An intelligent display method, device and equipment for e-commerce warehouse information

Abstract

The present invention relates to the field of warehouse management technology, and specifically to an intelligent display method, device and equipment for e-commerce warehouse information. The method of the present invention obtains the degree of attention and inventory change of a product by analyzing historical access frequency data and historical access frequency data; obtains the update priority sequence of the product according to the degree of attention and inventory change; analyzes the real-time access frequency data of the product on the day and the access frequency data in the same historical time period to obtain the access anomaly of the product on the day; updates the inventory information of the product on the day according to the update priority sequence and the access anomaly. The method of the present invention not only determines the update priority sequence according to the historical data of the product, but also considers the daily emergencies and formulates the final update strategy, thereby improving the accuracy and timeliness of the warehouse information update and avoiding the situation where the inventory information of a hot product is not updated in time, resulting in overselling of the product.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to an intelligent display method, device and equipment for e-commerce warehouse information. Background Art

[0002] Intelligent warehousing information display is an important part of intelligent warehousing. By intuitively presenting massive warehousing information, it is helpful for managers to grasp the warehouse operation status in real time and optimize the decision-making process. With the continuous development of e-commerce, the large-scale coverage of intelligent warehousing systems has realized real-time monitoring of commodity inventory. The e-commerce logistics system and logistics network operations have become more mature and diversified, and the accuracy and real-time nature of logistics information have been continuously improved.

[0003] In the process of intelligent display of e-commerce warehouse information, inventory information may change due to sales, delayed or early supplier delivery, promotional activities, etc. Due to the large number of products on the e-commerce platform, if the system is not updated quickly enough, the inventory information of a hot product will not be updated in time, which will cause a synchronization delay between the inventory information on the e-commerce platform and the inventory data in the actual warehouse, which may lead to overselling of products. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent display method, device and equipment for e-commerce warehousing information.

[0005] According to the first aspect of the embodiment of the present application, a method for intelligently displaying e-commerce warehousing information is provided, and the technical solution adopted is as follows:

[0006] Collect historical inventory data, historical access frequency data and real-time access frequency data of the product;

[0007] Analyze the volatility of the historical access frequency data, and obtain the degree of attention paid to the product by combining the historical access frequency data;

[0008] Analyze the historical inventory data to obtain the inventory change degree of the commodity;

[0009] Obtaining an updated priority sequence of the commodities according to the attention level of the commodities and the inventory change level;

[0010] Analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day;

[0011] The inventory information of the commodities on the day is updated according to the update priority sequence and the access abnormality of the commodities.

[0012] In some embodiments of the present invention, analyzing the volatility of the historical access frequency data and combining the historical access frequency data to obtain the degree of attention paid to the product includes:

[0013] Performing polynomial fitting on the fluctuation curve of the historical access frequency data to obtain an access frequency fitting curve;

[0014] Using Newton's method to obtain all extreme points on the access frequency fitting curve;

[0015] Dividing the access frequency fitting curve into a plurality of sub-curve segments by using the extreme value points, and obtaining slope values ​​of the sub-curve segments;

[0016] Performing a weighted analysis on the slope value of the sub-curve segment to obtain a weighted slope value;

[0017] The degree of attention paid to the product is obtained according to the weighted slope values ​​of all the sub-curve segments in combination with the historical access frequency data.

[0018] In some embodiments of the present invention, analyzing the historical inventory data to obtain the inventory change degree of the commodity includes:

[0019] Dividing the historical inventory data into multiple interval segments by day according to the sub-curve segments;

[0020] Analyze the changes in the historical inventory data of each day contained in each of the interval segments to obtain the inventory change degree of the goods.

[0021] In some embodiments of the present invention, obtaining an update priority sequence of commodities according to the attention level of the commodities and the inventory change level includes:

[0022] According to the attention degree of the product and the inventory change degree, the attention conversion ratio of the product is obtained by combining the historical access frequency data and the historical inventory data;

[0023] The attention conversion ratios of all commodities are sorted to obtain an updated priority sequence of commodities.

