CN112101980A - Method and system for analyzing purchase preference of user - Google Patents
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Abstract
The invention discloses a method and a system for analyzing purchasing preference of a user, wherein the method comprises the following steps: analyzing the purchasing power of the user based on the historical purchasing expenditure; acquiring a term and a used term of a first product historically purchased by a user, and analyzing historical purchase preference of the user based on the term and the used term; acquiring the number of persons purchasing associated products in a user interaction circle, and acquiring association purchase preference of the interaction circle based on the number of persons purchasing the associated products; acquiring the number of people purchasing a first product in a user interaction circle, and analyzing the purchasing preference of the interaction circle based on the number of people purchasing the first product in the interaction circle; acquiring the associated use condition of a user on a first product, and analyzing the product associated preference of the user; analyzing the user purchase preference based on the analysis result. The purchasing preference of the user is comprehensively analyzed from multiple dimensions, and the purchasing preference of the user for a specific product is analyzed through deep mining on historical data, so that the purchasing requirement of the user for the specific product is accurately reflected.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a system for analyzing purchase preference of a user.
Background
The purchase preference analysis is to mine historical behavior data and predict the future event purchase behavior of the user according to reliable calculation. With the continuous development of information technology, the amount of information generated by users is continuously increased, and the amount of information of products is also continuously increased; how to mine the information and perform preference analysis on the user purchase becomes a key problem of product recommendation. In the existing purchasing scene, products which are interested by a user are analyzed through historical browsing information of the user, and then the analyzed products are recommended to the user.
The data mining depth of the analysis method is low, only interested products are recommended to the user, and the purchase preference of the user for specific products cannot be analyzed.
Disclosure of Invention
Aiming at the technical problems in the prior art, the invention provides a method and a system for analyzing the purchasing preference of a user, which are used for deeply mining the purchasing preference of the user for a specific product based on historical data.
The invention discloses a method for analyzing purchasing preference of a user, which comprises the following steps: acquiring historical purchase expenditure of a user, and analyzing purchasing power of the user based on the historical purchase expenditure; acquiring a term and a used term of a first product historically purchased by a user, and analyzing historical purchase preference of the user based on the term and the used term; acquiring the number of persons purchasing associated products in a user interaction circle, and acquiring association purchase preference of the interaction circle based on the number of persons purchasing the associated products; acquiring the number of people who purchase the first product in a user interaction circle, and analyzing the purchasing preference of the interaction circle based on the number of people who purchase the first product in the interaction circle; acquiring the associated use condition of a user on a first product, and analyzing the product associated preference of the user; analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences.
Preferably, the invention also comprises a method for establishing the user purchase preference model, which comprises the following steps: respectively setting a scoring model and a weighting of the purchasing power, the historical purchasing preference, the relationship circle associated purchasing preference, the relationship circle purchasing preference and the product associated preference; and calculating the total score of the purchase preference according to the scoring model and the weight.
Preferably, the method for constructing the scoring model of purchasing power includes: and setting a scoring interval of the purchasing power, and assessing the scoring of the purchasing power according to the historical purchasing expenditure and the scoring interval.
Preferably, the method for constructing the historical purchasing preference scoring model comprises the following steps: setting a rating interval of the historical purchase duration, and rating the rating of the historical purchase preference according to the duration of the historical purchase of the first product, the used duration and the rating interval.
Preferably, the method for constructing the cross circle associated purchasing preference scoring model comprises the following steps: and setting a grading interval of the number of people purchasing the associated products in the contact circle, and evaluating the grading of the associated purchasing preference of the contact circle according to the number of people purchasing the associated products in the contact circle and the grading interval.
Preferably, the method for constructing the cross-circle purchase preference scoring model comprises the following steps: and setting a scoring interval of the number of people who buy the first product in the contact circle, and assessing the scoring of the purchasing preference of the contact circle according to the number of people who buy the first product in the contact circle and the scoring interval.
Preferably, the method for constructing the association preference score model comprises the following steps: and setting a scoring interval of the product association use condition of the user, and evaluating the score of the product association preference according to the product association use condition of the user and the scoring interval.
