E-commerce commodity supply chain intelligent scheduling system based on order demand analysis

Abstract

The invention belongs to the field of e-commerce supply chains, relates to a data analysis technology, is used for solving the problem that the overall order completion timeliness rate cannot be guaranteed in the prior art, and particularly relates to an e-commerce commodity supply chain intelligent scheduling system based on order demand analysis, which comprises a scheduling execution subsystem and a scheduling analysis subsystem, the scheduling execution subsystem comprises a mode selection module, an inventory scheduling module and a production scheduling module; the scheduling analysis subsystem comprises an execution statistics module and a scheduling optimization module; according to the invention, inventory scheduling analysis can be carried out on the e-commerce commodities, production scheduling analysis is carried out on the e-commerce commodities in combination with the production scheduling module, inventory scheduling and production scheduling are carried out by adopting different processing modes, basic coverage scheduling preferentially meets basic requirements of all merchants, the maintenance effect on the merchants is better, and the efficiency is improved. And the saturated coverage scheduling preferentially meets the complete requirements of part of merchants, the transportation concentration degree of the produced commodities is higher, the transportation efficiency is higher, and the cost is lower.

Description

Technical Field

[0001] The present invention belongs to the field of e-commerce supply chain and relates to data analysis technology, specifically to an e-commerce commodity supply chain intelligent scheduling system based on order demand analysis. Background Art

[0002] With the rapid development of e-commerce and the continuous improvement of consumers' service requirements, commodity supply chain optimization and intelligent scheduling algorithms play an increasingly important role in today's business environment. Effective supply chain optimization can not only reduce operating costs and improve supply chain response speed, but also improve customer satisfaction and enhance the market competitiveness of enterprises.

[0003] The existing e-commerce commodity supply chain intelligent scheduling system can only perform commodity scheduling analysis from the perspective of order demand, and allocates inventory commodities and production tasks in a single way. However, commodities of different types and different demand volumes require different allocation methods, and different allocation methods require different transportation resources and production supply efficiencies. This results in the overall order completion timeliness rate not being guaranteed.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an e-commerce commodity supply chain intelligent scheduling system based on order demand analysis, which is used to solve the problem that the existing technology cannot guarantee the overall order completion timeliness rate; The technical problem to be solved by the present invention is: how to provide an e-commerce commodity supply chain intelligent scheduling system based on order demand analysis that can improve the overall order completion timeliness rate through scheduling in multiple allocation modes.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The intelligent scheduling system for the supply chain of e-commerce commodities based on order demand analysis includes a scheduling execution subsystem and a scheduling analysis subsystem. The scheduling execution subsystem includes a mode selection module, an inventory scheduling module and a production scheduling module; the scheduling analysis subsystem includes an execution statistics module and a scheduling optimization module; The mode selection module is used to select the scheduling mode of the e-commerce commodity supply chain: generate a continuous scheduling cycle, and select the scheduling mode of the scheduling cycle at the beginning of the scheduling cycle, and the scheduling mode includes basic coverage scheduling and saturated coverage scheduling; The inventory scheduling module is used to perform inventory scheduling analysis on e-commerce products: at the beginning of the scheduling cycle, the total demand for the e-commerce products corresponding to the demand orders is counted to obtain the demand value of the e-commerce products, and then the sum of the inventory of the e-commerce products in all warehouses is counted and marked as the inventory value of the e-commerce products, and the demand value is compared with the inventory value: if the demand value is less than or equal to the inventory value, the inventory scheduling is directly performed to allocate the e-commerce products in stock to the merchants corresponding to the demand orders; if the demand value is greater than the inventory value, the inventory commodity scheduling analysis is performed according to the selected scheduling mode; The production scheduling module is used to perform production scheduling analysis on e-commerce products and generate production tasks; The execution statistics module is used to perform statistical analysis on the scheduling data of the e-commerce commodity supply chain: when the execution number of the scheduling cycle reaches L1, the analysis coefficient FX of the e-commerce commodity relative to the scheduling cycle is obtained; The scheduling optimization module is used to optimize and analyze the scheduling mode of the e-commerce commodity supply chain.

