CN117788124A - Data management system and method for customized products - Google Patents
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Abstract
The invention relates to the technical field of data management, and particularly provides a data management system and method for customizing a product, wherein the system comprises the following steps: the storage unit comprises a first storage module, a second storage module and a recommended data storage module, wherein the first storage module, the second storage module and the recommended data storage module are used for data storage; the acquisition module is used for acquiring the historical login times N of a user in the product online platform and browsing data of each time; the grade module is used for determining the grade of the user according to the historical login times N; the processing module is used for determining an initial recommendation period when the customized product is recommended to the user according to the storage position of the browsing data of the user; and the recommending module is used for recommending the customized product to the user according to the initial recommending period and the initial recommending times P0. The invention not only can effectively store the user data in a grading way and improve the management efficiency of the data, but also can greatly improve the accuracy of recommending the product while effectively screening the user.
Description
Technical Field
The invention relates to the technical field of data management, in particular to a data management system and method for customized products.
Background
At present, with the continuous development of internet technology, more and more transactions and orders are generated on line, and compared with the traditional off-line transactions, the method has higher efficiency. Particularly, with the popularization of online product platforms, users can purchase goods on online platforms without going out.
In conventional vehicle customization, a user must physically select a vehicle model, an internal cabinet module, an external function module, painting, etc. on site at a factory or at an office, and these expressions are generally described orally, sales personnel record, the sales personnel feed back the recorded contents to a designer, and in the process of transferring, the transferred user demands may deviate, so that the on-line product platform of the vehicle can provide a good purchase path for the user.
However, in the above prior art, after a user logs in to the product platform, a large amount of user data is generated, and how to effectively manage the user data in a hierarchical manner becomes a problem to be solved urgently.
Disclosure of Invention
In view of this, the present invention proposes a data management system and method for customizing a product, which aims to solve the problem of how to effectively manage user data in an online platform of the product.
In one aspect, the present invention provides a data management system for customizing a product, comprising:
the storage unit comprises a first storage module, a second storage module and a recommended data storage module, wherein the first storage module, the second storage module and the recommended data storage module are used for data storage;
the acquisition module is used for acquiring the historical login times N of the user in the product online platform and browsing data of each time when the user logs in the product online platform to browse customized products;
a grade module, configured to determine a grade of the user according to the historical login frequency N, and determine a storage location of browsing data of the user according to the grade of the user, where the grade of the user includes a first user grade A1, a second user grade A2, and a third user grade A3,
when n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into the first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is at the second user level A2, storing browsing data of the user into the second storage module;
When 3 < N, setting the user level as the third user level A3; when the user is at the third user level A3, storing the browsing data of the user into the recommended data storage module;
the processing module is used for determining an initial recommendation period when the user is subjected to customized product recommendation according to the storage position of the browsing data of the user;
the processing module is further configured to obtain a historical single average online time duration Tb of the user in the product online platform after the initial recommendation period is determined, and determine an initial recommendation frequency P0 when performing customized product recommendation on the user according to the historical single average online time duration Tb, where t0=n×s, N is the historical login frequency, and s is the single online time duration of the user;
and the recommending module is used for recommending the customized product to the user according to the initial recommending period and the initial recommending times P0.
Further, the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2, further obtaining the online time length of the user when logging in the product online platform each time, recording as the Nth online time length TN, presetting single reference online time length Ta, and determining whether to adjust the currently set second user grade A2 of the user according to the Nth online time length TN of the user when logging in the product online platform each time:
When n=1, comparing the 1 st login online time length T1 with the single reference online time length Ta;
if T1 is smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1; if T1 is larger than or equal to Ta, the second user level A2 of the currently set user is not adjusted.
Further, the ranking module is further configured to, when 0 < n+.3, set the ranking of the user to the second user ranking A2, include:
when n=2, determining a1 st login online time length T1 and A2 nd login online time length T2, and comparing the 1 st login online time length T1, the 2 nd login online time length T2 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the currently set user:
when T1+T2 is less than or equal to Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when t1+t2 > Ta,
if Ta-T1 is larger than Ta-T2, not adjusting the second user level A2 of the currently set user;
and if Ta-T1 is less than or equal to Ta-T2, adjusting the second user level A2 of the currently set user to be a third user level A3.
