A cross-platform multi-channel order information intelligent analysis method and system
Through the intelligent analysis method of cross-platform multi-channel order information, the problem of difficulty for enterprises to analyze multi-channel order information is solved, accurate prediction of the degree of hot sales of goods and inventory adjustments are achieved, and operational efficiency and customer satisfaction are improved.
Patent Information
- Application Number
- CN202411143193.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-20
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2044-08-20
AI Technical Summary
It is difficult for enterprises to effectively analyze multi-channel order information through existing technology, resulting in inventory backlogs and reduce operational efficiency and customer satisfaction.
Using cross-platform multi-channel order information intelligent analysis method, through obtaining and analyzing order information, including product sales volume, price, type and replenishment time, product clustering, explosive order parameter calculation, sales stability evaluation and hot sales index prediction , adjust inventory and promotion strategies.
It realizes accurate prediction of the degree of hot sales of goods and timely adjustment of inventory, improves operational efficiency and customer satisfaction, and optimizes marketing and pricing strategies.
Smart Images
Figure CN118674529B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and in particular to a method and system for intelligently analyzing cross-platform and multi-channel order information. Background Art
[0002] With the continuous development of technologies such as cloud computing, big data, and artificial intelligence, an order management system that can operate across platforms and multiple channels has been built. These technologies enable the system to collect, analyze, and process order data from various channels in real time, providing decision support for enterprises, helping them to better manage orders, increase delivery speed, and reduce error rates, thereby improving customer satisfaction.
[0003] Since order information comes from a variety of sources and the types of order information are complex and changeable, it is difficult for companies to analyze user preferences and adjust inventory levels in a timely manner, resulting in inventory backlogs, which in turn reduces operational efficiency and customer satisfaction. Summary of the invention
[0004] The present invention provides a cross-platform multi-channel order information intelligent analysis method and system to solve the existing problems.
[0005] The cross-platform multi-channel order information intelligent analysis method and system of the present invention adopts the following technical solutions:
[0006] An embodiment of the present invention provides a method for intelligently analyzing cross-platform multi-channel order information, the method comprising the following steps:
[0007] Obtain order information across multiple platforms and channels within the collection time and the replenishment time of each product. Order information includes product sales, product price, product type, order quantity, and products in each order; cluster products according to product type to obtain several clusters;
[0008] Record the time from the first sale to the end of sale of each product every day as a sales duration period of each product, and obtain several sales duration periods of each product; obtain the hot order parameters of each product in each sales duration period according to the sales volume of each product in the sales duration period; update the sales duration period according to the hot order parameters of each product in each sales duration period, and obtain each updated sales time period of each product; obtain the sales stability of each product according to the sales volume of each product in each updated sales time period;
[0009] According to the sales stability of each product and the sales volume of the same type of products in the cluster to which the product belongs, the hot-selling index of each product is obtained every day; according to the sales stability of each product and the sales volume of different types of products in the same order, the hot-selling evaluation of each product among all products is obtained every day;
[0010] The popularity of each product is obtained based on its daily hot-selling index, its daily hot-selling rating among all products, and its price. The popularity of each product on the next day is predicted based on its current popularity, and the products in cross-platform and multi-channel orders are adjusted based on the next day's popularity.
[0011] Furthermore, obtaining the hot order parameters of each product in each sales duration period includes:
[0012] No. Products in The calculation formula for the burst order parameter of a sales duration period is:
[0013]
[0014] in, Indicates Products in The sales burst parameters for the duration of the sales period; Indicates Products in The length of time that each sales period lasts; Indicates Products in Unit sales for a sales duration period; Indicates The average sales volume of a product in all sales duration periods; represents the linear normalization function; represents the absolute value function.
[0015] Furthermore, the specific method for obtaining the unit sales volume during the sales duration period is as follows:
[0016] The first Products in The sales volume of the first sales duration period is The ratio of the duration of the sales duration periods is recorded as Products in The unit sales volume for a sales duration period.