[0007] Furthermore, a random selection method is used to select the scheduling mode within the first L1 scheduling cycles; after L1 scheduling cycles, a matching selection method is used to select the scheduling mode: at the beginning of the scheduling cycle, the total number of commodity demands corresponding to the demand orders of the e-commerce products is obtained, and the matching mode of the demand interval corresponding to the total number of commodity demands is retrieved as the scheduling mode of the e-commerce products within the scheduling cycle.

[0008] Furthermore, the specific process of inventory commodity scheduling analysis includes: when the selected scheduling mode is basic coverage scheduling, the ratio of inventory value to demand value is marked as the basic coverage ratio, the product of the merchant's commodity demand number corresponding to each demand order and the basic coverage ratio is marked as the merchant's basic coverage value, and the inventory commodities in the warehouse are allocated to each merchant according to the basic coverage value; when the selected scheduling mode is saturated coverage scheduling, the merchants are arranged in order from small to large according to the commodity demand number of the demand order to obtain a demand sequence, and the inventory commodities in the warehouse are allocated according to the demand sequence and the commodity demand number of the demand order until all the inventory commodities in the warehouse are allocated.

[0009] Furthermore, the specific process of the production scheduling module performing production scheduling analysis on the e-commerce goods includes: marking the difference between the demand value and the inventory value as the production value, and when the e-commerce goods adopt the basic coverage scheduling for scheduling analysis, allocating the production value to all manufacturers corresponding to the e-commerce goods and generating production tasks; When saturated coverage scheduling is used for scheduling analysis of e-commerce products, the merchants who have not completed the allocation in the demand sequence are marked as production replenishment objects, the straight-line distance values ​​between the manufacturer's position and all production replenishment objects are calculated and summed to obtain the average value to obtain the manufacturer's supply priority value, and the manufacturer with the smallest supply priority value is marked as the production supply object; then it is determined whether the expected total production of all production supply objects is less than the production value: if so, the manufacturer with the smallest supply priority value among the remaining manufacturers is marked as the production supply object, and so on, until the expected total production is not less than the production value; if not, the production value is allocated to all production supply objects and production tasks are generated.

[0010] Furthermore, the specific process of executing the statistical module to perform statistical analysis on the e-commerce commodity supply chain scheduling data includes: when the execution quantity of the scheduling cycle reaches L1, the delay data YC, order data DD and out-of-stock data QH of the e-commerce commodity in each scheduling cycle are obtained; through the formula The analysis coefficient FX of the e-commerce product in the scheduling cycle is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1.

[0011] Furthermore, the order data DD is the number of orders for e-commerce products that have not been supplied within the specified time for all demand orders within the scheduling period, the out-of-stock data QH is the total number of out-of-stock orders for e-commerce products that have not been supplied, and the delay data YC is the total timeout duration when the orders that have not been supplied within the specified time are completed.

[0012] Furthermore, the specific process of the scheduling optimization module optimizing and analyzing the scheduling mode of the e-commerce commodity supply chain includes: obtaining the total commodity demand of the e-commerce commodity corresponding to the demand order at the beginning of the scheduling cycle and marking it as the demand concept value of the e-commerce commodity, forming a demand range by the maximum and minimum values ​​of the demand concept value, dividing the demand range into several demand intervals, calculating the average value of the analysis coefficient FX of the scheduling cycle for scheduling analysis using basic covering scheduling and whose demand concept value is within the demand interval and marking it as the basic priority value of the demand interval; calculating the average value of the analysis coefficient FX of the scheduling cycle for scheduling analysis using saturated covering scheduling and whose demand concept value is within the demand interval and marking it as the saturated priority value of the demand interval; comparing the basic priority value with the saturated priority value and marking the matching pattern of the demand interval through the comparison result.

[0013] Furthermore, the specific process of comparing the basic priority value with the saturated priority value includes: if the basic priority value is less than the saturated priority value, marking the basic coverage scheduling as the matching mode of the demand interval; otherwise, marking the saturated coverage scheduling as the matching mode of the demand interval; and sending the matching modes of all demand intervals corresponding to all e-commerce products to the mode selection module.

