CN116452303B - Electronic commerce data management method based on big data - Google Patents
Electronic commerce data management method based on big data Download PDFInfo
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- CN116452303B CN116452303B CN202310701902.7A CN202310701902A CN116452303B CN 116452303 B CN116452303 B CN 116452303B CN 202310701902 A CN202310701902 A CN 202310701902A CN 116452303 B CN116452303 B CN 116452303B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention relates to the technical field of data processing, in particular to an electronic commerce data management method based on big data, which comprises the following steps: step 1: receiving commodity information data, identifying the attribute of each commodity information data, and distinguishing and storing commodity information; step 2: setting a price interval, and further distinguishing the commodity information which is distinguished and stored; step 3: acquiring commodity browsing data of an access user in a commodity sales platform, traversing and reading the commodity browsing data of the user, and analyzing commodity interest trends of the user; in the execution process of the steps, the method can store and manage commodity information data, synchronously push appointed commodities for users according to the commodity browsing habit of analysis users, has better pertinence, ensures that a commodity sales platform does not depend on big data analysis any more, can realize pushing aiming at the respective commodity interest trends of all users, and is beneficial to improving the commodity success rate of the commodity sales platform.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an electronic commerce data management method based on big data.
Background
Electronic commerce generally refers to a novel business operation mode for realizing online shopping of consumers, online transaction and online electronic payment among merchants, various business activities, transaction activities, financial activities and related comprehensive service activities based on client/server application modes in a global and wide-ranging business trade activities in an internet open network environment.
However, in the existing commodity sales platform, commodities pushed in the main industry are often pushed by selecting commodities according to big data analysis or random modes, and the mode can meet the purchase demands and interest recommendations of most commodity purchasing users, but the adaptation degree of the pushed commodities is poor for each independent user.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides an electronic commerce data management method based on big data, which solves the technical problems in the background art.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the electronic commerce data management method based on big data comprises the following steps:
step 1: receiving commodity information data, identifying the attribute of each commodity information data, and distinguishing and storing commodity information;
step 2: setting a price interval, and further distinguishing the commodity information which is distinguished and stored;
step 3: acquiring commodity browsing data of an access user in a commodity sales platform, traversing and reading the commodity browsing data of the user, and analyzing commodity interest trends of the user;
step 4: searching commodity information data with the same attribute in the cloud storage space according to the commodity information data attribute corresponding to the commodity interest tendency of the user;
step 5: selecting commodity information data from the searched commodity information data with the same attribute, and sending the selected commodity information data to a commodity sales platform by taking the selected commodity information data as a pushing target;
step 6: monitoring whether commodity information data sent to a commodity sales platform in the step 5 has commodity information distinguishing storage position change or not;
and 6, synchronously feeding back the commodity information detected in the step of changing the storage position to the user side.
Further, the commodity information data attribute analyzed in the step 1 includes: the commodity information is subjected to commodity use and price attribute in commodity information data attribute when being stored in a distinguishing mode in the step 1.
Further, the price interval set in the step 2 is manually set by the user side, and the step 2 is provided with the sub-steps at the lower stage, including:
step 21: setting a commodity information differentiated storage refreshing period, and refreshing and storing the differentiated stored commodity information according to the refreshing period;
step 22: synchronously acquiring commodity information with changed storage positions when each refreshing period starts, and feeding back the acquired commodity information into a user side by forming a message;
when the commodity information data is stored, the commodity information data is manually uploaded to the cloud storage space through the user side, the operation of distinguishing and storing the commodity information data is completed in the cloud storage space synchronously, the message formed in the step 22 is stored in the cloud storage space synchronously, the user side accesses the cloud storage space through a computer connection network, and the message stored in the cloud storage space is downloaded and read.
Further, the cloud storage space is any virtual network disk, and after the message stored in the cloud storage space is downloaded and read by the user side, the cloud storage deletes the message stored in the cloud storage space and already downloaded and read.
Further, after the commodity browsing data of the user is traversed and read, the commodity use attribute and the upper limit and the lower limit of the commodity price with the highest occurrence probability in the commodity browsing data are obtained in the step 3, and the obtained commodity use attribute and the obtained upper limit and lower limit of the commodity price are recorded as the commodity interest tendency of the user.
And further, after searching the commodity information data, the step 4 further sets the interest trend commodity proportion in the commodity sales platform by the user side, calculates the interest trend commodity quantity according to the interest trend commodity proportion by the total displayed commodity quantity of the commodity sales platform, and sends the calculated interest trend commodity quantity to the step 5.