Preferably, the purchase preference model is used for analyzing the purchase preference of the mobile phone contract service: analyzing the purchasing power of the user based on the expenditure of the user for paying out; analyzing historical purchase preference based on the remaining duration of the mobile phone contract service purchased historically; analyzing the association purchase preference of the contact circle based on the number of people who purchase the mobile phone in the contact circle; analyzing purchasing preference of the contact circle based on the number of people who purchase the mobile phone contract service in the contact circle; analyzing product association preference of a user based on the use condition of the mobile phone internet traffic; analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences.
Preferably, the present invention further comprises a method of generating a recommendation list based on the purchase preference analysis: obtaining a product which is interested by a user; generating a first recommendation list according to a product in which a user is interested, wherein the first recommendation list comprises at least two recommended products; according to the method for analyzing the purchasing preference, analyzing the purchasing preference of the recommended product; and sequencing the first recommendation list according to the purchase preference to generate a second recommendation list.
The invention also provides a system adopting the purchasing preference analysis method, which comprises a purchasing power analysis module, a historical purchasing preference analysis module, a communication circle associated purchasing preference analysis module, a communication circle purchasing preference analysis module, a product associated preference analysis module and a purchasing preference analysis module, wherein the purchasing power analysis module is used for acquiring the historical purchasing expenditure of the user and analyzing the purchasing power of the user based on the historical purchasing expenditure; the historical purchasing preference analysis module is used for acquiring the period and the used period of the product purchased by the user in history and analyzing the historical purchasing preference of the user based on the period and the used period of the product purchased by the user; the contact circle associated purchase preference analysis module is used for acquiring the number of people who purchase associated products in a user contact circle and analyzing contact circle associated purchase preference based on the number of people who purchase associated products; the contact circle purchasing preference analysis module is used for acquiring the number of people who purchase the product in a contact circle of a user and judging contact circle purchasing preference based on the number of people who purchase the product in the contact circle; the product association preference analysis module is used for acquiring the product association use condition of the user and analyzing the product association preference of the user; the user purchase preference analysis module analyzes user purchase preferences based on the purchasing power, historical purchase preferences, cross-circle associated purchase preferences, cross-circle purchase preferences, and product associated preferences.
Compared with the prior art, the invention has the beneficial effects that:
and comprehensively analyzing the purchasing preference of the user from the dimensionalities of the purchasing power, the historical purchasing preference, the association purchasing preference of the circle of contact, the purchasing preference of the circle of contact and the association preference of the product, and analyzing the purchasing preference of the user for the specific product through deep mining of historical data, thereby accurately reflecting the purchasing demand of the user for the specific product.
Drawings
FIG. 1 is a flow chart of a method of user purchase preference analysis of the present invention;
FIG. 2 is a flow diagram of a method for analyzing purchase preferences of a mobile phone contract service;
FIG. 3 is a flow diagram of a method of generating a recommendation list;
FIG. 4 is a logical block diagram of a user purchase preference analysis system of the present invention;
FIG. 5 is a flow diagram of a method of modeling purchase preferences of a user.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The invention is described in further detail below with reference to the attached drawing figures:
a method of analyzing purchasing preferences of a user, as shown in fig. 1, the method comprising:
step 101: and acquiring historical purchasing expenditure of the user, and analyzing purchasing power of the user based on the historical purchasing expenditure.
The historical purchase expenditure can be acquired from the charge information of the user, and it is worth proposing that sensitive information of the user needs to be acquired on the premise of authorization of the user; the user's purchase expenditure may also be derived from the financial system's billed income. Wherein the term of the historical purchase expenditure may be set within 1 year or within 2 years, but is not limited thereto.
Step 102: acquiring the period and the used period of the first product purchased by the user in history, and analyzing the historical purchase preference of the user based on the period and the used period.
Wherein the first product may comprise a physical product or a service product, the product has a certain period, for example, the period of the contract service may be 24 periods, the remaining period is calculated by the period and the used period, and the remaining period of the contract service is used for analyzing the historical purchasing preference of the user, namely, when the first product is expired or is about to expire, the user has a higher preference to purchase the product again.
Step 103: the method comprises the steps of obtaining the number of persons purchasing associated products in a user interaction circle, and obtaining association purchase preference of the interaction circle based on the number of persons purchasing the associated products.
