CN115760223A - Intelligent garment e-commerce monitoring and analyzing system based on data analysis - Google Patents
Intelligent garment e-commerce monitoring and analyzing system based on data analysis Download PDFInfo
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Abstract
The invention relates to the technical field of garment e-commerce monitoring and analysis, and particularly discloses a garment e-commerce intelligent monitoring and analysis system based on data analysis, which comprises a shop information acquisition module, a customer purchase information acquisition module, a customer basic information acquisition module, a customer historical information acquisition module, a customer return probability evaluation module, a shop garment sales volume estimation module, a warehouse replenishment analysis module and an e-commerce information base.
Description
Technical Field
The invention belongs to the technical field of garment e-commerce monitoring and analysis, and relates to a garment e-commerce intelligent monitoring and analysis system based on data analysis.
Technical Field
In recent years, with the rapid development of economy and the rapid improvement of scientific level, the garment e-commerce industry is more and more popular, and the way of buying female garments by e-commerce is gradually accepted by the public, but as consumers often have a goods return problem, in order to guarantee the safety of e-commerce female garments, the monitoring and analysis of e-commerce goods of female garments is more and more important.
At present, female garment e-commerce goods monitoring mainly aims at analyzing sales volume of female garment e-commerce, and return information of the female garment e-commerce is not combined for analysis, so that it is obvious that the current female garment e-commerce monitoring and analysis has the following defects: 1. at present, the demand of goods return possibly generated by each customer is not analyzed, the demand of follow-up goods replenishment of the shop cannot be guaranteed to a certain extent, the phenomenon that the clothing is overstocked in the shop due to too much follow-up goods replenishment of the shop is easily caused, and the normal operation of the follow-up shop cannot be guaranteed.
2. The historical purchase information of each customer is not analyzed at present, and then target clothing return probability coefficients corresponding to each customer cannot be obtained, the return anticipation ability of the shop is reduced to a certain extent, adverse effects on the shop are easily caused, the sales level of the shop is not improved easily, potential influences are easily caused, and the after-sales handling ability of the shop is reduced.
Disclosure of Invention
In view of the problems existing in the prior art, the invention provides an intelligent garment e-commerce monitoring and analyzing system based on data analysis, which is used for solving the technical problems.
In order to achieve the above objects and other objects, the present invention adopts the following technical solutions: the invention provides a garment e-commerce intelligent monitoring and analyzing system based on data analysis, which comprises a shop information acquisition module, a customer purchase information acquisition module, a customer basic information acquisition module, a customer historical information acquisition module, a customer return probability evaluation module, a shop garment sales estimation module, a warehouse replenishment analysis module and an e-commerce information base.
The store information acquisition module is used for acquiring the inventory corresponding to the target clothes of the target electric store.
And the customer purchase information acquisition module is used for acquiring the purchase information of each customer at the target electric shop at the current time.
And the customer basic information acquisition module is used for extracting basic information corresponding to each customer from the e-commerce information base, wherein the basic information comprises height and weight.
And the customer history information acquisition module is used for extracting the history purchase information corresponding to each customer in the target electric shop from the electric business information base.
And the customer return probability evaluation module is used for evaluating and obtaining a target clothing return probability coefficient corresponding to each customer according to the purchase information and the historical purchase information of each customer on the target electric shop at the current time.
The shop clothing sales estimation module is used for extracting historical sales information of the target electronic shop from the e-commerce information base, and further analyzing to obtain the target clothing pre-sales amount corresponding to the current month of the target electronic shop.
The warehouse replenishment analysis module is used for comprehensively judging and analyzing the replenishment demand of the target E-commerce shop according to the target garment return probability coefficient corresponding to each customer, the target garment pre-sale amount corresponding to the target E-commerce shop and the stock corresponding to the target garment.
In a preferred technical solution of the present application, the purchase information of each customer at the target electric shop at the present time includes a number of the target clothes to be purchased, a size corresponding to the target clothes to be purchased, a price corresponding to the target clothes, and a style type corresponding to the target clothes.
In a preferred embodiment of the present application, the historical purchasing information includes a historical purchasing clothing style type and a historical purchasing clothing size.
In a preferred technical solution of the present application, the target garment return probability coefficient corresponding to each customer is obtained by the evaluation, and the specific evaluation process is as follows: a1, obtaining purchase information and historical purchase information of each customer at a target electric shop at the current time, further obtaining a target clothes size and style type purchased at the target electric shop at the current time by each customer, and simultaneously obtaining a historical purchased clothes style type and size of each customer.
