CN112001777A - E-commerce platform data analysis system based on cloud computing - Google Patents
E-commerce platform data analysis system based on cloud computing Download PDFInfo
- Publication number
- CN112001777A CN112001777A CN202010855051.8A CN202010855051A CN112001777A CN 112001777 A CN112001777 A CN 112001777A CN 202010855051 A CN202010855051 A CN 202010855051A CN 112001777 A CN112001777 A CN 112001777A
- Authority
- CN
- China
- Prior art keywords
- commodities
- information
- deposit
- data
- merchant
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000007405 data analysis Methods 0.000 title claims abstract description 13
- 238000004458 analytical method Methods 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims description 15
- 238000012098 association analyses Methods 0.000 claims description 9
- 101000878595 Arabidopsis thaliana Squalene synthase 1 Proteins 0.000 claims description 3
- 101000713575 Homo sapiens Tubulin beta-3 chain Proteins 0.000 claims description 3
- 101000713585 Homo sapiens Tubulin beta-4A chain Proteins 0.000 claims description 3
- 102100036790 Tubulin beta-3 chain Human genes 0.000 claims description 3
- 102100036788 Tubulin beta-4A chain Human genes 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 3
- 238000010521 absorption reaction Methods 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- 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
-
- 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
-
- 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]
- G06Q30/0623—Item investigation
- G06Q30/0625—Directed, with specific intent or strategy
- G06Q30/0629—Directed, with specific intent or strategy for generating comparisons
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an electronic commerce platform data analysis system based on cloud computing, which comprises a root acquisition unit, a data selection unit, a data skimming unit, a data temporary storage unit, a data association unit, an association rule base, a management unit, a cloud storage unit, a cloud processor, a parallel-pushing rule base and a display unit, wherein the data selection unit is used for selecting data to be transmitted to a client; according to the invention, the relevant sales information of the merchant is acquired through the data skimming unit, the sales data is analyzed, the popular goods of the merchant and the goods with larger return can be acquired, the analysis is automatically carried out according to the goods, after the items which are usually purchased by the user can be purchased are acquired, the purchase stores which the user frequently visits corresponding to the goods can be automatically analyzed according to the contents of the items, and the corresponding purchase stores which can be selected under the condition of the public can form comprehensive recommendation, so that the viscosity among the stores is enhanced, a binding type mutual help is formed, and other relevant resources are integrated.
Description
Technical Field
The invention belongs to the field of electronic commerce, and particularly relates to an electronic commerce platform data analysis system based on cloud computing.
Background
The patent that publication number is CN109360059A discloses an electronic commerce platform of realization intelligent service, including electronic commerce platform body, touch display screen and solar energy electroplax, the top of electronic commerce platform body is equipped with touch display screen, the front portion of electronic commerce platform body is equipped with puts the thing board, the anterior distribution of electronic commerce platform body has the megaphone, the universal wheel is all installed to the bottom corner of electronic commerce platform body, the rear portion of electronic commerce platform body is equipped with the mounting panel, the top of mounting panel is equipped with the expansion plate, the top of expansion plate is equipped with the solar energy electroplax. This realize intelligent service's electronic commerce platform, the customer of being convenient for is shopping on electronic platform and is realized high-efficient secret system to but the automatic clear record, but on the one hand efficient absorption solar power, on the other hand can be opened and shelters from the shady and cool, hides the wind and keeps out the rain, convenient to use, through mounting panel and the expansion plate that sets up at the rear portion, the lifting and sliding use of being convenient for, the height-adjusting of being convenient for.
However, the use of the system is only directed at the external structure, but the system lacks a system which can automatically acquire which commodities are popular commodities according to the conditions of commodities sold by merchants, and a user can select which commodities while purchasing the commodities, and selects which merchants to mutually push the commodities; therefore, good merchant circulation is formed, and meanwhile, a user can conveniently obtain commodity recommendation wanted by the user in time.
Disclosure of Invention
The invention aims to provide an e-commerce platform data analysis system based on cloud computing.