Further, the ranking module is further configured to, when 0 < n+.3, set the ranking of the user to the second user ranking A2, include:
When n=3, determining a1 st login online time length T1, A2 nd login online time length T2 and a3 rd login online time length T3, and comparing the 1 st login online time length T1, the 2 nd login online time length T2, the 3 rd login online time length T3 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the user currently set:
when T1, T2 and T3 are all smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when T1, T2 and T3 are all greater than Ta,
if Ta-T2 > Ta-T3, not adjusting the second user level A2 of the currently set user;
and if Ta-T2 is less than or equal to Ta-T3, adjusting the second user level A2 of the currently set user to be a third user level A3.
Further, the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
when 3 < N, setting the user level as the third user level A3, determining whether to adjust the currently set third user level A3 of the user according to a comparison result between the nth login online time period TN and the single reference online time period Ta:
If t1+t2+t3+ + TN is less than or equal to Ta, adjusting the currently set third user level A3 of the user to be a first user level A1;
if Ta is less than t1+t2+t3+ & ltta is less than or equal to 2Ta, adjusting the third user level A3 of the user currently set to be a second user level A2;
if 2ta < t1+t2+t3+ & gt, +tn, then no adjustment is made to the currently set third user level A3 for the user.
Further, the processing module is further configured to, when determining an initial recommendation period for making a customized product recommendation to the user according to a storage location of browsing data of the user, include:
a first preset recommended period Q1, a second preset recommended period Q2, a third preset recommended period Q3 and a fourth preset recommended period Q4 are preset, and Q1 is more than Q2 and more than Q3 is more than Q4, wherein,
when the browsing data of the user is stored in the first storage module, the first preset recommendation period Q1 is selected as an initial recommendation period when the user is recommended for customized products;
when the browsing data of the user is stored in the second storage module, the second preset recommendation period Q2 is selected as an initial recommendation period when the user is recommended for customized products;
When the browsing data of the user is stored in the recommended data storage module, selecting the third preset recommended period Q3 as an initial recommended period when the user is recommended for customized products;
the processing module is further configured to select the fourth preset recommendation period Q4 as an initial recommendation period when the user's browsing data is stored in the recommendation data storage module, if 3ta is less than t1+t2+t3+ &.+ TN.
Further, the processing module is further configured to, after selecting the i-th preset recommendation period Qi as an initial recommendation period when making a customized product recommendation to the user, include:
presetting a first preset adjustment coefficient a1, a second preset adjustment coefficient a2, a third preset adjustment coefficient a3 and a fourth preset adjustment coefficient a4, wherein a1 is more than 0.5 and a2 is more than 3 and a4 is more than 1;
the processing module is further configured to determine a historical single average online duration Tb, tb= (t1+t2+t3+ & gt TN)/N of the user N times logging in the product online platform, and determine whether to adjust the initial recommended period according to a comparison result between the historical single average online duration Tb and the single reference online duration Ta, so as to determine a final recommended period:
When Tb is less than or equal to Ta, the initial recommended period is not adjusted;
when Ta is smaller than Tb and smaller than or equal to 1.1Ta, selecting the fourth preset adjusting coefficient a4 to adjust the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.1 and less than or equal to 1.3Ta, selecting the third preset regulating coefficient a3 to regulate the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the second preset regulating coefficient a2 to regulate the initial recommended period, wherein the final recommended period is Qi;
when 1.5Ta < Tb, the first preset adjustment coefficient a1 is selected to adjust the initial recommended period, and the final recommended period is qi×a1.
Further, when obtaining the historical single average online time length Tb of the user in the product online platform, and determining the initial recommendation frequency P0 when recommending the customized product to the user according to the historical single average online time length Tb, the processing module is further configured to include:
presetting a first preset recommended frequency P1, a second preset recommended frequency P2, a third preset recommended frequency P3 and a fourth preset recommended frequency P4, wherein P1 is more than P2 and less than P3 and less than P4;
Determining the initial recommended times P0 according to the comparison result between the historical single average online time length Tb and the single reference online time length Ta:
when Tb is less than or equal to Ta, selecting the first preset recommended frequency P1 as the initial recommended frequency P0, where p0=p1;
when Ta is smaller than Tb and smaller than or equal to 1.3Ta, selecting the second preset recommended frequency P2 as the initial recommended frequency P0, and at the moment, P0=P2;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the third preset recommended frequency P3 as the initial recommended frequency P0, and at the moment, P0=P3;
when 1.5Ta < Tb, the fourth preset recommended number P4 is selected as the initial recommended number P0, and p0=p4 at this time.