A2, comparing the target clothing size corresponding to the current time of each customer at the target electric shop with the historical purchased clothing size, if the target clothing size purchased by a certain customer at the current time at the target electric shop is not consistent with the historical purchased clothing size, judging that the target clothing purchased by the customer at the current time at the target electric shop is pre-returned clothing, and further estimating the return coefficient alpha' of the customer for the clothing, otherwise, judging that the target clothing purchased by the customer at the current time at the target electric shop is the pre-returned clothingThe clothing purchased by the customer at the target electric shop is determined as non-return clothing, and the return estimation coefficient alpha' of the customer for the target clothing is determined, so as to obtain the return estimation coefficient alpha of the preliminary size corresponding to the target clothing purchased by the customer at the target electric shop at the current time s ，α s Take the values alpha ' or alpha ', and alpha '>α', where s denotes a number corresponding to each customer, and s =1,2.
A3, purchasing information of each marked customer according to the target electric shop history extracted from the E-commerce information base, further obtaining the height, weight and size of each marked customer purchased by the target electric shop history, comparing the height and weight of each marked customer with the height and weight of each customer, comparing the height and weight of each customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each customer, comparing the height and weight of each marked customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each marked customer, comparing the weight grade corresponding to each customer with the weight grade corresponding to each marked customer, and if the weight grade corresponding to a certain customer is consistent with the weight grade corresponding to a certain marked customer, judging that the height and the weight of the customer and the marked customer belong to the same grade, further obtaining the number corresponding to each marked customer with the height and the weight of each customer belonging to the same grade, marking each marked customer with the height and the weight of each customer as each reference customer, further obtaining the goodness of the size corresponding to each reference customer, mutually comparing and screening the goodness of the size corresponding to each reference customer, further screening to obtain the size with the front goodness arrangement, comparing the size with the front goodness arrangement with the size of the target clothing corresponding to the current time of each customer at the target electric shop, and further obtaining the size return evaluation coefficient beta corresponding to the current time of each customer at the target electric shop according to the analysis mode of the preliminary return evaluation coefficient corresponding to the current time of each customer at the target electric shop to purchase the target clothing s 。
In a preferred embodiment of the present application, the evaluation is performedThe specific evaluation process of the target clothing return probability coefficient corresponding to each customer is as follows: b1, comparing the target clothing style type corresponding to the current target shop of each customer with the historical purchased clothing style type, if the target clothing style type corresponding to the current target shop of a certain customer is not matched with the historical purchased clothing style type, judging that the target clothing purchased by the current customer at the current target shop of the target electric shop is required returned clothing, estimating the return estimation index x ' of the customer for the clothing, and if not, judging that the target clothing purchased by the current customer at the current target shop of the target electric shop is not required returned clothing, estimating the return estimation index x ' of the customer for the target clothing, and thus obtaining the size return analysis index x ' corresponding to the target clothing purchased by the current customer at the target electric shop of the customer at the current time s Wherein χ is χ 'or χ'>χ″。
B2, further using a calculation formula
Calculating to obtain a target clothing return probability coefficient delta corresponding to each customer s Wherein a1 and a2 are respectively expressed as weighting factors corresponding to the size and style of the customer, a3 and a4 are respectively expressed as influence factors corresponding to the preliminary size return evaluation coefficient and the size return evaluation coefficient, and a1>a2。
In a preferred technical solution of the present application, the historical sales information of the target electronic shop includes a target clothing sales volume for each month of the past year.
In a preferred technical scheme of the application, the target pre-sale amount of the clothes corresponding to the current month of the target electronic shop is obtained through analysis, and the specific analysis process is as follows: c1, extracting target clothing sales volume of each month of the past year of the target E-commerce shop according to historical sales information of the target E-commerce shop, substituting the target clothing sales volume of each month of the past year of the target E-commerce shop into a clothing sales trend model diagram to obtain a sales trend corresponding to the target clothing sales volume of each month of the past year of the target E-commerce shop, and further utilizing a calculation formula
Calculating to obtain the sales increase rate epsilon corresponding to each past year of the target electric shop i Wherein i represents a number corresponding to each previous year, i =1, 2.. Eta.. J, m represents a number corresponding to each month, and m =1, 2.. Eta.. N, XS im Expressed as the target clothing sales volume corresponding to the mth month in the ith past year,
expressed as the target sales of clothing corresponding to the m-1 month in the ith past year, with m expressed as the total number of months.
