Electronic commerce data management method based on big data

Abstract

The invention discloses an electronic commerce data management method based on big data, and belongs to the technical field of commerce data management, and the method comprises the following steps: obtaining electronic commerce data, preprocessing the data, carrying out backup storage on the data, carrying out data multi-encryption processing, and analyzing the data; according to the method, multiple encryption processes for the processed data are set, the safety of the processed original data can be greatly improved, important business data are prevented from being leaked, unnecessary business losses are avoided, meanwhile, the analysis result of the data is subjected to visualization processing, and the data processing efficiency is improved. Therefore, the user can quickly and visually know the data analysis result, meanwhile, the business recommendation model can be automatically established according to the analysis result, a large number of business links are integrated in the model, and the user can quickly perform business navigation through the links and perform required business negotiation and cooperation.

Description

Technical Field

[0001] The present invention belongs to the technical field of business data management, and in particular to an e-commerce data management method based on big data. Background Art

[0002] As the name suggests, business data refers to business-related data and information. It refers to various data generated and collected in e-commerce activities. These data cover user behavior, transaction information, product information, market trends and other aspects. Due to the wide variety and large amount of business data, in order to manage business data well, it is necessary to apply business data management methods.

[0003] Chinese patent (CN116452303B) discloses an e-commerce data management method based on big data, including the following steps: Step 1: receiving commodity information data, identifying the attributes of each commodity information data, and distinguishing and storing the commodity information; Step 2: setting a price range, and further distinguishing the distinguished and stored commodity information; Step 3: obtaining the commodity browsing data of the user who accesses the commodity sales platform, traversing and reading the user's commodity browsing data, and analyzing the user's commodity interest tendency; during the execution of its steps, this method can store and manage the commodity information data, and simultaneously push the specified commodity to the user according to the analysis of the user's commodity browsing habits, which has better pertinence, so that the commodity sales platform no longer relies on big data analysis, and can push the commodity according to the commodity interest tendency of each user, which is conducive to improving the commodity transaction rate of the commodity sales platform. Although the current e-commerce data management method can manage e-commerce data, it does not perform multiple encryption on the processed business data, and the data is easy to leak, which can easily cause great commercial losses. At the same time, the results after data analysis are not visualized, and the user cannot quickly know the analysis results. The actual application effect of the overall method is not good, and an e-commerce data management method based on big data is urgently needed. Summary of the invention

[0004] The purpose of the present invention is to propose an e-commerce data management method based on big data in order to solve the problem that although the current e-commerce data management method can manage e-commerce data, the processed business data is not encrypted multiple times, the data is easy to leak, which can easily cause great business losses, and the results after data analysis are not visualized, so that users cannot quickly know the analysis results, and the actual application effect of the overall method is poor.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an e-commerce data management method based on big data, comprising the following steps: S1. Acquiring e-commerce data; S2, preprocessing the data; S3, back up and store data; S4, perform multiple data encryption processing; S5. Analyze the data; S6. Visualize the data analysis results; S7. Establish a business recommendation model; S8. Develop business marketing strategies; S9. Evaluate the overall process.

[0006] As a further description of the above technical solution: In S1, the e-commerce data is acquired, and the specific process is to collect various data of the e-commerce platform, including user behavior data, transaction data and product data, through one or more methods such as website search platform, search engine and log file. The above data reflects the consumption habits of users, the market performance of products and the overall trend of the market, and is the basis for subsequent analysis.

[0007] As a further description of the above technical solution: In S2, the data is preprocessed, and the specific steps are: first, the acquired data is concentrated, then the data is checked, then the data is cleaned, abnormal data is marked and cleared, clear data is supplemented and corrected, and data is formatted and converted to unify the data format.

[0008] As a further description of the above technical solution: In the S3, data is backed up and stored, and the storage database used is one or more of Hadoop and Spark.

[0009] As a further description of the above technical solution: In S4, multiple data encryption processing is performed, and the data encryption algorithms are MD5 algorithm and RAS algorithm.