[0014] The present invention has the following beneficial effects: 1. The mode selection module can be used to select the scheduling mode of the e-commerce product supply chain. Random selection is used for the scheduling mode of all e-commerce products in the first L1 scheduling cycle to provide data support for the matching selection process. Then, after the end of the L1 scheduling cycle, scheduling optimization analysis is performed to mark the matching mode for all demand intervals of each e-commerce product. A more appropriate scheduling mode is selected for scheduling processing according to the actual order completion status parameters; 2. The inventory scheduling module can be used to analyze the inventory scheduling of e-commerce products, and the production scheduling module can be used to analyze the production scheduling of e-commerce products. Different processing methods are used for inventory scheduling and production scheduling according to different scheduling modes. Basic coverage scheduling gives priority to meeting the basic needs of all merchants, which has a better effect on maintaining merchants, while saturated coverage scheduling gives priority to meeting the complete needs of some merchants. The transportation of goods after production is more centralized, with higher transportation efficiency and lower costs; 3. By executing the statistical module, the e-commerce commodity supply chain scheduling data can be statistically analyzed, and the multiple data of untimely supply of e-commerce commodities within the scheduling cycle can be statistically analyzed and calculated to obtain the analysis coefficient. Then, the timeliness of order completion of a single e-commerce commodity within the scheduling cycle can be fed back according to the analysis coefficient, providing data support for scheduling optimization analysis; 4. The scheduling optimization module can be used to optimize and analyze the scheduling mode of the e-commerce product supply chain, analyze the overall order completion timeliness when the same e-commerce product is scheduled and analyzed using different scheduling modes within L1 scheduling cycles, and mark the matching mode based on the analysis results. A matching mode is assigned to the demand interval of each e-commerce product. When the subsequent scheduling cycle is executed, the most appropriate scheduling mode can be directly selected based on the total order demand to improve product supply efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A system block diagram of the present invention as a whole; Figure 2 is a system block diagram of Embodiment 1 of the present invention; Figure 3 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0017] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the intelligent scheduling system for the supply chain of e-commerce products based on order demand analysis includes a scheduling execution subsystem and a scheduling analysis subsystem. The scheduling execution subsystem includes a mode selection module, an inventory scheduling module and a production scheduling module; the scheduling analysis subsystem includes an execution statistics module and a scheduling optimization module.

[0019] Embodiment 1: Figure 2 As shown, the mode selection module, the inventory scheduling module and the production scheduling module are communicatively connected in sequence.

[0020] The mode selection module is used to select the scheduling mode of the e-commerce product supply chain: generate continuous scheduling cycles, and use random selection to select the scheduling mode within the first L1 scheduling cycles. The scheduling modes include basic coverage scheduling and saturated coverage scheduling; after L1 scheduling cycles, use matching selection to select the scheduling mode: at the beginning of the scheduling cycle, obtain the total number of product demands for the corresponding demand orders of e-commerce products, and retrieve the matching mode of the demand interval corresponding to the total number of product demands as the scheduling mode of the e-commerce products within the scheduling cycle; random selection is used for the scheduling mode of all e-commerce products in the first L1 scheduling cycles to provide data support for the matching selection process, and then after the end of L1 scheduling cycles, scheduling optimization analysis is performed, matching mode marks are performed for all demand intervals of each e-commerce product, and a more appropriate scheduling mode is selected for scheduling processing according to the actual order completion status parameters.

[0021] The inventory scheduling module is used to perform inventory scheduling analysis on e-commerce products: at the beginning of the scheduling cycle, the total number of commodity demands corresponding to the demand orders of e-commerce products is counted to obtain the demand value of the e-commerce products, and then the sum of the inventory of e-commerce products in all warehouses is counted and marked as the inventory value of the e-commerce products, and the demand value is compared with the inventory value: if the demand value is less than or equal to the inventory value, the inventory scheduling is directly performed, and the inventory of e-commerce products is allocated to the merchants corresponding to the demand orders; if the demand value is greater than the inventory value, the inventory commodity scheduling analysis is performed according to the selected scheduling mode: when the selected scheduling mode is basic coverage scheduling, the ratio of the inventory value to the demand value is marked as the basic coverage ratio, the product of the number of merchant commodity demands corresponding to each demand order and the basic coverage ratio is marked as the merchant's basic coverage value, and the inventory commodities in the warehouse are allocated to each merchant according to the basic coverage value; when the scheduling mode is selected as saturated coverage scheduling, the merchants are arranged in order from small to large according to the number of commodity demands of the demand orders to obtain a demand sequence, and the inventory commodities in the warehouse are allocated according to the demand sequence and the number of commodity demands of the demand orders until all the inventory commodities in the warehouse are allocated.