C2, obtaining a peak season month corresponding to the target garment from the E-business information base according to the current month, comparing a month date corresponding to the current month with the peak season month corresponding to the target garment, if the month date corresponding to the current month is consistent with the peak season month corresponding to the target garment in comparison, judging that the current month is the peak season month, and further obtaining a sales growth coefficient corresponding to the target garment
Otherwise, the current month is judged to be the slack month, and the sales growth coefficient corresponding to the target clothing is obtained
Thereby obtaining the corresponding sales growth evaluation coefficient of the target clothes
Take a value of
Or
And is
C3, extracting the target clothing sales amount corresponding to the current month of the set year from the target clothing sales amount of each month of the previous year of the target e-commerce shop, and further according to an analysis formula
And calculating to obtain a target clothing pre-sale amount phi corresponding to the current month of the target e-shop, wherein XS represents a target clothing sale amount corresponding to the current month of the set year, j represents the total amount of the past year, and e represents a natural constant.
In a preferred technical scheme of the present application, the comprehensive judgment and analysis of the replenishment demand of the target e-commerce store comprises the following specific judgment processes: r1, comparing the target clothing return probability coefficient corresponding to each customer with the set clothing standard return probability coefficient, if the target clothing return probability coefficient corresponding to a certain customer is larger than or equal to the set clothing standard return probability coefficient, judging that the customer needs to return, further counting the total number of the customers needing to return, extracting the number of the customers needing to return for purchasing the corresponding target clothing from the target clothing according to the purchase information of the customers laid in the target electric shop, further extracting the number corresponding to each customer needing to return, further counting the total number corresponding to the customers needing to return, and further using a calculation formula M to lay the stock corresponding to the target clothing according to the target electric shop 0 ＝M 1 +M 2 *M 3 And calculating to obtain the estimated inventory M of the corresponding target clothes of the target electronic shop 0 Wherein M is 1 Expressed as the stock quantity, M, corresponding to the target clothes of the target electric shop 2 Expressed as a total number, M, of customers needing returns 3 Representing the corresponding target garment quantity purchased for the customer requiring return.
And R2, comparing the estimated target clothing inventory corresponding to the target electronic shop with the target clothing pre-sale amount corresponding to the current month of the target electronic shop, if the estimated target clothing inventory corresponding to the target electronic shop is greater than or equal to the target clothing pre-sale amount corresponding to the current month of the target electronic shop, judging that the target electronic shop needs to replenish goods, and if the estimated target clothing inventory corresponding to the target electronic shop is less than the target clothing pre-sale amount corresponding to the current month of the target electronic shop, judging that the target electronic shop does not need to replenish goods.
In a preferred technical solution of the present application, the e-commerce information base is configured to store the peak season month corresponding to the target garment and the basic information corresponding to each customer, and is further configured to store historical purchase information of each marked customer, historical sales information, basic information corresponding to each customer, historical purchase information of each customer corresponding to the target e-shop and historical purchase information of each marked customer corresponding to the target e-shop, where the historical purchase information of each customer includes height and weight, size, and size goodness.
As described above, the garment e-commerce intelligent monitoring and analyzing system based on data analysis provided by the invention at least has the following beneficial effects: according to the intelligent clothes e-commerce monitoring and analyzing system based on data analysis, each customer purchasing a target garment at a target e-commerce shop is analyzed to obtain a target garment return probability coefficient corresponding to each customer, the target garment pre-sale amount corresponding to the current month of the target e-commerce shop is obtained through analysis according to historical sales information of the target e-commerce shop, and then the supply demand of the target e-commerce shop is comprehensively judged and analyzed according to the stock amount corresponding to the target garment.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram showing the connection of modules of the system of the present invention.
Detailed Description
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Referring to fig. 1, the system for intelligent monitoring and analyzing clothing e-commerce based on data analysis comprises a shop information acquisition module, a customer purchase information acquisition module, a customer basic information acquisition module, a customer history information acquisition module, a customer return probability evaluation module, a shop clothing sales estimation module, a warehouse replenishment analysis module and an e-commerce information base.
