CN111223235A - Commodity putting method of unmanned cabinet, unmanned cabinet and control device of unmanned cabinet - Google Patents
Commodity putting method of unmanned cabinet, unmanned cabinet and control device of unmanned cabinet Download PDFInfo
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Abstract
The application discloses commodity releasing method of unmanned cabinet, unmanned cabinet and control device thereof, wherein the commodity releasing method of unmanned cabinet comprises the following steps: acquiring user data generated by a user using the unmanned cabinet; according to the analysis requirement of commodity release, sample data is extracted from user data related to a release area; carrying out statistical analysis on the sample data to obtain the portrait characteristics of the user in the launching area; and generating a commodity putting scheme based on the portrait characteristics. According to the method, the user data of the unmanned cabinet is obtained, portrait characteristics of the user are described, the commodity release scheme of the unmanned cabinet is designed in a targeted mode, the existing commodity release scheme of the unmanned cabinet is upgraded, reasonable release points of the unmanned cabinet can be planned, and the income of the unmanned cabinet is increased.
Description
Technical Field
The application belongs to the technical field of portrait analysis, and particularly relates to a commodity putting method of an unmanned cabinet machine, the unmanned cabinet machine and a control device of the unmanned cabinet machine.
Background
With the development of science and technology, the unmanned cabinet machine is integrated with the aspects of life of people, and commodities sold by the unmanned cabinet machine can be food with long shelf life, such as beverages, bagged snacks and the like, can also be fresh food, and even can be sold as daily necessities. The unmanned cabinet machine can be operated for twenty-four hours without interruption, and simultaneously, the manpower resource is saved. In addition, the unmanned cabinet machine can be placed in a plurality of positions, and people can purchase commodities nearby, so that the unmanned cabinet machine is very convenient.
However, when the unmanned cabinet is used for laying commodities, the unmanned cabinet can only cover various commodity types, then the putting quantity of a single commodity is adjusted according to the selling condition, the turnover is limited, and the commodity can not be put in a targeted manner according to the characteristics of users in the coverage area of the unmanned cabinet.
Disclosure of Invention
The application provides a commodity release method of an unmanned cabinet, the unmanned cabinet and a control device thereof, which aim to solve the technical problem that the existing unmanned cabinet can not carry out commodity release in a targeted manner according to the characteristics of users.
In order to solve the technical problem, the application adopts a technical scheme that: a commodity release method of an unmanned cabinet machine comprises the following steps: acquiring user data generated by a user using the unmanned cabinet; according to the analysis requirement of commodity release, sample data is extracted from user data related to a release area; carrying out statistical analysis on the sample data to obtain the portrait characteristics of the user in the launching area; and generating a commodity putting scheme based on the portrait characteristics.
According to an embodiment of the present application, the performing statistical analysis on the sample data includes: and removing invalid data in the sample data.
According to an embodiment of the present application, the removing invalid data in the sample data includes: and removing invalid data of which the data value is null or does not accord with a preset rule in the sample data.
According to an embodiment of the present application, the performing statistical analysis on the sample data to obtain portrait features of the user in the delivery area includes: acquiring a data dimension of the sample data; determining a dimension characteristic value of each data dimension according to the distribution condition of the sample data in each data dimension; generating the portrait feature based on the dimensional feature values.
According to an embodiment of the present application, the determining a dimension feature value of each data dimension according to a distribution of the sample data in each data dimension includes: segmenting the sample data under the data dimension, and acquiring a segmented data value and a segmented weighted value, wherein the segmented data represents the value of the sample data in the segment, and the segmented weighted value represents the distribution proportion of the segment in the sample data; and carrying out weighted summation on the segmentation data value and the segmentation weighted value to obtain the dimension characteristic value.
According to an embodiment of the present application, the determining a dimension feature value of each data dimension according to a distribution of the sample data in each data dimension includes: classifying the sample data under the data dimension to obtain a score and a frequency value of each classification; and taking the result of adding the initial score value of the data dimension to the product of the score value and the degree value as the dimension characteristic value.