The contact circle refers to a crowd who directly communicates with the user within a certain period, and when the number of people who purchase the associated products in the contact circle is large, the preference of the user for purchasing the first product is higher. Direct communication refers to communication in a manner that can be detected through phone contact, short message contact, or online contact.
Step 104: and acquiring the number of people who purchase the first product in the user interaction circle, and analyzing the purchasing preference of the interaction circle based on the number of people who purchase the first product in the interaction circle. I.e. the number of people in the circle of interaction who buy the first product is higher, the user's preference for buying the first product is higher.
Step 105: and acquiring the associated use condition of the user on the first product and acquiring the product associated preference of the user. Wherein the usage condition has relevance with the first product, such as the usage condition of the mobile phone has relevance with the mobile phone, and the purchase preference of the mobile phone contract service is influenced by the usage condition of the mobile phone; also, the purchasing preference of paper and ink cartridges is influenced by the usage of the printer.
Step 106: analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences.
And comprehensively analyzing the purchasing preference of the user from the dimensionalities of the purchasing power, the historical purchasing preference, the association purchasing preference of the circle of contact, the purchasing preference of the circle of contact and the association preference of the product, analyzing the purchasing preference of the user for the specific product through deep mining of historical data, and accurately reflecting the purchasing demand of the user for the specific product.
As shown in FIG. 5, the present invention further includes a method for establishing a user purchase preference model:
step 501: and respectively setting the scoring models and weights of the purchasing power, the historical purchasing preference, the relationship circle associated purchasing preference, the relationship circle purchasing preference and the product associated preference. The scoring model is used for quantitatively measuring scoring values of the purchasing power, the historical purchasing preference, the association purchasing preference of the contact circle, the purchasing preference of the contact circle and the association preference of the product, and the weight is used for measuring the proportion of each scoring in the total scoring.
Step 502: and calculating the total score of the purchase preference according to the scoring model and the weight. The purchase preference total score is used to quantitatively measure the purchase preference.
Example 1
The method for constructing the purchasing power scoring model comprises the following steps: setting a scoring interval of purchasing power; and evaluating the score of the purchasing power according to the historical purchasing expenditure and the score interval (P1).
Take the communication cost of the mobile phone as an example: when the monthly communication fee is below 60 yuan, the score is 0; score 0.2 at 60-80; at 80-120, score 0.4; at 120-; between 180 and 300, score 0.8; above 300, a score of 1 is given. But not limited thereto, the score interval may be set according to the category price of the product.
Example 2
The method for constructing the historical purchasing preference scoring model comprises the following steps: setting a score interval of the historical purchase term, and evaluating the score of the historical purchase preference according to the term of the historical purchase first product, the used term and the score interval of the historical purchase term (P2).
Take a mobile phone contract service as an example: the mobile phone contract service is not used, and the score is 0; the mobile phone contract service expiration residual month number (M) is more than 24 months, and the score is 0; the number of remaining expired months of the mobile phone contract service is less than 24 months, and the mobile phone contract service is scored: m/24; the service life of the mobile phone contract service is less than 6 months, and the score is 0 or negative. The preference of buying the mobile phone contract service again is greater than that of the user who has not bought the mobile phone contract service; and the user with less residual months of the contract purchases the mobile phone contract service again, and the preference of the user with more months of the contract is greater than that of the user with more months of the contract. Wherein the repurchase preference is low when the lifetime of the first product is low; the total score may be adjusted by a negative value when the first product is not provided to the user for a lower lifetime of the first product.
The historical purchasing preference can also be scored by associated product analysis (P2), such as mobile phone rental service, the more months (N) a user purchases mobile phone rental service, the greater the purchasing preference of mobile phone contract service, the score is: n/24.
Example 3
The method for constructing the cross circle associated purchase preference scoring model comprises the following steps: and setting a scoring interval of the number of persons purchasing the related products in the contact circle, and assessing the scoring of the contact circle related purchasing preference according to the number of persons purchasing the related products in the contact circle and the scoring interval (P3).