The purpose of the invention can be realized by the following technical scheme:
a cloud computing-based e-commerce platform data analysis system comprises a root acquisition unit, a data selection unit, a data skimming unit, a data temporary storage unit, a data association unit, an association rule base, a management unit, a cloud storage unit, a cloud processor, a parallel-pushing rule base and a display unit;
the data skimming unit is used for acquiring all transaction information of the electronic platform, wherein the transaction information comprises merchant information, purchased commodities, corresponding purchase time and number of purchased commodities, the merchant information is a merchant identity, and the purchased commodities are commodities which complete a transaction in a corresponding transaction record;
the data skimming unit is used for transmitting the transaction information to the data selecting unit, and the data selecting unit receives the transaction information transmitted by the data skimming unit and selects and analyzes the transaction information to obtain the interest-saving commodities corresponding to all the merchant information;
after the data selecting unit acquires the profit commodities corresponding to all the merchant information, the data selecting unit automatically combines the root acquiring unit to perform a data tracing process to obtain all the profit commodities Pij corresponding to the merchant information Qi and the profit information Cij thereof; wherein Cij corresponds to Pij one by one, i is 1.. n, and j is 1.. m;
the data selecting unit is used for transmitting all the deposit commodities Pij corresponding to the merchant information Qi and the deposit information Cij thereof to the data temporary storage unit, and the data temporary storage unit receives all the deposit commodities Pij and the deposit information Cij thereof transmitted by the data selecting unit and stores the deposit commodities Pij and the deposit information Cij in real time;
wherein, the association rule base stores corresponding association analysis rules; the data association unit is used for performing association analysis on the deposit commodity Pij and the deposit information Cij thereof in the data temporary storage unit by combining with an association rule base to obtain recommendation information formed by binding all recommended commodities and corresponding deposit commodities and associated commodities of all merchant information Qi;
the data association unit is used for transmitting the recommendation information, the merchant information and the associated commodities corresponding to the merchant information to the cloud processor, the cloud processor is used for transmitting the recommendation information to the cloud storage unit, and the cloud storage unit receives the recommendation information transmitted by the cloud processor and stores the recommendation information in real time;
the cloud processor is used for transmitting the recommendation information to the display unit, and when the display unit receives the recommendation information transmitted by the cloud processor, if the current display of the related commodities is detected, the corresponding recommendation information is immediately displayed;
the parallel-pushing rule base stores parallel-pushing rules, the cloud processor receives the associated commodities transmitted by the data association unit and performs parallel-pushing analysis on the associated commodities, and the specific parallel-pushing analysis process is as follows:
s001: acquiring a related commodity;
s002: optionally selecting a related commodity;
s003: acquiring a current merchant corresponding to the highest commodity sales volume, and marking the merchant as a parallel recommended merchant;
s004: acquiring a merchant with the most people who go to a user who purchases the associated commodity, and marking the merchant as a same-direction recommending merchant;
s005: selecting the next associated commodity, repeating the steps S003-S005, and obtaining parallel recommended merchants and equidirectional recommended merchants of all the associated commodities;
the cloud processor is used for transmitting the parallel recommended merchants and the equidirectional recommended merchants of the associated commodities to the cloud storage unit for real-time storage;
the cloud processor is further used for automatically driving the display unit to display ' parallel recommended merchants ' and equidirectional recommended merchants ' when detecting that the user purchases the associated commodity;
the management unit is in communication connection with the cloud processor and used for recording all preset values.
Further, the specific process of the selection analysis is as follows:
the method comprises the following steps: acquiring merchant information, purchased commodities, and corresponding purchase time and number of purchased commodities in the transaction information;
step two: acquiring information of any merchant;
step three: all the commodities on sale are obtained, and the quantity of all the sold articles is obtained at the same time;
step four: obtaining the profit of each commodity on sale, wherein the profit is equal to the price minus the cost minus the additional value, the additional value is the costs of renting houses and water and electricity except the cost of the goods, and the profit is averaged to each commodity on sale every month;
step five: acquiring the quantity sold by each commodity on sale, acquiring the created value corresponding to the commodity on sale according to the quantity, acquiring the sum of all created values, marking the sum as a revenue generating value, and dividing the created value corresponding to all commodities on sale by the revenue generating value;
step six: obtaining the revenue generating ratio of all the commodities on sale;
step seven: marking the commodity on sale with the revenue generating ratio exceeding a preset threshold X1 as a benefit commodity; wherein X1 is a preset value, and 0< X1 < 1;
step eight: optionally selecting the next merchant information, and repeating the third step to the eighth step until all merchant information is processed;
step nine: obtaining the deposit commodities corresponding to all the merchant information;
step ten: and when the corresponding merchant information non-profit commodity exists, automatically ignoring the merchant.