Further, the processing module is further configured to, when the i-th preset recommended number Pi is selected as the initial recommended number P0, p0=pi, include:
presetting a first correction coefficient c1, a second correction coefficient c2, a third correction coefficient c3 and a fourth correction coefficient c4, wherein c1 is more than 1 and c2 is more than 3 and c4 is more than 2;
acquiring a single maximum online time length Tm of the user N times logging in the product online platform, comparing the single maximum online time length Tm with the single reference online time length Ta, and correcting the initial recommended times P0 according to a comparison result:
When Ta is less than or equal to Tm and less than 2Ta, the first correction coefficient c1 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 c1;
when Tm is less than or equal to 2Ta and less than 3Ta, the second correction coefficient c2 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c2;
when Tm is smaller than or equal to 3Ta and smaller than 4Ta, the third correction coefficient c3 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c3;
when the value of the fourth correction coefficient c4 is equal to or less than Tm, the initial recommended frequency P0 is corrected by selecting the fourth correction coefficient c4, and the corrected final recommended frequency is P0 c4.
Compared with the prior art, the method has the advantages that the first storage module, the second storage module and the recommended data storage module are arranged for data storage, the grade of the user is determined according to the historical login times N of the user in the product online platform, the storage position of browsing data of the user is determined according to the grade of the user, after the initial recommendation period is determined, the historical single average online time length Tb of the user in the product online platform is obtained, the initial recommendation times P0 when customized product recommendation is performed to the user is determined according to the historical single average online time length Tb, and customized product recommendation is performed to the user according to the initial recommendation period and the initial recommendation times P0. According to the invention, the attention degree of the user to the customized product in the platform can be determined according to the login times of the user in the product online platform, so that the grade of the user is determined according to the login times, the attention degree of the user is fed back based on the grade of the user, browsing data of different grades of the user are stored in different storage modules, different recommendation periods and recommendation times are set based on different modules, so that the recommendation of the customized product is performed to the user, the user data can be effectively stored in a grading manner, the management efficiency of the data is improved, the product recommendation can be effectively performed according to the attention degree of the user, and the accuracy of the product recommendation can be greatly improved while the user is effectively screened.
In another aspect, the present invention also provides a data management method for a customized product, where the method is implemented based on the data management system for a customized product, and includes:
when a user logs in a product online platform to browse customized products, acquiring historical login times N of the user in the product online platform and browsing data of each time;
determining a grade of the user according to the historical login times N, determining a storage position of browsing data of the user according to the grade of the user, wherein the grade of the user comprises a first grade A1 of the user, a second grade A2 of the user and a third grade A3 of the user,
when n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into a first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is the second user level A2, storing the browsing data of the user into a second storage module;
when 3 < N, setting the user level as the third user level A3; when the user is the third user grade A3, storing browsing data of the user into a recommended data storage module;
Determining an initial recommendation period when the user is recommended for the customized product according to the storage position of the browsing data of the user;
after the initial recommendation period is determined, acquiring historical single average online time Tb of the user in the product online platform, and determining initial recommendation times P0 when customized product recommendation is performed on the user according to the historical single average online time Tb, wherein T0=N, N is the historical login times, and s is the single online time of the user;
and recommending customized products to the user according to the initial recommendation period and the initial recommendation frequency P0.