Taking the purchase of the mobile phone contract service as an example, the user contact circles are arranged in descending order according to the contact times, the top 10 people are taken, and the number of people who exchange mobile phones among the people is obtained. When the number of mobile phone users is 0, the mobile phone users are scored for 0; the number of people changing mobile phones is 1, and the score is 0.4; the number of mobile phone users is 2, and the score is 0.6; the number of people who change mobile phones exceeds 3, and the score is 1. That is, the greater the number of people who exchange mobile phones within the circle, the greater the preference for purchasing mobile phone contract services. The mobile phone contract service is used as a first product, and the mobile phone is used as a related product.
Example 4
The method for constructing the cross circle purchasing preference scoring model comprises the following steps: and setting a scoring interval of the number of people who buy the first product in the contact circle, and assessing the scoring of the purchasing preference of the contact circle according to the number of people who buy the first product in the contact circle and the scoring interval (P4).
Taking the purchase of the mobile phone contract service as an example, the number of people who purchase the mobile phone contract service in the 10 people before the contact circle is obtained. When the number of the purchasers is 0, the score is 0; when the number of the purchasers is 1, the score is 0.4; the number of purchasers is 2, and the score is 0.6; when the number of purchasers exceeds 3, the score is 1.
Example 5
The method for constructing the associated preference scoring model comprises the following steps: and setting a scoring interval of the product association use condition of the user, and assessing the scoring of the association preference according to the first product association use condition of the user and the scoring interval (P5). The usage may include frequency of use or amount of use. Wherein the dimensions of the first product association and its use should be set according to the product's category and most associated use.
Taking a mobile phone contract service as an example, the mobile phone traffic service condition is used as the most relevant service: when the using flow is 0, scoring 0; when the flow rate is 0-30M, the score is 0.8; when the flow rate is 30-800M, the score is: flow rate/800; when the usage flow rate exceeds 800M, the score is 0.
The user purchase preference model comprises the scores of the scoring models and the weights of the scoring models:
P＝P1×K1+P2×K2+P3×K3+P4×K4+P5×K5 (1)
wherein K1-K5 are weight coefficients and P is the total score of the purchasing preferences.
The invention may also include a method of ranking purchasing preferences according to P: and dividing the grade of the purchase preference according to the P and the grading interval thereof, wherein the grade is as follows:
| purchase preference Total score (P) | Purchasing preference level |
| 0-30 | Non-potential users |
| 30-40 | Low grade |
| 40-50 | Intermediate grade |
| 50-100 | High grade |
Example 6
As shown in FIG. 2, the purchase preference model of the present invention is applied to the analysis of the purchase preferences of a mobile phone contract service:
step 201: the purchasing power of the user is analyzed based on the expenditure of the user, and a purchasing power score P1 is obtained.
Step 202: and analyzing the historical purchasing preference based on the remaining term of the mobile phone contract service of the historical purchase to obtain a historical purchasing preference score P2. Wherein, the mobile phone contract service is used as a first product.
Step 203: and analyzing the association purchase preference of the contact circle based on the number of people who purchase the mobile phone in the contact circle to obtain a contact circle association purchase preference score P3. Wherein, the mobile phone is taken as a related product.
Step 204: and analyzing the purchasing preference of the contact circle based on the number of people who purchase the mobile phone contract service in the contact circle to obtain a contact circle purchasing preference score P4.
Step 205: and analyzing the product association preference of the user based on the use condition of the mobile phone internet traffic to obtain a product association preference score P5. The mobile phone internet traffic is used as a first product to correlate the use condition.
Step 206: analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences. Taking K1 as 20, K2 as 20, K3 as 25, K4 as 25, and K5 as 10, calculating the user's score (P) for mobile contract service purchase preference according to formula 1:
P＝P1×20+P2×20+P3×25+P4×25+P5×10 (2)。
the expenditure of the user, the remaining period of the mobile phone contract service purchased historically, the number of people who purchase the mobile phone or purchase the mobile phone contract service in the contact circle and the use condition of the mobile phone internet traffic are historical data stored in advance or can be obtained through calculation of the data.