[0010] As a further description of the above technical solution: In S5, the data is analyzed, and the specific steps are: classifying and grouping the data, extracting features from the data, performing feature analysis on the data, and extracting valid information from the data. The advance validity information includes user behavior pattern information, product trend information, and market trend information.

[0011] As a further description of the above technical solution: In S6, the data analysis results are visualized, and the visualization is presented in one or more forms of report presentation, chart presentation, and legend presentation.

[0012] As a further description of the above technical solution: In S7, a business recommendation model is established, and the specific steps are: based on the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's quick business navigation.

[0013] As a further description of the above technical solution: In S8, a personalized marketing strategy is formulated based on the user portrait and market analysis results. The marketing strategy includes targeted advertising and promotional activities. The established marketing strategy needs to be screened 2-3 times to select the most suitable marketing strategy and record it.

[0014] As a further description of the above technical solution: In S9, the overall process is evaluated, and the evaluation results are excellent, good, and poor. When the evaluation result is poor, the overall process is sorted out and improved until the evaluation result reaches excellent or good.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: In the present invention, the method is provided with multiple encryption processes for the processed data, which can greatly improve the security of the processed original data, avoid the leakage of important business data, and thus avoid unnecessary business losses. At the same time, the method visualizes the analysis results of the data, so that users can quickly and intuitively know the data analysis results. At the same time, a business recommendation model can be automatically established based on the analysis results, and a large number of business links are also integrated into the model. Users can quickly conduct business navigation through the links and conduct necessary business negotiations and cooperation. At the same time, business marketing strategies can be formulated, and a large number of targeted advertisements and promotional activities can be obtained, which are then screened multiple times to finalize the final marketing strategy, thereby improving the actual application effect and functionality of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Flowchart of the e-commerce data management method based on big data. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0018] See also Figure 1 The present invention provides a technical solution: an e-commerce data management method based on big data, comprising the following steps: S1. The specific process of acquiring e-commerce data is to collect various data of the e-commerce platform through search engines and log files, including user behavior data, transaction data and product data. The above data reflects the consumption habits of users, the market performance of products and the overall trend of the market, and is the basis for subsequent analysis; S2. Preprocess the data. The specific steps are: first collect the acquired data, then check the data, then clean the data, mark and remove abnormal data, fill and correct the clear data, and format the data to make the data format unified; S3, for data backup and storage, the application storage database is Spark; S4, perform multiple data encryption processing, the data encryption algorithms are MD5 algorithm and RAS algorithm; S5. Analyze the data. The specific steps are: classify and group the data, extract features from the data, analyze the features of the data, and extract valid information from the data. The valid information in advance includes user behavior pattern information, product trend information, and market trend information. S6. Visualize the data analysis results. The visualization is presented in the form of charts and legends. S7, establishing a business recommendation model, the specific steps are: according to the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's fast business navigation; S8. Develop personalized marketing strategies based on user portraits and market analysis results. The marketing strategies include targeted advertising and promotional activities. The established marketing strategies need to be screened twice to select the most suitable marketing strategies and record them; S9. Evaluate the overall process, and the evaluation results are excellent, good, or poor. When the evaluation result is poor, sort out and improve the overall process until the evaluation result reaches excellent or good.

[0019] In this embodiment, the method can greatly improve the security of the processed original data by setting up multiple encryption processes for the processed data, avoid leakage of important business data, and thus avoid unnecessary business losses. Example 2