It can be appreciated that the above data management system and method for customizing a product have the same beneficial effects, and will not be described herein.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 is a functional block diagram of a data management system for customizing a product provided by an embodiment of the present invention;
FIG. 2 is a flow chart of a method for data management for customized products according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, the present embodiment provides a data management system for customizing a product, including:
the storage unit comprises a first storage module, a second storage module and a recommended data storage module, wherein the first storage module, the second storage module and the recommended data storage module are used for data storage;
the acquisition module is used for acquiring the historical login times N of the user in the product online platform and browsing data of each time when the user logs in the product online platform to browse customized products;
A grade module, configured to determine a grade of the user according to the historical login frequency N, and determine a storage location of browsing data of the user according to the grade of the user, where the grade of the user includes a first user grade A1, a second user grade A2, and a third user grade A3,
when n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into the first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is at the second user level A2, storing browsing data of the user into the second storage module;
when 3 < N, setting the user level as the third user level A3; when the user is at the third user level A3, storing the browsing data of the user into the recommended data storage module;
the processing module is used for determining an initial recommendation period when the user is subjected to customized product recommendation according to the storage position of the browsing data of the user;
the processing module is further configured to obtain a historical single average online time duration Tb of the user in the product online platform after the initial recommendation period is determined, and determine an initial recommendation frequency P0 when performing customized product recommendation on the user according to the historical single average online time duration Tb, where t0=n×s, N is the historical login frequency, and s is the single online time duration of the user;
And the recommending module is used for recommending the customized product to the user according to the initial recommending period and the initial recommending times P0.
It can be seen that, in this embodiment, the first storage module, the second storage module and the recommended data storage module are configured to store data, determine the level of the user according to the historical login times N of the user in the product online platform, determine the storage location of the browsing data of the user according to the level of the user, obtain the historical single average online time length Tb of the user in the product online platform after determining the initial recommendation period, determine the initial recommendation times P0 when recommending the customized product to the user according to the historical single average online time length Tb, and recommend the customized product to the user according to the initial recommendation period and the initial recommendation times P0. According to the invention, the attention degree of the user to the customized product in the platform can be determined according to the login times of the user in the product online platform, so that the grade of the user is determined according to the login times, the attention degree of the user is fed back based on the grade of the user, browsing data of different grades of the user are stored in different storage modules, different recommendation periods and recommendation times are set based on different modules, so that the recommendation of the customized product is performed to the user, the user data can be effectively stored in a grading manner, the management efficiency of the data is improved, the product recommendation can be effectively performed according to the attention degree of the user, and the accuracy of the product recommendation can be greatly improved while the user is effectively screened.
Specifically, the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2, further obtaining the online time length of the user when logging in the product online platform each time, recording as the Nth online time length TN, presetting single reference online time length Ta, and determining whether to adjust the currently set second user grade A2 of the user according to the Nth online time length TN of the user when logging in the product online platform each time:
when n=1, comparing the 1 st login online time length T1 with the single reference online time length Ta;
if T1 is smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1; if T1 is larger than or equal to Ta, the second user level A2 of the currently set user is not adjusted.
Specifically, the ranking module is further configured to, when 0 < n+.3, set the ranking of the user to the second user ranking A2, include:
when n=2, determining a1 st login online time length T1 and A2 nd login online time length T2, and comparing the 1 st login online time length T1, the 2 nd login online time length T2 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the currently set user:
When T1+T2 is less than or equal to Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when t1+t2 > Ta,
if Ta-T1 is larger than Ta-T2, not adjusting the second user level A2 of the currently set user;
and if Ta-T1 is less than or equal to Ta-T2, adjusting the second user level A2 of the currently set user to be a third user level A3.
Specifically, the ranking module is further configured to, when 0 < n+.3, set the ranking of the user to the second user ranking A2, include:
when n=3, determining a1 st login online time length T1, A2 nd login online time length T2 and a3 rd login online time length T3, and comparing the 1 st login online time length T1, the 2 nd login online time length T2, the 3 rd login online time length T3 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the user currently set:
when T1, T2 and T3 are all smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when T1, T2 and T3 are all greater than Ta,
if Ta-T2 > Ta-T3, not adjusting the second user level A2 of the currently set user;
And if Ta-T2 is less than or equal to Ta-T3, adjusting the second user level A2 of the currently set user to be a third user level A3.
Specifically, the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
when 3 < N, setting the user level as the third user level A3, determining whether to adjust the currently set third user level A3 of the user according to a comparison result between the nth login online time period TN and the single reference online time period Ta:
if t1+t2+t3+ + TN is less than or equal to Ta, adjusting the currently set third user level A3 of the user to be a first user level A1;
if Ta is less than t1+t2+t3+ & ltta is less than or equal to 2Ta, adjusting the third user level A3 of the user currently set to be a second user level A2;
if 2ta < t1+t2+t3+ & gt, +tn, then no adjustment is made to the currently set third user level A3 for the user.