The invention also tests the purchasing preference analysis method on line for 1 month: the method comprises the following steps of establishing a purchasing preference model according to a method for analyzing purchasing preference of the mobile phone contract service, recommending the mobile phone contract service to users with different purchasing preference levels, wherein the purchasing rate of the mobile phone contract service is shown in the following table:
| purchasing preference level | Purchase rate |
| Non-potential users | 0.5％ |
| Low grade | 3.7％ |
| Intermediate grade | 9.5％ |
| High grade | 18.1％ |
As can be seen from the table, the model provided by the invention can effectively reflect the purchasing preference of the user.
Example 7
As shown in FIG. 3, the present invention also includes a method for generating a recommendation list based on the purchase preference analysis:
step 301: the product which the user is interested in is obtained according to the product browsed or consulted by the user.
Step 302: generating a first recommendation list according to the product in which the user is interested, wherein the first recommendation list comprises at least two recommended products. The product or related product related to the product can be generated according to the product in which the user is interested, and for example, the similar mobile phone, the mobile phone shell and the mobile phone package can be recommended to the user when the user consults or browses the mobile phone.
Step 303: and according to the method for analyzing the purchasing preference, analyzing the purchasing preference of the recommended product.
Step 304: and sequencing the first recommendation list according to the purchase preference to generate a second recommendation list. The ranking may be in a descending order of the total purchase preference score.
The invention also provides a system for analyzing the purchasing preference of the user, as shown in fig. 4, the system comprises a purchasing power analysis module 1, a historical purchasing preference analysis module 2, a communication circle associated purchasing preference analysis module 3, a communication circle purchasing preference analysis module 4, a product associated preference analysis module 5 and a purchasing preference analysis module 6, wherein the purchasing power analysis module 1 is used for acquiring the historical purchasing expenditure of the user and analyzing the purchasing power of the user based on the historical purchasing expenditure; the historical purchasing preference analysis module 2 is used for acquiring the period and the used period of the product purchased by the user in history and analyzing the historical purchasing preference of the user based on the period and the used period of the product purchased by the user; the contact circle associated purchase preference analysis module 3 is used for acquiring the number of people purchasing associated products in a user contact circle and analyzing contact circle associated purchase preference based on the number of people purchasing associated products; the contact circle purchasing preference analysis module 4 is used for acquiring the number of people who purchase the product in a contact circle of a user, and judging contact circle purchasing preference based on the number of people who purchase the product in the contact circle; the product association preference analysis module 5 is used for acquiring the product association use condition of the user and analyzing the product association preference of the user; user purchase preference analysis module 6 analyzes user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact purchase preferences, and product association preferences.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for analyzing purchasing preferences of a user, the method comprising:
acquiring historical purchase expenditure of a user, and analyzing purchasing power of the user based on the historical purchase expenditure;
acquiring a term and a used term of a first product historically purchased by a user, and analyzing historical purchase preference of the user based on the term and the used term;
acquiring the number of persons purchasing associated products in a user interaction circle, and acquiring association purchase preference of the interaction circle based on the number of persons purchasing the associated products;
acquiring the number of people who purchase the first product in a user interaction circle, and analyzing the purchasing preference of the interaction circle based on the number of people who purchase the first product in the interaction circle;
acquiring the associated use condition of a user on a first product, and analyzing the product associated preference of the user;
analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences.
2. The method of purchase preference analysis according to claim 1, further comprising a method of building a user purchase preference model:
respectively setting a scoring model and a weighting of the purchasing power, the historical purchasing preference, the relationship circle associated purchasing preference, the relationship circle purchasing preference and the product associated preference;
and calculating the total score of the purchase preference according to the scoring model and the weight.
3. The method of purchasing preference analysis as claimed in claim 2, wherein the method of constructing the scoring model of purchasing power comprises:
and setting a scoring interval of the purchasing power, and assessing the scoring of the purchasing power according to the historical purchasing expenditure and the scoring interval.
4. The method of purchasing preference analysis as claimed in claim 2, wherein the method of building a historical purchasing preference score model comprises:
setting a rating interval of the historical purchase duration, and rating the rating of the historical purchase preference according to the duration of the historical purchase of the first product, the used duration and the rating interval.
