CN114065018A - Commodity popularity determination method and device, electronic equipment and computer readable storage medium - Google Patents
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Abstract
The embodiment of the application discloses a method and a device for determining commodity heat, electronic equipment and a computer readable storage medium, wherein in the embodiment of the application, the device for determining commodity heat acquires sales information and inventory information of commodities in a warehouse within a preset time period; determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information; determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method and a device for determining commodity popularity, electronic equipment and a computer readable storage medium.
Background
The reasonable management of the warehouse is an important embodiment of the competitiveness of the warehouse, the accurate warehouse management can effectively control and reduce the circulation cost and the inventory cost, but the operation management of the warehouse at present extremely depends on the level of warehouse management personnel, the sales condition of commodities in the warehouse is simply statistically analyzed by depending on the experience of the warehouse management personnel, and the artificial analysis lacks reliable and interpretable data guidance, so that a method which can automatically predict the commodity heat without depending on the level of the management personnel is urgently needed.
Disclosure of Invention
The embodiment of the application provides a method and a device for determining the popularity of a commodity, electronic equipment and a computer readable storage medium, which can automatically determine the popularity of the commodity.
In a first aspect, an embodiment of the present application provides a method for determining a popularity of a commodity, including:
acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information;
determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic;
and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In some embodiments, said determining a unit sales of said item from said first sales characteristic and said first inventory characteristic comprises:
and determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
In some embodiments, said determining a heat of said good from said unit sales, said second sales characteristic and said second inventory characteristic comprises:
carrying out data continuity processing on the commodity sales-available days in the second inventory characteristic to obtain a processed second inventory characteristic;
and determining the heat degree of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
In some embodiments, said determining a heat of said good from said unit sales, said second sales characteristic and said second inventory characteristic comprises:
dividing the commodities into a first commodity and a second commodity according to a preset proportion;
for the first commodity, carrying out data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic;
determining the heat degree of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function;
for the second commodity, determining a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information;
and performing weighted calculation on the third sales characteristic and the third inventory characteristic based on a BDP big data platform to determine the heat of the second commodity.
In some embodiments, before determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
based on a BDP big data platform, filtering the commodities according to the sales volume information to obtain filtered commodities;
the determining the popularity of the goods according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic comprises:
and determining the heat of the filtered commodities according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In some embodiments, after determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
and determining the upper replenishment quantity of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic and the first inventory characteristic based on the trained second Catboost model.
In some embodiments, after determining the upper replenishment limit amount of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic and the first inventory characteristic, the method further includes:
and determining a replenishment scheme of the commodity according to the unit sales volume and the replenishment upper limit volume.
In some embodiments, after determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
and determining the commodity space plan of the warehouse according to the heat of the commodity.
In a second aspect, an embodiment of the present application further provides a device for determining a popularity of a commodity, including:
the acquisition unit is used for acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
a first determining unit, configured to determine a first sales characteristic, a second sales characteristic, a first inventory characteristic, and a second inventory characteristic of the product according to the sales information and the inventory information;
a second determination unit configured to determine a unit sales amount of the commodity according to the first sales amount characteristic and the first stock characteristic;
a third determining unit configured to determine the degree of heat of the product based on the unit sales amount, the second sales amount characteristic, and the second inventory characteristic.
In some embodiments, the second determining unit is specifically configured to:
and determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
In some embodiments, the third determining unit is specifically configured to:
carrying out data continuity processing on the commodity sales-available days in the second inventory characteristic to obtain a processed second inventory characteristic;
and determining the heat degree of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
In some embodiments, the third determining unit is further specifically configured to:
dividing the commodities into a first commodity and a second commodity according to a preset proportion;
for the first commodity, carrying out data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic;
determining the heat degree of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function;
for the second commodity, determining a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information;
and performing weighted calculation on the third sales characteristic and the third inventory characteristic based on a BDP big data platform to determine the heat of the second commodity.
