E-commerce order management method and system and storage medium

Abstract

The invention provides an e-commerce order management method and system and a storage medium, and belongs to the technical field of data processing, and the method specifically comprises the steps: determining the commodities of different e-commerce orders based on the analysis result of the order data of the e-commerce orders in an e-commerce platform, determining historical order data of the commodities and other commodities in the e-commerce platform in the same order and the same user, determining association coefficients of the commodities and other commodities and associated commodities in other commodities in combination with the types of the commodities, and obtaining the associated commodities of the commodities in different e-commerce orders and the association coefficients of the associated commodities; according to the method and the device, the similar orders of the e-commerce orders are combined to determine the merging processing probability of the e-commerce orders, and the monitoring frequency of obtaining and processing the e-commerce orders of the e-commerce platform is determined according to the merging processing probability of different e-commerce orders in the e-commerce platform, so that a foundation is laid for reducing the express cost.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular relates to an e-commerce order management method, system and storage medium. Background Art

[0002] In order to realize the order delivery processing in the e-commerce platform, the existing technical solution determines the target express company and performs express delivery by using the information input by the delivery code and the delivery authority in CN202211277535.4 "Express Delivery Method, Device, System and Delivery Number Directory", but there are the following technical problems: For e-commerce orders placed by merchants on e-commerce platforms, it is common for the same user to place repeated orders. In existing technical solutions, different express delivery numbers are often used for different e-commerce orders, which makes it impossible for merchants to effectively control their express delivery costs.

[0003] In response to the above technical problems, the present application specifically provides an e-commerce order management method, system and storage medium. Summary of the invention

[0004] To achieve the purpose of the present invention, the present invention adopts the following technical solutions: According to one aspect of the present invention, an e-commerce order management method is provided.

[0005] An e-commerce order management method, specifically comprising: S1 determines similar e-commerce orders in the e-commerce platform based on the acquisition result of the e-commerce orders on the e-commerce platform, and when it is determined that it is not necessary to use the preset monitoring frequency to acquire the e-commerce orders on the e-commerce platform using the order data of the similar e-commerce orders and e-commerce orders on the e-commerce platform, proceeds to the next step; S2 determines the commodities of different e-commerce orders based on the parsing results of the order data of the e-commerce orders in the e-commerce platform, determines the historical order data of the commodity and other commodities in the e-commerce platform in the same order and the same user, and determines the correlation coefficient between the commodity and other commodities and the related commodities among other commodities in combination with the type of the commodity; S3 obtains the associated products of the products in different e-commerce orders and the correlation coefficients with the associated products, and determines the probability of merging the e-commerce orders in combination with similar orders of the e-commerce orders; S4 determines the monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform according to the probability of merging and processing different e-commerce orders in the e-commerce platform.

[0006] The beneficial effects of the present invention are: The probability of merging e-commerce orders is determined by utilizing related products of goods in different e-commerce orders, their correlation coefficients, and similar orders of e-commerce orders. This not only takes into account the difference in the possible probability of merging orders in the future caused by the difference in the degree of correlation between related products of goods in e-commerce orders, but also takes into account the difference in the possible probability of merging existing e-commerce orders caused by the number of similar orders of e-commerce orders. This realizes the determination of the probability of merging e-commerce orders from multiple angles, and also lays the foundation for further determining differentiated monitoring frequencies.

[0007] According to the probability of merging and processing different e-commerce orders in the e-commerce platform, the monitoring frequency of the acquisition and processing of the e-commerce orders of the e-commerce platform is determined, and the difference in the possibility of merging orders on the e-commerce platform is evaluated based on the number of e-commerce orders and the probability of merging and processing different e-commerce orders. This ensures the real-time monitoring and processing of e-commerce platforms with a higher possibility of merging orders, while also reducing the difficulty of monitoring and processing e-commerce orders.

[0008] A further technical solution is that the similar e-commerce orders are e-commerce orders that are consistent with the e-commerce platform in terms of either order information or order account.

