The preset condition is that the fluctuation value of the duration of stay of the product exceeds the preset range of the fluctuation value of the duration of stay, and the change in the demand for the product is greater than a preset comparison threshold value of the change in the demand for the product.

6. The supply chain scheduling system based on the Internet of Things according to claim 5 is characterized in that: The selecting unit selects a scheduling strategy according to the commodity supply chain category, including: If the commodity supply chain category is a weak response tendency category, then obtain the change curve of demand over time in the current cycle, determine the number of sub-periods in the next cycle according to the fluctuation value of the residence time in the cycle, and determine the inventory quantity in each sub-period of the next cycle according to the change curve; If the commodity supply chain category is a weakly stable tendency category, the time of the commodity in each link of the supply chain is obtained, the abnormal link is obtained, and the abnormal link is adjusted.

7. The supply chain scheduling system based on the Internet of Things according to claim 1 is characterized in that: The selection unit determines the number of divisions of the sub-time periods in the next cycle according to the residence time fluctuation value in the current cycle, wherein the residence time fluctuation value is positively correlated with the number of divisions.

8. The supply chain scheduling system based on the Internet of Things according to claim 1 is characterized in that: The selection unit determines the storage quantity in each sub-period of the next cycle according to the change curve, Divide the current period into several sub-periods; Get the average demand for each sub-period; The inventory quantity in each sub-period of the next cycle is the same as the average demand quantity in the corresponding sub-period of the current cycle.

9. The supply chain scheduling system based on the Internet of Things according to claim 1, characterized in that: The selected unit identifies an abnormal link, Get the time taken by the product in the production and transportation stages respectively; Compare the time the product spends in the production phase with the preset production phase time comparison threshold, and compare the time the product spends in the transportation phase with the preset transportation phase time comparison threshold. 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 time the goods spend in the production link is greater than the preset production link time comparison threshold, and the second preset condition is that the time the goods spend in the transportation link is greater than the preset transportation link time comparison threshold.

10. A supply chain scheduling method based on the Internet of Things, characterized in that: The supply chain scheduling system based on the Internet of Things according to any one of claims 1 to 9 is applied, and the supply chain scheduling method based on the Internet of Things comprises: Step S1, obtaining the demand for each commodity and the length of time each commodity stays in the warehouse according to the order; Step S2, obtaining a residence time fluctuation value 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 fluctuation value of the residence time of the commodities in the warehouse whose supply is in an unstable state and combining it with the change in commodity demand to classify the commodity supply chain category; Step S4, selecting a scheduling strategy based on the commodity supply chain category, including: Obtain a curve of demand changes over time in the current cycle, determine the number of sub-periods in the next cycle according to the fluctuation value of the stay time in the cycle, and determine the inventory quantity in each sub-period of the next cycle according to the change curve; Or, obtain the time that the goods spend in each link of the supply chain, identify abnormal links, and adjust the abnormal links.

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