Further, when the commodity information data is selected and sent to the commodity sales platform, the step 5 selects the commodity information data with the corresponding quantity according to the calculated quantity of the interest trend commodities;
when the commodity information data is selected, the required commodity information data is obtained through the following formula:
；
wherein:commodity information data obtained based on a lasso model; />Is a collection of commodity information data;is a corresponding variable; />Is a predictive variable; />Is an offset vector; />Is weighted; />A sparsity set of commodity information data; />And the sparse coefficient of the j-th sample or test point is represented.
Further, when the commodity information data sent to the commodity sales platform is calculated, a user side user is provided with a plurality of groups of commodity price thresholds, each commodity price threshold is configured with a designated number of commodity information data selection quotas, and the aggregate value of the quotas corresponding to all commodity price thresholds is equal to the calculated number of interest trend commodities;
and the commodity information data obtaining formula further obtains a group of commodity information data through the formula until all commodity price threshold corresponding quotas are saturated, and the commodity information data obtaining formula stops outputting commodity information data.
Further, the monitoring operation of the commodity information distinguishing storage position change in the step 6 is synchronously executed following the refresh period set in the step 21, the operation of the monitoring of the commodity information distinguishing storage position change in the step 6 is executed in the cloud storage space, the step 6 further monitors whether the corresponding commodity information data is browsed by the commodity sales platform access user in two continuous refresh periods when the monitoring of the commodity information is in the cloud storage space and the monitoring of the commodity information is executed in the cloud storage space, and the step 4 is further executed when the judging result is yes; otherwise, deleting the commodity information data subjected to judgment in the cloud storage space, and further jumping to the step 4 for further execution.
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects:
1. the invention provides an electronic commerce data management method based on big data, which is implemented by steps in the method, can store and manage commodity information data, synchronously push appointed commodities for users according to analysis of commodity browsing habits of the users, has better pertinence, ensures that a commodity sales platform does not depend on big data analysis any more, can realize pushing aiming at respective commodity interest trends of the users, and is beneficial to improving commodity yield of the commodity sales platform.
2. The method can autonomously complete the selection of the information data of the pushed commodity in the executing process of the steps, so that the commodity pushed by the user has certain randomness, and the pushed commodity can be diversified while meeting the interest trend of the user.
3. In the method, in the step execution process, the influence of commodity price change on the interest trend of the user can be monitored, so that the commodity pushed by the commodity sales platform for the user is adaptively changed based on the commodity price change, and better browsing experience is brought to the user.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method for managing e-commerce data based on big data.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1: the electronic commerce data management method based on big data in this embodiment, as shown in fig. 1, includes the following steps:
step 1: receiving commodity information data, identifying the attribute of each commodity information data, and distinguishing and storing commodity information;
step 2: setting a price interval, and further distinguishing the commodity information which is distinguished and stored;
step 3: acquiring commodity browsing data of an access user in a commodity sales platform, traversing and reading the commodity browsing data of the user, and analyzing commodity interest trends of the user;
step 4: searching commodity information data with the same attribute in the cloud storage space according to the commodity information data attribute corresponding to the commodity interest tendency of the user;
step 5: selecting commodity information data from the searched commodity information data with the same attribute, and sending the selected commodity information data to a commodity sales platform by taking the selected commodity information data as a pushing target;
step 6: monitoring whether commodity information data sent to a commodity sales platform in the step 5 has commodity information distinguishing storage position change or not;
the commodity information detected in the step 6 is fed back to the user side in synchronization with the change of the storage position;
the commodity information data attribute analyzed in the step 1 comprises: the commodity information is subjected to commodity use and price attribute in commodity information data attribute when being stored in a distinguishing mode in the step 1;
the price interval set in the step 2 is manually set through a user side, and the step 2 is provided with the substeps at the lower stage, comprising the following steps:
step 21: setting a commodity information differentiated storage refreshing period, and refreshing and storing the differentiated stored commodity information according to the refreshing period;
step 22: synchronously acquiring commodity information with changed storage positions when each refreshing period starts, and feeding back the acquired commodity information into a user side by forming a message;
when commodity information data is stored, the operation of manually uploading the commodity information data to the cloud storage space through the user side, and distinguishing and storing the commodity information data is completed in the cloud storage space, wherein messages formed in the step 22 are stored in the cloud storage space synchronously, the user side accesses the cloud storage space through a computer connection network, and downloads and reads the messages stored in the cloud storage space;
step 5, selecting commodity information data of corresponding quantity according to the calculated quantity of the interest trend commodities when the commodity information data is selected to be sent to a commodity sales platform;
when the commodity information data is selected, the required commodity information data is obtained through the following formula:
；
wherein:for solving based on lasso modelCommodity information data; />Is a collection of commodity information data;is a corresponding variable; />Is a predictive variable; />Is an offset vector; />Is weighted; />A sparsity set of commodity information data; />And the sparse coefficient of the j-th sample or test point is represented.