[0017] Furthermore, the specific method for obtaining the average sales volume of all sales duration periods is as follows:
[0018] The first The ratio of the sales volume of a product in all sales duration periods to the collection time is recorded as The average sales volume of a product during the entire collection process.
[0019] Furthermore, the specific method for obtaining each updated sales time period of each commodity is as follows:
[0020] When Products in When the burst order parameter of the sales duration period is greater than or equal to the preset burst order threshold, the Products in The sum of the sales duration and the replenishment time is recorded as The first An updated sales period;
[0021] When Products in When the burst order parameter of the sales duration period is less than the preset burst order threshold, Products in The sales duration period is recorded as The first An updated sales period.
[0022] Furthermore, the specific method of obtaining the sales stability of each commodity is as follows:
[0023] No. The calculation formula for the sales stability of a commodity is:
[0024]
[0025] in, Indicates The sales stability of individual products; Indicates the collection time; Indicates The first the length of the updated sales period; Indicates The first Unit sales for the updated sales time period; Indicates the number of updated sales time periods; represents the linear normalization function.
[0026] Furthermore, the specific method of obtaining the hot selling index of each commodity every day is as follows:
[0027] No. Products in The calculation formula for the hot selling index of the day is:
[0028]
[0029] in, Indicates Products in Hot sales index of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Included in the day The number of orders for other products in the cluster to which the product belongs; Indicates Days including The quantity of all items in an order of items; Indicated in days, including The quantity of all products in the orders of other products in the cluster to which the product belongs; represents the linear normalization function.
[0030] Furthermore, the specific method of obtaining the best-selling evaluation of each product among all products every day is:
[0031] No. Products are ranked first among all products The calculation formula for the hot sales rating of the day is:
[0032]
[0033] in, Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Days including The quantity of all items in an order of items; Indicates Number of orders per day; Indicates The number of items in all orders for the day; represents the linear normalization function.
[0034] Furthermore, the specific method for obtaining the popularity of each commodity every day is as follows:
[0035] No. Products in The formula for calculating the popularity of the day is:
[0036]
[0037] in, Indicates Products in How popular the day is; Indicates Products in Hot sales index of the day; Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates The preset price and sales volume influencing parameters of each product.
[0038] The present invention also proposes a cross-platform multi-channel order information intelligent analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned cross-platform multi-channel order information intelligent analysis method.
[0039] The beneficial effects of the technical solution of the present invention are: obtaining order information across multiple platforms and channels within the collection time and the replenishment time of each product, the order information including product sales, product price, product type, order quantity, and products in each order, clustering the products according to product type to obtain several clustering clusters, obtaining several sales duration periods for each product, and obtaining the explosion-order parameter of each product in each sales duration period according to the sales volume of each product in the sales duration period, with the purpose of confirming whether the product is in an explosion-order phenomenon during the time period with no active sales record, resulting in insufficient inventory and the need to stop sales for replenishment, or whether the product itself has no significant sales performance during the sales period, and then adjusting each sales duration period of each product; updating the sales duration period according to the explosion-order parameter of each product in each sales duration period, obtaining each updated sales time period of each product, and obtaining the explosion-order parameter of each product in each updated sales time period according to the sales volume of each product in each updated sales time period. The purpose is to determine the possibility that the sales status of the product is good; according to the sales stability of each product and the sales volume of the same type of products in the cluster to which the product belongs, the hot-selling index of each product on a daily basis is obtained; according to the sales stability of each product and the sales volume of different types of products in the same order, the hot-selling evaluation of each product among all products on a daily basis is obtained; by calculating the hot-selling index and hot-selling evaluation of each product, its popularity and competitiveness in the market can be understood, and then the marketing and pricing strategies can be adjusted; according to the hot-selling index of each product on a daily basis, the hot-selling evaluation of each product among all products on a daily basis and the product price, the hot-selling degree of each product on a daily basis can be obtained, and the inventory and promotion strategies can be adjusted in time to maximize sales revenue and market share; the hot-selling degree of each product on the next day is predicted by the hot-selling degree of each product on the current day, and the products in the cross-platform and multi-channel orders are adjusted according to the hot-selling degree of the next day, so as to help enterprises adjust inventory in time and improve operational efficiency and customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] 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.