Further, the data tracing process specifically includes:
s1: obtaining the deposit commodities of all merchant information;
s2: marking merchant information as Qi, i ═ 1.. n; correspondingly marking the good commodity of the merchant information Qi as Pij, i is 1.. n, j is 1.. m; wherein Pij represents a good interest commodity corresponding to the merchant information Qi as Pij;
s3: let i equal to 1, j equal to 1;
s4: acquiring a corresponding deposit commodity P11;
s5: acquiring all users who purchase the commodity, and marking the users as target users;
s6: selecting a target user, and acquiring all articles purchased by the target user within a time period close to T2, wherein T2 is a preset value;
s7: sequentially selecting all articles purchased by the next user in a time period close to T2, and acquiring all articles purchased by all users;
s8: acquiring the purchased times of all the articles, and deleting the articles with the times less than or equal to 1;
s9: acquiring the number of times of purchasing the residual articles and the number of purchasers of the residual articles, wherein the number of purchasers is the number of persons purchasing the articles correspondingly;
s10: using the formula: calculating the purchase value of all the remaining articles according to the purchase number/purchase times;
s11: comparing the purchase value with a preset purchase value X2, wherein X2 is a preset value, marking the goods corresponding to the purchase value larger than X2 as the deposit goods, and fusing the corresponding deposit goods and the purchase value thereof to form deposit information;
s12: repeating the steps S4-S12 when j is j +1, and obtaining all the interest information of all the interest commodities P1j corresponding to the same merchant information;
s13: and then repeating the steps S4-S13 when i is i +1, obtaining the interest information of all the articles corresponding to all the merchant information, and marking the interest information as Cij, wherein Cij corresponds to Pij one by one, i is 1.
Further, the specific steps of the association analysis are as follows:
SS 1: obtaining a deposit commodity Pij and deposit information Cij thereof;
SS 2: acquiring deposit goods in the deposit information Cij and a purchase value thereof;
SS 3: then, making i equal to 1, and selecting corresponding merchant information;
SS 4: selecting one deposit commodity P1j, acquiring all deposit information Cij corresponding to the deposit commodity, acquiring deposit commodities and purchase values of the deposit commodities in the deposit commodity, marking all the deposit commodities as recommended commodities, and acquiring all the recommended commodities of the corresponding deposit commodity P1 j;
SS 5: then comparing the purchase values of all the recommended commodities, sequencing the purchase values from big to small, and marking the corresponding recommended commodities ranked in the third place as related commodities;
SS 6: let i equal i + 1;
SS 7: repeating the steps SS3-SS7 until all the merchant information is processed;
SS 8: obtaining recommended commodities of all the deposit commodities Pij in all the merchant information, and binding the recommended commodities with corresponding deposit commodities to form recommended information;
SS 9: and obtaining the related commodities of all the merchant information Qi.
The invention has the beneficial effects that:
according to the invention, the relevant sales information of the merchant is acquired through the data skimming unit, the sales data is analyzed, the hot commodities of the merchant and the large return can be obtained, the commodities are automatically analyzed according to the commodities, after the commodity is acquired, the commodity is also purchased by the user who generally purchases the commodity, the purchase store which the user frequently visits corresponding to the commodity purchase is automatically analyzed according to the content of the commodity, and the corresponding purchase store which is selected under the condition of the public is formed to be comprehensively recommended, so that the viscosity among the stores is enhanced, a binding type mutual help is formed, other relevant resources are integrated, and the shopping experience of the user is more convenient and rapid.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
As shown in fig. 1, an e-commerce platform data analysis system based on cloud computing includes a root acquisition unit, a data selection unit, a data skimming unit, a data temporary storage unit, a data association unit, an association rule base, a management unit, a cloud storage unit, a cloud processor, a parallel-pushing rule base, and a display unit;
the data skimming unit is used for acquiring all transaction information of the electronic platform, wherein the transaction information comprises merchant information, purchased commodities, corresponding purchase time and number of purchased commodities, the merchant information is a merchant identity, and the purchased commodities are commodities which complete a transaction in a corresponding transaction record;
the data skimming unit is used for transmitting the transaction information to the data selecting unit, the data selecting unit receives the transaction information transmitted by the data skimming unit and selects and analyzes the transaction information, and the specific selecting and analyzing process comprises the following steps:
the method comprises the following steps: acquiring merchant information, purchased commodities, and corresponding purchase time and number of purchased commodities in the transaction information;
step two: acquiring information of any merchant;
step three: all the commodities on sale are obtained, and the quantity of all the sold articles is obtained at the same time;
step four: obtaining the profit of each commodity on sale, wherein the profit is equal to the price minus the cost minus the additional value, the additional value is the costs of renting houses, water and electricity and the like except the goods feeding cost, and the profit is averaged to each commodity on sale every month; step five: acquiring the quantity sold by each commodity on sale, acquiring the created value corresponding to the commodity on sale according to the quantity, acquiring the sum of all created values, marking the sum as a revenue generating value, and dividing the created value corresponding to all commodities on sale by the revenue generating value;