The customer return probability evaluation module is connected with the store information acquisition module, the customer purchase information acquisition module, the customer basic information acquisition module and the customer historical information acquisition module, the warehouse replenishment analysis module is connected with the customer return probability evaluation module and the store clothing sales estimation module, and the e-commerce information base is connected with the store information acquisition module, the customer purchase information acquisition module, the customer basic information acquisition module and the customer historical information acquisition module.
The shop information acquisition module is used for acquiring the inventory corresponding to target clothes paved in the target electric shop.
And the customer purchase information acquisition module is used for acquiring the purchase information of each customer at the target electric shop at the current time.
As a further optimization of the above scheme, the purchase information of each customer at the target electric shop at the present time includes the number of the target clothes to be purchased, the size corresponding to the target clothes to be purchased, the price corresponding to the target clothes, and the style type corresponding to the target clothes.
The customer basic information acquisition module is used for extracting basic information corresponding to each customer from the E-commerce information base, wherein the basic information comprises height and weight.
And the customer history information acquisition module is used for extracting the historical purchase information corresponding to the target electric shop of each customer from the electric business information base.
As a further optimization of the above solution, the historical purchasing information includes historical purchasing costume style types and historical purchasing costume sizes.
In a specific embodiment, the style type with the most clothing purchased historically is taken as the style type of clothing purchased historically, and the size with the most clothing purchased historically is taken as the size of clothing purchased historically.
And the customer return probability evaluation module is used for evaluating and obtaining a target clothing return probability coefficient corresponding to each customer according to the purchase information and the historical purchase information of each customer on the target electric shop at the current time.
As a further optimization of the above scheme, the evaluation obtains a target garment return probability coefficient corresponding to each customer, and the specific evaluation process is as follows: a1, obtaining purchase information and historical purchase information of each customer at a target electric shop at the current time, further obtaining a target clothes size and style type purchased at the target electric shop at the current time by each customer, and simultaneously obtaining a historical purchased clothes style type and size of each customer.
A2, comparing the corresponding target clothing size of the current target electric shop of each customer with the historical clothing size of purchasing, if the size of the target clothing purchased by the current target electric shop of a certain customer is inconsistent with the size of the historical clothing purchased, judging that the target clothing purchased by the current target electric shop of the customer is a pre-returned clothing, further estimating the return coefficient alpha ' of the customer to the clothing, if not, judging that the clothing purchased by the current target electric shop of the customer is a non-returned clothing, and estimating the return coefficient alpha ' of the customer to the target clothing to estimate the return coefficient alpha ' of the customer to the target clothingThe initial size goods returning evaluation coefficient alpha corresponding to the target clothes purchased by each customer at the target electric shop at the current time is obtained s ，α s Take the values alpha ' or alpha ', and alpha '>α', where s denotes a number corresponding to each customer, and s =1, 2.
A3, purchasing information of each marked customer according to the target electric shop history extracted from the E-commerce information base, further obtaining the height, weight and size of each marked customer purchased in the target electric shop history, comparing the height and weight of each marked customer with the height and weight of each customer, comparing the height and weight of each customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each customer, simultaneously comparing the height and weight of each marked customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each marked customer, comparing the weight grade corresponding to each customer with the weight grade corresponding to each marked customer, if the weight grade corresponding to a certain customer is consistent with the weight grade corresponding to a certain marked customer, judging that the height and the weight of the customer and the marked customer belong to the same grade, further obtaining the number corresponding to each marked customer with the height and the weight of each customer belonging to the same grade, recording each marked customer with the height and the weight of each customer as each reference customer, further obtaining the favorable rating of the size corresponding to each reference customer, comparing and screening the favorable rating of the size corresponding to each reference customer mutually, further screening out the size with the favorable rating arranged in front, comparing the size with the size corresponding to the target electronic shop of each customer at the current time, and further obtaining the size return evaluation coefficient beta corresponding to the target electronic shop of each customer at the current time according to the analysis mode of the initial return evaluation coefficient corresponding to the target electronic shop of each customer at the current time for purchasing the target clothing s 。
In one specific embodiment, the top ranked size of the good scores from the filter is specifically indicated as the first ranked size of the good scores from the filter.