[0041] Figure 1 The present invention is a flowchart of the steps of a cross-platform multi-channel order information intelligent analysis method. DETAILED DESCRIPTION
[0042] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the cross-platform multi-channel order information intelligent analysis method and system proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.
[0043] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
[0044] The specific scheme of a cross-platform multi-channel order information intelligent analysis method and system provided by the present invention is described in detail below with reference to the accompanying drawings.
[0045] See also Figure 1 , which shows a flowchart of a method for intelligent analysis of cross-platform multi-channel order information provided by an embodiment of the present invention, the method comprising the following steps:
[0046] Step S001: Obtain order information across multiple platforms and channels within the collection time and the replenishment time of each product. The order information includes product sales, product price, product type, order quantity, and products in each order; cluster the products according to product type to obtain several clusters.
[0047] Cross-platform and multi-channel order information is collected. In this embodiment, every other day is a collection moment. Order information and the time period with no sales of goods are collected once at each moment. Cross-platform and multi-channel order information of all days in a quarter is collected. The order information includes: product sales, product price, product type, order quantity, and the number of goods in each order.
[0048] The elbow method obtains the optimal K value, and clusters all product types through the K-means clustering algorithm, clustering the same type of products into one category to obtain several clusters. The elbow method and K-means clustering algorithm are well-known technologies, and the specific methods are not introduced here.
[0049] Step S002: Record the time from the first sale to the discontinuation of sale of each commodity every day as a sales duration period for each commodity, and obtain several sales duration periods for each commodity; obtain the hot-order parameters of each commodity in each sales duration period based on the sales volume of each commodity in the sales duration period; update the sales duration period based on the hot-order parameters of each commodity in each sales duration period, and obtain each updated sales time period for each commodity; obtain the sales stability of each commodity based on the sales volume of each commodity in each updated sales time period.
[0050] It should be noted that the commodity order information of enterprises on each platform and channel is changeable, users may buy multiple commodities at the same time, and the sales volume of commodity types corresponding to each order on each platform and each channel is also different. For different commodity types, the sales status on each platform and each channel is different. For each commodity, there may be two different situations during the period when there is no active sales record: one is that the commodity has a burst of orders during the sales period, that is, the sales volume is much higher than usual, resulting in insufficient inventory, and it is necessary to stop sales for replenishment; the other is that the commodity itself has no significant sales performance during the sales period, which indicates that the possibility of insufficient inventory is low. When enterprises analyze the sales of different types of commodities, the stability of commodity sales is a key factor. For commodity types with good sales stability, there is a possibility that their corresponding sales status is good. Therefore, this embodiment first determines whether there is a possibility of a burst of orders for the commodity during its sales duration, and then obtains the sales stability of each commodity in each channel of each platform.
[0051] Specifically, the time from the first sale to the end of sale of each product is recorded as a sales duration period of each product, and several sales duration periods of each product are obtained during the collection time. Products in The unit sales volume of the sales duration period is the same as that of the The difference in the average sales volume of the product in all sales duration periods, combined with the The length of the sales duration period, get the Products in The sales burst parameters for a sales duration period.
[0052] No. Products in The calculation formula for the burst order parameter of a sales duration period is:
[0053]
[0054] in, Indicates Products in The sales burst parameters for the duration of the sales period; Indicates Products in The length of time that each sales period lasts; Indicates Products in The unit sales volume for the sales duration period, the Products in The method for obtaining the unit sales volume for a sales duration period is as follows: Products in The sales volume of the first sales duration period is The ratio of the duration of the sales duration periods is recorded as Products in Unit sales for a sales duration period; Indicates The average sales volume of the product in all sales duration periods, The average sales volume of a product in all sales duration periods is obtained by: The ratio of the sales volume of a product in all sales duration periods to the collection time is recorded as The average sales volume of a product during the entire collection process; Represents a linear normalization function that normalizes the data value to within the interval; represents the absolute value function.