In some embodiments, the apparatus further comprises:
the filtering unit is used for filtering the commodities according to the sales volume information based on a BDP big data platform to obtain filtered commodities;
at this time, the third determining unit is specifically configured to:
and determining the heat of the filtered commodities according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In some embodiments, the apparatus further comprises:
and the fourth determining unit is used for determining the replenishment upper limit quantity of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic and the first inventory characteristic.
In some embodiments, the apparatus further comprises:
and the fifth determining unit is used for determining the replenishment scheme of the commodity according to the unit sales volume and the replenishment upper limit volume.
In some embodiments, the apparatus further comprises:
a sixth determining unit, configured to determine a commodity space plan of the warehouse according to the heat of the commodity.
In a third aspect, an embodiment of the present application further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor executes, when calling the computer program in the memory, any one of the steps in the method for determining the popularity of a product provided in the embodiment of the present application.
In a fourth aspect, the present application further provides a computer-readable storage medium, where a plurality of instructions are stored, and the instructions are suitable for being loaded by a processor to perform the steps in any one of the methods for determining the popularity of a commodity provided by the embodiments of the present application.
In the embodiment of the application, a commodity heat determining device acquires sales information and inventory information of commodities in a warehouse within a preset time period; determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information; determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic view of an application scenario of a method for determining a hot degree of a commodity according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of a method for determining the popularity of a commodity according to an embodiment of the present application;
FIG. 3 is another schematic flow chart diagram of a method for determining a hot degree of a commodity according to an embodiment of the present application;
FIG. 4 is another schematic flow chart of a method for determining a hot degree of a commodity according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of an application scenario of the calculation of heat according to the embodiment of the present application;
FIG. 6 is a schematic diagram of dynamic adjustment of replenishment upper and lower limits according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a device for determining the heat of a commodity according to an embodiment of the present application;
fig. 8 is another schematic structural diagram of a device for determining the heat of a commodity according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the description that follows, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise indicated. Accordingly, these steps and operations will be referred to, several times, as being performed by a computer, the computer performing operations involving a processing unit of the computer in electronic signals representing data in a structured form. This operation transforms the data or maintains it at locations in the computer's memory system, which may be reconfigured or otherwise altered in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the application have been described in language specific to above, it is not intended to be limited to the specific form set forth herein, and it will be recognized by those of ordinary skill in the art that various of the steps and operations described below may be implemented in hardware.
The principles of the present application may be employed in numerous other general-purpose or special-purpose computing, communication environments or configurations. Examples of well known computing systems, environments, and configurations that may be suitable for use with the application include, but are not limited to, hand-held telephones, personal computers, servers, multiprocessor systems, microcomputer-based systems, mainframe-based computers, and distributed computing environments that include any of the above systems or devices.
The terms "first", "second", and "third", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions.
The execution main body of the method for determining the commodity heat degree can be a device for determining the commodity heat degree provided by the embodiment of the application or an electronic device integrated with the device for determining the commodity heat degree, wherein the device for determining the commodity heat degree can be realized in a hardware or software mode, the electronic device can be a server or a terminal, and the terminal can be a smart phone, a tablet computer, a palm computer, a notebook computer or the like.
Referring to fig. 1, fig. 1 is a schematic view of an application scenario of a method for determining a commodity popularity in this embodiment, first, a user may set a sales cycle (i.e., a time length that different popularity commodities can be sold after being replenished once) of each popularity commodity through a warehousing operation Platform (WPM) system, then push required Data to a Mysql database of a Data Visualization Platform (DVP) through a Big Data Platform (BDP), a computing cloud obtains Data in the Mysql database through an interface, calculates commodity popularity and the like on the computing cloud Platform according to a python algorithm package, and returns a calculation result to the WPM system, so that the user obtains the calculation result through the WPM system.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a method for determining a hot degree of a product according to an embodiment of the present application. The method for determining the popularity of the commodity can comprise the following steps:
201. and acquiring sales information and inventory information of commodities in the warehouse within a preset time period.