As a further aspect of the above solutionOptimizing, wherein the target clothing return probability coefficient corresponding to each customer is obtained through evaluation, and the specific evaluation process is as follows: b1, comparing the target clothing style type corresponding to the target electronic shop where each customer currently shops with the historical purchased clothing style types, if the target clothing style type corresponding to the target electronic shop where a certain customer currently shops with the target electronic shop is not matched with the historical purchased clothing style type, judging that the target clothing purchased by the customer currently shops with the target electronic shop is a required return clothing, estimating the return estimation index chi 'of the customer for the clothing, and otherwise judging that the target clothing purchased by the customer currently shops with the target electronic shop is a non-required return clothing, estimating the return estimation index chi' of the customer for the target clothing, and obtaining the size return analysis index chi 'corresponding to the target clothing purchased by the customer currently shops with the target electronic shop, thereby obtaining the size return analysis index chi' of the customer currently purchased target clothing s Wherein χ is χ 'or χ'>χ″。
In a specific embodiment, the style type specific classification process comprises the steps of extracting title characters corresponding to a target garment corresponding to a target electric shop, dividing the title characters corresponding to the target garment into keywords, matching the keywords with the keywords corresponding to the style types stored in a database, and obtaining the style type corresponding to the target garment by using a keyword matching formula.
B2, further using a calculation formula
Calculating to obtain a target clothing return probability coefficient delta corresponding to each customer s Wherein a1 and a2 are respectively expressed as weighting factors corresponding to the size and style of the customer, a3 and a4 are respectively expressed as influence factors corresponding to the preliminary size return evaluation coefficient and the size return evaluation coefficient, and a1>a2。
The embodiment of the invention guarantees the requirement of subsequent replenishment of the shop to a certain extent, avoids the phenomenon that overstocked clothing exists in the shop due to excessive subsequent replenishment of the shop, and further guarantees the normal operation of the subsequent shop.
According to the embodiment of the invention, the target clothing return probability coefficient corresponding to each customer is obtained by analyzing the historical purchase information of each customer, so that the return anticipation ability of the shop is improved to a certain extent, the sales level of the shop is promoted, and the after-sales processing ability of the shop is improved.
The shop clothing sales estimation module is used for extracting historical sales information of the target electronic shop from the e-commerce information base and further analyzing to obtain a target clothing pre-sales volume corresponding to the current month of the target electronic shop.
As a further optimization of the above solution, the historical sales information of the target e-commerce store includes a target clothing sales volume for each month of the past year.
As a further optimization of the above scheme, the target pre-sale amount of clothing corresponding to the current month of the target electricity shop is obtained through analysis, and the specific analysis process is as follows: c1, extracting target clothes sales volume of each month in each past year of the target E-commerce shop according to historical sales information of the target E-commerce shop, substituting the target clothes sales volume of each month in each past year of the target E-commerce shop into a clothes sales trend model diagram to obtain sales trends corresponding to the target clothes sales volume of each month in each past year of the target E-commerce shop, and further utilizing a calculation formula
Calculating to obtain the sales increase rate epsilon corresponding to each past year of the target electric shop i Wherein i represents a number corresponding to each previous year, i =1, 2.... J, m represents a number corresponding to each month, and m =1, 2.... N, XS im Expressed as the target clothing sales amount corresponding to the mth month in the ith past year,
expressed as the target sales of clothing corresponding to the m-1 month in the ith past year, with m expressed as the total number of months.
C2, further obtaining the peak season month corresponding to the target garment from the E-commerce information base according to the month and date corresponding to the current month, comparing the month and date corresponding to the current month with the peak season month corresponding to the target garment, and if yes, comparing the month and date corresponding to the current month with the peak season month corresponding to the target garmentComparing the month and date corresponding to the current month with the peak season month corresponding to the target garment, if the comparison result is consistent, judging that the current month is the peak season month, and further obtaining the sales growth coefficient corresponding to the target garment
Otherwise, the current month is judged to be the slack month, and the sales growth coefficient corresponding to the target clothing is obtained
Thereby obtaining the corresponding sales growth evaluation coefficient of the target clothes
Take a value of
Or
And is
C3, extracting the target clothing sales amount corresponding to the current month of the set year from the target clothing sales amount of each month of the previous year of the target E-commerce shop, and further according to an analysis formula
And calculating to obtain a target clothing pre-sale amount phi corresponding to the current month of the target e-shop, wherein XS represents a target clothing sale amount corresponding to the current month of the set year, j represents the total amount of the past year, and e represents a natural constant.