In the embodiment, through the calculation of the steps 1 to 6 and the formulas, the storage management of commodity information data and the pushing of recommended commodities in the commodity sales platform are realized, so that the commodities pushed in the commodity sales platform are more targeted, and the use experience of the commodity sales platform when used by a user is improved;
in addition, the substep arranged at the lower level of the step 2 can enable the method to complete the change of the commodity information distinguishing storage position in the execution process of the step, so that the commodity information distinguishing storage position can be synchronously and completely changed when the attribute of commodity information data is changed, the follow-up data in the follow-up step in the method is more accurate in the execution process, and the final commodity information data pushing precision of the method is further ensured.
Example 2: in the implementation aspect, on the basis of embodiment 1, this embodiment further specifically describes, with reference to fig. 1, an electronic commerce data management method based on big data in embodiment 1:
the cloud storage space is any virtual network disk, and after the message stored in the cloud storage space is downloaded and read by the user side, the cloud storage deletes the message stored in the cloud storage space and read by the downloading.
Through the arrangement, the problem that the cloud storage space is fully loaded in storage can be avoided.
As shown in fig. 1, step 3 obtains the commodity usage attribute and the upper and lower commodity price limits with the highest occurrence probability in the commodity browsing data after traversing and reading the commodity browsing data of the user, and the obtained commodity usage attribute and the obtained upper and lower commodity price limits are marked as commodity interest trends of the user.
By the above arrangement, the commodity interest trend data sources obtained by the step execution in the method are defined.
As shown in fig. 1, step 4 further sets the interest trend commodity proportion in the commodity sales platform by the user side after searching the commodity information data, calculates the interest trend commodity number according to the interest trend commodity proportion by the total amount of the displayed commodities in the commodity sales platform, and sends the calculated interest trend commodity number to step 5.
By calculating the number of interest-prone products receivable in the product sales platform as described above, the data support necessary for pushing the product information data to the product sales platform in step 5 can be provided.
As shown in fig. 1, when acquiring commodity information data sent to a commodity sales platform, a user at a user end sets a plurality of groups of commodity price thresholds, each commodity price threshold is configured with a designated number of commodity information data selection quotas, and the aggregate value of the quotas corresponding to all commodity price thresholds is equal to the calculated number of interest trend commodities;
the commodity information data obtaining formula calculates and outputs a group of commodity information data each time, the output commodity information data is measured in real time in the corresponding quota of each commodity price threshold value, when the commodity information data obtained by the formula overflows in the corresponding quota of the commodity information data, the obtained commodity information data is abandoned, the next group of commodity information data is further obtained through the formula, and after the corresponding quota of all commodity price threshold values is saturated, the commodity information data obtaining formula stops outputting the commodity information data;
in the step 6, the monitoring operation of the commodity information distinguishing storage position change is synchronously executed along with the refresh period set in the step 21, the operation of the monitoring of the commodity information distinguishing storage position change in the step 6 is executed in the cloud storage space, when the condition that the commodity information is changed in the storage position in the cloud storage space is monitored, whether corresponding commodity information data are browsed by a commodity sales platform access user in two groups of continuous refresh periods is further monitored, and when the judgment result is yes, the step 4 is skipped to be executed further; otherwise, deleting the commodity information data subjected to judgment in the cloud storage space, and further jumping to the step 4 for further execution.
Through the arrangement, based on the commodity information data of the designated quantity pushed for the commodity sales platform, the pushed commodity information data is diversified according to the attribute of the commodity information data, and the commodity information data pushed in the commodity sales platform is ensured to be different no matter based on commodity use or commodity price.