[0020] See also Figure 1 The present invention provides a technical solution: an e-commerce data management method based on big data, comprising the following steps: S1. The specific process of acquiring e-commerce data is to collect various data of the e-commerce platform through website search platforms, search engines and log files, including user behavior data, transaction data and product data. The above data reflects the consumption habits of users, the market performance of products and the overall trend of the market, and is the basis for subsequent analysis; S2. Preprocess the data. The specific steps are: first collect the acquired data, then check the data, then clean the data, mark and remove abnormal data, fill and correct the clear data, and format the data to make the data format unified; S3, for data backup and storage, the storage database used is Hadoop; S4, perform multiple data encryption processing, the data encryption algorithms are MD5 algorithm and RAS algorithm; S5. Analyze the data. The specific steps are: classify and group the data, extract features from the data, analyze the features of the data, and extract valid information from the data. The valid information in advance includes user behavior pattern information, product trend information, and market trend information. S6. Visualize the data analysis results. The visualization forms include: report display, chart display and legend display; S7, establishing a business recommendation model, the specific steps are: according to the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's fast business navigation; S8. Develop personalized marketing strategies based on user portraits and market analysis results. The marketing strategies include targeted advertising and promotional activities. The established marketing strategies need to be screened twice to select the most suitable marketing strategies and record them; S9. Evaluate the overall process, and the evaluation results are excellent, good, or poor. When the evaluation result is poor, sort out and improve the overall process until the evaluation result reaches excellent or good.

[0021] In this embodiment, the method visualizes the analysis results of the data so that users can quickly and intuitively obtain the data analysis results. At the same time, a business recommendation model can be automatically established based on the analysis results. A large number of business links are also integrated into the model, so that users can quickly navigate through the links and conduct necessary business negotiations and cooperation. Example 3

[0022] See also Figure 1 The present invention provides a technical solution: an e-commerce data management method based on big data, comprising the following steps: S1. The specific process of acquiring e-commerce data is to collect various data of the e-commerce platform through the website search platform, including user behavior data, transaction data and product data. The above data reflects the user's consumption habits, the market performance of the product and the overall trend of the market, and is the basis for subsequent analysis; S2. Preprocess the data. The specific steps are: first collect the acquired data, then check the data, then clean the data, mark and remove abnormal data, fill and correct the clear data, and format the data to make the data format unified; S3, for data backup and storage, the application storage database is Spark; S4, perform multiple data encryption processing, the data encryption algorithms are MD5 algorithm and RAS algorithm; S5. Analyze the data. The specific steps are: classify and group the data, extract features from the data, analyze the features of the data, and extract valid information from the data. The valid information in advance includes user behavior pattern information, product trend information, and market trend information. S6. Visualize the data analysis results. The visualization is presented in the form of reports and charts. S7, establishing a business recommendation model, the specific steps are: according to the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's fast business navigation; S8. Develop personalized marketing strategies based on user portraits and market analysis results. The marketing strategies include targeted advertising and promotional activities. The established marketing strategies need to be screened twice to select the most suitable marketing strategies and record them; S9. Evaluate the overall process, and the evaluation results are excellent, good, or poor. When the evaluation result is poor, sort out and improve the overall process until the evaluation result reaches excellent or good.

[0023] In this embodiment, a business marketing strategy can be formulated, a large number of targeted advertisements and promotional activities can be obtained, and then they are screened multiple times to finalize the final marketing strategy, thereby improving the practical application effect and functionality of the method. Example 4

[0024] See also Figure 1 The present invention provides a technical solution: an e-commerce data management method based on big data, comprising the following steps: S1. The specific process of acquiring e-commerce data is to collect various data of e-commerce platforms through website search platforms and search engines, including user behavior data, transaction data and product data. The above data reflects the consumption habits of users, the market performance of products and the overall trend of the market, and is the basis for subsequent analysis; S2. Preprocess the data. The specific steps are: first collect the acquired data, then check the data, then clean the data, mark and remove abnormal data, fill and correct the clear data, and format the data to make the data format unified; S3, for data backup and storage, the storage database used is Hadoop; S4, perform multiple data encryption processing, the data encryption algorithms are MD5 algorithm and RAS algorithm; S5. Analyze the data. The specific steps are: classify and group the data, extract features from the data, analyze the features of the data, and extract valid information from the data. The valid information in advance includes user behavior pattern information, product trend information, and market trend information. S6. Visualize the data analysis results. The visualization is presented in the form of report display. S7, establishing a business recommendation model, the specific steps are: according to the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's fast business navigation; S8. Develop personalized marketing strategies based on user portraits and market analysis results. The marketing strategies include targeted advertising and promotional activities. The established marketing strategies need to be screened three times to select the most suitable marketing strategies and record them. S9. Evaluate the overall process, and the evaluation results are excellent, good, or poor. When the evaluation result is poor, sort out and improve the overall process until the evaluation result reaches excellent or good.