[0024] In some embodiments of the present invention, obtaining the attention conversion ratio of a product according to the attention degree of the product and the inventory change degree in combination with the historical access frequency data and the historical inventory data includes:

[0025] According to the attention degree of the commodity and the inventory change degree, the difference between the proportion of the attention degree of the commodity and the proportion of the inventory change degree is analyzed to obtain the degree difference between the attention degree and the inventory change degree;

[0026] According to the historical access frequency data and the historical inventory data, analyzing the difference between the change trend of the historical access frequency data on the sub-curve segment and the change trend of the historical inventory data on the corresponding interval segment, to obtain the data change difference between the historical access frequency data and the historical inventory data;

[0027] According to the degree difference and the data change difference, the attention conversion ratio of the product is obtained.

[0028] In some embodiments of the present invention, the ratio of the mean of the real-time access frequency data of the product on the day to the mean of the access frequency data of the product in the same historical time period is analyzed to obtain the access abnormality of the product on the day.

[0029] In some embodiments of the present invention, updating the inventory information of the commodities on the day according to the update priority sequence and the access abnormality of the commodities includes:

[0030] Preset access exception threshold;

[0031] Comparing the access abnormality degree with the access abnormality threshold;

[0032] Sort the commodities corresponding to the access abnormality degree being greater than or equal to the access abnormality threshold according to the magnitude of the access abnormality degree to obtain a first update sequence of commodities;

[0033] The first update sequence is removed from the update priority sequence to obtain a second update sequence for the product;

[0034] The inventory information of the commodities for the day is updated according to the first updating sequence first and then the second updating sequence.

[0035] In some embodiments of the present invention, the weight of the weighted analysis of the slope value of the sub-curve segment is: the ratio of the exponential function of the position sequence of the sub-curve segment to the sum of the exponential functions of the position sequences of all sub-curve segments in the commodity access frequency fitting curve.

[0036] According to a second aspect of an embodiment of the present application, an intelligent display device for e-commerce warehousing information is provided, the device comprising:

[0037] The commodity data collection module is used to collect the commodity's historical inventory data, historical access frequency data, and real-time access frequency data of the day;

[0038] An update priority sequence module is used to analyze the volatility of the historical access frequency data, and obtain the degree of attention of the product in combination with the historical access frequency data; and analyze the historical inventory data to obtain the degree of inventory change of the product; and obtain an update priority sequence of the product based on the degree of attention and the degree of inventory change of the product in combination with the historical access frequency data and the historical inventory data;

[0039] The access anomaly module is used to analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day;

[0040] The inventory information updating module is used to update the inventory information of the commodities on the day according to the update priority sequence and the access abnormality of the commodities.

[0041] According to a third aspect of an embodiment of the present application, an intelligent display device for e-commerce warehousing information is provided, the device comprising a memory and a processor, wherein:

[0042] The memory is used to store program codes;

[0043] The processor is used to read the program code stored in the memory and execute an intelligent display method of e-commerce warehousing information provided by the first aspect of the embodiment of the present application.

[0044] Compared with the prior art, the intelligent display method, device and equipment of e-commerce warehouse information provided by the present invention has the following beneficial effects:

[0045] The present invention first calculates the attention level and inventory change level of each commodity through the historical inventory data and historical access frequency data of each commodity; then quantifies the attention conversion ratio of each commodity in combination with the attention level and inventory change level of each commodity; obtains the update priority sequence of all commodities according to the attention conversion ratio of each commodity; this update priority sequence not only considers the historical access frequency data but also considers the historical inventory data, so that the update priority sequence is more in line with the actual situation, and provides a basis for accurate and timely warehouse information update. The present invention also obtains the access anomaly of the commodity on the day by analyzing the real-time access frequency data of the commodity on the day and the access frequency data of the commodity in the same historical time period, and then formulates the update strategy of the commodity on the day according to the update priority sequence and the access anomaly of the commodity, and updates the inventory information of the commodity on the day; the update priority sequence is combined with the access anomaly, and the inventory information of the commodity on the day can be updated first due to various sudden reasons, which leads to an abnormal increase in the number of visits to certain commodities, and then other commodities are updated according to the preset update priority sequence, which further improves the accuracy and timeliness of warehouse information update, and avoids the situation that the inventory information of a hot commodity is not updated in time, resulting in overselling of the commodity. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are 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.

[0047] Figure 1 A basic flow chart of an intelligent display method for e-commerce warehousing information provided by an embodiment of the present invention;

[0048] Figure 2 A basic flow chart of a method for obtaining the degree of attention of a product provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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 intelligent display method, device and equipment of e-commerce warehouse information proposed by the present invention, its specific implementation method, structure, characteristics 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.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. Terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such article or device. In the absence of further restrictions, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element.