In the embodiment of the present application, the Unit of the commodity is a Stock Keeping Unit (sku), the duration of the preset time period may be 3 months, the duration may be set by a user, and the specific details are not limited herein.
The sales information in this embodiment may be a delivery receipt detail and a delivery receipt commodity detail, and the inventory information may be an inventory commodity detail, where the inventory commodity detail includes the number of commodities and the warehousing time of the commodities.
202. And determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information.
In this embodiment, the first sales characteristic includes: historical sales value, mean value, variance, extremum, quantile and sales variation of the commodity in a preset time period; the second sales characteristics include: historical sales volume value, frequency, average value and sales volume ratio of the commodity in a preset time period; the first inventory characteristic includes: the number of available inventory of goods; the second inventory characteristic includes: the number of available inventory of the goods, the number of days the goods can be sold, and the time of warehousing the goods.
203. And determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic.
Specifically, based on the trained first Catboost model, the unit sales of the commodity is determined according to the first sales characteristic and the first inventory characteristic.
In this embodiment, when a replenishment scheme is subsequently formulated for a commodity, the unit sales volume, that is, the lowest stock quantity of the commodity (that is, the replenishment lower limit mentioned in this application), for example, the sales volume of the commodity for one day, needs to be combined, and when the quantity of the commodity in the warehouse is lower than the unit sales volume, replenishment is started, so that a commodity gap caused by untimely replenishment is avoided.
204. And determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
Specifically, determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic includes:
carrying out data continuity processing on the number of marketable days of the commodities in the second inventory characteristic to obtain a processed second inventory characteristic; and then determining the heat degree of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
In some embodiments, prior to the determining the heat of the good based on the unit sales, the second sales characteristic, and the second inventory characteristic, the method further comprises: based on the BDP big data platform, filtering the commodity according to the sales volume information to obtain a filtered commodity;
at this time, determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic, and the second inventory characteristic includes: and determining the heat of the filtered commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In some embodiments, the determining the heat of the good according to the unit sales, the second sales characteristic, and the second inventory characteristic includes: dividing the commodity into a first commodity and a second commodity according to a preset proportion; for the first commodity, carrying out data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic; determining the heat degree of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function; for the second commodity, determining a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information; and performing weighted calculation on the third sales characteristic and the third inventory characteristic based on a BDP big data platform to determine the heat of the second commodity.
In some embodiments, the upper replenishment limit amount of the commodity is determined according to the degree of heat of the commodity, the commodity sales cycle corresponding to the degree of heat, the first sales characteristic and the first inventory characteristic based on the trained second Catboost model.
Wherein the upper replenishment limit is the maximum inventory of the commodity.
In this embodiment, a replenishment scheme for the commodity needs to be determined according to the unit sales volume and the replenishment upper limit, that is, when the quantity of the commodity in the warehouse is lower than the unit sales volume, a replenishment worker is reminded to replenish the commodity, wherein the total inventory quantity of the commodity in the warehouse after replenishment does not exceed the replenishment upper limit.
In some embodiments, the commodity space plan of the warehouse is determined according to the heat degree of the commodity, and the warehouse space is reasonably arranged, so that the cost caused by frequent change of the warehouse location and warehouse entry and exit transportation can be reduced.
In the embodiment of the application, a commodity heat determining device acquires sales information and inventory information of commodities in a warehouse within a preset time period; determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information; determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; and determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
The method for determining the hot degree of a commercial product according to the above embodiment will be described in further detail below.
Referring to fig. 3, fig. 3 is another schematic flow chart of a method for determining a hot degree of a product according to an embodiment of the present disclosure. The method for determining the popularity of the commodity can be applied to a server, as shown in fig. 3, the flow of the method for determining the popularity of the commodity can be as follows:
301. the server acquires sales information and inventory information of commodities in the warehouse within a preset time period.