5. The method of purchasing preference analysis as claimed in claim 2, wherein the method of constructing a circle of contact associated purchasing preference scoring model comprises:
and setting a grading interval of the number of people purchasing the associated products in the contact circle, and evaluating the grading of the associated purchasing preference of the contact circle according to the number of people purchasing the associated products in the contact circle and the grading interval.
6. The method of purchasing preference analysis as claimed in claim 2, wherein the method of constructing a circle of business purchasing preference scoring model includes:
and setting a scoring interval of the number of people who buy the first product in the contact circle, and assessing the scoring of the purchasing preference of the contact circle according to the number of people who buy the first product in the contact circle and the scoring interval.
7. The method of purchase preference analysis according to claim 2, wherein the method of constructing a relevance preference scoring model comprises:
and setting a scoring interval of the product association use condition of the user, and evaluating the score of the product association preference according to the product association use condition of the user and the scoring interval.
8. The method of purchase preference analysis according to claim 2, wherein the purchase preference model is used for analysis of purchase preferences of a mobile contract service,
analyzing the purchasing power of the user based on the expenditure of the user for paying out;
analyzing historical purchase preference based on the remaining duration of the mobile phone contract service purchased historically;
analyzing the association purchase preference of the contact circle based on the number of people who purchase the mobile phone in the contact circle;
analyzing purchasing preference of the contact circle based on the number of people who purchase the mobile phone contract service in the contact circle;
analyzing product association preference of a user based on the use condition of the mobile phone internet traffic;
analyzing user purchase preferences based on the purchasing power, historical purchase preferences, circle of contact associated purchase preferences, circle of contact purchase preferences, and product associated preferences.
9. The method of purchase preference analysis of claim 2, further comprising the method of generating a recommendation list from the purchase preference analysis:
obtaining a product which is interested by a user;
generating a first recommendation list according to a product in which a user is interested, wherein the first recommendation list comprises at least two recommended products;
according to the method for analyzing the purchasing preference, analyzing the purchasing preference of the recommended product;
and sequencing the first recommendation list according to the purchase preference to generate a second recommendation list.
10. A system using the purchasing preference analysis method of any one of claims 1 to 9, wherein the system comprises a purchasing power analysis module, a historical purchasing preference analysis module, a circle of contact associated purchasing preference analysis module, a circle of contact purchasing preference analysis module, a product associated preference analysis module and a purchasing preference analysis module,
the purchasing power analysis module is used for acquiring historical purchasing expenditure of the user and analyzing the purchasing power of the user based on the historical purchasing expenditure;
the historical purchasing preference analysis module is used for acquiring the period and the used period of the product purchased by the user in history and analyzing the historical purchasing preference of the user based on the period and the used period of the product purchased by the user;
the contact circle associated purchase preference analysis module is used for acquiring the number of people who purchase associated products in a user contact circle and analyzing contact circle associated purchase preference based on the number of people who purchase associated products;
the contact circle purchasing preference analysis module is used for acquiring the number of people who purchase the product in a contact circle of a user and judging contact circle purchasing preference based on the number of people who purchase the product in the contact circle;
the product association preference analysis module is used for acquiring the product association use condition of the user and analyzing the product association preference of the user;
the user purchase preference analysis module analyzes user purchase preferences based on the purchasing power, historical purchase preferences, cross-circle associated purchase preferences, cross-circle purchase preferences, and product associated preferences.
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| CN112950323A (en) * | 2021-03-12 | 2021-06-11 | 广东来个碗网络科技有限公司 | Tableware circulation management method and device based on intelligent rental cabinet |
| CN113793180A (en) * | 2021-09-15 | 2021-12-14 | 北京沃东天骏信息技术有限公司 | User preference analysis method, device, equipment and computer storage medium |
| CN114022228A (en) * | 2022-01-06 | 2022-02-08 | 深圳市思迅软件股份有限公司 | Economic information data processing method, system, computer equipment and storage medium |
| CN115187177A (en) * | 2022-09-07 | 2022-10-14 | 国连科技(浙江)有限公司 | Method and device for managing warehouse of goods of merchants in sales promotion activities |
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| CN115187177A (en) * | 2022-09-07 | 2022-10-14 | 国连科技(浙江)有限公司 | Method and device for managing warehouse of goods of merchants in sales promotion activities |
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