According to an embodiment of the present application, the method further comprises: continuously acquiring new user data generated by the user by using the unmanned cabinet machine; continuously extracting new sample data from new user data related to the release region according to the analysis requirement; carrying out statistical analysis on the new sample data to obtain new image characteristics of the user in the release area; and generating a new commodity putting scheme based on the new image characteristics.
According to an embodiment of the present application, the method further comprises: saving the portrait characteristics; and aiming at other commodity release requirements similar to the commodity release requirements, extracting the portrait characteristics, and generating other commodity release schemes based on the portrait characteristics.
In order to solve the above technical problem, another technical solution adopted by the present application is: the control device of the unmanned cabinet machine comprises a processor and a memory, wherein the memory stores program instructions, the processor executes the program instructions, and the program instructions are used for realizing any one of the methods.
In order to solve the above technical problem, the present application adopts another technical solution: an unmanned cabinet machine comprises the control device.
In order to solve the above technical problem, the present application adopts another technical solution: apparatus having a memory function, said apparatus storing program data executable to implement a method as claimed in any one of the preceding claims
The beneficial effect of this application is: different from the prior art, the commodity putting method of the unmanned cabinet based on the unmanned cabinet is characterized in that the user is portrayed based on the user data of the unmanned cabinet, then the commodity putting scheme of the unmanned cabinet is designed in a targeted mode, the existing commodity putting scheme of the unmanned cabinet is upgraded, reasonable putting points of the unmanned cabinet can be planned, and accordingly the income of the unmanned cabinet is increased.
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, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
fig. 1 is a schematic flow chart of an embodiment of a commodity delivery method for an unmanned cabinet machine according to the present application;
fig. 2 is a schematic flow chart of a commodity release method of the unmanned cabinet machine according to another embodiment of the present application;
fig. 3 is a schematic structural diagram of an embodiment of a control device of the unmanned cabinet of the present application;
fig. 4 is a schematic structural diagram of an embodiment of the apparatus with a storage function according to 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.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a commodity releasing method of an unmanned aerial vehicle according to an embodiment of the present application.
An embodiment of the application provides a commodity release method for an unmanned cabinet machine, which comprises the following steps:
s101: and acquiring user data generated by the user by using the unmanned cabinet machine.
The obtaining of the user data generated by the user using the unmanned cabinet machine may be obtaining of the user data generated by the user using the unmanned cabinet machine, or obtaining of the user data generated by the user using all the unmanned cabinet machines in one area. In some cases, if the area history is not put into use of the unmanned cabinet, user data of users in the area on shopping software can be acquired, or user data generated by users in adjacent similar areas using the unmanned cabinet is acquired to be used as the user data when the area is put into the unmanned cabinet for the first time.
The user data can be acquired through the mobile phone APP connected with the unmanned cabinet, and the operation can also be directly performed through the unmanned cabinet.
The user data includes user attribute data, user behavior data, user consumption data, and the like, where the user attribute data includes user registration information, such as: user name, gender, age, mobile phone number, etc.; the user behavior data comprises frequency data of using the unmanned cabinet by a user, times of scanning the unmanned cabinet by the user, times of opening an unmanned cabinet interface by the user and the like; the user consumption data comprises order transaction data, user commodity purchasing data, user goods returning and chargeback data, user recent transaction time interval and the like.
S102: according to the analysis requirement of commodity release, sample data is extracted from user data related to the release area.
And extracting correspondingly useful sample data from the user data related to the putting area according to the analysis requirement of the commodity putting. The analysis requirements of the commodity placement may include at least the following two cases:
the first method is that the analysis requirement is put in for the whole commodity type of the unmanned cabinet, corresponding sample data is extracted from user data to carry out requirement analysis, and the sample data can be the commodity type which is most purchased by the users in the area, so that the most popular type of commodity can be planned to be put in and sold on the unmanned cabinet, and the turnover of the unmanned cabinet is improved.