In a particular embodiment, the set year is represented as the previous year to the current year.
The warehouse replenishment analysis module is used for comprehensively judging and analyzing the replenishment demand of the target E-commerce shop according to the target clothing return probability coefficient corresponding to each customer, the target clothing pre-sale amount corresponding to the target E-commerce shop and the stock amount corresponding to the target clothing.
As a further optimization of the above scheme, the comprehensive judgment and analysis of the replenishment demand of the target e-commerce store includes the following specific judgment processes: r1, comparing the target clothing return probability coefficient corresponding to each customer with the set clothing standard return probability coefficient, if the target clothing return probability coefficient corresponding to a certain customer is larger than or equal to the set clothing standard return probability coefficient, judging that the customer needs to return, further counting the total number of customers needing to return, extracting the number corresponding to the customers needing to return according to the purchase information of each customer laid in the target electric shop at the current time, further extracting the number corresponding to each customer needing to return, further counting the total number corresponding to the customers needing to return, and further utilizing a calculation formula M to lay the stock corresponding to the target clothing according to the target electric shop 0 ＝M 1 +M 2 *M 3 And calculating to obtain the estimated inventory M of the corresponding target clothes of the target electronic shop 0 Wherein M is 1 Expressed as the stock quantity, M, corresponding to the target clothes of the target electric shop 2 Expressed as a total number, M, of customers needing returns 3 Indicating that the corresponding target garment quantity is purchased for the customer requiring return.
And R2, comparing the estimated target clothing inventory corresponding to the target electric shop with the target clothing pre-sale amount corresponding to the current month of the target electric shop, if the estimated target clothing inventory corresponding to the target electric shop is greater than or equal to the target clothing pre-sale amount corresponding to the current month of the target electric shop, judging that the target electric shop needs to replenish goods, and if the estimated target clothing inventory corresponding to the target electric shop is less than the target clothing pre-sale amount corresponding to the current month of the target electric shop, judging that the target electric shop does not need to replenish goods.
As a further optimization of the above scheme, the e-commerce information base is used for storing the peak season month corresponding to the target clothing and the basic information corresponding to each customer, and is also used for storing historical purchase information of each marked customer, historical sales information, basic information corresponding to each customer, historical purchase information of each customer corresponding to the target e-shop and historical purchase information of each marked customer by the target e-shop, wherein the historical purchase information of each customer comprises height and weight, size and size favorable rating.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (9)
1. The utility model provides a clothing electricity merchant intelligent monitoring analytic system based on data analysis which characterized in that: the system comprises a shop information acquisition module, a customer purchase information acquisition module, a customer basic information acquisition module, a customer history information acquisition module, a customer return probability evaluation module, a shop clothing sales estimation module, a warehouse replenishment analysis module and an e-commerce information base;
the store information acquisition module is used for acquiring the stock corresponding to the target clothes laid by the target electric store;
the customer purchase information acquisition module is used for acquiring the purchase information of each customer on the target electric shop at the current time;
the customer basic information acquisition module is used for extracting basic information corresponding to each customer from an e-commerce information base, wherein the basic information comprises height and weight;
the customer historical information acquisition module is used for extracting historical purchase information corresponding to the target electronic shop of each customer from the electronic commerce information base;
the customer return probability evaluation module is used for evaluating and obtaining a target clothing return probability coefficient corresponding to each customer according to the purchase information and the historical purchase information of each customer on the target electric shop at the current time;
the shop clothing sales estimation module is used for extracting historical sales information of the target electronic shop from the e-commerce information base, and further analyzing to obtain a target clothing pre-sales volume corresponding to the current month of the target electronic shop;
the warehouse replenishment analysis module is used for comprehensively judging and analyzing the replenishment demand of the target E-commerce shop according to the target clothing return probability coefficient corresponding to each customer, the target clothing pre-sale amount corresponding to the target E-commerce shop and the stock amount corresponding to the target clothing.
2. The intelligent garment e-commerce monitoring and analyzing system based on data analysis as claimed in claim 1, wherein: the purchase information of each customer at the target electric shop at the current time comprises the number of the purchased target clothes, the size corresponding to the purchased target clothes, the price corresponding to the target clothes and the style type corresponding to the target clothes.
3. The garment e-commerce intelligent monitoring and analyzing system based on data analysis as claimed in claim 2, wherein: the historical purchasing information comprises historical purchasing clothing style types and historical purchasing clothing sizes.