[0009] A further technical solution is to determine that it is not necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform, which specifically includes: Determine the number of e-commerce orders on the e-commerce platform using order data of e-commerce orders on the e-commerce platform; Determine the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders in the e-commerce platform; Based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

[0010] A further technical solution is to determine whether it is necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the e-commerce orders, which specifically includes: Determine a preset quantity ratio threshold corresponding to the number of the e-commerce platforms based on the number of the e-commerce platforms; According to the preset quantity proportion threshold and the quantity proportion of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

[0011] A further technical solution is that the probability of merging the e-commerce orders ranges from 0 to 1, wherein the greater the probability of merging the e-commerce orders on the e-commerce platform, the greater the probability that the e-commerce orders will be merged in the future.

[0012] A further technical solution is that the method for determining the monitoring frequency of the acquisition and processing of the e-commerce orders of the e-commerce platform is: Based on the combined processing probability of different e-commerce orders in the e-commerce platform, determine the e-commerce orders whose combined processing probability is greater than a preset probability threshold, and regard them as high-probability orders; The monitoring frequency of the acquisition process of the e-commerce orders of the e-commerce platform is determined by the number of the high-probability orders.

[0013] A further technical solution is to determine the monitoring frequency of the acquisition and processing of the e-commerce orders of the e-commerce platform according to the number of the high-probability orders, which specifically includes: Based on the number of the high probability orders, determining a preset monitoring frequency corresponding to the number of the high probability orders; The monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform is determined by the preset monitoring frequency.

[0014] In a second aspect, the present invention provides a computer system comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-mentioned e-commerce order management method when running the computer program.

[0015] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned e-commerce order management method.

[0016] Other features and advantages will be described in the following description. The objects and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the accompanying drawings; Figure 1 It is a flow chart of an e-commerce order management method; Figure 2 It is a flow chart for determining that it is not necessary to use a preset monitoring frequency to obtain and process e-commerce orders of the e-commerce platform; Figure 3 is a flow chart of a method for determining a correlation coefficient; Figure 4 is a flow chart of a method for determining a probability of combined processing of e-commerce orders; Figure 5 It is a framework diagram of a computer system. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0020] For e-commerce orders placed by merchants on e-commerce platforms, it is often the case that the same user places repeated orders. In existing technical solutions, different express delivery numbers are often used for different e-commerce orders. Therefore, it is necessary to merge the express delivery of e-commerce orders on the e-commerce platform to reduce the express delivery cost.

[0021] Determine that there is no need to use the preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform: determine the preset quantity proportion threshold value corresponding to the number of e-commerce platforms based on the number of e-commerce platforms, and when the quantity proportion of similar e-commerce orders in the e-commerce orders is greater than the preset quantity proportion threshold value, it is determined that the preset monitoring frequency needs to be used to obtain and process the e-commerce orders of the e-commerce platform.

[0022] The correlation coefficient between a product and other products: The product type is used to determine the products that need to be used simultaneously with the product during use and the products that have the same user objects as the product, and these products are taken as basic associated products. The historical orders of the product and the basic associated products in the same order and the same user are taken as associated historical orders. The correlation coefficient between the product and the basic associated products is determined based on the proportion of the number of associated historical orders in the number of orders that include the product.

[0023] Related products among other products: Other products with a correlation coefficient greater than 0.6 are considered related products.

[0024] Merger processing probability: the similarity merging probability of the e-commerce order is determined by the ratio of the number of similar orders of the e-commerce order to a preset number, the weighted sum of the association coefficients of the associated commodities in the e-commerce order is determined, the weighted sum of the association coefficients of different commodities in the e-commerce order is normalized and summed to obtain the associated merging probability, and the average value of the similar merging probability and the associated merging probability is used to determine the merging processing probability of the e-commerce order.

[0025] Monitoring frequency of acquisition and processing of e-commerce orders on the e-commerce platform: Based on the combined processing probability of different e-commerce orders in the e-commerce platform, determine the e-commerce orders whose combined processing probability is greater than a preset probability threshold, and treat them as high-probability orders; based on the number of high-probability orders, determine the preset monitoring frequency corresponding to the number of high-probability orders; determine the monitoring frequency of acquisition and processing of e-commerce orders on the e-commerce platform through the preset monitoring frequency. Embodiment 1

[0026] To solve the above problems, according to one aspect of the present invention, Figure 1 As shown, a first aspect is provided, and the present invention provides an e-commerce order management method, which specifically includes: S1 determines similar e-commerce orders in the e-commerce platform based on the acquisition result of the e-commerce orders on the e-commerce platform, and when it is determined that it is not necessary to use the preset monitoring frequency to acquire the e-commerce orders on the e-commerce platform using the order data of the similar e-commerce orders and e-commerce orders on the e-commerce platform, proceeds to the next step; Furthermore, the similar e-commerce orders are e-commerce orders that are consistent in either order information or order account on the e-commerce platform.