Specifically, the processing module is further configured to, when determining an initial recommendation period for making a customized product recommendation to the user according to a storage location of browsing data of the user, include:
a first preset recommended period Q1, a second preset recommended period Q2, a third preset recommended period Q3 and a fourth preset recommended period Q4 are preset, and Q1 is more than Q2 and more than Q3 is more than Q4, wherein,
When the browsing data of the user is stored in the first storage module, the first preset recommendation period Q1 is selected as an initial recommendation period when the user is recommended for customized products;
when the browsing data of the user is stored in the second storage module, the second preset recommendation period Q2 is selected as an initial recommendation period when the user is recommended for customized products;
when the browsing data of the user is stored in the recommended data storage module, selecting the third preset recommended period Q3 as an initial recommended period when the user is recommended for customized products;
the processing module is further configured to select the fourth preset recommendation period Q4 as an initial recommendation period when the user's browsing data is stored in the recommendation data storage module, if 3ta is less than t1+t2+t3+ &.+ TN.
Specifically, the processing module is further configured to, after selecting the i-th preset recommendation period Qi as an initial recommendation period when making a customized product recommendation to the user, include:
presetting a first preset adjustment coefficient a1, a second preset adjustment coefficient a2, a third preset adjustment coefficient a3 and a fourth preset adjustment coefficient a4, wherein a1 is more than 0.5 and a2 is more than 3 and a4 is more than 1;
The processing module is further configured to determine a historical single average online duration Tb, tb= (t1+t2+t3+ & gt TN)/N of the user N times logging in the product online platform, and determine whether to adjust the initial recommended period according to a comparison result between the historical single average online duration Tb and the single reference online duration Ta, so as to determine a final recommended period:
when Tb is less than or equal to Ta, the initial recommended period is not adjusted;
when Ta is smaller than Tb and smaller than or equal to 1.1Ta, selecting the fourth preset adjusting coefficient a4 to adjust the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.1 and less than or equal to 1.3Ta, selecting the third preset regulating coefficient a3 to regulate the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the second preset regulating coefficient a2 to regulate the initial recommended period, wherein the final recommended period is Qi;
when 1.5Ta < Tb, the first preset adjustment coefficient a1 is selected to adjust the initial recommended period, and the final recommended period is qi×a1.
Specifically, when the processing module is further configured to obtain the historical single average online time duration Tb of the user in the product online platform, and determine, according to the historical single average online time duration Tb, an initial recommendation number P0 when performing customized product recommendation for the user, the processing module includes:
Presetting a first preset recommended frequency P1, a second preset recommended frequency P2, a third preset recommended frequency P3 and a fourth preset recommended frequency P4, wherein P1 is more than P2 and less than P3 and less than P4;
determining the initial recommended times P0 according to the comparison result between the historical single average online time length Tb and the single reference online time length Ta:
when Tb is less than or equal to Ta, selecting the first preset recommended frequency P1 as the initial recommended frequency P0, where p0=p1;
when Ta is smaller than Tb and smaller than or equal to 1.3Ta, selecting the second preset recommended frequency P2 as the initial recommended frequency P0, and at the moment, P0=P2;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the third preset recommended frequency P3 as the initial recommended frequency P0, and at the moment, P0=P3;
when 1.5Ta < Tb, the fourth preset recommended number P4 is selected as the initial recommended number P0, and p0=p4 at this time.
Specifically, the processing module is further configured to, when the i-th preset recommended number Pi is selected as the initial recommended number P0, p0=pi, include:
presetting a first correction coefficient c1, a second correction coefficient c2, a third correction coefficient c3 and a fourth correction coefficient c4, wherein c1 is more than 1 and c2 is more than 3 and c4 is more than 2;
acquiring a single maximum online time length Tm of the user N times logging in the product online platform, comparing the single maximum online time length Tm with the single reference online time length Ta, and correcting the initial recommended times P0 according to a comparison result:
When Ta is less than or equal to Tm and less than 2Ta, the first correction coefficient c1 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 c1;
when Tm is less than or equal to 2Ta and less than 3Ta, the second correction coefficient c2 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c2;
when Tm is smaller than or equal to 3Ta and smaller than 4Ta, the third correction coefficient c3 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c3;
when the value of the fourth correction coefficient c4 is equal to or less than Tm, the initial recommended frequency P0 is corrected by selecting the fourth correction coefficient c4, and the corrected final recommended frequency is P0 c4.