The sales information in this embodiment may be a delivery receipt detail and a delivery receipt commodity detail, the inventory information may be a stock commodity detail, the stock commodity detail includes the number of commodities and the warehousing time of the commodities, the unit of the commodity is sku, the duration of the preset time period may be 3 months, the duration may be set by a user, and the details are not limited herein.
In this embodiment, after acquiring sales information and inventory information of a commodity, data preprocessing needs to be performed on the acquired data, and the data may be specifically preprocessed through a time order statistics and screening module, a SKU statistics and screening module, a dimension information filling module, a complete time filling module, a missing value processing module, a promotion marking module, a window empty marking module (lower limit processing), and a time characteristic processing module.
302. And the server performs filtering processing on the commodity according to the sales volume information based on the BDP big data platform.
In this embodiment, a part of the commodities are filtered out first in the BDP big data platform, and the popularity level of the filtered commodities is determined as the lowest popularity level, for example, when the popularity levels include a class a, a class B, a class C, and a class D, the filtered commodities are directly classified as the class D popularity.
The specific filtering method comprises the following steps:
the BDP big data platform obtains sales information of a commodity in a preset time period, determines the maximum value, the average value and the frequency of the sales of the commodity according to the sales information, then determines whether the maximum value, the average value and the frequency of the sales all meet requirements (for example, the maximum value of the sales is larger than the preset sales value, the average value is larger than the preset average value and the frequency is larger than the preset frequency), if a certain commodity all meets the requirements, the commodity is not filtered, if the commodity does not meet the requirements, the commodity needs to be filtered, and the heat of the commodity is determined to be the lowest heat.
303. The server determines a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information.
In this embodiment, the first sales characteristic includes: historical sales value, mean value, variance, extremum, quantile and sales variation of the commodity in a preset time period; the second sales characteristics include: historical sales volume value, frequency, average value and sales volume ratio of the commodity in a preset time period; the first inventory characteristic includes: the number of available inventory of goods; the second inventory characteristic includes: the number of available inventory of the goods, the number of days the goods can be sold, and the time of warehousing the goods.
304. The server determines the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
And when the inventory of the commodities is lower than the lowest inventory, the staff is reminded to replenish the commodities.
In some embodiments, the trained first Catboost model determines the unit sales of the commodity according to a unit predicted value of the commodity, the first sales characteristic and the first inventory characteristic, in addition to the first sales characteristic and the first inventory characteristic, wherein the unit predicted value is a unit sales preliminarily predicted from the commodity.
305. And the server performs data continuity processing on the number of saleable days of the commodities in the second inventory characteristic to obtain the processed second inventory characteristic.
Specifically, data continuity processing is carried out on the number of marketable days of the commodity through a sigmoid function, marketable time is converted into probability, and the conversion formula is as follows:
wherein x is the number of marketable days.
306. The server determines the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
Specifically, each numerical value in the unit sales amount, the second sales amount characteristic, and the processed second inventory characteristic has a corresponding weight, and the embodiment performs weighted product calculation on the numerical values according to the corresponding weights, and determines the popularity of the product according to the obtained result.
Specifically, in some embodiments, after the weighted product results of the commodities are calculated, the results are sorted from large to small, the commodities corresponding to the top 10% of the results are determined as the class a hot commodities, the commodities in the top 10% to 40% of the range are determined as the class B hot commodities, and then the commodities after 40% are determined as the class C hot commodities.
307. And the server determines the replenishment upper limit quantity of the commodity according to the popularity of the commodity, the commodity sales cycle corresponding to the popularity, the first sales characteristic and the first inventory characteristic based on the trained second Catboost model.
The replenishment upper limit amount, that is, the maximum stock amount of the commodities corresponds to the unit sales amount (replenishment lower limit value) mentioned in the present embodiment.
