CN113761380A - Commodity information sharing method and system for each E-commerce platform based on block chain big data - Google Patents
Commodity information sharing method and system for each E-commerce platform based on block chain big data Download PDFInfo
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Abstract
The invention discloses a commodity information sharing method for each E-commerce platform based on block chain big data, which comprises the following steps: the method comprises the steps of 1, obtaining commodity information of each E-commerce platform through big data, 2, building a commodity information base according to the commodity information of each E-commerce platform, 3, obtaining on-sale commodities, different from other platforms, in each commodity platform through a data analysis module according to the built commodity information base to obtain a specific commodity information base, 4, obtaining click quantity and purchase quantity of each specific commodity information according to the specific information base, 5, building a click and purchase trend prediction model according to a cloud computer to obtain prediction data, 6, judging whether the prediction data are active data or not, if the prediction data are in an active data interval, the active commodities are obtained, and if the prediction data are in an active data interval, the inactive commodities are obtained. Through equipment overall structure, bring commodity analysis convenience for the trade company, increased the user and bought commodity convenience.
Description
Technical Field
The invention relates to the technical field of e-commerce, in particular to a method and a system for sharing commodity information of each e-commerce platform based on block chain big data.
Background
The block chain system is composed of a data layer, a network layer, a consensus layer, an excitation layer, a contract layer and an application layer. The data layer encapsulates a bottom layer data block, basic data such as related data encryption and time stamp and a basic algorithm; the network layer comprises a distributed networking mechanism, a data transmission mechanism, a data verification mechanism and the like; the consensus layer mainly encapsulates various consensus algorithms of the network nodes; the incentive layer integrates economic factors into a block chain technology system, and mainly comprises an economic incentive issuing mechanism, an economic incentive distributing mechanism and the like; the contract layer mainly encapsulates various scripts, algorithms and intelligent contracts.
The electronic commerce is called e-commerce for short, and is characterized in that transaction activities and related service activities are carried out in an electronic transaction mode on the internet, an intranet and a value-added network, so that each link of the traditional business activities is electronized and networked, in the big data era, the commodity selling quantity and types of an e-commerce platform are very huge, and the e-commerce platform is allowed to provide data sharing for buyers, so that each buyer is helped to carry out data analysis on the transaction of commodities, more commodities can be sold by the buyers, the win-win is achieved, and commodity information of each e-commerce platform can be acquired through block chain big data.
In each e-commerce platform, due to the rules of each e-commerce platform, the sale of some products is limited, so that when a user needs to purchase a commodity which is not sold on the e-commerce platform, the user does not know what e-commerce platform can purchase the needed commodity or needs to download an APP of another e-commerce platform, which is very troublesome, and therefore the method and the system for sharing the commodity information of each e-commerce platform based on the block chain big data are provided.
Disclosure of Invention
The invention aims to provide a method and a system for sharing commodity information of each E-commerce platform based on block chain big data, which bring commodity analysis convenience to merchants and increase the commodity purchasing convenience of users.
The invention discloses a commodity information sharing method and system for each E-commerce platform based on block chain big data, which adopts the technical scheme that the commodity information sharing method for each E-commerce platform based on the block chain big data comprises the following steps:
step 1, obtaining commodity information of each E-commerce platform through big data,
step 2, building a commodity information base according to the commodity information of each E-commerce platform,
step 3, according to the constructed commodity information base, the on-sale commodities which are different from other platforms in each commodity platform are obtained through a data analysis module, a specific commodity information base is obtained,
step 4, obtaining the click rate and the purchase amount of each specific commodity information according to the specific information base, comparing the click rate and the purchase amount of the specific commodity information with the total click rate and the purchase amount of the corresponding platform commodity to obtain active data, presetting an active data interval,
step 5, constructing a click and purchase trend prediction model according to the cloud computer to obtain prediction data,
step 6, judging whether the prediction data is in an active data interval, if so, determining that the commodity is active, if not, determining that the commodity is inactive,
step 7, counting and storing the active commodity information and the inactive commodity information to form an active commodity library and an inactive commodity library,
step 8, respectively carrying out grade marking on the stored commodity information in the active commodity library and the inactive commodity library according to the active commodity library and the inactive commodity library to form grade commodities, wherein each grade commodity comprises a first grade, a second grade and an Nth grade,
and 9, according to the grade commodity information, the data display module and the data search module which are built by each E-commerce platform, carrying out grade display on the specific commodities which are different from the platform according to grade marks, establishing a link relation between the specific commodities and the platform which is sold, and simultaneously enabling the display area to have a search function.