[0022] The production scheduling module is used to perform production scheduling analysis on e-commerce products: the difference between the demand value and the inventory value is marked as the production value. When the e-commerce products adopt the basic coverage scheduling for scheduling analysis, the production value is allocated to all the producers corresponding to the e-commerce products and the production tasks are generated; when the e-commerce products adopt the saturated coverage scheduling for scheduling analysis, the merchants who have not completed the allocation in the demand sequence are marked as production replenishment objects, the straight-line distance values ​​between the producer's position and all the production replenishment objects are calculated and summed to obtain the average value to obtain the producer's supply priority value, and the producer with the smallest supply priority value is marked as the production supply object; then it is determined whether the expected total production of all production supply objects is less than the production value: if so, the producer with the smallest supply priority value among the remaining producers is marked as the production supply object, and so on, until the expected total production is not less than the production value; if not, the production value is allocated to all production supply objects and the production tasks are generated; different processing methods are used for inventory scheduling and production scheduling according to different scheduling modes. The basic coverage scheduling gives priority to meeting the basic needs of all merchants, which has a better maintenance effect on merchants, while the saturated coverage scheduling gives priority to meeting the complete needs of some merchants, and the transportation of goods after production is more concentrated, with higher transportation efficiency and lower cost.

[0023] Embodiment 2: Figure 3 As shown, the execution statistics module is connected to the scheduling optimization module for communication.

[0024] The execution statistics module is used to perform statistical analysis on the scheduling data of the e-commerce commodity supply chain: when the execution quantity of the scheduling cycle reaches L1, the delay data YC, order data DD and out-of-stock data QH of the e-commerce commodity in each scheduling cycle are obtained; the order data DD is the number of orders for all demand orders of e-commerce commodities that have not been completed within the specified time within the scheduling cycle, the out-of-stock data QH is the total number of out-of-stock orders for the e-commerce commodities that have not been completed, and the delay data YC is the total timeout duration of the orders that have not been completed within the specified time when the supply is completed; through the formula The analysis coefficient FX of the e-commerce goods in the scheduling cycle is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1; the analysis coefficient is obtained by statistics and calculation of multiple data of untimely supply of e-commerce goods in the scheduling cycle, and then the timeliness of order completion of a single e-commerce product in the scheduling cycle can be fed back according to the analysis coefficient, providing data support for scheduling optimization analysis.

[0025] The scheduling optimization module is used to optimize and analyze the scheduling mode of the e-commerce commodity supply chain: obtain the total demand of e-commerce commodities corresponding to the demand orders at the beginning of the scheduling cycle and mark it as the demand concept value of the e-commerce commodities, the demand range is composed of the maximum and minimum values ​​of the demand concept value, and the demand range is divided into several demand intervals. The average value of the analysis coefficient FX of the scheduling cycle using basic covering scheduling for scheduling analysis with demand concept values ​​within the demand interval is calculated and marked as the basic priority value of the demand interval; the average value of the analysis coefficient FX of the scheduling cycle using saturated covering scheduling for scheduling analysis with demand concept values ​​within the demand interval is calculated and marked as the saturated priority value of the demand interval; the basic The priority value is compared with the saturation priority value: if the basic priority value is less than the saturation priority value, the basic coverage scheduling is marked as the matching mode of the demand interval; otherwise, the saturated coverage scheduling is marked as the matching mode of the demand interval; the matching modes of all demand intervals corresponding to all e-commerce products are sent to the mode selection module; the overall order completion timeliness of the same e-commerce product when different scheduling modes are used for scheduling analysis within L1 scheduling cycles is analyzed, and the matching mode is marked according to the analysis results, and a matching mode is assigned to each e-commerce product's demand interval. When the subsequent scheduling cycle is executed, the most appropriate scheduling mode can be directly selected according to the total order demand to improve the efficiency of product supply.