In some embodiments, the trained second Catboost model determines, according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic, and the first inventory characteristic, the upper limit predicted value of the unit sales volume of the commodity, and further determines the unit sales volume of the commodity according to the upper limit predicted value of the commodity, where the upper limit predicted value is an upper limit sales volume preliminarily predicted according to the commodity, and the upper limit sales volume may be a total sales volume of the commodity predicted in the commodity sales cycle corresponding to the heat of the commodity.
In this embodiment, steps 304 to 307 are implemented based on a python algorithm package.
308. And the server determines the replenishment scheme of the commodity according to the unit sales and the replenishment upper limit.
Specifically, in the replenishment scheme of the commodity, when the inventory of the commodity is lower than the unit sales volume, the staff is reminded to replenish the commodity, and after replenishment, the total inventory of the commodity does not exceed the replenishment upper limit amount of the commodity.
At this moment, the staff need not to decide the quantity of replenishment and when to replenish according to the field situation and experience, therefore this scheme can also be solved because frequent replenishment that replenishment quantity is not enough leads to and the problem of the long-term space of picking up that occupies that replenishment quantity leads to of replenishment quantity.
309. The server determines a commodity space plan for the warehouse based on the heat of the commodity.
Specifically, determining the commodity space plan of the warehouse according to the heat of the commodity includes:
the shelf space information of the warehouse is obtained, then the commodity space plan of the warehouse is determined according to the heat degree of the commodities and the shelf space information, and specifically, the commodities with high heat degree are placed at positions which are easy to take out of the warehouse and store.
Therefore, the scheme can reasonably plan the storage layout of the commodities according to different conditions of the commodities, and reduces the labor cost caused by frequent change of the storage position and transportation in and out of the warehouse.
The sequence of step 308 and step 309 is not limited when steps 308 and 309 are executed, that is, step 309 may be executed before step 308 or simultaneously with step 309.
In the embodiment of the application, a commodity heat determining device acquires sales information and inventory information of commodities in a warehouse within a preset time period; determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information; determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; and determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
Referring to fig. 4, fig. 4 is another schematic flow chart of a method for determining a hot degree of a product according to an embodiment of the present disclosure. The commodity popularity determination method can be applied to a server, and in the embodiment, in order to reduce the calculation amount of the python algorithm package, popularity is predicted by combining a BDP big data platform during popularity calculation. As shown in fig. 4, the flow of the method for determining the popularity of the product may be as follows:
401. the server acquires sales information and inventory information of commodities in the warehouse within a preset time period.
402. And the server performs filtering processing on the commodity according to the sales volume information based on the BDP big data platform.
403. The server determines a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information.
404. The server determines the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
In this embodiment, steps 401 to 404 are similar to steps 301 to 304 in the corresponding embodiment of fig. 3, and are not described herein again.
405. The server divides the commodity into a first commodity and a second commodity according to a preset proportion.
The preset ratio of the first commodity to the second commodity may be 7:3, or other ratios may be set according to specific cases, and specific values of the ratios are not limited herein.
406. And for the first commodity, the server performs data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic.
407. The server determines the heat of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
In this embodiment, steps 406 to 407 are similar to steps 305 to 306 in the embodiment corresponding to fig. 3, and are not described herein again.
408. For the second commodity, the server determines a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information.
In this embodiment, the third sales volume characteristic includes: the third inventory characteristic comprises the available inventory number of the second commodity and the warehousing time of the second commodity.
409. And the server performs weighted calculation on the third sales characteristic and the third inventory characteristic based on the BDP big data platform to determine the heat degree of the second commodity.
In this embodiment, each numerical value in the third sales characteristic and the third inventory characteristic is preset with a corresponding weight, and in this embodiment, weighting calculation is performed according to the third sales characteristic, the third inventory characteristic, and the corresponding weight, so as to obtain the heat of the second product.