Preferably, the obtaining of the specific commodity information library further comprises the following steps:
step 31, extracting all commodity information data in the commodity information base of each E-commerce platform,
step 32, counting, classifying and comparing similar products,
and 32, counting the commodities which are different from other E-commerce platforms, and storing to obtain a specific commodity information base.
Preferably, the prediction data further includes the following steps:
step 51, inputting the click quantity and the purchase quantity of each specific commodity information as input data, and inputting a click and purchase trend prediction model to obtain prediction data of each specific commodity;
step 52: the click and purchase trend prediction model is obtained by training a plurality of groups of training data to convergence, wherein each group of data in the plurality of groups of training data comprises a sum of click amount and purchase amount as identification information for identifying the access trend;
step 53: and obtaining prediction data of the click quantity and the purchase quantity of each specific commodity, wherein the prediction data is trend data which can be concerned or purchased by the customer for the specific commodity.
Preferably, the active data interval includes the following steps:
step 61, counting click and purchase data of all commodities of each platform to obtain concerned coefficient data of the commodities;
step 62, dividing active data intervals according to the concerned coefficient data of each commodity to obtain active data;
step 63, counting click and purchase data of each specific commodity to obtain the concerned coefficient data of each specific commodity;
and step 64, comparing the concerned coefficient data of the specific commodity with the active data interval to judge whether the specific commodity is an active commodity.
Preferably, the data display module and the data search module further include the following steps:
step 71, building a display module and a data search module according to the cloud computer;
72, embedding and building a display module according to the cloud computer according to the interior of the APP display module of each E-commerce platform;
step 73, then building a data searching module in the display module according to the cloud computer;
and step 74, displaying the stored information of the specific commodities in a display module built according to the cloud computer.
Preferably, the establishing of the link relation between the specific commodity and the platform on sale further comprises the following steps:
step 75, marking each specific commodity according to the specific commodity information base to obtain specific commodity marks;
step 76, building a link relation between the marked specific commodity and the corresponding e-commerce platform on sale according to the specific commodity mark;
and 77, clicking the specific commodity to jump to the corresponding e-commerce platform for sale.
As a preferred scheme, the cloud computer is connected with the display module through an input and output line, the cloud computer is welded on the mainboard, the mainboard is further welded with the storage and the data processing module, a click and purchase trend prediction module and a search module are built in the E-commerce platform commodity information sharing system APP built according to the cloud computer, and a block chain is built in the cloud computer.
As a preferable scheme, the system of the commodity information sharing method of each E-commerce platform based on the block chain big data is stored in the APP of the computer framework and is driven to run through a burning program, it also includes a bus architecture, which may include any number of interconnected buses and bridges, a cloud computer, storage, and a bus interface that links together various circuits including one or more processors represented by the processors and memory, and various other circuits such as peripherals, voltage regulators, power management circuits, and the like, the bus interface providing an interface between the bus architecture and a receiver and transmitter, which may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
Preferably, the commodity information base and the specific commodity information base are both stored in the memory, and the data analysis module is built in the data quantity module.
The commodity information sharing method and system based on the block chain big data for each E-commerce platform disclosed by the invention have the beneficial effects that:
through the integral structure of the equipment, active commodities are obtained through analysis, so that buyers of the e-commerce platform can stock and invest in the commodities according to the sales volume of the commodities, and convenience is brought to commodity analysis for the merchants; the specific commodity different from the platform is displayed, the link relation between the specific commodity and the platform on sale is established, and meanwhile, the display area has a searching function. The data display module and the data search module can facilitate a user to purchase all commodities wanted by the user in the platform only by downloading an e-commerce platform which the user likes, and the user can directly search for the commodities wanted by the user conveniently through the search function, so that the convenience of purchasing the commodities by the user is greatly improved.
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FIG. 1 is a schematic overall framework of the present invention;
fig. 2 is a schematic diagram of the present invention.
Detailed Description
The invention will be further elucidated and described with reference to the embodiments and drawings of the specification:
referring to fig. 1-2, the present invention: a commodity information sharing method for each E-commerce platform based on block chain big data comprises the following steps:
step 1, obtaining commodity information of each E-commerce platform through big data, wherein the commodity information comprises information such as names, purposes, pictures and shapes of commodities.
And 2, constructing a commodity information base according to the commodity information of each e-commerce platform, wherein the commodity information base is convenient for comparison in the following steps in order to store the commodity information of each e-commerce platform in sale.