The other type is that the type of the merchant staying in the unmanned cabinet is fixed, the analysis requirement is the putting scheme of certain commodity, and the sample data is the sales volume of different single products of certain commodity. Of course, in other embodiments, other situations may be included, and are not limited herein.
In an embodiment, before performing the statistical analysis on the sample data, removing invalid data in the sample data is further included. Removing invalid data in the sample data comprises removing invalid data of which the data value is null or the data value does not accord with a preset rule in the sample data. For example, when a user registers an account, software batch registration exists, many users are false users, and if invalid data is not removed, the calculation amount is increased during subsequent statistical analysis, and errors of subsequent statistical analysis results are caused. The specific invalid data to be removed may be similar invalid data, such as the user name of the user is null, the phone number is null, the user age is greater than 100 years old, and the like, according to the requirements of specific sample data.
Specifically, a user order record of the last half year and corresponding user information may be extracted from the user data, where the order record includes order placement time, purchase item type, unit price of the item, total amount of consumption, and the like. The corresponding user information comprises the gender, the age and the like of the user, and invalid data of the user with the user name of null, the telephone number of null and the user age of more than 100 years are removed from the user data.
S103: and carrying out statistical analysis on the sample data to obtain the portrait characteristics of the user in the throwing area.
And carrying out statistical analysis on sample data extracted from the user data to obtain the portrait characteristics of the user in the release area. The image feature is an image feature corresponding to an analysis demand for commodity distribution, and the image feature is also an image feature corresponding to a user in a distribution area since sample data is extracted from user data related to the distribution area.
Further, performing statistical analysis on the sample data to obtain portrait features of the user in the launching area comprises:
carrying out statistical analysis on the sample data to obtain portrait characteristics of users in the throwing area, wherein the portrait characteristics comprise: acquiring data dimension of sample data; determining a dimension characteristic value of each data dimension according to the distribution condition of the sample data in each data dimension; an image feature is generated based on the dimensional feature values.
In particular, the data dimension of the sample data may be of several kinds, such as user age group division and/or consumption capability division, etc. The consumption capacity division may include a single purchase price division, an intra-cycle purchase frequency division, an intra-cycle total consumption amount division, and the like.
In an embodiment, determining the dimension characteristic value of each data dimension according to the distribution of the sample data in each data dimension includes: segmenting the sample data under the data dimension, and acquiring a segmented data value and a segmented weighted value, wherein the segmented data represents the value of the sample data in the segment, and the segmented weighted value represents the distribution proportion of the segment in the sample data; and carrying out weighted summation on the segmentation data values and the segmentation weighted values to obtain the dimension characteristic values. The segment data value may select a median number in the segment.
Specifically, the above data dimension is segmented, and it should be noted that the method is not limited to the following segmentation method:
the user age classification comprises the following steps: immature (0-18 years old), young (18-30 years old), strong (30-40 years old), middle (40-60 years old), and old (over 60 years old).
The single purchase price segment includes: for example, 5-or less, 5-to 10-or 10-to 15-or 15-to 20-or 20-to 30-or 30-to 50-or 50-to 100-or more, etc.
The in-cycle purchase frequency segment comprises: for example, within 7 days, 1 time, 2 times, 3-5 times, 5-7 times, 7-10 times, 10-15 times, etc.
The total consumption amount in the period is segmented and comprises: for example, 20 or less, 20 to 40, 40 to 60, 60 to 80, 80 to 100, or 100 or more, etc.
The feature value corresponding to each data feature can be calculated according to the following formula:
wherein m isiThe represented data values of the segments can be selected from the median of each segment, c represents the weighted value of the segments, namely the weight of each segment, and is calculated by a formula (N/N), wherein N represents the times of each segment, N represents the times of all segments, and ceil represents rounding-up.
Dimension feature values can be calculated according to the formula, and the portrait features can be generated based on the dimension feature values.