[0027] Specifically, Figure 2 As shown, determining that there is no need to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform specifically includes: Determine the number of e-commerce orders on the e-commerce platform using order data of e-commerce orders on the e-commerce platform; Determine the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders in the e-commerce platform; Based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

[0028] It can be understood that, based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the number of e-commerce orders, determining whether it is necessary to adopt a preset monitoring frequency to obtain the e-commerce orders of the e-commerce platform specifically includes: Determine a preset quantity ratio threshold corresponding to the number of the e-commerce platforms based on the number of the e-commerce platforms; According to the preset quantity proportion threshold and the quantity proportion of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

[0029] Furthermore, when the number of similar e-commerce orders accounts for a proportion of the number of e-commerce orders that is greater than the preset number proportion threshold, it is determined that a preset monitoring frequency needs to be used to obtain and process the e-commerce orders of the e-commerce platform.

[0030] In another embodiment, determining that it is not necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform specifically includes: Determine the number of e-commerce orders on the e-commerce platform using order data of e-commerce orders on the e-commerce platform; Determine the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders in the e-commerce platform; E-commerce orders that are similar to each other are divided into similar order groups, and similar order groups with a larger number of orders than a preset number are used as screening order groups. The proportion product is determined according to the product of the proportion of the number of e-commerce orders in the screening order group in the number of e-commerce orders and the proportion of the number of similar e-commerce orders in the e-commerce orders. The proportion product is used to determine whether it is necessary to adopt a preset monitoring frequency to obtain and process e-commerce orders from the e-commerce platform.

[0031] In another embodiment, determining that it is not necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform specifically includes: S11 uses order data of e-commerce orders on the e-commerce platform to determine the number of e-commerce orders on the e-commerce platform, determines the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders on the e-commerce platform, and determines a basic monitoring demand coefficient based on the number of the e-commerce orders and the proportion of the similar e-commerce orders in the e-commerce orders; S12: dividing the e-commerce orders that are similar to each other into similar order groups, and determining the repetition coefficients of different similar order groups according to the number of orders in different similar order groups; S13 determines a comprehensive monitoring demand coefficient using a basic monitoring demand coefficient and repetition coefficients of different similar order groups, and determines whether a preset monitoring frequency is required to obtain and process e-commerce orders of the e-commerce platform according to the comprehensive monitoring demand coefficient.

[0032] Optionally, determining a basic monitoring demand coefficient based on the number of the e-commerce orders and the proportion of the similar e-commerce orders in the e-commerce orders specifically includes: The e-commerce order proportion coefficient is determined by the ratio of the number of the e-commerce orders to the preset number of e-commerce orders, and the basic monitoring demand coefficient is determined by multiplying the proportion of the number of similar e-commerce orders in the e-commerce orders by the e-commerce order proportion coefficient.

[0033] Furthermore, the repetition coefficient of the similar order group is determined according to a preset repetition coefficient corresponding to the order quantity of the similar order group.

[0034] Optionally, the above step S11 includes the following contents: S111 uses the order data of the e-commerce orders of the e-commerce platform to determine the number of e-commerce orders of the e-commerce platform. When the number of e-commerce orders of the e-commerce platform is greater than the set value of the order quantity, it is determined that the acquisition process of the e-commerce orders of the e-commerce platform needs to be performed using a preset monitoring frequency. When the number of e-commerce orders of the e-commerce platform is not greater than the set value of the order quantity, the process proceeds to step S112. S112: When the number of e-commerce orders on the e-commerce platform is within the preset order quantity range, proceed to step S113; when the number of e-commerce orders on the e-commerce platform is not within the preset order quantity range, proceed to step S114; S113 determines the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders in the e-commerce platform. When the proportion is greater than the preset proportion, it is determined that the preset monitoring frequency needs to be used to obtain the e-commerce orders of the e-commerce platform. When the proportion is not greater than the preset proportion, the process proceeds to step S114. S114 determines the basic monitoring demand coefficient based on the number of the e-commerce orders and the proportion of the similar e-commerce orders in the e-commerce orders. When the basic monitoring demand coefficient is greater than the preset demand coefficient threshold, it is determined that the preset monitoring frequency needs to be used to obtain and process the e-commerce orders of the e-commerce platform. When the basic monitoring demand coefficient is not greater than the preset demand coefficient threshold, the process proceeds to step S12.