step six: obtaining the revenue generating ratio of all the commodities on sale;
step seven: marking the commodity on sale with the revenue generating ratio exceeding a preset threshold X1 as a benefit commodity; wherein X1 is a preset value, and 0< X1 < 1;
step eight: optionally selecting the next merchant information, and repeating the third step to the eighth step until all merchant information is processed;
step nine: obtaining the deposit commodities corresponding to all the merchant information;
step ten: when the corresponding merchant information does not have the good, automatically ignoring the merchant;
after the data selecting unit acquires the profit commodities corresponding to all the merchant information, the data selecting unit can automatically combine the root acquiring unit to perform a data tracing process, and the specific data tracing process is as follows:
s1: obtaining the deposit commodities of all merchant information;
s2: marking merchant information as Qi, i ═ 1.. n; correspondingly marking the good commodity of the merchant information Qi as Pij, i is 1.. n, j is 1.. m; wherein Pij represents a good interest commodity corresponding to the merchant information Qi as Pij;
s3: let i equal to 1, j equal to 1;
s4: acquiring a corresponding deposit commodity P11;
s5: acquiring all users purchasing the commodity by using a root acquisition unit, and marking the users as target users;
s6: selecting a target user, and acquiring all articles purchased by the target user within a time period close to T2, wherein T2 is a preset value;
s7: sequentially selecting all articles purchased by the next user in a time period close to T2, and acquiring all articles purchased by all users;
s8: acquiring the purchased times of all the articles, and deleting the articles with the times less than or equal to 1;
s9: acquiring the number of times of purchasing the residual articles and the number of purchasers of the residual articles, wherein the number of purchasers is the number of persons purchasing the articles correspondingly;
s10: using the formula: calculating the purchase value of all the remaining articles according to the purchase number/purchase times;
s11: comparing the purchase value with a preset purchase value X2, wherein X2 is a preset value, marking the goods corresponding to the purchase value larger than X2 as the deposit goods, and fusing the corresponding deposit goods and the purchase value thereof to form deposit information;
s12: repeating the steps S4-S12 when j is j +1, and obtaining all the interest information of all the interest commodities P1j corresponding to the same merchant information;
s13: then, repeating the steps S4-S13 when i is i +1, obtaining the interest information of all articles corresponding to all the merchant information, and marking the interest information as Cij, wherein Cij corresponds to Pij one by one, i is 1.
The data selecting unit is used for transmitting all the deposit commodities Pij corresponding to the merchant information Qi and the deposit information Cij thereof to the data temporary storage unit, and the data temporary storage unit receives all the deposit commodities Pij and the deposit information Cij thereof transmitted by the data selecting unit and stores the deposit commodities Pij and the deposit information Cij in real time;
wherein, the association rule base stores corresponding association analysis rules; the data association unit is used for performing association analysis on the deposit commodity Pij in the data temporary storage unit and the deposit information Cij thereof by combining an association rule base, and the specific steps of the association analysis are as follows:
SS 1: obtaining a deposit commodity Pij and deposit information Cij thereof;
SS 2: acquiring deposit goods in the deposit information Cij and a purchase value thereof;
SS 3: then, making i equal to 1, and selecting corresponding merchant information;
SS 4: selecting one deposit commodity P1j, acquiring all deposit information Cij corresponding to the deposit commodity, acquiring deposit commodities and purchase values of the deposit commodities in the deposit commodity, marking all the deposit commodities as recommended commodities, and acquiring all the recommended commodities of the corresponding deposit commodity P1 j;
SS 5: then comparing the purchase values of all the recommended commodities, sequencing the purchase values from big to small, and marking the corresponding recommended commodities ranked in the third place as related commodities;
SS 6: let i equal i + 1;
SS 7: repeating the steps SS3-SS7 until all the merchant information is processed;
SS 8: obtaining recommended commodities of all the deposit commodities Pij in all the merchant information, and binding the recommended commodities with corresponding deposit commodities to form recommended information;
SS 9: obtaining the related commodities of all merchant information Qi;
the data association unit is used for transmitting the recommendation information, the merchant information and the associated commodities corresponding to the merchant information to the cloud processor, the cloud processor is used for transmitting the recommendation information to the cloud storage unit, and the cloud storage unit receives the recommendation information transmitted by the cloud processor and stores the recommendation information in real time;
the cloud processor is used for transmitting the recommendation information to the display unit, and when the display unit receives the recommendation information transmitted by the cloud processor, if the current display of the related commodities is detected, the corresponding recommendation information is immediately displayed;
the parallel-pushing rule base stores parallel-pushing rules, the cloud processor receives the associated commodities transmitted by the data association unit and performs parallel-pushing analysis on the associated commodities, and the specific parallel-pushing analysis process is as follows:
s001: acquiring a related commodity;
s002: optionally selecting a related commodity;
s003: acquiring a current merchant corresponding to the highest commodity sales volume, and marking the merchant as a parallel recommended merchant;
s004: acquiring a merchant with the most people who go to a user who purchases the associated commodity, and marking the merchant as a same-direction recommending merchant;