[0055] When Products in The unit sales volume of the first sales duration period is much greater than that of the The average sales volume of a product in all sales duration periods, and the sales duration is long, then The period of no sales after a sales period is more likely to be a time of replenishment.
[0056] The default threshold for orders in this embodiment , which is described as an example, can be set to other values in other implementations, and this embodiment does not limit it. Products in The burst order parameter of the sales duration period is greater than or equal to the preset burst order threshold , explain The first sales duration period is the time for replenishment after the product is sold out. For enterprises, if there is a lot of inventory, there will still be a lot of users placing orders during the replenishment period. Products in The replenishment time after the burst of orders within the sales duration period is also regarded as the sales duration. Products in The sum of the sales duration and the replenishment time is recorded as The first updated sales time period; when the Products in The burst order parameter of the sales duration period is less than the preset burst order threshold , explain The first sales duration period is the case where the product itself has no significant sales performance during the sales period. The sales duration period remains unchanged. Products in The sales duration period is recorded as The first An updated sales period.
[0057] Based on the sales performance of each product at different times, the sales stability of each product is obtained. The calculation formula for the sales stability of a commodity is:
[0058]
[0059] in, Indicates The sales stability of individual products; Indicates the collection time; Indicates The first the length of the updated sales period; Indicates The first Unit sales for the updated sales time period; Indicates the number of updated sales time periods; Represents a linear normalization function that normalizes the data value to Within the range.
[0060] It means that the sales volume is time-weighted. When a product is sold for a long time within a certain sales period and the sales volume is considerable, it means that the sales stability of the product is better. The larger the value, the The higher the sales stability of a product, the more balanced and stable the sales volume in each updated sales period is. The smaller the value, the greater the fluctuation of sales volume in the updated sales period and the poorer the stability.
[0061] Similarly, obtain the sales stability of each product.
[0062] Step S003: According to the sales stability of each commodity and the sales quantity of the same type of commodities in the cluster to which the commodity belongs, obtain the hot-selling index of each commodity on a daily basis; according to the sales stability of each commodity and the sales quantity of different types of commodities in the same order, obtain the hot-selling evaluation of each commodity on a daily basis among all commodities.
[0063] It should be noted that the sales stability of each product can be obtained according to the above steps. To accurately evaluate the sales popularity of the product, it is necessary to further analyze the sales of different orders. Since users may take various purchasing behaviors, it is necessary to compare the sales of different orders in the same time period to derive the daily hot-selling index of each product.
[0064] No. Products in The calculation formula for the hot selling index of the day is:
[0065]
[0066] in, Indicates Products in Hot sales index of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Included in the day The number of orders for other products in the cluster to which the product belongs; Indicates Days including The quantity of all items in an order of items; Indicated in days, including The quantity of all products in the orders of other products in the cluster to which the product belongs; Represents a linear normalization function that normalizes the data value to Within the range.
[0067] Indicates Days The order quantity of the cluster to which the product belongs is the same as that of the Days including The ratio of the quantity of all orders for a product. The larger the value, the The higher the proportion of orders for a product in the cluster to which it belongs, the higher the sales concentration of the product in the cluster. Indicated in days, including The quantity of all products in the orders of other products in the cluster to which the product belongs is Days including The ratio of the quantity of all commodities in the order of commodities. The larger the value, the higher the quantity of commodities. The higher the proportion of a commodity in the number of commodities in the cluster to which it belongs, the higher the purchase concentration of the commodity in the cluster. The larger the value, the Products in The more popular it is.
[0068] To judge the popularity index of each product, we should also consider the relationship between the number of orders containing the product and the number of products in the orders containing the product, and analyze the popularity evaluation of the product among all products every day.
[0069] No. Products are ranked first among all products The calculation formula for the hot sales rating of the day is:
[0070]
[0071] in, Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Days including The quantity of all items in an order of items; Indicates Number of orders per day; Indicates The number of items in all orders for the day; Represents a linear normalization function that normalizes the data value to Within the range.
[0072] Use the Days including The quantity of all orders for the item is the same as the quantity of the The ratio of the number of orders per day , No. Days including The quantity of all items in the order of item The ratio of the number of items in all orders for the day , and Days including The average sales stability of all products in the order of products Calculation, when including The number of all orders for a product and the number of products ordered in the order account for a large proportion of the total, which means that the product is more popular among users among all the products of the company, and it can also mean that the product has a high sales evaluation.
[0073] Step S004: Obtain the daily popularity of each commodity based on its daily popularity index, its daily popularity rating among all commodities, and its price; predict the popularity of each commodity on the next day based on its popularity on the current day, and adjust the commodities in the cross-platform multi-channel orders based on its popularity on the next day.
[0074] It should be noted that in the above analysis, the hot-selling index of each product in the order has been determined. However, the price of the product is a key factor in the purchase decision, and the impact of the price factor needs to be further considered. The calculation of the hot-selling index already includes the sales stability of the product and the ratio of the number of orders to the number of products in the cluster to which the product belongs. These factors can understand the sales performance of the product in the internal market, but do not take into account the impact of price on consumer purchasing behavior. In order to more comprehensively evaluate the hot-selling degree of the product on a daily basis, it is necessary to further combine the price to judge the hot-selling degree of the product on a daily basis.
[0075] Specifically, the average price of the products in the order type corresponding to each product and the average price of all products are considered, and the price is combined with the hot-selling index to obtain the hot-selling degree of the product every day.
[0076] No. Products in The formula for calculating the popularity of the day is:
[0077]
[0078] in, Indicates Products in How popular the day is; Indicates Products in Hot sales index of the day; Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates The preset price and sales volume influencing parameters of the product are obtained in the following manner: When the average price of all items in an order of items is greater than or equal to the average price of all items, Take the example to describe; when the content contains When the average price of all items in an order of items is less than the average price of all items, Let's take this as an example.
[0079] Similarly, obtain the daily sales popularity of each product.
[0080] The popularity of each commodity every day is used as the input data of the ARIMA forecasting model, and combined with cross-platform multi-channel order information for intelligent analysis, the future popularity of each commodity is predicted, helping enterprises understand which commodities may have higher sales in the next period of time. Based on historical data and the prediction results of the ARIMA forecasting model, the popularity of some commodity combinations can be identified. For example, if commodity A and commodity B are often purchased together in past data, then when the user purchases commodity A, the system can intelligently recommend commodity B to increase the chance of matching sales. Integrating order data from different sales channels can provide a more comprehensive understanding of the overall sales of commodities. Using this data, the popularity and sales trends of commodities in different channels can be analyzed to help enterprises optimize inventory management and replenishment strategies.
[0081] Based on the popularity of goods predicted by the ARIMA model, companies can adjust the inventory level of goods in a timely manner. Predicting higher sales volumes can guide supply chain management, avoid losing sales opportunities due to insufficient supply of goods, and reduce the waste of funds caused by excessive inventory.
[0082] So far, the present invention is completed.
[0083] To sum up, in an embodiment of the present invention, the present invention also provides a cross-platform multi-channel order information intelligent analysis system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the aforementioned cross-platform multi-channel order information intelligent analysis method.