[0035] S2 determines the commodities of different e-commerce orders based on the parsing results of the order data of the e-commerce orders in the e-commerce platform, determines the historical order data of the commodity and other commodities in the e-commerce platform in the same order and the same user, and determines the correlation coefficient between the commodity and other commodities and the related commodities among other commodities in combination with the type of the commodity; Furthermore, the historical order data includes the historical order quantity.

[0036] Specifically, Figure 3 As shown, the method for determining the correlation coefficient is: Determine the commodities that need to be used simultaneously with the commodity during use and the commodities that are used by the same objects as the commodity based on the type of the commodity, and use them as basic associated commodities; The historical orders of the product and the basic associated product in the same order and the same user are taken as associated historical orders; The correlation coefficient between the commodity and the basic correlated commodity is determined according to the number of correlated historical orders.

[0037] Optionally, when the other commodities do not belong to the basic associated commodities, the association coefficient between the other commodities and the commodity is determined to be 0; when the other commodities belong to the basic associated commodities, the association coefficient between the commodity and the basic associated commodities is determined using the preset association coefficient corresponding to the number of associated historical orders.

[0038] Further, when the correlation coefficient between the product and the basic related product is greater than a preset correlation coefficient, the basic related product is determined to be a related product.

[0039] In another embodiment, the method for determining the correlation coefficient is: Determine the commodities that need to be used simultaneously with the commodity during use and the commodities that are used by the same object as the commodity according to the type of the commodity, and use them as basic associated commodities; when the other commodities do not belong to the basic associated commodities, determine that the other commodities do not belong to the associated commodities; When the other products are related basic products: The historical orders of the product and the basic associated product in the same order and the same user are taken as associated historical orders, and when the number of the associated historical orders is less than the preset number of associated orders, it is determined that the other products do not belong to associated products; When the number of associated orders is not less than the preset number of associated orders: According to the simultaneous use of the basic associated commodity and the commodity and the matching of the usage objects, the basic association coefficient between the basic associated commodity and the commodity is determined. When the basic association coefficient between the basic associated commodity and the commodity is less than a preset basic coefficient threshold: Obtaining the quantity of the product and the basic associated product in the same order and the number of historical orders of the same user, and when the quantity of the product and the basic associated product in the same order and the number of historical orders of the same user are both within a preset quantity range, determining that the other product does not belong to the associated product; When the quantity of the product and the basic related product in the same order or the number of historical orders of the same user is not within the preset quantity range: When the number of the same users is less than a preset user number threshold, it is determined that the other products do not belong to the associated products; When the basic correlation coefficient between the basic related product and the product is not less than the preset basic coefficient threshold or the number of the same users is not less than the preset user number threshold: The order correlation coefficient is determined by the number of associated orders of different identical users, and the correlation coefficient between the basic associated products and the basic associated products is determined by combining the basic associated products and the basic associated products.

[0040] S3 obtains the associated products of the products in different e-commerce orders and the correlation coefficients with the associated products, and determines the probability of merging the e-commerce orders in combination with similar orders of the e-commerce orders; Specifically, Figure 4 As shown, the method for determining the probability of merging and processing the e-commerce orders is: Determining the similarity merging probability of the e-commerce order based on the ratio of the number of similar orders to the e-commerce order and a preset number; Determine the weighted sum of the association coefficients of different commodities in the e-commerce order according to the weighted sum of the association coefficients of the associated commodities of the commodity in the e-commerce order, and obtain the association merging probability after normalizing and summing the weighted sums of the association coefficients of different commodities; The probability of merging and processing the e-commerce order is determined by using the average value of the similar merging probability and the associated merging probability.

[0041] Furthermore, the probability of merging the e-commerce orders ranges from 0 to 1, wherein the greater the probability of merging the e-commerce orders on the e-commerce platform, the greater the probability that the e-commerce orders will be merged in the future.

[0042] S4 determines the monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform according to the probability of merging and processing different e-commerce orders in the e-commerce platform.