Referring to fig. 2, in another preferred implementation manner based on the above embodiment, the present embodiment provides a data management method for a customized product, which is implemented based on the above data management system for a customized product, including the following steps:
step a: when a user logs in a product online platform to browse customized products, acquiring historical login times N of the user in the product online platform and browsing data of each time;
step b: determining a grade of the user according to the historical login times N, determining a storage position of browsing data of the user according to the grade of the user, wherein the grade of the user comprises a first grade A1 of the user, a second grade A2 of the user and a third grade A3 of the user,
When n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into a first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is the second user level A2, storing the browsing data of the user into a second storage module;
when 3 < N, setting the user level as the third user level A3; when the user is the third user grade A3, storing browsing data of the user into a recommended data storage module;
step c: determining an initial recommendation period when the user is recommended for the customized product according to the storage position of the browsing data of the user; after the initial recommendation period is determined, acquiring historical single average online time Tb of the user in the product online platform, and determining initial recommendation times P0 when customized product recommendation is performed on the user according to the historical single average online time Tb, wherein T0=N, N is the historical login times, and s is the single online time of the user;
Step d: and recommending customized products to the user according to the initial recommendation period and the initial recommendation frequency P0.
It can be seen that, in the above embodiment, the first storage module, the second storage module and the recommended data storage module are set to store data, the grade of the user is determined according to the historical login times N of the user in the product online platform, the storage position of the browsing data of the user is determined according to the grade of the user, after the initial recommendation period is determined, the historical single average online time length Tb of the user in the product online platform is obtained, and the initial recommendation times P0 when the customized product is recommended to the user is determined according to the historical single average online time length Tb, and the customized product is recommended to the user according to the initial recommendation period and the initial recommendation times P0. According to the invention, the attention degree of the user to the customized product in the platform can be determined according to the login times of the user in the product online platform, so that the grade of the user is determined according to the login times, the attention degree of the user is fed back based on the grade of the user, browsing data of different grades of the user are stored in different storage modules, different recommendation periods and recommendation times are set based on different modules, so that the recommendation of the customized product is performed to the user, the user data can be effectively stored in a grading manner, the management efficiency of the data is improved, the product recommendation can be effectively performed according to the attention degree of the user, and the accuracy of the product recommendation can be greatly improved while the user is effectively screened.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (10)
1. A data management system for customizing a product, comprising:
the storage unit comprises a first storage module, a second storage module and a recommended data storage module, wherein the first storage module, the second storage module and the recommended data storage module are used for data storage;
the acquisition module is used for acquiring the historical login times N of the user in the product online platform and browsing data of each time when the user logs in the product online platform to browse customized products;
a grade module, configured to determine a grade of the user according to the historical login frequency N, and determine a storage location of browsing data of the user according to the grade of the user, where the grade of the user includes a first user grade A1, a second user grade A2, and a third user grade A3,
when n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into the first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is at the second user level A2, storing browsing data of the user into the second storage module;
When 3 < N, setting the user level as the third user level A3; when the user is at the third user level A3, storing the browsing data of the user into the recommended data storage module;
the processing module is used for determining an initial recommendation period when the user is subjected to customized product recommendation according to the storage position of the browsing data of the user;
the processing module is further configured to obtain a historical single average online time duration Tb of the user in the product online platform after the initial recommendation period is determined, and determine an initial recommendation frequency P0 when performing customized product recommendation on the user according to the historical single average online time duration Tb, where t0=n×s, N is the historical login frequency, and s is the single online time duration of the user;
and the recommending module is used for recommending the customized product to the user according to the initial recommending period and the initial recommending times P0.
2. The data management system for a customized product as claimed in claim 1, wherein,
the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2, further obtaining the online time length of the user when logging in the product online platform each time, recording as the Nth online time length TN, presetting single reference online time length Ta, and determining whether to adjust the currently set second user grade A2 of the user according to the Nth online time length TN of the user when logging in the product online platform each time:
When n=1, comparing the 1 st login online time length T1 with the single reference online time length Ta;
if T1 is smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1; if T1 is larger than or equal to Ta, the second user level A2 of the currently set user is not adjusted.