[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A cross-platform multi-channel order information intelligent analysis method, characterized in that: The method comprises the following steps: Obtain order information across multiple platforms and channels within the collection time and the replenishment time of each product. Order information includes product sales, product price, product type, order quantity, and products in each order; cluster products according to product type to obtain several clusters; Record the time from the first sale to the end of sale of each product every day as a sales duration period of each product, and obtain several sales duration periods of each product; obtain the hot order parameters of each product in each sales duration period according to the sales volume of each product in the sales duration period; update the sales duration period according to the hot order parameters of each product in each sales duration period, and obtain each updated sales time period of each product; obtain the sales stability of each product according to the sales volume of each product in each updated sales time period; According to the sales stability of each product and the sales volume of the same type of products in the cluster to which the product belongs, the hot-selling index of each product is obtained every day; according to the sales stability of each product and the sales volume of different types of products in the same order, the hot-selling evaluation of each product among all products is obtained every day; Obtain the popularity of each product on a daily basis based on its daily popularity index, its popularity rating among all products, and its price; predict the popularity of each product on the next day based on its popularity on the current day, and adjust the products in cross-platform and multi-channel orders based on its popularity on the next day; The method of obtaining the hot order parameters of each product in each sales duration period includes: No. Products in The calculation formula for the burst order parameter of a sales duration period is: in, Indicates Products in The sales burst parameters for the duration of the sales period; Indicates Products in The length of time that each sales period lasts; Indicates Products in Unit sales for a sales duration period; Indicates The average sales volume of a product in all sales duration periods; represents the linear normalization function; represents the absolute value function; The specific method for obtaining the unit sales volume during the sales duration period is as follows: The first Products in The sales volume of the first sales duration period is The ratio of the duration of the sales duration periods is recorded as Products in Unit sales for a sales duration period; The specific method for obtaining the sales stability of each commodity is as follows: No. The calculation formula for the sales stability of a commodity is: in, Indicates The sales stability of individual products; Indicates the collection time; Indicates The first the length of the updated sales period; Indicates The first Unit sales for the updated sales time period; Indicates the number of updated sales time periods; represents the linear normalization function; The specific method for obtaining the hot selling index of each commodity every day is as follows: No. Products in The calculation formula for the hot selling index of the day is: in, Indicates Products in Hot sales index of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Included in the day The number of orders for other products in the cluster to which the product belongs; Indicates Days including The quantity of all items in an order of items; Indicated in days, including The quantity of all products in the orders of other products in the cluster to which the product belongs; represents the linear normalization function; The specific method of obtaining the best-selling evaluation of each product among all products every day is as follows: No. Products are ranked first among all products The calculation formula for the hot sales rating of the day is: in, Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates Days including The mean of the sales stability of all products in all orders of a product; Indicates Days including The number of all orders for a product; Indicates Days including The quantity of all items in an order of items; Indicates Number of orders per day; Indicates The number of items in all orders for the day; represents the linear normalization function; The specific method for obtaining the popularity of each product every day is as follows: No. Products in The formula for calculating the popularity of the day is: in, Indicates Products in How popular the day is; Indicates Products in Hot sales index of the day; Indicates Products are ranked first among all products Hot sales evaluation of the day; Indicates The preset price and sales volume influencing parameters of each product.
2. According to claim 1, a cross-platform multi-channel order information intelligent analysis method is characterized in that: The specific method for obtaining the average sales volume of all sales duration periods is as follows: The first The ratio of the sales volume of a product in all sales duration periods to the collection time is recorded as The average sales volume of a product during the entire collection process.
3. According to claim 1, a cross-platform multi-channel order information intelligent analysis method is characterized in that: The specific method for obtaining each updated sales time period of each commodity is as follows: When Products in When the burst order parameter of the sales duration period is greater than or equal to the preset burst order threshold, the Products in The sum of the sales duration and the replenishment time is recorded as The first An updated sales period; When Products in When the burst order parameter of the sales duration period is less than the preset burst order threshold, Products in The sales duration period is recorded as The first An updated sales period.
4. A cross-platform multi-channel order information intelligent analysis system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a cross-platform multi-channel order information intelligent analysis method as described in any one of claims 1 to 3 are implemented.