And 3, according to the constructed commodity information base, obtaining the commodities which are sold on other platforms in each commodity platform through the data analysis module, and obtaining a specific commodity information base which is used for storing the commodity information that the commodities sold on the corresponding platforms do not sell on other platforms.
Step 4, obtaining the click rate and the purchase amount of each specific commodity information according to the specific information base, comparing the click rate and the purchase amount of the specific commodity information with the total click rate and the purchase amount of the corresponding platform commodity to obtain active data, presetting an active data interval,
step 5, constructing a click and purchase trend prediction model according to the cloud computer to obtain prediction data,
and 6, judging whether the prediction data is in an active data interval, if so, obtaining active commodities, and if not, obtaining inactive commodities, analyzing and obtaining the active commodities, so that buyers of the E-commerce platform can stock and invest the commodities according to the sales volume of the commodities, and convenience is brought to commodity analysis for the merchants.
And 7, counting and storing the active commodity information and the inactive commodity information to form an active commodity library and an inactive commodity library, wherein commodities with relatively large purchase amount and commodities with small purchase amount are respectively stored in the active commodity library and the inactive commodity library.
And 8, respectively carrying out grade marking on the stored commodity information in the active commodity library and the inactive commodity library according to the active commodity library and the inactive commodity library to form grade commodities, wherein each grade commodity comprises a first grade, a second grade and a level from N, the grade commodities are graded according to the purchase quantity, the grade is higher when the purchase quantity is larger, so that the grade can be displayed in front of a display area when display arrangement is carried out, the commodities in the active commodity library and the inactive commodity library are divided into two rows to be respectively displayed, and the sales volume of the commodities in the inactive commodity library can also be increased.
And 9, according to the grade commodity information, the data display module and the data search module which are built by each E-commerce platform, carrying out grade display on the specific commodities which are different from the platform according to grade marks, establishing a link relation between the specific commodities and the platform which is sold, and simultaneously enabling the display area to have a search function. According to the display module and the data search module, a user can conveniently purchase all commodities wanted by the user in the platform only by downloading a favorite e-commerce platform, and the user can conveniently and directly search and find the commodity number wanted by the user through the search function. And displaying according to the commodity grades marked by the grades in the display sequence, wherein the higher the grade is, the more the display is advanced.
As shown in fig. 1:
the method for obtaining the specific commodity information base further comprises the following steps:
step 31, extracting all commodity information data in the commodity information base of each E-commerce platform,
step 32, counting, classifying and comparing similar products,
and 32, counting the commodities which are different from other E-commerce platforms, and storing to obtain a specific commodity information base.
The specific commodity information base stores the on-sale commodities which are different from other e-commerce platforms by each e-commerce platform, namely, a platform is jointly developed according to the e-commerce platforms of cooperative statistics, the specific commodities refer to the e-commerce platforms of the statistics, and the commodities which are not sold by one or more e-commerce platforms are the specific commodities.
As shown in fig. 1:
the prediction data further comprises the steps of:
step 51, inputting the click quantity and the purchase quantity of each specific commodity information as input data, and inputting a click and purchase trend prediction model to obtain prediction data of each specific commodity;
step 52: the click and purchase trend prediction model is obtained by training a plurality of groups of training data to convergence, wherein each group of data in the plurality of groups of training data comprises a sum of click amount and purchase amount as identification information for identifying the access trend;
step 53: and obtaining prediction data of the click quantity and the purchase quantity of each specific commodity, wherein the prediction data is trend data which can be concerned or purchased by the customer for the specific commodity.
The training data of the click and purchase trend prediction model is obtained by training after data calculation is carried out in a cloud computer, the click and purchase trend prediction model is a model established on the basis of a neural network model, the neural network is an operation model formed by interconnection of a large number of neurons, and the output of the network is expressed according to a logic strategy of the connection mode of the network. Further, the training process is essentially a supervised learning process, each of the plurality of sets of training data includes commodity click amount, commodity purchase times and identification information used for identifying an access trend, the click and purchase trend prediction model performs continuous self-correction and adjustment until an obtained output result is consistent with the identification information, the supervised learning of the set of data is finished, and the supervised learning of the next set of data is performed. When the output information of the click and purchase trend prediction model reaches the preset accuracy rate/reaches the convergence state, the supervised learning process is ended, and the aim that the corresponding prediction result is more accurately output through the training of the click and purchase trend prediction model is achieved.