In summary, in the method in the above embodiment, during the execution of the steps, the method can store and manage the commodity information data, and synchronously push the appointed commodity for the user according to the commodity browsing habit of the analysis user, so that the commodity sales platform does not depend on big data analysis any more, can push the commodity interest trend of each user, and is beneficial to improving the commodity yield of the commodity sales platform; in addition, the method can also autonomously complete the selection of the information data of the pushed commodity, so that the commodity pushed by the user has certain randomness, and the pushed commodity can be diversified while meeting the interest trend of the user; meanwhile, in the execution process of the steps, the method can also monitor the influence of commodity price change on the interest trend of the user, so that the commodity pushed by the commodity sales platform for the user is adaptively changed based on the commodity price change, and better browsing experience is brought to the user.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (4)
1. The electronic commerce data management method based on big data is characterized by comprising the following steps:
step 1: receiving commodity information data, identifying the attribute of each commodity information data, and distinguishing and storing commodity information;
step 2: setting a price interval, and further distinguishing the commodity information which is distinguished and stored;
step 3: acquiring commodity browsing data of an access user in a commodity sales platform, traversing and reading the commodity browsing data of the user, and analyzing commodity interest trends of the user;
step 4: searching commodity information data with the same attribute in the cloud storage space according to the commodity information data attribute corresponding to the commodity interest tendency of the user;
step 5: selecting commodity information data from the searched commodity information data with the same attribute, and sending the selected commodity information data to a commodity sales platform by taking the selected commodity information data as a pushing target;
step 6: monitoring whether commodity information data sent to a commodity sales platform in the step 5 has commodity information distinguishing storage position change or not;
the commodity information detected in the step 6 is fed back to the user side in synchronization with the change of the storage position;
the price interval set in the step 2 is manually set by a user side, and the step 2 is provided with the substeps at the lower stage, including:
step 21: setting a commodity information differentiated storage refreshing period, and refreshing and storing the differentiated stored commodity information according to the refreshing period;
step 22: synchronously acquiring commodity information with changed storage positions when each refreshing period starts, and feeding back the acquired commodity information into a user side by forming a message;
when the commodity information data is stored, the commodity information data is manually uploaded to the cloud storage space through the user side, the operation of distinguishing and storing the commodity information data is completed in the cloud storage space synchronously, the message formed in the step 22 is stored in the cloud storage space synchronously, the user side accesses the cloud storage space through a computer connection network, and the message stored in the cloud storage space is downloaded and read;
step 3, after traversing and reading commodity browsing data of a user, acquiring commodity use attributes and commodity price upper limit and lower limit with highest occurrence probability in the commodity browsing data, and recording the acquired commodity use attributes and commodity price upper limit and lower limit as commodity interest trends of the user;
after searching the commodity information data, the user side further sets the interest trend commodity proportion in the commodity sales platform, calculates the number of interest trend commodities according to the interest trend commodity proportion by the total displayed commodity amount of the commodity sales platform, and sends the calculated number of interest trend commodities to the step 5;
step 5, selecting commodity information data of corresponding quantity according to the calculated quantity of the interest trend commodities when the commodity information data is selected and sent to a commodity sales platform;
when the commodity information data is selected, the required commodity information data is obtained through the following formula:
；
wherein:commodity information data obtained based on a lasso model; />Is a collection of commodity information data; />Is a corresponding variable; />Is a predictive variable; />Is an offset vector; />Is weighted; />A sparsity set of commodity information data; />A sparse coefficient representing a jth sample or test point;
when the commodity information data sent to the commodity sales platform is calculated, a user side user sets a plurality of groups of commodity price thresholds, each commodity price threshold is configured with a designated number of commodity information data selection quotas, and the aggregate value of the quotas corresponding to all commodity price thresholds is equal to the calculated number of interest trend commodities;
and the commodity information data obtaining formula further obtains a group of commodity information data through the formula until all commodity price threshold corresponding quotas are saturated, and the commodity information data obtaining formula stops outputting commodity information data.
2. The electronic commerce data management method based on big data according to claim 1, wherein the commodity information data attribute analyzed in step 1 includes: the commodity information is subjected to commodity use attributes in commodity information data attributes when the commodity information is stored in a distinguishing mode in the step 1.
3. The method for managing electronic commerce data based on big data according to claim 2, wherein the cloud storage space is any virtual network disk, and the cloud storage performs deletion processing on the internally stored downloaded and read messages after the downloaded and read operations are performed on the messages stored in the cloud storage space by the user side.
4. The method for managing electronic commerce data based on big data according to claim 1 or 3, wherein the monitoring operation for the commodity information distinguishing storage location change in step 6 is synchronously executed following the refresh period set in step 21, the operation for monitoring the commodity information distinguishing storage location change in step 6 is executed in the cloud storage space, step 6 is further monitored if the commodity information is monitored to have the storage location change in the cloud storage space, the corresponding commodity information data is further browsed by the commodity sales platform access user in two continuous refresh periods, and when the determination result is yes, the step 4 is skipped to be executed further; otherwise, deleting the commodity information data subjected to judgment in the cloud storage space, and further jumping to the step 4 for further execution.
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