In another embodiment, determining the dimension characteristic value of each data dimension according to the distribution of the sample data in each data dimension comprises: classifying sample data under data dimensionality, and acquiring a score and a frequency value of each classification; and taking the result of adding the product of the score and the degree value to the initial score value of the data dimension as a dimension characteristic value.
Specifically, the data dimension value may be a credit, and the sample data is a consumption behavior in a single user cycle, including: unpaid, stolen, bill and payment. Assuming that the initial score value of the credit is 100, the unpaid single score is negative 2, the stolen single score is negative 5, the supplementary single score is positive 1, and the supplementary single score is positive 4, the credit of the user can be obtained by multiplying the initial score value by the negative score of the bad behavior and by the positive score of the good behavior. According to the credit degree of the user, for the user with the credit degree lower than a certain value, a measure for limiting shopping can be adopted to reduce the loss of the unmanned cabinet machine. In other embodiments, the data dimension value may be based on the consumption condition of the user, and for the user consuming more, a promotion activity may be developed to increase the customer viscosity.
S104: and generating a commodity putting scheme based on the portrait characteristics.
And generating a commodity putting scheme based on the portrait characteristics. The commodity putting scheme can be that the existing unmanned cabinet machine is used for carrying out commodity laying optimization, and can also be used for carrying out commodity laying planning on an unmanned cabinet machine newly arranged in an area. The commodity putting method of the unmanned cabinet machine is based on the user data of the unmanned cabinet machine, the user is portrayed with the portrait features, and then the commodity putting scheme of the unmanned cabinet machine is designed in a targeted mode, and the existing commodity putting scheme of the unmanned cabinet machine is upgraded, so that reasonable putting points of the unmanned cabinet machine can be planned, and the income of the unmanned cabinet machine is increased.
The method of the present application further comprises: and storing the portrait characteristics, extracting the portrait characteristics aiming at other commodity release requirements similar to the commodity release requirements, and generating other commodity release schemes based on the portrait characteristics.
The portrait features may be saved and a user portrait database may be established. And extracting sample data according to the analysis requirement of commodity release every time, and storing the user portrait characteristics obtained after statistical analysis into a user portrait database. Therefore, when the same commodity is put in the analysis demand, the portrait characteristics can be directly obtained from the user portrait database according to the analysis demand of commodity putting, the steps of extracting sample data and carrying out statistical analysis are not needed again, convenience and rapidness are achieved, and the operation analysis cost and time are saved.
The portrait features stored in the user portrait database are valid within a predetermined time period, and portrait features exceeding the predetermined time period cannot be used for generating a commodity release scheme, so that the portrait features stored in the user portrait database need to be updated regularly. The predetermined period of time may be one month, three months, or half a year, etc., and is not limited herein. Of course, a plurality of same portrait features may be stored in the user portrait database, and when the portrait features are acquired from the user portrait database according to the analysis requirement of commodity release, a commodity release scheme can be generated based on the latest portrait features; or after comparing a plurality of the latest portrait characteristics, the future trend can be comprehensively analyzed and predicted according to the trend of the portrait characteristics, so that a commodity putting scheme is generated.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a commodity release method for an unmanned aerial vehicle according to another embodiment of the present application.
Another embodiment of the present application provides a commodity distribution method for an unmanned cabinet, including the following steps:
s201: and acquiring user data generated by the user using the unmanned cabinet machine.
Step S201 is substantially the same as the corresponding step described above, and is not described herein again.
S202: and extracting sample data from the user data according to the analysis requirement of commodity release.
Step S202 is substantially the same as the corresponding steps described above, and is not described here again.
S203: and carrying out statistical analysis on the sample data to obtain the portrait characteristics of the user using the unmanned cabinet machine.
Step S203 is substantially the same as the corresponding step described above, and is not described herein again.
S204: and generating a commodity putting scheme based on the portrait characteristics.
Step S204 is substantially the same as the corresponding step described above, and is not described herein again.