In some embodiments, the specific steps of performing weighted calculation according to the third sales characteristic, the third inventory characteristic, and the corresponding weight to obtain the heat of the second product include:
after weighted calculation is carried out according to the third sales characteristic, the third inventory characteristic and the corresponding weight, the heat value of each commodity is obtained, then the commodities are sorted from large to small according to the heat value, the commodity at the top 50 is determined as the A-type heat commodity, the commodity with the sorted score accounting for 60 percent (namely the commodity heat value accounting for 60 percent of the total heat value) is determined as the A-type heat commodity, the commodity with the sorted score accounting for 60 to 90 percent is determined as the B-type heat commodity, and then the rest commodities are determined as the C-type heat commodities.
An application scenario diagram of the calculation of the heat degree in the method for determining the heat degree of the commodity in the scheme is shown in fig. 5, in the scenario, the preset time period is 30 days, and the sales information and the inventory information are obtained according to the historical ex-warehouse information, the historical in-warehouse information and the current inventory information.
410. And the server determines the replenishment upper limit quantity of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic and the first inventory characteristic based on the trained second Catboost model.
411. And the server determines the replenishment scheme of the commodity according to the unit sales and the replenishment upper limit.
412. The server determines a commodity space plan for the warehouse based on the heat of the commodity.
In this embodiment, steps 410 to 412 are similar to steps 307 to 309 in the corresponding embodiment of fig. 3, and are not described herein again.
In the embodiment of the application, a commodity heat determining device acquires sales information and inventory information of commodities in a warehouse within a preset time period; determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information; determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; and determining the heat of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
As shown in fig. 6, fig. 6 is a schematic diagram illustrating dynamic adjustment of upper and lower replenishment limits, in a process of replenishing goods in a subsequent warehouse by using a calculation result of the upper and lower replenishment limits, the upper and lower replenishment limits (upper replenishment limit and lower replenishment limit) may be adjusted according to a utilization rate of a warehouse location in a warehouse picking area and an occupation ratio of an emergency replenishment task, where the lower replenishment limit is a unit sales volume, and the upper replenishment limit is an upper replenishment limit, so as to achieve a function of feedback regression adjustment.
In order to better implement the method for determining the popularity of the commodity provided by the embodiment of the present application, the embodiment of the present application further provides a device based on the method for determining the popularity of the commodity. The terms are the same as those in the above-mentioned method for determining the popularity of a product, and the details of implementation may refer to the description in the method examples.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a device for determining a hot degree of a product according to an embodiment of the present disclosure, where the device 700 for determining a hot degree of a product may include an obtaining unit 701, a first determining unit 702, a second determining unit 703, a third determining unit 704, and the like.
Wherein the content of the first and second substances,
an obtaining unit 701, configured to obtain sales information and inventory information of a commodity in a warehouse within a preset time period;
a first determining unit 702, configured to determine a first sales characteristic, a second sales characteristic, a first inventory characteristic, and a second inventory characteristic of the product according to the sales information and the inventory information;
a second determining unit 703 configured to determine a unit sales amount of the product according to the first sales amount characteristic and the first inventory characteristic;
a third determining unit 704 configured to determine the heat of the product according to the unit sales amount, the second sales amount characteristic, and the second inventory characteristic.
In some embodiments, the second determining unit 703 is specifically configured to:
and determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
In some embodiments, the third determining unit 704 is specifically configured to:
carrying out data continuity processing on the commodity sales-available days in the second inventory characteristic to obtain a processed second inventory characteristic;
and determining the heat degree of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
In some embodiments, the third determining unit 704 is further specifically configured to:
dividing the commodities into a first commodity and a second commodity according to a preset proportion;
for the first commodity, carrying out data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic;
determining the heat degree of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function;
for the second commodity, determining a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information;
and performing weighted calculation on the third sales characteristic and the third inventory characteristic based on a BDP big data platform to determine the heat of the second commodity.
As shown in fig. 8, in some embodiments, the apparatus 600 further comprises:
the filtering unit 705 is configured to filter the commodity according to the sales volume information based on a BDP big data platform to obtain a filtered commodity;
at this time, the third determining unit 704 is specifically configured to:
and determining the heat of the filtered commodities according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In some embodiments, the apparatus 700 further comprises:
a fourth determining unit 706, configured to determine the replenishment upper limit amount of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic, and the first inventory characteristic.