s005: selecting the next associated commodity, repeating the steps S003-S005, and obtaining parallel recommended merchants and equidirectional recommended merchants of all the associated commodities;
the cloud processor is used for transmitting the parallel recommended merchants and the equidirectional recommended merchants of the associated commodities to the cloud storage unit for real-time storage;
the cloud processor is further used for automatically driving the display unit to display 'parallel recommended merchants' and 'same-direction recommended merchants' when detecting that the user purchases the associated commodity.
The management unit is in communication connection with the cloud processor and used for recording all preset values.
A data analysis system of an electronic commerce platform based on cloud computing is characterized in that when the data analysis system works, relevant sales information of a merchant is obtained through a data skimming unit, sales data are analyzed, hot commodities of the merchant and high return can be obtained, the commodities are automatically analyzed according to the commodities, after what kind of goods can be purchased by a user who generally purchases the commodities are obtained, according to the contents of the goods, purchasing stores which the user frequently visits corresponding to the goods can be automatically analyzed, and under the condition of masses, corresponding purchasing stores which can be selected form comprehensive recommendation, therefore, the viscosity among all stores is enhanced, binding type mutual help is formed, other relevant resources are integrated, and the shopping experience of the user is more convenient and faster.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. A cloud computing-based e-commerce platform data analysis system is characterized by comprising a root acquisition unit, a data selection unit, a data skimming unit, a data temporary storage unit, a data association unit, an association rule base, a management unit, a cloud storage unit, a cloud processor, a parallel-pushing rule base and a display unit;
the data skimming unit is used for acquiring all transaction information of the electronic platform, wherein the transaction information comprises merchant information, purchased commodities, corresponding purchase time and number of purchased commodities, the merchant information is a merchant identity, and the purchased commodities are commodities which complete a transaction in a corresponding transaction record;
the data skimming unit is used for transmitting the transaction information to the data selecting unit, and the data selecting unit receives the transaction information transmitted by the data skimming unit and selects and analyzes the transaction information to obtain the interest-saving commodities corresponding to all the merchant information;
after the data selecting unit acquires the profit commodities corresponding to all the merchant information, the data selecting unit automatically combines the root acquiring unit to perform a data tracing process to obtain all the profit commodities Pij corresponding to the merchant information Qi and the profit information Cij thereof; wherein Cij corresponds to Pij one by one, i is 1.. n, and j is 1.. m;
the data selecting unit is used for transmitting all the deposit commodities Pij corresponding to the merchant information Qi and the deposit information Cij thereof to the data temporary storage unit, and the data temporary storage unit receives all the deposit commodities Pij and the deposit information Cij thereof transmitted by the data selecting unit and stores the deposit commodities Pij and the deposit information Cij in real time;
wherein, the association rule base stores corresponding association analysis rules; the data association unit is used for performing association analysis on the deposit commodity Pij and the deposit information Cij thereof in the data temporary storage unit by combining with an association rule base to obtain recommendation information formed by binding all recommended commodities and corresponding deposit commodities and associated commodities of all merchant information Qi;
the data association unit is used for transmitting the recommendation information, the merchant information and the associated commodities corresponding to the merchant information to the cloud processor, the cloud processor is used for transmitting the recommendation information to the cloud storage unit, and the cloud storage unit receives the recommendation information transmitted by the cloud processor and stores the recommendation information in real time;
the cloud processor is used for transmitting the recommendation information to the display unit, and when the display unit receives the recommendation information transmitted by the cloud processor, if the current display of the related commodities is detected, the corresponding recommendation information is immediately displayed;
the parallel-pushing rule base stores parallel-pushing rules, the cloud processor receives the associated commodities transmitted by the data association unit and performs parallel-pushing analysis on the associated commodities, and the specific parallel-pushing analysis process is as follows:
s001: acquiring a related commodity;
s002: optionally selecting a related commodity;
s003: acquiring a current merchant corresponding to the highest commodity sales volume, and marking the merchant as a parallel recommended merchant;
s004: acquiring a merchant with the most people who go to a user who purchases the associated commodity, and marking the merchant as a same-direction recommending merchant;
s005: selecting the next associated commodity, repeating the steps S003-S005, and obtaining parallel recommended merchants and equidirectional recommended merchants of all the associated commodities;
the cloud processor is used for transmitting the parallel recommended merchants and the equidirectional recommended merchants of the associated commodities to the cloud storage unit for real-time storage;
the cloud processor is further used for automatically driving the display unit to display ' parallel recommended merchants ' and equidirectional recommended merchants ' when detecting that the user purchases the associated commodity;
the management unit is in communication connection with the cloud processor and used for recording all preset values.