S205: and continuously acquiring new user data generated by the user using the unmanned cabinet.
After the unmanned cabinet implements the commodity putting scheme in step S204, new user data after the unmanned cabinet implements the commodity putting scheme is continuously obtained. The new user data includes user attribute data, user behavior data, user consumption data, and the like, where the user attribute data includes user registration information, such as: user name, gender, age, mobile phone number, etc.; the user behavior data comprises frequency data of using the unmanned cabinet by a user, times of scanning the unmanned cabinet by the user, times of opening an unmanned cabinet interface by the user and the like; the user consumption data comprises order transaction data, user commodity purchasing data, user goods returning and chargeback data, user recent transaction time interval and the like.
S206: and continuously extracting new sample data from the new user data related to the putting area according to the analysis requirement of commodity putting.
And continuously extracting correspondingly useful new sample data from the new user data related to the putting area according to the analysis requirement of the commodity putting. Here, the commodity release analysis requirement is a requirement for optimizing the product release type, for example: goods are laid on the unmanned cabinet machine according to a commodity release scheme, the yield is still enough within a period of time, and then the unmanned cabinet machine slips down, so that the change of user requirements along with objective conditions can be timely and continuously analyzed, and product release optimization is carried out to obtain higher yield. For another example: goods laying has been carried out on the unmanned cabinet machine according to the goods putting scheme, and the income is enough, so user demands can also be continuously analyzed, and then the goods with higher unit price and income are tried to be laid, or new goods are tried to be laid, and the turnover is increased as much as possible in the only space of the unmanned cabinet machine, so that the income maximization is achieved.
And extracting new sample data from the new user data related to the release area according to the analysis requirement of release of the specific new commodity.
In another embodiment, before performing the statistical analysis on the new sample data, the method further includes removing invalid data in the new sample data. Removing invalid data in the new sample data comprises removing invalid data of which the data value is null or the data value does not accord with a preset rule in the new sample data. For example, when a user registers an account, software batch registration exists, many users are false users, and if invalid data is not removed, the calculation amount is increased during subsequent statistical analysis, and errors of subsequent statistical analysis results are caused. The specific invalid data to be removed may be similar invalid data, such as the user name of the user is null, the phone number is null, the user age is over 100 years old, and the like, according to the requirement of the specific new sample data.
S207: and carrying out statistical analysis on the new sample data to obtain new image characteristics of the user in the release area.
And carrying out statistical analysis on new sample data extracted from the new user data to obtain new image characteristics of the users in the release area. The new image feature is a new image feature corresponding to the analysis requirement for commodity release, and the new sample data is extracted from new user data related to the release area, so the new image feature is also a new image feature corresponding to the user in the release area.
Further, performing statistical analysis on the new sample data to obtain portrait features of the user in the launching area comprises:
carrying out statistical analysis on the sample data to obtain portrait characteristics of users in the throwing area, wherein the portrait characteristics comprise: acquiring data dimension of sample data; determining a dimension characteristic value of each data dimension according to the distribution condition of the sample data in each data dimension; an image feature is generated based on the dimensional feature values. The specific statistical analysis method is substantially the same as step S103, and is not described herein again.
S208: and generating a new commodity putting scheme based on the new image characteristics.
And generating a new commodity putting scheme based on the new image characteristics. The new commodity putting scheme upgrades the existing commodity putting scheme of the unmanned cabinet, and pointedly improves the turnover as much as possible in the only space of the unmanned cabinet according to the user transaction condition of the unmanned cabinet, thereby achieving the maximum profit.
Referring to fig. 3, fig. 3 is a schematic structural diagram of an embodiment of the control device of the unmanned cabinet according to the present application. The control device 30 includes a processor 31 and a memory 32. The memory 32 stores program instructions, and the processor 31 executes the program instructions to implement the commodity delivery method of the unmanned aerial vehicle in any of the above embodiments. Specifically, the processor 31 extracts sample data from user data related to the delivery area according to an analysis requirement of commodity delivery, performs statistical analysis on the sample data to obtain portrait features of users in the delivery area, and finally generates a commodity delivery scheme based on the portrait features. The control device 30 may be installed on the unmanned cabinet, or may be installed on a server.