As shown in fig. 1:
the active data interval includes the steps of:
step 61, counting click and purchase data of all commodities of each platform to obtain concerned coefficient data of the commodities;
step 62, dividing active data intervals according to the concerned coefficient data of each commodity to obtain active data;
step 63, counting click and purchase data of each specific commodity to obtain the concerned coefficient data of each specific commodity;
and step 64, comparing the concerned coefficient data of the specific commodity with the active data interval to judge whether the specific commodity is an active commodity.
The active commodities are obtained through analysis, so that buyers of the E-commerce platform can stock and invest in the commodities according to the selling amount of the commodities, and convenience is brought to commodity analysis for the merchants. The click and purchase data of each specific commodity are compared with click and purchase data of all commodities of each platform, a comparison algorithm is utilized to analyze and generate corresponding calculation data, and further specific commodity active data is obtained, the click and purchase trend prediction model is an updated click and purchase trend prediction model obtained by machine learning of all commodity active data based on the concerned coefficient data of each commodity, and as the specific commodity active data needs to be combined with old training data of the click and purchase trend prediction model to complete a comprehensive incremental learning result, after incremental learning of the specific commodity active data, the basic performance of the first click and purchase trend prediction model can be reserved and model performance can be updated, and further the specific commodity active data is obtained, the specific commodity activity data is prediction data obtained based on the new model, and incremental learning based on propagation data is achieved, so that the accurate prediction performance of the model is improved.
As shown in fig. 1:
the data display module and the data search module further comprise the following steps:
step 71, building a display module and a data search module according to the cloud computer;
72, embedding and building a display module according to the cloud computer according to the interior of the APP display module of each E-commerce platform;
step 73, then building a data searching module in the display module according to the cloud computer;
and step 74, displaying the stored information of the specific commodities in a display module built according to the cloud computer.
As shown in fig. 1:
the link relation between the specific commodity and the platform on sale further comprises the following steps:
step 75, marking each specific commodity according to the specific commodity information base to obtain specific commodity marks;
step 76, building a link relation between the marked specific commodity and the corresponding e-commerce platform on sale according to the specific commodity mark;
and 77, clicking the specific commodity to jump to the corresponding e-commerce platform for sale.
As shown in fig. 2:
the cloud computer passes through input and output line connection display module, the cloud computer welding is on the mainboard, it has memory and data processing module still to weld on the mainboard, and has clicked and purchase trend prediction module and search module according to the inside construction of electricity merchant platform commodity information sharing system APP that the cloud computer found, the inside construction of cloud computer has the block chain, and block chain system comprises data layer, network layer, consensus layer, excitation layer, contract layer and application layer. The data layer encapsulates a bottom layer data block, basic data such as related data encryption and time stamp and a basic algorithm; the network layer comprises a distributed networking mechanism, a data transmission mechanism, a data verification mechanism and the like; the consensus layer mainly encapsulates various consensus algorithms of the network nodes; the incentive layer integrates economic factors into a block chain technology system, and mainly comprises an economic incentive issuing mechanism, an economic incentive distributing mechanism and the like; the contract layer mainly encapsulates various scripts, algorithms and intelligent contracts.
As shown in fig. 2:
the system of the commodity information sharing method for each e-commerce platform based on block chain big data is stored in an APP of a computer framework and is driven to run through a burning program, and further comprises a bus framework, a cloud computer, a storage and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework can link various circuits including one or more processors represented by a processor and a storage represented by the storage together, the bus framework can also link various other circuits such as peripheral equipment, a voltage stabilizer, a power management circuit and the like together, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
As shown in fig. 1 and 2:
the commodity information base and the specific commodity information base are stored in the storage, and the data analysis module is built in the data quantity module.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 a system 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 an instruction system 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. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (9)
1. A commodity information sharing method for each E-commerce platform based on block chain big data is characterized in that: the method comprises the following steps:
step 1, obtaining commodity information of each E-commerce platform through big data,
step 2, building a commodity information base according to the commodity information of each E-commerce platform,
step 3, according to the constructed commodity information base, the on-sale commodities which are different from other platforms in each commodity platform are obtained through a data analysis module, a specific commodity information base is obtained,
step 4, obtaining the click rate and the purchase amount of each specific commodity information according to the specific information base, comparing the click rate and the purchase amount of the specific commodity information with the total click rate and the purchase amount of the corresponding platform commodity to obtain active data, presetting an active data interval,
step 5, constructing a click and purchase trend prediction model according to the cloud computer to obtain prediction data,
step 6, judging whether the prediction data is in an active data interval, if so, determining that the commodity is active, otherwise, determining that the commodity is inactive,
step 7, counting and storing the active commodity information and the inactive commodity information to form an active commodity library and an inactive commodity library,
step 8, respectively carrying out grade marking on the stored commodity information in the active commodity library and the inactive commodity library according to the active commodity library and the inactive commodity library to form grade commodities, wherein each grade commodity comprises a first grade, a second grade and an Nth grade,
and 9, according to the grade commodity information, the data display module and the data search module which are built by each E-commerce platform, carrying out grade display on the specific commodities which are different from the platform according to grade marks, establishing a link relation between the specific commodities and the platform which is sold, and simultaneously enabling the display area to have a search function.