2. The cloud-computing-based e-commerce platform data analysis system as claimed in claim 1, wherein the specific process of the selection analysis is as follows:
the method comprises the following steps: acquiring merchant information, purchased commodities, and corresponding purchase time and number of purchased commodities in the transaction information;
step two: acquiring information of any merchant;
step three: all the commodities on sale are obtained, and the quantity of all the sold articles is obtained at the same time;
step four: obtaining the profit of each commodity on sale, wherein the profit is equal to the price minus the cost minus the additional value, the additional value is the costs of renting houses and water and electricity except the cost of the goods, and the profit is averaged to each commodity on sale every month;
step five: acquiring the quantity sold by each commodity on sale, acquiring the created value corresponding to the commodity on sale according to the quantity, acquiring the sum of all created values, marking the sum as a revenue generating value, and dividing the created value corresponding to all commodities on sale by the revenue generating value;
step six: obtaining the revenue generating ratio of all the commodities on sale;
step seven: marking the commodity on sale with the revenue generating ratio exceeding a preset threshold X1 as a benefit commodity; wherein X1 is a preset value, and 0< X1 < 1;
step eight: optionally selecting the next merchant information, and repeating the third step to the eighth step until all merchant information is processed;
step nine: obtaining the deposit commodities corresponding to all the merchant information;
step ten: and when the corresponding merchant information non-profit commodity exists, automatically ignoring the merchant.
3. The cloud-computing-based e-commerce platform data analysis system as claimed in claim 1, wherein the data tracing process specifically comprises:
s1: obtaining the deposit commodities of all merchant information;
s2: marking merchant information as Qi, i ═ 1.. n; correspondingly marking the good commodity of the merchant information Qi as Pij, i is 1.. n, j is 1.. m; wherein Pij represents a good interest commodity corresponding to the merchant information Qi as Pij;
s3: let i equal to 1, j equal to 1;
s4: acquiring a corresponding deposit commodity P11;
s5: acquiring all users who purchase the commodity, and marking the users as target users;
s6: selecting a target user, and acquiring all articles purchased by the target user within a time period close to T2, wherein T2 is a preset value;
s7: sequentially selecting all articles purchased by the next user in a time period close to T2, and acquiring all articles purchased by all users;
s8: acquiring the purchased times of all the articles, and deleting the articles with the times less than or equal to 1;
s9: acquiring the number of times of purchasing the residual articles and the number of purchasers of the residual articles, wherein the number of purchasers is the number of persons purchasing the articles correspondingly;
s10: using the formula: calculating the purchase value of all the remaining articles according to the purchase number/purchase times;
s11: comparing the purchase value with a preset purchase value X2, wherein X2 is a preset value, marking the goods corresponding to the purchase value larger than X2 as the deposit goods, and fusing the corresponding deposit goods and the purchase value thereof to form deposit information;
s12: repeating the steps S4-S12 when j is j +1, and obtaining all the interest information of all the interest commodities P1j corresponding to the same merchant information;
s13: and then repeating the steps S4-S13 when i is i +1, obtaining the interest information of all the articles corresponding to all the merchant information, and marking the interest information as Cij, wherein Cij corresponds to Pij one by one, i is 1.