[0043] It should be noted that the method for determining the monitoring frequency of the acquisition and processing of e-commerce orders of the e-commerce platform is: Based on the combined processing probability of different e-commerce orders in the e-commerce platform, determine the e-commerce orders whose combined processing probability is greater than a preset probability threshold, and regard them as high-probability orders; The monitoring frequency of the acquisition process of the e-commerce orders of the e-commerce platform is determined by the number of the high-probability orders.

[0044] Further, determining the monitoring frequency of the acquisition and processing of the e-commerce orders of the e-commerce platform according to the number of the high-probability orders specifically includes: Based on the number of the high probability orders, determining a preset monitoring frequency corresponding to the number of the high probability orders; The monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform is determined by the preset monitoring frequency.

[0045] In another embodiment, the method for determining the monitoring frequency of the acquisition process of the e-commerce order of the e-commerce platform is: Based on the number of e-commerce orders in the e-commerce platform, determine a preset monitoring frequency based on the number of e-commerce orders; Based on the combined processing probabilities of different e-commerce orders in the e-commerce platform, determine the average value of the combined processing probabilities of different e-commerce platforms, and use it as the average value of the combined processing probability; The monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform is determined by the preset monitoring frequency and the average value of the combined processing probability.

[0046] Further, the monitoring frequency of obtaining and processing the e-commerce orders of the e-commerce platform is determined by the preset monitoring frequency and the average value of the combined processing probability, specifically including: Based on the ratio of the preset monitoring frequency to the average value of the combined processing probability, a monitoring frequency for obtaining and processing e-commerce orders of the e-commerce platform is determined. Embodiment 2

[0047] Second, as Figure 5 As shown, the present invention provides a computer system, comprising: a memory and a processor that are communicatively connected, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-mentioned e-commerce order management method when running the computer program.

[0048] Optionally, the above step S12 includes the following contents: S121 obtains the number of similar e-commerce orders in the e-commerce platform. When the number of similar e-commerce orders in the e-commerce platform is within a preset similar order number range, proceeds to step S122. When the number of similar e-commerce orders in the e-commerce platform is not within the preset similar order number range, proceeds to step S123. S122 divides the e-commerce orders that are similar to each other into similar order groups. When the number of the similar order groups is greater than the preset number of order groups, it is determined that the preset monitoring frequency needs to be used to obtain the e-commerce orders of the e-commerce platform. When the number of the similar order groups is not greater than the preset number of order groups, the process proceeds to step S123. S123 determines the repetition coefficients of different similar order groups according to the order quantities in the different similar order groups. When there is a similar order group whose repetition coefficient is greater than the preset repetition coefficient, the process proceeds to step S124. When there is no similar order group whose repetition coefficient is not greater than the preset repetition coefficient, the process proceeds to step S13. S124 When the number of groups of similar order groups whose repetition coefficient is greater than the preset repetition coefficient does not meet the requirement, it is determined that the preset monitoring frequency needs to be used to obtain and process the e-commerce orders of the e-commerce platform; when the number of groups of similar order groups whose repetition coefficient is greater than the preset repetition coefficient meets the requirement, proceed to step S13. Embodiment 3

[0049] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the above-mentioned e-commerce order management method.

[0050] Specifically, the method for determining the monitoring frequency of the acquisition and processing of the e-commerce orders of the e-commerce platform is: Obtaining the number of e-commerce orders in the e-commerce platform, and when the number of e-commerce orders in the e-commerce platform is greater than a preset order number, determining a monitoring frequency for obtaining and processing e-commerce orders on the e-commerce platform using a preset monitoring frequency; When the number of e-commerce orders in the e-commerce platform is not greater than the preset order number: When it is determined based on the number of e-commerce orders in the e-commerce platform that the number of e-commerce orders in the e-commerce platform is within a preset order quantity range: Obtaining the combined processing probabilities of different e-commerce orders, and when the average of the combined processing probabilities of different e-commerce orders is greater than a preset combined probability threshold, determining the monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform using a preset monitoring frequency; When the number of e-commerce orders in the e-commerce platform is not within the preset order quantity range or the average value of the merging probability of different e-commerce orders is not greater than the preset merging probability threshold: Based on the combined processing probability of different e-commerce orders in the e-commerce platform, e-commerce orders whose combined processing probability is greater than a preset probability threshold are determined and regarded as high-probability orders. When the number of the high-probability orders is greater than the preset high-probability order number threshold, the monitoring frequency of the acquisition processing of the e-commerce orders of the e-commerce platform is determined using the preset monitoring frequency; When the quantity of the high probability orders is not greater than the preset high probability order quantity threshold: Based on the number of e-commerce orders in the e-commerce platform, determine a preset monitoring frequency under the number of e-commerce orders; based on the combined processing probability of different e-commerce orders in the e-commerce platform, determine an average value of the combined processing probabilities of different e-commerce platforms, and use it as the average value of the combined processing probability; The monitoring frequency of obtaining and processing e-commerce orders of the e-commerce platform is determined by the preset monitoring frequency and the average value of the combined processing probability.