4. The intelligent garment e-commerce monitoring and analyzing system based on data analysis as claimed in claim 3, wherein: the target garment return probability coefficient corresponding to each customer is obtained through the evaluation, and the specific evaluation process is as follows:
a1, acquiring purchase information and historical purchase information of each customer on a target electric shop at the current time, further acquiring a target clothing size and style type purchased by each customer on the target electric shop at the current time, and simultaneously acquiring a historical purchased clothing style type and size of each customer;
a2, comparing the target clothing size corresponding to the current time of each customer at the target electric shop with the historical purchased clothing size, if the target clothing size purchased by a certain customer at the current time at the target electric shop is not consistent with the historical purchased clothing size, judging that the target clothing purchased by the customer at the current time at the target electric shop is pre-returned clothing, and further estimating the return coefficient alpha' of the customer for the clothing, otherwise, judging that the target clothing purchased by the customer at the current time at the target electric shop is the pre-returned clothingThe clothing purchased by the customer at the target electric shop is determined as non-return clothing, and the return estimation coefficient alpha' of the customer for the target clothing is determined, so as to obtain the return estimation coefficient alpha of the preliminary size corresponding to the target clothing purchased by the customer at the target electric shop at the current time s ，α s Take the values alpha ' or alpha ', and alpha '>α', where s denotes a number corresponding to each customer, s =1, 2.. Y;
a3, purchasing information of each marked customer according to the target electric shop history extracted from the E-commerce information base, further obtaining the height, weight and size of each marked customer purchased in the target electric shop history, comparing the height and weight of each marked customer with the height and weight of each customer, comparing the height and weight of each customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each customer, simultaneously comparing the height and weight of each marked customer with the weight grade corresponding to each height and weight stored in the database, further obtaining the weight grade corresponding to each marked customer, comparing the weight grade corresponding to each customer with the weight grade corresponding to each marked customer, if the weight grade corresponding to a certain customer is consistent with the weight grade corresponding to a certain marked customer, judging that the height and the weight of the customer and the marked customer belong to the same grade, further obtaining the number corresponding to each marked customer with the height and the weight of each customer belonging to the same grade, marking each marked customer with the height and the weight of each customer as each reference customer, further obtaining the goodness of the size corresponding to each reference customer, mutually comparing and screening the goodness of the size corresponding to each reference customer, further screening to obtain the size with the front goodness arrangement, comparing the size with the front goodness arrangement with the size of the target clothing corresponding to the current time of each customer at the target electric shop, and further obtaining the size return evaluation coefficient beta corresponding to the current time of each customer at the target electric shop according to the analysis mode of the preliminary return evaluation coefficient corresponding to the current time of each customer at the target electric shop to purchase the target clothing s 。
5. The intelligent garment e-commerce monitoring and analyzing system based on data analysis as claimed in claim 4, wherein: the target garment return probability coefficient corresponding to each customer is obtained through the evaluation, and the specific evaluation process is as follows:
b1, comparing the target clothing style type corresponding to the target electronic shop where each customer currently shops with the historical purchased clothing style types, if the target clothing style type corresponding to the target electronic shop where a certain customer currently shops with the target electronic shop is not matched with the historical purchased clothing style type, judging that the target clothing purchased by the customer currently shops with the target electronic shop is a required return clothing, estimating the return estimation index chi 'of the customer for the clothing, and otherwise judging that the target clothing purchased by the customer currently shops with the target electronic shop is a non-required return clothing, estimating the return estimation index chi' of the customer for the target clothing, and obtaining the size return analysis index chi 'corresponding to the target clothing purchased by the customer currently shops with the target electronic shop, thereby obtaining the size return analysis index chi' of the customer currently purchased target clothing s Wherein χ is χ 'or χ'>χ″；
B2, further using a calculation formula
Calculating to obtain the target clothing return probability coefficient delta corresponding to each customer s Wherein a1 and a2 are respectively expressed as weighting factors corresponding to the size and style of the customer, a3 and a4 are respectively expressed as influence factors corresponding to the preliminary size return evaluation coefficient and the size return evaluation coefficient, and a1>a2。
6. The intelligent garment e-commerce monitoring and analyzing system based on data analysis as claimed in claim 1, wherein: the historical sales information of the target electric shop includes the target clothing sales volume of each month of the past year.