4. The cloud-computing-based e-commerce platform data analysis system as claimed in claim 1, wherein the association analysis comprises the specific steps of:
SS 1: obtaining a deposit commodity Pij and deposit information Cij thereof;
SS 2: acquiring deposit goods in the deposit information Cij and a purchase value thereof;
SS 3: then, making i equal to 1, and selecting corresponding merchant information;
SS 4: selecting one deposit commodity P1j, acquiring all deposit information Cij corresponding to the deposit commodity, acquiring deposit commodities and purchase values of the deposit commodities in the deposit commodity, marking all the deposit commodities as recommended commodities, and acquiring all the recommended commodities of the corresponding deposit commodity P1 j;
SS 5: then comparing the purchase values of all the recommended commodities, sequencing the purchase values from big to small, and marking the corresponding recommended commodities ranked in the third place as related commodities;
SS 6: let i equal i + 1;
SS 7: repeating the steps SS3-SS7 until all the merchant information is processed;
SS 8: obtaining recommended commodities of all the deposit commodities Pij in all the merchant information, and binding the recommended commodities with corresponding deposit commodities to form recommended information;
SS 9: and obtaining the related commodities of all the merchant information Qi.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010855051.8A CN112001777B (en) | 2020-08-24 | 2020-08-24 | Electronic commerce platform data analysis system based on cloud computing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010855051.8A CN112001777B (en) | 2020-08-24 | 2020-08-24 | Electronic commerce platform data analysis system based on cloud computing |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112001777A true CN112001777A (en) | 2020-11-27 |
| CN112001777B CN112001777B (en) | 2023-12-08 |
Family
ID=73470227
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010855051.8A Active CN112001777B (en) | 2020-08-24 | 2020-08-24 | Electronic commerce platform data analysis system based on cloud computing |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112001777B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422576A (en) * | 2023-12-19 | 2024-01-19 | 青岛巨商汇网络科技有限公司 | Business and finance integrated management system and method for commodity sales based on cloud computing |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101783004A (en) * | 2010-03-03 | 2010-07-21 | 陈嵘 | Fast intelligent commodity recommendation system |
| TW201140477A (en) * | 2010-05-12 | 2011-11-16 | Hung-Wu Wu | Public real estate price index referred saving-investment type financial product and the implementation of its information system |
| US20160063599A1 (en) * | 2014-08-29 | 2016-03-03 | Wal-Mart Stores, Inc. | Matching a product with other products |
| CN107424014A (en) * | 2017-08-07 | 2017-12-01 | 璧垫尝 | A kind of businessman's promotion method and server based on communication network |
| CN107862566A (en) * | 2017-10-17 | 2018-03-30 | 杨明 | A kind of Method of Commodity Recommendation and system |
| US20190188771A1 (en) * | 2017-12-15 | 2019-06-20 | Capital One Services, Llc | Systems and Methods for Transaction-Based Real Time Pre-Intent Recommendations for a Sequential Purchase |
| CN110827104A (en) * | 2018-08-07 | 2020-02-21 | 北京京东尚科信息技术有限公司 | Method and device for recommending commodities to user |
| CN111402013A (en) * | 2020-06-04 | 2020-07-10 | 成都晓多科技有限公司 | Commodity collocation recommendation method, system, device and storage medium |
| CN111429203A (en) * | 2020-03-02 | 2020-07-17 | 北京明略软件系统有限公司 | Commodity recommendation method and device based on user behavior data |
| CN111445296A (en) * | 2020-04-13 | 2020-07-24 | 智粤云(广州)数字信息科技有限公司 | Electronic commerce system based on big data cloud platform |
-
2020
- 2020-08-24 CN CN202010855051.8A patent/CN112001777B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101783004A (en) * | 2010-03-03 | 2010-07-21 | 陈嵘 | Fast intelligent commodity recommendation system |
| TW201140477A (en) * | 2010-05-12 | 2011-11-16 | Hung-Wu Wu | Public real estate price index referred saving-investment type financial product and the implementation of its information system |
| US20160063599A1 (en) * | 2014-08-29 | 2016-03-03 | Wal-Mart Stores, Inc. | Matching a product with other products |
| CN107424014A (en) * | 2017-08-07 | 2017-12-01 | 璧垫尝 | A kind of businessman's promotion method and server based on communication network |
| CN107862566A (en) * | 2017-10-17 | 2018-03-30 | 杨明 | A kind of Method of Commodity Recommendation and system |