[0051] The specific scheme of the intelligent display method of e-commerce warehousing information provided by the present invention is described in detail below with reference to the accompanying drawings.

[0052] See also Figure 1 , which shows the basic process of an intelligent display method for e-commerce warehousing information provided by an embodiment of the present invention.

[0053] like Figure 1 As shown, an embodiment of the present invention provides an intelligent display method for e-commerce warehousing information, which specifically includes:

[0054] S100: Collect historical inventory data, historical access frequency data, and real-time access frequency data of the product.

[0055] Get the daily historical inventory data and historical access frequency data of each product from the backend database of the e-commerce company. Assume that a total of N days of historical data are obtained, and there are M products in total. The historical inventory data of each product will decrease as long as an order is placed. The historical inventory data is counted once every minute; the historical access frequency data of each product is counted once every hour. At the same time, the real-time access frequency data of each product on the day is obtained from the backend database of the e-commerce company, and the real-time access frequency data is also counted once every hour.

[0056] S200: Analyze the volatility of historical access frequency data, and obtain the degree of attention paid to the product by combining the historical access frequency data.

[0057] Since customers have different preferences for each product, the corresponding degree of attention paid to each product every day is different. The higher the degree of attention paid to a product, the more attractive it is, and the greater the possibility of subsequent purchase. Therefore, the degree of attention paid to each product is first calculated based on the changes in the historical access frequency data of each product.

[0058] Based on the above analysis, this embodiment analyzes the volatility of historical access frequency data and combines the historical access frequency data to obtain the degree of attention paid to the product.

[0059] See also Figure 2 , which shows the basic process of a method for obtaining the degree of attention of a product provided by an embodiment of the present invention.

[0060] like Figure 2 As shown, the volatility of historical access frequency data is analyzed, and the degree of attention paid to the product is obtained by combining the historical access frequency data. Further steps include:

[0061] S201: Perform polynomial fitting on the fluctuation curve of historical access frequency data to obtain an access frequency fitting curve.

[0062] For For each product, first obtain the fluctuation curve of its historical access frequency data for N days. It should be noted that the historical access frequency data in the fluctuation curve is analyzed on a daily basis, and the historical access frequency data for each day is the average of the 24 historical access frequency data obtained in one day. First, the fluctuation curve of the historical access frequency data is fitted with a polynomial to obtain the access frequency fitting curve.

[0063] S202: Using Newton's method to obtain all extreme value points on the access frequency fitting curve.

[0064] Use Newton's method to obtain all extreme points on the access frequency fitting curve, where the first and last points on the access frequency fitting curve are extreme points by default.

[0065] S203: Divide the access frequency fitting curve into a plurality of sub-curve segments by using extreme value points, and obtain slope values ​​of the sub-curve segments.

[0066] Two extreme points constitute a sub-curve segment, and the access frequency fitting curve is divided into multiple sub-curve segments using extreme points, assuming that a total of I sub-curve segments are constituted. The slope value of the sub-curve segment is obtained, and the slope value of the sub-curve segment is represented by the average value of the slopes of all points on the sub-curve segment. The slope of a point is the average value of the slopes of the line segments formed by the point and its two adjacent points before and after it, where the slopes of the first point and the last point on the sub-curve segment are the slopes of the line segments formed by a point adjacent to them.

[0067] S204: Perform a weighted analysis on the slope values ​​of the sub-curve segments to obtain a weighted slope value.

[0068] The later the sub-curve segment is, the closer it is to the current moment, and the corresponding historical access frequency change trend is more of a reference to the attention level of subsequent products. The exponential function can better reflect this feature. Therefore, the weighted value is the ratio of the exponential function of the sub-curve segment position sequence to the sum of the exponential functions of the position sequences of all sub-curve segments in the product access frequency fitting curve, that is, using To express the The weight of the change trend of each sub-curve segment is used to obtain the weighted slope value.

[0069] S205: Obtain the degree of attention paid to the product according to the weighted slope values ​​of all sub-curve segments in combination with the historical access frequency data.