In some embodiments, the apparatus 700 further comprises:
a fifth determining unit 707 configured to determine a replenishment plan of the commodity according to the unit sales amount and the replenishment upper limit amount.
In some embodiments, the apparatus 700 further comprises:
a sixth determining unit 708, configured to determine the commodity space plan of the warehouse according to the heat of the commodity.
In the embodiment of the application, the obtaining unit 701 obtains sales information and inventory information of commodities in a warehouse within a preset time period; the first determining unit 702 determines a first sales characteristic, a second sales characteristic, a first inventory characteristic, and a second inventory characteristic of the product according to the sales information and the inventory information; the second determination unit 703 determines the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic; the third determination unit 704 determines the degree of heat of the commodity based on the unit sales amount, the second sales amount characteristic, and the second inventory characteristic. According to the scheme, when the popularity of the commodity is predicted, the popularity of the commodity can be automatically predicted according to the sales information and the inventory information of the commodity without depending on a manager.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Referring to fig. 9, embodiments of the present application provide a server 900, which may include one or more processors 901 of a processing core, one or more memories 902 of a computer-readable storage medium, a Radio Frequency (RF) circuit 903, a power supply 904, an input unit 905, and a display unit 906. Those skilled in the art will appreciate that the server architecture shown in FIG. 9 does not constitute a limitation on the servers, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components. Wherein:
the processor 901 is a control center of the server, connects various parts of the entire server by various interfaces and lines, and performs various functions of the server and processes data by running or executing software programs and/or modules stored in the memory 902 and calling data stored in the memory 902, thereby performing overall monitoring of the server. Optionally, processor 901 may include one or more processing cores; preferably, the processor 901 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 901.
The memory 902 may be used to store software programs and modules, and the processor 901 executes various functional applications and data processing by operating the software programs and modules stored in the memory 902.
The RF circuit 903 may be used for receiving and transmitting signals during transmission and reception of information.
The server also includes a power supply 904 (e.g., a battery) for powering the various components, which may preferably be logically coupled to the processor 901 via a power management system to manage charging, discharging, and power consumption via the power management system.
The server may also include an input unit 905, the input unit 905 being operable to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.
The server may also include a display unit 906, and the display unit 906 may be used to display information input by or provided to the user, as well as various graphical user interfaces of the server, which may be made up of graphics, text, icons, video, and any combination thereof. Specifically, in this embodiment, the processor 901 in the server loads the executable file corresponding to the process of one or more application programs into the memory 902 according to the following instructions, and the processor 901 runs the application programs stored in the memory 902, so as to implement various functions as follows:
acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information;
determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic;
and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
In the above embodiments, the descriptions of the embodiments have respective emphasis, and parts that are not described in detail in a certain embodiment may refer to the above detailed description of the method for determining the popularity of a commodity, which is not described herein again.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor.
To this end, embodiments of the present application provide a computer-readable storage medium, in which a plurality of instructions are stored, where the instructions can be loaded by a processor to execute the steps in any one of the methods for determining the popularity of a commodity provided by the embodiments of the present application. For example, the instructions may perform the steps of:
acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information;
determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic;
and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
The above operations can be implemented in the foregoing embodiments, and are not described in detail herein.
Wherein the computer-readable storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the computer-readable storage medium may execute the steps in the method for determining the popularity of any product provided by the embodiment of the present application, beneficial effects that can be achieved by the method for determining the popularity of any product provided by the embodiment of the present application may be achieved, for details, see the foregoing embodiments, and are not described herein again.