2. The commodity information sharing method for each e-commerce platform based on block chain big data according to claim 1, wherein: the method for obtaining the specific commodity information base further comprises the following steps:
step 31, extracting all commodity information data in the commodity information base of each E-commerce platform,
step 32, counting, classifying and comparing similar products,
and 32, counting the commodities which are different from other E-commerce platforms, and storing to obtain a specific commodity information base.
3. The commodity information sharing method for each e-commerce platform based on block chain big data according to claim 1, wherein: the prediction data further comprises the steps of:
step 51, inputting the click quantity and the purchase quantity of each specific commodity information as input data, and inputting a click and purchase trend prediction model to obtain prediction data of each specific commodity;
step 52: the click and purchase trend prediction model is obtained by training a plurality of groups of training data to convergence, wherein each group of data in the plurality of groups of training data comprises a sum of click amount and purchase amount as identification information for identifying the access trend;
step 53: and obtaining prediction data of the click quantity and the purchase quantity of each specific commodity, wherein the prediction data is trend data which can be concerned or purchased by the customer for the specific commodity.
4. The commodity information sharing method for each e-commerce platform based on block chain big data according to claim 1, wherein: the active data interval includes the steps of:
step 61, counting click and purchase data of all commodities of each platform to obtain concerned coefficient data of the commodities;
step 62, dividing active data intervals according to the concerned coefficient data of each commodity to obtain active data;
step 63, counting click and purchase data of each specific commodity to obtain the concerned coefficient data of each specific commodity;
and step 64, comparing the concerned coefficient data of the specific commodity with the active data interval to judge whether the specific commodity is an active commodity.
5. The commodity information sharing method for each e-commerce platform based on block chain big data according to claim 1, wherein: the data display module and the data search module further comprise the following steps:
step 71, building a display module and a data search module according to the cloud computer;
72, embedding and building a display module according to the cloud computer according to the interior of the APP display module of each E-commerce platform;
step 73, then building a data searching module in the display module according to the cloud computer;
and step 74, displaying the stored information of the specific commodities in a display module built according to the cloud computer.
6. The commodity information sharing method for each e-commerce platform based on block chain big data according to claim 1, wherein: the link relation between the specific commodity and the platform on sale further comprises the following steps:
step 75, marking each specific commodity according to the specific commodity information base to obtain specific commodity marks;
step 76, building a link relation between the marked specific commodity and the corresponding e-commerce platform on sale according to the specific commodity mark;
and 77, clicking the specific commodity to jump to the corresponding e-commerce platform for sale.
7. The system for sharing commodity information of each e-commerce platform based on block chain big data according to claims 1-6, wherein: the cloud computer passes through input and output line connection display module, the cloud computer welding is on the mainboard, it has memory and data processing module still to weld on the mainboard, and has clicked and purchase trend prediction module and search module according to the inside establishment of electricity merchant platform commodity information sharing system APP that the cloud computer found, the inside establishment of cloud computer has the block chain.
8. The system for sharing commodity information of each e-commerce platform based on block chain big data according to claim 7, wherein: the system of the commodity information sharing method for each e-commerce platform based on block chain big data is stored in an APP of a computer framework and is driven to run through a burning program, and further comprises a bus framework, a cloud computer, a storage and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework can link various circuits including one or more processors represented by a processor and a storage represented by the storage together, the bus framework can also link various other circuits such as peripheral equipment, a voltage stabilizer, a power management circuit and the like together, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provide a unit for communicating with various other systems on a transmission medium.
9. The system for sharing commodity information of each e-commerce platform based on block chain big data according to claims 1-8, wherein: the commodity information base and the specific commodity information base are stored in the storage, and the data analysis module is built in the data quantity module.
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| CN115471302A (en) * | 2022-11-14 | 2022-12-13 | 山东智豆数字科技有限公司 | Electronic marketing data processing method based on big data analysis |
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2021
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115471302A (en) * | 2022-11-14 | 2022-12-13 | 山东智豆数字科技有限公司 | Electronic marketing data processing method based on big data analysis |
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