[0070] According to the weighted slope values ​​of all sub-curve segments and combined with the historical access frequency data, the degree of attention of the product is obtained. The calculation formula for the degree of attention is:

[0071] ;

[0072] In the formula, Indicates The degree of attention paid to a product; Indicates The access frequency fitting curve of the historical access frequency data of products The changing trend of each sub-curve segment is shown in It is represented by the slope value of each sub-curve segment; Indicates The mean of the historical access frequency data of a product over all N days; Indicates The number of sub-curve segments in the access frequency fitting curve of the historical access frequency data of a product; Indicated by natural constant An exponential function with base ; represents the linear normalization function.

[0073] Indicates The weight of the change trend of the sub-curve segment. The later the sub-curve segment is located, the closer it is to the current moment. The change trend of the corresponding historical access frequency data has a greater reference to the attention of subsequent products. The exponential function can better express this feature, so we use To express the The weight of the change trend of the sub-curve segment. The slope value of the sub-curve segment is used to represent the change trend of the sub-curve segment. ,illustrate The larger the value, the The change trend of the sub-curve segments tends to increase. The larger the value, the The larger the mean of the historical access frequency data of a product over all N days is, the larger the mean of the historical access frequency data of a product over all N days is. The larger the value of The larger the value, the The higher the historical access frequency data of a product is and the trend of the historical access frequency data is increasing, the corresponding The more attention a product receives, the higher the level of attention it receives.

[0074] According to the above method, the degree of attention paid to each product can be calculated.

[0075] S300: Analyze historical inventory data to obtain the inventory change degree of the product.

[0076] After calculating the attention level of each product, the attention conversion ratio of each product needs to be considered. The attention conversion ratio indicates the degree to which the attention level of each product is converted into orders. If the attention level of a product is high, the corresponding inventory change is small, which means that the product is visited many times, but the orders formed for various reasons are small, and the corresponding attention conversion ratio of the product is relatively small. Therefore, it is necessary to determine the attention conversion ratio of each product based on the relationship between the attention level of each product and the inventory change.

[0077] Based on the above analysis, this embodiment first obtains the inventory change degree of the goods by analyzing the historical inventory data. Further, first, the historical inventory data is divided into multiple interval segments by day according to the sub-curve segments. Specifically, in step S203, the access frequency fitting curve is divided into multiple sub-curve segments, each sub-curve segment will contain one or more days of historical access frequency data, and the corresponding one or more days contain historical inventory data. Therefore, the historical inventory data within the time of the same sub-curve segment is divided into one interval segment, that is, the historical inventory data can be divided into multiple interval segments by day according to the sub-curve segments. Then, the changes in the daily historical inventory data contained in each interval segment are analyzed to obtain the inventory change degree of the goods. The calculation formula for the inventory change degree is:

[0078] ;

[0079] In the formula, Indicates The degree of inventory change of each product; Indicates the first The number of sub-curve segments corresponding to the access frequency fitting curve of the historical access frequency data of a product can be The historical inventory change data of commodities is divided into intervals; Indicates Products in The first Initial inventory quantity for the day, Indicates Products in The first Inventory quantity at the end of the day; Indicates Products in The total number of days included in the interval; Expressed as a natural constant An exponential function with base .

[0080] The logic is the same as in step S204 above. In the interval The weight value of the inventory change of a commodity is closer to the current moment, and the corresponding weight value is larger, which will not be repeated here. Indicates Products in The first The ratio of the inventory change value of the day to the initial inventory quantity, The larger the value, the Products in The first The greater the value of the daily inventory change degree.

[0081] S400: Obtaining an update priority sequence of the commodities according to the degree of attention paid to the commodities and the degree of inventory change.

[0082] Since the attention conversion ratio indicates the degree to which the attention level of each product is converted into an order, and the update priority sequence of the product is determined by the attention conversion ratio, the present embodiment obtains the update priority sequence of the product according to the attention level of the product and the inventory change level. Further comprising: obtaining the attention conversion ratio of the product according to the attention level of the product and the inventory change level, combining the historical access frequency data and the historical inventory data; sorting the attention conversion ratios of all products to obtain the update priority sequence of the product. Furthermore, the method for obtaining the attention conversion ratio of a product is as follows: according to the degree of attention of the product and the degree of inventory change, the difference between the proportion of the degree of attention of the product and the proportion of the degree of inventory change is analyzed, that is, the difference between the proportion of the degree of attention of the product in the degree of attention of all products and the proportion of the degree of inventory change of the product in the degree of inventory change of all products is analyzed to obtain the degree difference between the degree of attention and the degree of inventory change; and, according to the historical access frequency data and the historical inventory data, the difference between the change trend of the historical access frequency data on the sub-curve segment and the change trend of the historical inventory data on the corresponding interval segment is analyzed to obtain the data change difference between the historical access frequency data and the historical inventory data; then, according to the degree difference and the data change difference, the attention conversion ratio of the product is obtained. The calculation formula for the attention conversion ratio of a product is:

[0083] ;

[0084] In the formula, Indicates The attention conversion ratio of each product; Indicates the total quantity of the product; Indicates The degree of attention paid to a product; Indicates The degree of attention paid to a product; Indicates The degree of inventory change of each product; Indicates The degree of inventory change of each product; Indicates Products in The changing trend of the fluctuation curve composed of all historical inventory data within a period is expressed by the slope of the fluctuation curve, which is expressed by the average value of the slopes of all points on the fluctuation curve; Indicates The frequency fitting curve of the product The changing trend of each sub-curve segment is represented by the slope value of the sub-curve segment, and the slope value of the sub-curve segment is represented by the average of the slopes of all points on the sub-curve; Indicates The number of sub-curve segments in the access frequency fitting curve of the historical access frequency data of a product; Indicates The proportion of attention paid to a product in the attention paid to all products; Indicates The proportion of the inventory change of a commodity in the inventory change of all commodities; Indicated by natural constant An exponential function with base .

[0085] The larger the value, the The proportion of the inventory change of the first commodity in the inventory change of all commodities is greater than that of the The proportion of the attention level of a product in the attention level of all products, corresponding to The higher the order rate of a product, the higher the The better the attention conversion ratio of each product. The larger the value, the The first The trend of all inventory changes in the interval is better than that in the The change trend of the access frequency of the product, corresponding to The better the attention conversion ratio of a product.

[0086] According to the above method, the attention conversion ratio of each product can be calculated.

[0087] Then, the attention conversion ratios of all commodities are sorted in descending order to obtain an updated priority sequence for each commodity.

[0088] S500: Analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day.

[0089] The update priority sequence of the product is obtained through step S400, which can be used as an update strategy for setting the subsequent update of the inventory information of the product on the product page. However, the above update priority sequence of the product is obtained based on historical data (historical inventory data, historical access frequency data), which is of reference significance for the present. However, since the data of the day may have various sudden reasons that lead to an abnormal increase in the number of visits to certain products, there will be certain deviations if the update priority sequence obtained only by relying on historical data as an update strategy.

[0090] Based on the above analysis, this embodiment also obtains the visit abnormality of the product on the day by analyzing the real-time visit frequency data of the product on the day and the visit frequency data of the product in the same historical time period. Further, the ratio of the mean of the real-time visit frequency data of the product on the day and the mean of the visit frequency data of the product in the same historical time period is analyzed to obtain the visit abnormality of the product on the day. The calculation formula of the visit abnormality is:

[0091] ;

[0092] In the formula, Indicates The visit abnormality of a product on that day; Indicates The average of the real-time access frequency data of the product on that day; Indicates The average of the historical access frequency data of a product in the same historical time period. It should be noted that the historical same time period is the time period corresponding to the data obtained on the same day in multiple historical days; for example, at 8 o'clock in the morning of the same day, 9 real-time access frequency data from 0 o'clock to 8 o'clock were obtained. Indicates The average value of the 9 real-time access frequency data obtained for the product on that day, Indicates The average of the historical access frequency data of a product from 0:00 to 8:00 over multiple historical days.

[0093] S600: Update the inventory information of the commodities on the day according to the update priority sequence and the access abnormality of the commodities.

[0094] Combined with the update priority sequence and access anomaly of the products, the inventory information of the products of the day is updated. The specific operation is: preset the access anomaly threshold, which can be set to 2; compare the size relationship between the access anomaly and the access anomaly threshold, and the access anomaly is greater than or equal to the access anomaly threshold, that is, It means the The real-time access frequency value of a product on that day is 1 times greater than the historical access frequency value in the same time period. The higher the update priority of a product, the higher the update priority of the product; therefore, the products corresponding to the access anomaly degree greater than or equal to the access anomaly threshold are sorted according to the size of the access anomaly degree to obtain the first update sequence of the products; the first update sequence is removed from the update priority sequence to obtain the second update sequence of the products; the inventory information of the products of the day is updated according to the first update sequence first and then the second update sequence. According to the above update strategy, the inventory information of the products whose access volume increases abnormally due to various sudden reasons on the day can be updated first, and then other products can be updated according to the preset update priority sequence.

[0095] After calculating the update priority sequence of all products according to the above method, each time the inventory information of the product page in the mall is updated, the inventory information of all products is updated according to the above update strategy in combination with the abnormality of the product's visit on that day. This ensures that the inventory information of key products is updated accurately and timely, avoiding the phenomenon of overselling of products due to slow updates.

[0096] Based on the same inventive concept as the above method, this embodiment also provides an intelligent display device for e-commerce warehousing information, the device comprising:

[0097] The commodity data collection module is used to collect the commodity's historical inventory data, historical access frequency data, and real-time access frequency data of the day;

[0098] The update priority sequence module is used to analyze the volatility of the historical access frequency data, and combine the historical access frequency data to obtain the degree of attention of the product; and analyze the historical inventory data to obtain the degree of inventory change of the product; and according to the degree of attention and the degree of inventory change of the product, combine the historical access frequency data and the historical inventory data to obtain the update priority sequence of the product;

[0099] The access anomaly module is used to analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day;

[0100] The inventory information update module is used to update the inventory information of the commodities on the day according to the update priority sequence and access abnormality of the commodities.

[0101] Based on the same inventive concept as the above method, this embodiment also provides an intelligent display device for e-commerce warehouse information, the device includes a memory and a processor, wherein: the memory is used to store program code; the processor is used to read the program code stored in the memory, and execute the collection of historical inventory data, historical access frequency data and real-time access frequency data of the day; analyze the volatility of the historical access frequency data, and obtain the degree of attention of the product in combination with the historical access frequency data; analyze the historical inventory data to obtain the degree of inventory change of the product; obtain the update priority sequence of the product according to the degree of attention and the degree of inventory change of the product; analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day; update the inventory information of the product on the day according to the update priority sequence and the access anomaly of the product.

[0102] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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.

[0103] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent display method for e-commerce warehousing information, characterized in that: The method comprises: Collect historical inventory data, historical access frequency data and real-time access frequency data of the product; Analyze the volatility of the historical access frequency data, and obtain the degree of attention paid to the product by combining the historical access frequency data; Analyze the historical inventory data to obtain the inventory change degree of the commodity; Obtaining an updated priority sequence of the commodities according to the attention level of the commodities and the inventory change level; Analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day; updating the inventory information of the commodities on the day according to the update priority sequence and the access abnormality of the commodities; Analyze the volatility of the historical access frequency data, and combine the historical access frequency data to obtain the degree of attention paid to the product, including: Performing polynomial fitting on the fluctuation curve of the historical access frequency data to obtain an access frequency fitting curve; Using Newton's method to obtain all extreme points on the access frequency fitting curve; Dividing the access frequency fitting curve into a plurality of sub-curve segments by using the extreme value points, and obtaining slope values ​​of the sub-curve segments; Performing a weighted analysis on the slope value of the sub-curve segment to obtain a weighted slope value; Obtaining the degree of attention paid to the product according to the weighted slope values ​​of all the sub-curve segments and in combination with the historical access frequency data; Analyze the historical inventory data to obtain the inventory change degree of the product, including: Dividing the historical inventory data into multiple interval segments by day according to the sub-curve segments; Analyze the changes in the historical inventory data of each day contained in each of the intervals to obtain the inventory change degree of the commodity; According to the attention degree of the commodity and the inventory change degree, an update priority sequence of the commodity is obtained, including: According to the attention degree of the product and the inventory change degree, the attention conversion ratio of the product is obtained by combining the historical access frequency data and the historical inventory data; Sort the attention conversion ratios of all commodities to obtain an updated priority sequence of commodities; According to the attention degree of the product and the inventory change degree, in combination with the historical access frequency data and the historical inventory data, the attention conversion ratio of the product is obtained, including: According to the attention degree of the commodity and the inventory change degree, the difference between the proportion of the attention degree of the commodity and the proportion of the inventory change degree is analyzed to obtain the degree difference between the attention degree and the inventory change degree; According to the historical access frequency data and the historical inventory data, analyzing the difference between the change trend of the historical access frequency data on the sub-curve segment and the change trend of the historical inventory data on the corresponding interval segment, to obtain the data change difference between the historical access frequency data and the historical inventory data; The attention conversion ratio of the product is obtained according to the degree difference and the data change difference.