[0026] The intelligent scheduling system for the supply chain of e-commerce goods based on order demand analysis generates continuous scheduling cycles when working. In the first L1 scheduling cycles, the scheduling mode is selected by random selection. After L1 scheduling cycles, the scheduling mode is selected by matching selection. At the beginning of the scheduling cycle, the total demand of goods corresponding to the demand orders of e-commerce goods is counted to obtain the demand value of the e-commerce goods, and then the sum of the inventory of the e-commerce goods in all warehouses is counted and marked as the inventory value of the e-commerce goods. The demand value is compared with the inventory value. When the demand value is not less than the inventory value, the inventory scheduling analysis and production scheduling analysis are performed according to the selected scheduling mode. When the execution number of the scheduling cycle reaches L1, the analysis coefficient FX of the e-commerce goods in each scheduling cycle is obtained, the total demand of goods corresponding to the demand orders of the e-commerce goods at the beginning of the scheduling cycle is obtained and marked as the demand concept value of the e-commerce goods, the maximum and minimum values ​​of the demand concept value constitute the demand range, the demand range is divided into several demand intervals, and the matching mode of the demand interval is marked according to the basic priority value and the saturation priority value.

[0027] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0028] The above formulas are obtained by collecting a large amount of data and performing software simulation to select a formula close to the actual value. The coefficients in the formula are set by technicians in this field according to actual conditions; for example: Formula ; A technician in this field collects multiple groups of sample data and sets corresponding analysis coefficients for each group of sample data; Substitute the set analysis coefficients and the collected sample data into the formula, any three formulas constitute a three-variable linear equation system, screen the calculated coefficients and take the average, and obtain the values ​​of k1, k2 and k3 as 3.82, 2.69 and 2.04 respectively; The size of the coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the coefficient depends on the amount of sample data and the preliminary setting of the corresponding analysis coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value, such as the analysis coefficient is proportional to the value of the delay data.

[0029] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0030] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent scheduling system for e-commerce commodity supply chain based on order demand analysis, characterized by: It includes a scheduling execution subsystem and a scheduling analysis subsystem. The scheduling execution subsystem includes a mode selection module, an inventory scheduling module and a production scheduling module; the scheduling analysis subsystem includes an execution statistics module and a scheduling optimization module; The mode selection module is used to select the scheduling mode of the e-commerce commodity supply chain: generate a continuous scheduling cycle, and select the scheduling mode of the scheduling cycle at the beginning of the scheduling cycle, and the scheduling mode includes basic coverage scheduling and saturated coverage scheduling; The inventory scheduling module is used to perform inventory scheduling analysis on e-commerce products: at the beginning of the scheduling cycle, the total demand for e-commerce products corresponding to the demand orders is counted to obtain the demand value of the e-commerce products, and then the sum of the inventory of the e-commerce products in all warehouses is counted and marked as the inventory value of the e-commerce products, and the demand value is compared with the inventory value: if the demand value is less than or equal to the inventory value, the inventory scheduling is directly performed to allocate the e-commerce products in stock to the merchants corresponding to the demand orders; If the demand value is greater than the inventory value, the inventory commodity scheduling analysis is performed according to the selected scheduling mode; The production scheduling module is used to perform production scheduling analysis on e-commerce products and generate production tasks; The execution statistics module is used to perform statistical analysis on the scheduling data of the e-commerce commodity supply chain: when the execution number of the scheduling cycle reaches L1, the analysis coefficient FX of the e-commerce commodity relative to the scheduling cycle is obtained; The scheduling optimization module is used to optimize and analyze the scheduling mode of the e-commerce commodity supply chain.

2. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 1 is characterized in that: In the first L1 scheduling cycles, the scheduling mode is selected by random selection. After L1 scheduling cycles, the scheduling mode is selected by matching selection: at the beginning of the scheduling cycle, the total number of commodity demands corresponding to the demand orders of e-commerce products is obtained, and the matching mode of the demand interval corresponding to the total number of commodity demands is retrieved as the scheduling mode of the e-commerce products in the scheduling cycle.

3. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 2 is characterized in that: The specific process of inventory commodity scheduling analysis includes: when the selected scheduling mode is basic coverage scheduling, the ratio of inventory value to demand value is marked as the basic coverage ratio, the product of the merchant's commodity demand number corresponding to each demand order and the basic coverage ratio is marked as the merchant's basic coverage value, and the inventory commodities in the warehouse are allocated to each merchant according to the basic coverage value; when the selected scheduling mode is saturated coverage scheduling, the merchants are arranged in order from small to large according to the commodity demand number of the demand order to obtain a demand sequence, and the inventory commodities in the warehouse are allocated according to the demand sequence and the commodity demand number of the demand order until all the inventory commodities in the warehouse are allocated.

4. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 3 is characterized in that: The specific process of the production scheduling module for the production scheduling analysis of e-commerce products includes: marking the difference between the demand value and the inventory value as the production value, and when the e-commerce products are scheduled and analyzed using the basic coverage scheduling, allocating the production value to all manufacturers corresponding to the e-commerce products and generating production tasks; When saturated coverage scheduling is used for scheduling analysis of e-commerce products, the merchants who have not completed the allocation in the demand sequence are marked as production replenishment objects, the straight-line distance values ​​between the manufacturer's position and all production replenishment objects are calculated and summed to obtain the average value to obtain the manufacturer's supply priority value, and the manufacturer with the smallest supply priority value is marked as the production supply object; then it is determined whether the expected total production of all production supply objects is less than the production value: if so, the manufacturer with the smallest supply priority value among the remaining manufacturers is marked as the production supply object, and so on, until the expected total production is not less than the production value; if not, the production value is allocated to all production supply objects and production tasks are generated.

5. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 4 is characterized in that: The specific process of executing the statistical module to perform statistical analysis on the scheduling data of the e-commerce commodity supply chain includes: when the execution quantity of the scheduling cycle reaches L1, the delay data YC, order data DD and out-of-stock data QH of the e-commerce commodity in each scheduling cycle are obtained; through the formula The analysis coefficient FX of the e-commerce product in the scheduling cycle is obtained, where k1, k2 and k3 are all proportional coefficients, and k1>k2>k3>1.

6. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 5 is characterized in that: The order data DD is the number of orders for e-commerce products that have not been supplied within the specified time for all demand orders within the scheduling period. The out-of-stock data QH is the total number of out-of-stock orders for e-commerce products that have not been supplied. The delay data YC is the total timeout duration when the orders that have not been supplied within the specified time are completed.

7. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 6 is characterized in that: The specific process of the scheduling optimization module optimizing and analyzing the scheduling mode of the e-commerce commodity supply chain includes: obtaining the total demand of the e-commerce commodities corresponding to the demand orders at the beginning of the scheduling cycle and marking it as the demand concept value of the e-commerce commodities, forming a demand range by the maximum and minimum values ​​of the demand concept value, dividing the demand range into several demand intervals, calculating the average value of the analysis coefficient FX of the scheduling cycle using basic covering scheduling for scheduling analysis whose demand concept value is within the demand interval and marking it as the basic priority value of the demand interval; calculating the average value of the analysis coefficient FX of the scheduling cycle using saturated covering scheduling for scheduling analysis whose demand concept value is within the demand interval and marking it as the saturated priority value of the demand interval; comparing the basic priority value with the saturated priority value and marking the matching mode of the demand interval through the comparison result.

8. The e-commerce commodity supply chain intelligent scheduling system based on order demand analysis according to claim 7 is characterized in that: The specific process of comparing the basic priority value with the saturated priority value includes: if the basic priority value is less than the saturated priority value, the basic coverage scheduling is marked as the matching mode of the demand interval; otherwise, the saturated coverage scheduling is marked as the matching mode of the demand interval; the matching modes of all demand intervals corresponding to all e-commerce products are sent to the mode selection module.

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