3. The data management system for a customized product as claimed in claim 2, wherein,
the level module is further configured to, when N is greater than 0 and less than or equal to 3, set the user level to the second user level A2, include:
when n=2, determining a1 st login online time length T1 and A2 nd login online time length T2, and comparing the 1 st login online time length T1, the 2 nd login online time length T2 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the currently set user:
when T1+T2 is less than or equal to Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when t1+t2 > Ta,
if Ta-T1 is larger than Ta-T2, not adjusting the second user level A2 of the currently set user;
and if Ta-T1 is less than or equal to Ta-T2, adjusting the second user level A2 of the currently set user to be a third user level A3.
4. The data management system for a customized product as claimed in claim 3, wherein,
the level module is further configured to, when N is greater than 0 and less than or equal to 3, set the user level to the second user level A2, include:
when n=3, determining a1 st login online time length T1, A2 nd login online time length T2 and a3 rd login online time length T3, and comparing the 1 st login online time length T1, the 2 nd login online time length T2, the 3 rd login online time length T3 and a single reference online time length Ta to determine whether to adjust a second user level A2 of the user currently set:
when T1, T2 and T3 are all smaller than Ta, the second user level A2 of the user which is currently set is adjusted to be a first user level A1;
when T1, T2 and T3 are all greater than Ta,
if Ta-T2 > Ta-T3, not adjusting the second user level A2 of the currently set user;
and if Ta-T2 is less than or equal to Ta-T3, adjusting the second user level A2 of the currently set user to be a third user level A3.
5. The data management system for a customized product as recited in claim 4, wherein,
the ranking module is further configured to, when determining the ranking of the user according to the historical login times N, include:
When 3 < N, setting the user level as the third user level A3, determining whether to adjust the currently set third user level A3 of the user according to a comparison result between the nth login online time period TN and the single reference online time period Ta:
if t1+t2+t3+ + TN is less than or equal to Ta, adjusting the currently set third user level A3 of the user to be a first user level A1;
if Ta is less than t1+t2+t3+ & ltta is less than or equal to 2Ta, adjusting the third user level A3 of the user currently set to be a second user level A2;
if 2ta < t1+t2+t3+ & gt, +tn, then no adjustment is made to the currently set third user level A3 for the user.
6. The data management system for a customized product as recited in claim 5, wherein,
the processing module is further configured to, when determining an initial recommendation period for making a customized product recommendation to the user according to a storage location of browsing data of the user, include:
a first preset recommended period Q1, a second preset recommended period Q2, a third preset recommended period Q3 and a fourth preset recommended period Q4 are preset, and Q1 is more than Q2 and more than Q3 is more than Q4, wherein,
when the browsing data of the user is stored in the first storage module, the first preset recommendation period Q1 is selected as an initial recommendation period when the user is recommended for customized products;
When the browsing data of the user is stored in the second storage module, the second preset recommendation period Q2 is selected as an initial recommendation period when the user is recommended for customized products;
when the browsing data of the user is stored in the recommended data storage module, selecting the third preset recommended period Q3 as an initial recommended period when the user is recommended for customized products;
the processing module is further configured to select the fourth preset recommendation period Q4 as an initial recommendation period when the user's browsing data is stored in the recommendation data storage module, if 3ta is less than t1+t2+t3+ &.+ TN.
7. The data management system for a customized product as claimed in claim 6, wherein,
the processing module is further configured to, after selecting an ith preset recommendation period Qi as an initial recommendation period when making a customized product recommendation to the user, include:
presetting a first preset adjustment coefficient a1, a second preset adjustment coefficient a2, a third preset adjustment coefficient a3 and a fourth preset adjustment coefficient a4, wherein a1 is more than 0.5 and a2 is more than 3 and a4 is more than 1;
The processing module is further configured to determine a historical single average online duration Tb, tb= (t1+t2+t3+ & gt TN)/N of the user N times logging in the product online platform, and determine whether to adjust the initial recommended period according to a comparison result between the historical single average online duration Tb and the single reference online duration Ta, so as to determine a final recommended period:
when Tb is less than or equal to Ta, the initial recommended period is not adjusted;
when Ta is smaller than Tb and smaller than or equal to 1.1Ta, selecting the fourth preset adjusting coefficient a4 to adjust the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.1 and less than or equal to 1.3Ta, selecting the third preset regulating coefficient a3 to regulate the initial recommended period, wherein the final recommended period is Qi;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the second preset regulating coefficient a2 to regulate the initial recommended period, wherein the final recommended period is Qi;
when 1.5Ta < Tb, the first preset adjustment coefficient a1 is selected to adjust the initial recommended period, and the final recommended period is qi×a1.