Another embodiment of the present application provides an unmanned cabinet, including the above-mentioned control device, where the control device implements the commodity distribution method of the unmanned cabinet in any of the above-mentioned embodiments, so that a merchant can operate on the unmanned cabinet when laying a commodity, thereby obtaining an optimal commodity distribution method in real time and performing a corresponding commodity distribution operation.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an embodiment of a device with a memory function 40 according to the present application. The device 40 stores program data 41, and the program data 41 can be executed to realize the commodity putting method of the unmanned aerial vehicle according to any one of the embodiments. That is, when the commodity putting method of the unmanned aerial vehicle is implemented in the form of software and sold or used as an independent product, the software can be stored in the device 40 with a storage function, which can be read by an electronic device. The storage-enabled device 40 may be a usb-disk, an optical disk, or a server.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (11)
1. A commodity putting method of an unmanned cabinet machine is characterized by comprising the following steps:
acquiring user data generated by a user using the unmanned cabinet;
according to the analysis requirement of commodity release, sample data is extracted from user data related to a release area;
carrying out statistical analysis on the sample data to obtain the portrait characteristics of the user in the launching area;
and generating a commodity putting scheme based on the portrait characteristics.
2. The method of claim 1, wherein said statistically analyzing said sample data previously comprises:
and removing invalid data in the sample data.
3. The method of claim 2, wherein said removing invalid data from said sample data comprises:
and removing invalid data of which the data value is null or does not accord with a preset rule in the sample data.
4. The method of claim 1, wherein the performing a statistical analysis on the sample data to obtain an image characteristic of the user in the delivery area comprises:
acquiring a data dimension of the sample data;
determining a dimension characteristic value of each data dimension according to the distribution condition of the sample data in each data dimension;
generating the portrait feature based on the dimensional feature values.
5. The method of claim 4, wherein said determining a dimension feature value for each said data dimension according to a distribution of said sample data in each said data dimension comprises:
segmenting the sample data under the data dimension, and acquiring a segmented data value and a segmented weighted value, wherein the segmented data represents the value of the sample data in the segment, and the segmented weighted value represents the distribution proportion of the segment in the sample data;
and carrying out weighted summation on the segmentation data value and the segmentation weighted value to obtain the dimension characteristic value.
6. The method of claim 4, wherein said determining a dimension feature value for each said data dimension according to a distribution of said sample data in each said data dimension comprises:
classifying the sample data under the data dimension to obtain a score and a frequency value of each classification;
and taking the result of adding the initial score value of the data dimension to the product of the score value and the degree value as the dimension characteristic value.
7. The method of claim 1, further comprising:
continuously acquiring new user data generated by the user by using the unmanned cabinet machine;
continuously extracting new sample data from new user data related to the release region according to the analysis requirement;
carrying out statistical analysis on the new sample data to obtain new image characteristics of the user in the release area;
and generating a new commodity putting scheme based on the new image characteristics.
8. The method of claim 1, further comprising:
saving the portrait characteristics;
and aiming at other commodity release requirements similar to the commodity release requirements, extracting the portrait characteristics, and generating other commodity release schemes based on the portrait characteristics.
9. A control device for an unmanned cabinet machine, comprising a processor, a memory, the memory storing program instructions, the processor executing the program instructions, and the program instructions implementing the method according to any one of claims 1 to 8.
10. An unmanned cabinet machine, comprising the control device of claim 9.
11. An apparatus having a storage function, characterized in that the apparatus stores program data which can be executed to implement the method according to any one of claims 1-8.
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| CN112767596A (en) * | 2021-01-08 | 2021-05-07 | 格力电器（武汉）有限公司 | Self-service vending equipment and monitoring method, equipment, system and storage medium thereof |
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