[0051] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0052] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0053] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. An e-commerce order management method, characterized in that: Specifically include: Determine similar e-commerce orders in the e-commerce platform based on the acquisition result of the e-commerce orders of the e-commerce platform, and when it is determined that it is not necessary to use the preset monitoring frequency to acquire the e-commerce orders of the e-commerce platform using the order data of the similar e-commerce orders and e-commerce orders in the e-commerce platform, proceed to the next step; Determine the commodities of different e-commerce orders based on the analysis results of the order data of the e-commerce orders in the e-commerce platform, determine the historical order data of the commodity and other commodities in the e-commerce platform in the same order and the same user, and determine the correlation coefficient between the commodity and other commodities and the related commodities among other commodities in combination with the type of the commodity; Obtaining related products of products in different e-commerce orders and correlation coefficients with the related products, and determining the probability of merging the e-commerce orders in combination with similar orders of the e-commerce orders; According to the probability of merging and processing different e-commerce orders in the e-commerce platform, the monitoring frequency of obtaining and processing the e-commerce orders of the e-commerce platform is determined.

2. The e-commerce order management method according to claim 1, characterized in that: The similar e-commerce orders are e-commerce orders that are consistent in either order information or order account on the e-commerce platform.

3. The e-commerce order management method according to claim 1, characterized in that: Determining that it is not necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform specifically includes: Determine the number of e-commerce orders on the e-commerce platform using order data of e-commerce orders on the e-commerce platform; Determine the proportion of the similar e-commerce orders in the e-commerce orders based on the number of similar e-commerce orders in the e-commerce platform; Based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

4. The e-commerce order management method according to claim 3, characterized in that: Determining whether it is necessary to use a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform based on the number of the e-commerce platforms and the proportion of the number of similar e-commerce orders in the number of e-commerce orders, specifically includes: Determine a preset quantity ratio threshold corresponding to the number of the e-commerce platforms based on the number of the e-commerce platforms; According to the preset quantity proportion threshold and the quantity proportion of similar e-commerce orders in the e-commerce orders, it is determined whether it is necessary to adopt a preset monitoring frequency to obtain and process the e-commerce orders of the e-commerce platform.

5. The e-commerce order management method according to claim 3, characterized in that: When the proportion of the number of similar e-commerce orders in the number of e-commerce orders is greater than the preset proportion threshold, it is determined that the preset monitoring frequency needs to be used to obtain and process the e-commerce orders of the e-commerce platform.

6. The e-commerce order management method according to claim 1, characterized in that: The historical order data includes the historical order quantity.

7. The e-commerce order management method according to claim 1, characterized in that: The method for determining the correlation coefficient is: Determine the commodities that need to be used simultaneously with the commodity during use and the commodities that are used by the same objects as the commodity based on the type of the commodity, and use them as basic associated commodities; The historical orders of the product and the basic associated product in the same order and the same user are taken as associated historical orders; The correlation coefficient between the commodity and the basic correlated commodity is determined according to the number of correlated historical orders.

8. The e-commerce order management method according to claim 7, characterized in that: When the other products do not belong to the basic associated products, the association coefficient between the other products and the product is determined to be 0. When the other products belong to the basic associated products, the association coefficient between the product and the basic associated products is determined using the preset association coefficient corresponding to the number of associated historical orders.

9. A computer system comprising: A memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that: when the processor runs the computer program, an e-commerce order management method as described in any one of claims 1-8 is executed.

10. A computer storage medium having a computer program stored thereon, wherein when the computer program is executed in a computer, Instruct a computer to execute an e-commerce order management method as described in any one of claims 1-8.

Images