8. The data management system for a customized product as claimed in claim 7, wherein,
The processing module is further configured to, when obtaining a historical single average online time duration Tb of the user in the product online platform, and determining an initial recommendation number P0 when performing customized product recommendation for the user according to the historical single average online time duration Tb, include:
presetting a first preset recommended frequency P1, a second preset recommended frequency P2, a third preset recommended frequency P3 and a fourth preset recommended frequency P4, wherein P1 is more than P2 and less than P3 and less than P4;
determining the initial recommended times P0 according to the comparison result between the historical single average online time length Tb and the single reference online time length Ta:
when Tb is less than or equal to Ta, selecting the first preset recommended frequency P1 as the initial recommended frequency P0, where p0=p1;
when Ta is smaller than Tb and smaller than or equal to 1.3Ta, selecting the second preset recommended frequency P2 as the initial recommended frequency P0, and at the moment, P0=P2;
when Tb is more than 1.3 and less than or equal to 1.5Ta, selecting the third preset recommended frequency P3 as the initial recommended frequency P0, and at the moment, P0=P3;
when 1.5Ta < Tb, the fourth preset recommended number P4 is selected as the initial recommended number P0, and p0=p4 at this time.
9. The data management system for a customized product according to claim 8, wherein the processing module is further configured to, when an i-th preset recommended number Pi is selected as the initial recommended number P0, p0=pi, include:
Presetting a first correction coefficient c1, a second correction coefficient c2, a third correction coefficient c3 and a fourth correction coefficient c4, wherein c1 is more than 1 and c2 is more than 3 and c4 is more than 2;
acquiring a single maximum online time length Tm of the user N times logging in the product online platform, comparing the single maximum online time length Tm with the single reference online time length Ta, and correcting the initial recommended times P0 according to a comparison result:
when Ta is less than or equal to Tm and less than 2Ta, the first correction coefficient c1 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 c1;
when Tm is less than or equal to 2Ta and less than 3Ta, the second correction coefficient c2 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c2;
when Tm is smaller than or equal to 3Ta and smaller than 4Ta, the third correction coefficient c3 is selected to correct the initial recommended frequency P0, and the final recommended frequency after correction is P0 x c3;
when the value of the fourth correction coefficient c4 is equal to or less than Tm, the initial recommended frequency P0 is corrected by selecting the fourth correction coefficient c4, and the corrected final recommended frequency is P0 c4.
10. A data management method for a customised product, characterised in that the method is implemented on the basis of a data management system for a customised product according to any one of claims 1 to 9, comprising:
When a user logs in a product online platform to browse customized products, acquiring historical login times N of the user in the product online platform and browsing data of each time;
determining a grade of the user according to the historical login times N, determining a storage position of browsing data of the user according to the grade of the user, wherein the grade of the user comprises a first grade A1 of the user, a second grade A2 of the user and a third grade A3 of the user,
when n=0, then setting the user's level to the first user level A1; when the user is the first user grade A1, storing browsing data of the user into a first storage module;
when N is more than 0 and less than or equal to 3, setting the user grade as the second user grade A2; when the user is the second user level A2, storing the browsing data of the user into a second storage module;
when 3 < N, setting the user level as the third user level A3; when the user is the third user grade A3, storing browsing data of the user into a recommended data storage module;
determining an initial recommendation period when the user is recommended for the customized product according to the storage position of the browsing data of the user;
After the initial recommendation period is determined, acquiring historical single average online time Tb of the user in the product online platform, and determining initial recommendation times P0 when customized product recommendation is performed on the user according to the historical single average online time Tb, wherein T0=N, N is the historical login times, and s is the single online time of the user;
and recommending customized products to the user according to the initial recommendation period and the initial recommendation frequency P0.
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