7. The intelligent garment e-commerce monitoring and analyzing system based on data analysis of claim 6, wherein: the target pre-sale amount of the clothes corresponding to the current month of the target electronic shop is obtained through analysis, and the specific analysis process is as follows:
c1, extracting target clothes sales volume of each month in each past year of the target E-commerce shop according to historical sales information of the target E-commerce shop, substituting the target clothes sales volume of each month in each past year of the target E-commerce shop into a clothes sales trend model diagram to obtain sales trends corresponding to the target clothes sales volume of each month in each past year of the target E-commerce shop, and further utilizing a calculation formula
Calculating to obtain the corresponding sales increase rate epsilon of each round year of the target electric shop i Wherein i represents a number corresponding to each previous year, i =1, 2.... J, m represents a number corresponding to each month, and m =1, 2.... N, XS im Expressed as the target clothing sales amount corresponding to the mth month in the ith past year,
expressing the target clothing sales amount corresponding to the m-1 month in the ith past year, wherein m is the total number of months;
c2, obtaining a peak season month corresponding to the target garment from the E-business information base according to the current month, comparing a month date corresponding to the current month with the peak season month corresponding to the target garment, if the month date corresponding to the current month is consistent with the peak season month corresponding to the target garment in comparison, judging that the current month is the peak season month, and further obtaining a sales growth coefficient corresponding to the target garment
Otherwise, the current month is judged to be the slack month, and the sales growth coefficient corresponding to the target clothing is obtained
Thereby obtaining the corresponding sales growth evaluation coefficient of the target clothes
Take a value of
Or
And is
C3, extracting the target clothing sales amount corresponding to the current month of the set year from the target clothing sales amount of each month of the previous year of the target E-commerce shop, and further according to an analysis formula
And calculating to obtain a target clothing pre-sale amount phi corresponding to the current month of the target e-shop, wherein XS represents a target clothing sale amount corresponding to the current month of the set year, j represents the total amount of the past year, and e represents a natural constant.
8. The intelligent garment e-commerce monitoring and analyzing system based on data analysis of claim 7, wherein: the comprehensive judgment and analysis method is characterized in that the replenishment requirements of the target E-commerce shop are comprehensively judged and analyzed, and the specific judgment process is as follows:
r1, comparing the target clothing return probability coefficient corresponding to each customer with the set clothing standard return probability coefficient, if the target clothing return probability coefficient corresponding to a certain customer is larger than or equal to the set clothing standard return probability coefficient, judging that the customer needs to return goods, further counting the total number of the customers needing to return goods, extracting the number corresponding to the customers needing to return goods from the target clothing return probability coefficient according to the purchase information of the customers laid at the target electric shop at the current time, further extracting the number corresponding to each customer needing to return goods, counting the total number corresponding to the customers needing to return goods, and further laying the target clothing according to the target electric shopThe corresponding stock quantity is calculated by the formula M 0 ＝M 1 +M 2 *M 3 And calculating to obtain the estimated inventory M of the corresponding target clothes of the target electronic shop 0 Wherein, M is 1 Expressed as the stock quantity, M, corresponding to the target clothes of the target electric shop 2 Expressed as a corresponding total number of customers requiring returns, M 3 Representing the corresponding target clothing quantity purchased for the customer needing goods return;
and R2, comparing the estimated target clothing inventory corresponding to the target electric shop with the target clothing pre-sale amount corresponding to the current month of the target electric shop, if the estimated target clothing inventory corresponding to the target electric shop is greater than or equal to the target clothing pre-sale amount corresponding to the current month of the target electric shop, judging that the target electric shop needs to replenish goods, and if the estimated target clothing inventory corresponding to the target electric shop is less than the target clothing pre-sale amount corresponding to the current month of the target electric shop, judging that the target electric shop does not need to replenish goods.
9. The intelligent garment e-commerce monitoring and analyzing system based on data analysis as claimed in claim 1, wherein: the E-commerce information base is used for storing the peak season months corresponding to the target clothes and the basic information corresponding to each customer, and is also used for storing the information of each marked customer purchased in the history of the target electric shop, the historical sales information, the basic information corresponding to each customer, the information of each historical purchase corresponding to each customer in the target electric shop and the information of each marked customer purchased in the history of the target electric shop, wherein the information of each customer purchased in the history comprises height, weight, size and size rating.
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