2. The intelligent display method of e-commerce warehousing information according to claim 1 is characterized in that: The ratio of the mean of the real-time access frequency data of the product on the day to the mean of the access frequency data of the product in the same historical time period is analyzed to obtain the access anomaly of the product on the day.

3. The intelligent display method of e-commerce warehousing information according to claim 1 is characterized in that: The inventory information of the commodity of the day is updated according to the update priority sequence and the access abnormality of the commodity, including: Preset access exception threshold; Comparing the access abnormality degree with the access abnormality threshold; Sort the commodities corresponding to the access abnormality degree being greater than or equal to the access abnormality threshold according to the magnitude of the access abnormality degree to obtain a first update sequence of commodities; The first update sequence is removed from the update priority sequence to obtain a second update sequence for the product; The inventory information of the commodities for the day is updated according to the first updating sequence first and then the second updating sequence.

4. The intelligent display method of e-commerce warehousing information according to claim 1 is characterized in that: The weight of the weighted analysis of the slope value of the sub-curve segment is: the ratio of the exponential function of the position sequence of the sub-curve segment to the sum of the exponential functions of the position sequences of all sub-curve segments in the commodity access frequency fitting curve.

5. An intelligent display device for e-commerce warehouse information, characterized in that: The device comprises: The commodity data collection module is used to collect the commodity's historical inventory data, historical access frequency data, and real-time access frequency data of the day; An update priority sequence module is used to analyze the volatility of the historical access frequency data, and obtain the degree of attention of the product in combination with the historical access frequency data; and analyze the historical inventory data to obtain the degree of inventory change of the product; and obtain an update priority sequence of the product based on the degree of attention and the degree of inventory change of the product in combination with the historical access frequency data and the historical inventory data; The access anomaly module is used to analyze the real-time access frequency data of the product on the day and the access frequency data of the product in the same historical time period to obtain the access anomaly of the product on the day; An inventory information updating module, used to update the inventory information of the commodities of the day according to the update priority sequence and the access abnormality of the commodities; Analyze the volatility of the historical access frequency data, and combine the historical access frequency data to obtain the degree of attention paid to the product, including: Performing polynomial fitting on the fluctuation curve of the historical access frequency data to obtain an access frequency fitting curve; Using Newton's method to obtain all extreme points on the access frequency fitting curve; Dividing the access frequency fitting curve into a plurality of sub-curve segments by using the extreme value points, and obtaining slope values ​​of the sub-curve segments; Performing a weighted analysis on the slope value of the sub-curve segment to obtain a weighted slope value; Obtaining the degree of attention paid to the product according to the weighted slope values ​​of all the sub-curve segments and in combination with the historical access frequency data; Analyze the historical inventory data to obtain the inventory change degree of the product, including: Dividing the historical inventory data into multiple interval segments by day according to the sub-curve segments; Analyze the changes in the historical inventory data of each day contained in each of the intervals to obtain the inventory change degree of the commodity; According to the attention degree of the commodity and the inventory change degree, an update priority sequence of the commodity is obtained, including: According to the attention degree of the product and the inventory change degree, the attention conversion ratio of the product is obtained by combining the historical access frequency data and the historical inventory data; Sort the attention conversion ratios of all commodities to obtain an updated priority sequence of commodities; According to the attention degree of the product and the inventory change degree, in combination with the historical access frequency data and the historical inventory data, the attention conversion ratio of the product is obtained, including: According to the attention degree of the commodity and the inventory change degree, the difference between the proportion of the attention degree of the commodity and the proportion of the inventory change degree is analyzed to obtain the degree difference between the attention degree and the inventory change degree; According to the historical access frequency data and the historical inventory data, analyzing the difference between the change trend of the historical access frequency data on the sub-curve segment and the change trend of the historical inventory data on the corresponding interval segment, to obtain the data change difference between the historical access frequency data and the historical inventory data; According to the degree difference and the data change difference, the attention conversion ratio of the product is obtained.

6. An intelligent display device for e-commerce warehouse information, characterized in that: The device comprises a memory and a processor, wherein: The memory is used to store program codes; The processor is configured to read the program code stored in the memory and execute the method according to any one of claims 1 to 4.

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