The method, the apparatus, the electronic device, and the computer-readable storage medium for determining the hot degree of a commodity provided by the embodiments of the present application are described in detail above, and a specific example is applied to describe the principle and the implementation manner of the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (11)
1. A method for determining the popularity of a commodity is characterized by comprising the following steps:
acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
determining a first sales characteristic, a second sales characteristic, a first inventory characteristic and a second inventory characteristic of the commodity according to the sales information and the inventory information;
determining a unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic;
and determining the popularity of the commodity according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
2. The method of claim 1, wherein said determining a unit sales of the good from the first sales characteristic and the first inventory characteristic comprises:
and determining the unit sales volume of the commodity according to the first sales volume characteristic and the first inventory characteristic based on the trained first Catboost model.
3. The method of claim 1, wherein said determining a heat of said good from said unit sales volume, said second sales volume characteristic, and said second inventory characteristic comprises:
carrying out data continuity processing on the commodity sales-available days in the second inventory characteristic to obtain a processed second inventory characteristic;
and determining the heat degree of the commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function.
4. The method of claim 1, wherein said determining a heat of said good from said unit sales volume, said second sales volume characteristic, and said second inventory characteristic comprises:
dividing the commodities into a first commodity and a second commodity according to a preset proportion;
for the first commodity, carrying out data continuity processing on the commodity sale available days in the second inventory characteristic to obtain a processed second inventory characteristic;
determining the heat degree of the first commodity according to the unit sales volume, the second sales volume characteristic and the processed second inventory characteristic based on a linear weighted product function;
for the second commodity, determining a third sales characteristic and a third inventory characteristic according to the sales information and the inventory information;
and performing weighted calculation on the third sales characteristic and the third inventory characteristic based on a BDP big data platform to determine the heat of the second commodity.
5. The method of claim 1, wherein prior to determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
based on a BDP big data platform, filtering the commodities according to the sales volume information to obtain filtered commodities;
the determining the popularity of the goods according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic comprises:
and determining the heat of the filtered commodities according to the unit sales volume, the second sales volume characteristic and the second inventory characteristic.
6. The method of claim 1, wherein after determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
and determining the upper replenishment quantity of the commodity according to the heat of the commodity, the commodity sales cycle corresponding to the heat, the first sales characteristic and the first inventory characteristic based on the trained second Catboost model.
7. The method of claim 6, wherein after determining the upper replenishment limit amount of the product according to the degree of heat of the product, the product sales cycle corresponding to the degree of heat, the first sales characteristic, and the first inventory characteristic, the method further comprises:
and determining a replenishment scheme of the commodity according to the unit sales volume and the replenishment upper limit volume.
8. The method of any one of claims 1 to 7, wherein after determining the heat of the good based on the unit sales volume, the second sales volume characteristic, and the second inventory characteristic, the method further comprises:
and determining the commodity space plan of the warehouse according to the heat of the commodity.
9. An apparatus for determining a degree of warmth of a commodity, comprising:
the acquisition unit is used for acquiring sales information and inventory information of commodities in a warehouse within a preset time period;
a first determining unit, configured to determine a first sales characteristic, a second sales characteristic, a first inventory characteristic, and a second inventory characteristic of the product according to the sales information and the inventory information;
a second determination unit configured to determine a unit sales amount of the commodity according to the first sales amount characteristic and the first stock characteristic;
a third determining unit configured to determine the degree of heat of the product based on the unit sales amount, the second sales amount characteristic, and the second inventory characteristic.
10. An electronic device comprising a processor and a memory, wherein the memory stores a computer program, and the processor executes the method for determining the popularity of a product according to any one of claims 1 to 7 when calling the computer program in the memory.
11. A computer-readable storage medium storing a plurality of instructions, the instructions being adapted to be loaded by a processor to execute the method for determining the popularity of a product according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116862561A (en) * | 2023-07-10 | 2023-10-10 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116862561A (en) * | 2023-07-10 | 2023-10-10 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
| CN116862561B (en) * | 2023-07-10 | 2024-01-26 | 深圳爱巧网络有限公司 | Product heat analysis method and system based on convolutional neural network |
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