[0025] In this embodiment, the method can greatly improve the security of the processed original data by setting up multiple encryption processes for the processed data, avoid the leakage of important business data, and thus avoid unnecessary business losses. At the same time, the method visualizes the analysis results of the data, so that users can quickly and intuitively know the data analysis results. At the same time, a business recommendation model can be automatically established based on the analysis results, and a large number of business links are integrated into the model. Users can quickly navigate the business through the links and conduct necessary business negotiations and cooperation. At the same time, business marketing strategies can be formulated, and a large number of targeted advertisements and promotional activities can be obtained, which are then screened multiple times to finalize the final marketing strategy, thereby improving the actual application effect and functionality of the method.

[0026] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An e-commerce data management method based on big data, characterized by: The steps include: S1. Acquiring e-commerce data; S2, preprocessing the data; S3, back up and store data; S4, perform multiple data encryption processing; S5. Analyze the data; S6. Visualize the data analysis results; S7. Establish a business recommendation model; S8. Develop business marketing strategies; S9. Evaluate the overall process.

2. The e-commerce data management method based on big data according to claim 1, characterized in that: In S1, the e-commerce data is acquired, and the specific process is to collect various data of the e-commerce platform, including user behavior data, transaction data and product data, through one or more methods such as website search platform, search engine and log file. The above data reflects the consumption habits of users, the market performance of products and the overall trend of the market, and is the basis for subsequent analysis.

3. The e-commerce data management method based on big data according to claim 1, characterized in that: In S2, the data is preprocessed, and the specific steps are: first, the acquired data is concentrated, then the data is checked, then the data is cleaned, abnormal data is marked and cleared, clear data is supplemented and corrected, and data is formatted and converted to unify the data format.

4. The e-commerce data management method based on big data according to claim 1, characterized in that: In the S3, data is backed up and stored, and the storage database used is one or more of Hadoop and Spark.

5. The e-commerce data management method based on big data according to claim 1, characterized in that: In S4, multiple data encryption processing is performed, and the data encryption algorithms are MD5 algorithm and RAS algorithm.

6. The e-commerce data management method based on big data according to claim 1, characterized in that: In S5, the data is analyzed, and the specific steps are: classifying and grouping the data, extracting features from the data, performing feature analysis on the data, and extracting valid information from the data. The advance validity information includes user behavior pattern information, product trend information, and market trend information.

7. The e-commerce data management method based on big data according to claim 1, characterized in that: In S6, the data analysis results are visualized, and the visualization is presented in one or more forms of report presentation, chart presentation, and legend presentation.

8. The e-commerce data management method based on big data according to claim 1, characterized in that: In S7, a business recommendation model is established, and the specific steps are: based on the validity information extracted in S5, a business recommendation model is established, and different business links are set in the business recommendation model to establish a user's quick business navigation.

9. The e-commerce data management method based on big data according to claim 1, characterized in that: In S8, a personalized marketing strategy is formulated based on the user portrait and market analysis results. The marketing strategy includes targeted advertising and promotional activities. The established marketing strategy needs to be screened 2-3 times to select the most suitable marketing strategy and record it.

10. The e-commerce data management method based on big data according to claim 1, characterized in that: In S9, the overall process is evaluated, and the evaluation results are excellent, good, and poor. When the evaluation result is poor, the overall process is sorted out and improved until the evaluation result reaches excellent or good.

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