| US20190188771A1 (en) * | 2017-12-15 | 2019-06-20 | Capital One Services, Llc | Systems and Methods for Transaction-Based Real Time Pre-Intent Recommendations for a Sequential Purchase |
| CN110827104A (en) * | 2018-08-07 | 2020-02-21 | 北京京东尚科信息技术有限公司 | Method and device for recommending commodities to user |
| CN111429203A (en) * | 2020-03-02 | 2020-07-17 | 北京明略软件系统有限公司 | Commodity recommendation method and device based on user behavior data |
| CN111445296A (en) * | 2020-04-13 | 2020-07-24 | 智粤云(广州)数字信息科技有限公司 | Electronic commerce system based on big data cloud platform |
| CN111402013A (en) * | 2020-06-04 | 2020-07-10 | 成都晓多科技有限公司 | Commodity collocation recommendation method, system, device and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117422576A (en) * | 2023-12-19 | 2024-01-19 | 青岛巨商汇网络科技有限公司 | Business and finance integrated management system and method for commodity sales based on cloud computing |
| CN117422576B (en) * | 2023-12-19 | 2024-03-08 | 青岛巨商汇网络科技有限公司 | Business and finance integrated management system and method for commodity sales based on cloud computing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112001777B (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107958394A (en) | A kind of intelligence sales service management system | |
| CN107767227A (en) | Method of Commodity Recommendation, the commercial product recommending system of online shopping mall | |
| CN106980999A (en) | The method and apparatus that a kind of user recommends | |
| CN107220888A (en) | Social modularization distribution system based on electric business pattern | |
| JP4597427B2 (en) | Product assessment method, sales method with product assessment function and product assessment system | |
| CN110298707A (en) | A kind of method and device for tariff package scene price | |
| CN112785336A (en) | Internet consignment transaction platform and method for ancient and modern artworks | |
| CN109598595A (en) | A kind of sale management system for network | |
| CN112001777A (en) | E-commerce platform data analysis system based on cloud computing | |
| CN107403327A (en) | Mobile differential pin store processing system and its method | |
| CN112967108A (en) | BP-ANN-based electric power retail package searching and sequencing simulation system and method | |
| Kosasih et al. | THE IMPACT OF E-COMMERCE TOWARD CONSUMER PURCHASE INTENTION AT UD. CIPTA PRIMA MEDAN | |
| CN111008913A (en) | Electronic commerce platform for promoting intellectual property trade by product | |
| CN107590707A (en) | A SaaS architecture modes realize more distribution substation systems | |
| CN110866220A (en) | Selection recommendation system in e-commerce activity process | |
| CN110264324A (en) | A kind of convenience based on network technology send system | |
| CN110428281A (en) | Combine the method and apparatus for determining peers amount for a variety of related products | |
| CN116091171A (en) | Member statistics and management system | |
| CN110163696A (en) | A kind of electric business shopping air navigation aid and its system based on big data | |
| CN112950269A (en) | Incentive e-commerce order service management system | |
| CN110866809A (en) | Sale scene decision system of E-commerce platform | |
| CN110728560A (en) | Agricultural product transaction management system based on Internet of things | |
| Sari et al. | Implementation of Marketing Technology in Improving MSME Products in Medan City | |
| CN111489181A (en) | Novel E-commerce platform marketing business model and management system | |
| Hutapea et al. | ANALYSIS OF THE IMPACT OF E-CRM IN INCREASING CUSTOMER SATISFACTION AND LOYALTY TO THE INDONESIAN MARKETPLACE |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231101 Address after: Unit 03, 5th Floor, Building A, Fuhuali Center Office Building, No. 2021 Jiuzhou Avenue West, Xiangzhou District, Zhuhai City, Guangdong Province, 519000 Applicant after: Zhuhai Yibei Technology Co.,Ltd. Address before: 511400 Lane 3, Fufeng street, shiqiaoxing Avenue, Panyu District, Guangzhou City, Guangdong Province Applicant before: Zhi Xiao 2 (Guangzhou) Technology Co.,Ltd. |
|
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: Room 2703, Building 2, No. 2033 West Jiuzhou Avenue, Xiangzhou District, Zhuhai City, Guangdong Province, 519000 Applicant after: Zhuhai Yibei Technology Co.,Ltd. Address before: Unit 03, 5th Floor, Building A, Fuhuali Center Office Building, No. 2021 Jiuzhou Avenue West, Xiangzhou District, Zhuhai City, Guangdong Province, 519000 Applicant before: Zhuhai Yibei Technology Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |