Big data software management method

Abstract

The invention discloses a method for managing big data software, which relates to the technical field of computer software and comprises a data acquisition and preprocessing stage, a user information management stage, a merchant information management stage, a big data analysis and decision-making stage and a continuous optimization and improvement stage. In the data acquisition and preprocessing stage, information is acquired through multi-source data, and the information comprises user registration information, commodity information, transaction records and evaluation information. According to the big data software management method provided by the invention, information is acquired through multi-source data in a data acquisition and preprocessing stage, the completeness and accuracy of the data are ensured by utilizing a data acquisition tool, the format is unified, repeated data are effectively removed, and missing values are processed, so that the quality and availability of the data are improved, and the data processing efficiency is improved. By analyzing browsing, purchasing and evaluating behaviors of the user, a behavior model is established, abnormal behaviors can be detected and investigated and verified, and the safety of user information is further improved.

Description

Technical Field

[0001] The present invention relates to the field of computer software technology, and in particular to a method for managing big data software. Background Art

[0002] In the existing technology, the management of big data software faces a series of challenges. The complexity of data collection and processing is high, because the data comes from multiple channels, has various formats, and has a large number of duplicate and missing values, which makes it difficult to ensure the accuracy and completeness of the data. In terms of user information management, due to the single verification method, the authenticity and validity of user information are difficult to be effectively verified. The lack of in-depth analysis of user behavior and detection mechanism of abnormal behavior leads to an imperfect user credit evaluation system, making it difficult to effectively restrict low-credit users. There are also loopholes in merchant information management, the merchant qualification review is not strict, regular reviews are not timely, and abnormal transaction behavior monitoring is not in place, which increases the transaction risk on the e-commerce platform. In the big data analysis and decision-making stage, due to the lack of advanced data mining and analysis technology, it is difficult to extract valuable information from massive data, and the ability to provide decision-making support for the operation and management of the e-commerce platform is limited. The lack of continuous optimization and improvement mechanism leads to the inability to make timely adjustments to management methods based on feedback from users and merchants, and it is difficult to continuously improve management results. In this regard, we propose a method for the management of big data software. Summary of the invention

[0003] In order to solve the above technical problems, a method for managing big data software is provided. This technical solution solves the above problems.

[0004] In order to achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A method for managing big data software, comprising: a data collection and preprocessing stage, a user information management stage, a merchant information management stage, a big data analysis and decision-making stage, and a continuous optimization and improvement stage;

[0006] In the data collection and preprocessing stage, multi-source data collection includes user registration information, product information, transaction records, and evaluation information. Data collection tools are used to ensure that the data is complete, accurate, and in a unified format, and duplicate data is removed and missing values ​​are processed.

[0007] In the user information management stage, multiple verification methods are used when users register, user information is verified regularly, user browsing, purchasing and evaluation behaviors are analyzed, behavioral models are established, abnormal behaviors are detected and investigated and verified, a credit evaluation system is established, credit scores are given based on transaction records, etc., and low-credit users are restricted;

[0008] During the merchant information management phase, strictly review merchant qualification documents, conduct regular reviews, monitor merchant transaction data in real time, use data analysis to identify abnormal transaction behaviors and investigate, establish an evaluation review mechanism, remove false and malicious reviews, encourage true and detailed reviews and reward users, warn merchants with poor reviews to make corrections, and take measures such as removing them from shelves and suspending their operations;

[0009] In the big data analysis and decision-making stage, data mining technology is used to extract valuable information, establish analysis models, establish risk warning mechanisms based on analysis results, discover potential risk factors and formulate preventive measures, and provide decision support for e-commerce platform operation and management based on big data analysis results;

[0010] During the continuous optimization and improvement stage, management results are regularly evaluated, user and merchant feedback is collected, and management strategies and methods are adjusted based on the results.

[0011] Preferably, multiple verification methods are used when users register, and regular verification of user information specifically includes:

[0012] Identify the specific channels for obtaining data on the e-commerce platform, including: user registration page, product details page, transaction order system and user evaluation module;

[0013] Select professional data acquisition software and configure the parameters of the acquisition tool;

[0014] Conduct preliminary verification of the collected data to check whether the data format meets expectations, compare the same type of data collected from different channels to ensure data consistency, establish a unified data format standard, and convert the collected data in different formats to make them meet the unified standard;

[0015] Analyze each field in the data set, determine the key attributes used to judge duplicate data, traverse the data set, and compare the key attribute values ​​of each data with other data. If the key attribute values ​​of two or more data are found to be exactly the same, then these data are considered to be duplicates.

[0016] Once duplicate data is identified, select one of the data to be retained based on the data's timestamp and integrity, and delete the other duplicate data;

[0017] Traverse the data set and check whether the value of each field is empty or has a specific missing value identifier. For numeric fields, check whether it is a special value. For character fields, check whether it is an empty string or a specific string.

[0018] For numeric fields, if the data distribution is relatively concentrated, the missing values ​​are filled by calculating the mean of the field. The calculation formula for the mean is:

[0019]

[0020] In the formula, Mean represents the mean, SOANV represents the sum of all non-missing values, and NONV represents the number of non-missing values;

[0021] If the data distribution is skewed, fill in the missing values ​​by calculating the median of the field, sort the data from small to large, and the value in the middle is the median. If the number of data is an even number, the median is the average of the two middle numbers.

[0022] Preferably, the user information management stage specifically includes:

[0023] The application of multiple verification methods during user registration, including: mobile verification code verification, email verification and real-name authentication, and setting of information verification and maintenance cycles;

[0024] Mobile verification code: When a user registers, the system sends a random verification code to the mobile device entered by the user. The user enters the received verification code on the registration page, and the system verifies it. If the verification code is correct and within the valid time, the verification is passed;

[0025] Email verification: a verification email is sent to the email address provided by the user. The email contains a unique verification link. After the user clicks the link, the system confirms the validity of the email address and completes the verification.

[0026] Real-name authentication: Users are required to provide real-name information and the identity information submitted by users is verified by connecting with a third-party identity authentication agency.

[0027] Preferably, analyzing user browsing, purchasing and evaluation behaviors, establishing behavioral models, detecting abnormal behaviors and investigating and verifying them specifically include:

[0028] Analyze user browsing behavior, record the product categories, browsing time and number of browsing times that users browse on e-commerce platforms, and understand user interests and preferences by analyzing these data;

[0029] Analyze user purchasing behavior, record user purchase history, analyze user purchase frequency and consumption amount distribution, and understand user spending capacity and consumption habits;

[0030] Record the user's evaluation content, evaluation time, and evaluation level of the purchased goods, and understand the user's satisfaction with the goods and services by analyzing the evaluation behavior;

[0031] Integrate users' browsing, purchasing and evaluation behavior data, clean and pre-process the data, extract features from the processed data, and establish a user behavior model;

[0032] Based on the constructed user behavior model, the re-acquired user data is judged to detect whether the user has abnormal behavior, and the user is marked for further investigation and verification;

[0033] Among them, the expression of the user behavior model is:

[0034]

[0035] In the formula, Y represents the predicted value of the user's purchase behavior, X is the user behavior feature, i represents the feature of the i-th type, n represents the total number of feature types, β0,β1,β2,...,β n Represents the parameters of the model.

[0036] Preferably, a credit evaluation system is established to assign credit scores based on transaction records, etc., and restrictions are imposed on low-credit users, specifically including:

[0037] Determine credit score indicators based on the user's transaction records, evaluations and behavioral performance;

[0038] Set scoring rules, assign different weights to each credit scoring indicator, and score users based on their performance on each indicator;

[0039] According to the set scoring rules, a credit score is calculated for each user, where the credit score calculation formula is:

[0040] CS=∑(IS×W)

[0041] In the formula, CS represents the credit score, IS represents the indicator score, and W represents the weight;

[0042] For users with lower credit scores, different restriction measures are taken according to their credit level.

[0043] Preferably, the merchant information management stage specifically includes:

[0044] Identify the merchant qualification documents that need to be reviewed, including business license, tax registration certificate and organization code certificate, and check whether the merchant's business scope is consistent with the goods sold on the e-commerce platform;

[0045] Establish a dedicated review team or department to review merchant qualifications and require merchants to submit clear and complete qualification documents;

[0046] Determine the period for merchant qualification review based on actual conditions, check whether the merchant's qualification documents are still valid, and verify whether there are any major changes in the merchant's business conditions;

[0047] If it is found that the merchant's qualifications have expired or there are problems, the merchant will be notified in a timely manner to make corrections. During the correction period, the merchant's product sales will be restricted;

[0048] For transaction monitoring and analysis, we use the transaction system of the e-commerce platform to record merchant transaction data in real time, establish a data monitoring platform, monitor transaction data in real time, set judgment criteria for abnormal transaction behavior, use data analysis technology to analyze transaction data, and identify merchants with abnormal transaction behavior;

[0049] Investigate merchants marked as having abnormal transaction behaviors, require them to provide transaction receipts, verify the authenticity and legality of the transactions, and check whether there are any violations.

[0050] Preferably, the big data analysis and decision-making stage specifically includes:

[0051] Extract user preferences, product sales trends and merchant reputation information from the data, select appropriate data mining algorithms and techniques according to the mining objectives, clean and preprocess the collected data to ensure the quality and availability of the data, use the selected data mining techniques to mine the preprocessed data, analyze and interpret the results of data mining, and extract valuable information;

[0052] Clarify the operation status of the e-commerce platform to be analyzed, select the appropriate analysis model according to the analysis objectives, input the pre-processed data into the analysis model for training, improve the accuracy and reliability of the model by adjusting the model parameters, evaluate the trained model, and optimize the model based on the evaluation results;

[0053] Based on the results of data analysis, potential risk factors are identified, risk warning indicators and thresholds are set, and the operational data of the e-commerce platform is continuously monitored. When the risk warning indicators reach or exceed the thresholds, potential risk factors are discovered in a timely manner, and corresponding preventive measures are formulated for different risk factors.

[0054] Preferably, the continuous optimization and improvement phase specifically includes:

[0055] Determine specific evaluation indicators for different management aspects. For the data collection and preprocessing stage, evaluate the integrity, accuracy and deduplication effect of the data; for the user information management stage, evaluate the user verification pass rate and the rationality of the credit score; for the merchant information management stage, evaluate the merchant qualification review pass rate and the detection rate of abnormal transaction behavior; for the big data analysis and decision-making stage, evaluate the accuracy of the analysis model and the timeliness of risk warning;

[0056] Set up user feedback portals on e-commerce platforms and establish communication channels with merchants;

[0057] Regularly send satisfaction survey emails or text messages to users, inviting them to evaluate the platform's services and make suggestions;

[0058] Classify and organize the feedback collected from users and merchants, extract the main issues and suggestions, conduct in-depth analysis of the evaluation results, find out the problems and shortcomings in management, formulate specific adjustment plans based on the analysis results, implement the formulated adjustment plans, monitor and evaluate the implementation process to ensure the effectiveness of the adjustment plans.

[0059] Preferably, user registration information, product information, transaction records and evaluation information are encrypted. During data transmission, encrypted channels are used to prevent data from being stolen or tampered with. A strict data access permission control mechanism is established so that only authorized personnel can access specific data.

[0060] Preferably, based on the user's behavioral model and credit score, recommend goods, services and promotional activities that match the user's interest preferences and spending power, establish a user feedback mechanism, and collect user feedback on personalized service recommendations in a timely manner.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] A method for managing big data software proposed in the present invention collects information from multiple sources through the data collection and preprocessing stages, and uses data collection tools to ensure the integrity, accuracy and unified format of the data, effectively removes duplicate data and processes missing values, thereby improving the quality and availability of the data. By analyzing user browsing, purchasing and evaluation behaviors and establishing a behavior model, it is possible to detect abnormal behaviors and conduct investigations and verifications, further improving the security of user information. By setting up a user feedback portal and establishing communication channels with merchants, the interaction and communication between the platform and users and merchants are strengthened, thereby promoting the continuous improvement and development of the platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0064] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0065] Reference Figure 1 As shown, a method for managing big data software includes: data collection and preprocessing stage, user information management stage, merchant information management stage, big data analysis and decision-making stage and continuous optimization and improvement stage;

[0066] In the data collection and preprocessing stage, multi-source data collection includes user registration information, product information, transaction records, and evaluation information. Data collection tools are used to ensure that the data is complete, accurate, and in a unified format, and duplicate data is removed and missing values ​​are processed.

[0067] In the user information management stage, multiple verification methods are used when users register, user information is verified regularly, user browsing, purchasing and evaluation behaviors are analyzed, behavioral models are established, abnormal behaviors are detected and investigated and verified, a credit evaluation system is established, credit scores are given based on transaction records, etc., and low-credit users are restricted;

[0068] During the merchant information management phase, strictly review merchant qualification documents, conduct regular reviews, monitor merchant transaction data in real time, use data analysis to identify abnormal transaction behaviors and investigate, establish an evaluation review mechanism, remove false and malicious reviews, encourage true and detailed reviews and reward users, warn merchants with poor reviews to make corrections, and take measures such as removing them from shelves and suspending their operations;

[0069] In the big data analysis and decision-making stage, data mining technology is used to extract valuable information, establish analysis models, establish risk warning mechanisms based on analysis results, discover potential risk factors and formulate preventive measures, and provide decision support for e-commerce platform operation and management based on big data analysis results;

[0070] During the continuous optimization and improvement stage, management results are regularly evaluated, user and merchant feedback is collected, and management strategies and methods are adjusted based on the results.

[0071] When registering a user, a variety of verification methods are used, and user information is regularly verified, including:

[0072] Identify the specific channels for obtaining data on the e-commerce platform, including: user registration page, product details page, transaction order system and user evaluation module;

[0073] Select professional data acquisition software and configure the parameters of the acquisition tool;

[0074] Conduct preliminary verification of the collected data to check whether the data format meets expectations, compare the same type of data collected from different channels to ensure data consistency, establish a unified data format standard, and convert the collected data in different formats to make them meet the unified standard;

[0075] Analyze each field in the data set, determine the key attributes used to judge duplicate data, traverse the data set, and compare the key attribute values ​​of each data with other data. If the key attribute values ​​of two or more data are found to be exactly the same, then these data are considered to be duplicates.

[0076] Once duplicate data is identified, select one of the data to be retained based on the data's timestamp and integrity, and delete the other duplicate data;

[0077] Traverse the data set and check whether the value of each field is empty or has a specific missing value identifier. For numeric fields, check whether it is a special value. For character fields, check whether it is an empty string or a specific string.

[0078] For numeric fields, if the data distribution is relatively concentrated, the missing values ​​are filled by calculating the mean of the field. The calculation formula for the mean is:

[0079]

[0080] In the formula, Mean represents the mean, SOANV represents the sum of all non-missing values, and NONV represents the number of non-missing values;

[0081] If the data distribution is skewed, the median is used to fill the missing values. The value in the middle after the data is sorted is the median. If the number of data is an even number, the average of the two middle numbers is taken. The user information management stage includes multiple verification methods and set information verification cycles; the mobile verification code is sent and verified during registration; the email verification sends an email containing a link, and the user clicks to confirm the validity; real-name authentication is connected with a third-party organization to verify information, analyze user behavior, including browsing behavior, purchasing behavior, and evaluation behavior, and establish a behavioral model based on comprehensive data cleaning preprocessing and feature extraction. Based on the model, user data detection anomalies are determined and investigated and verified.

[0082] Among them, the expression of the user behavior model is:

[0083]

[0084] In the formula, Y represents the predicted value of the user's purchase behavior, X is the user behavior feature, i represents the feature of the i-th type, n represents the total number of feature types, β0,β1,β2,...,β n Represents the parameters of the model.

[0085] Establish a credit evaluation system, assign credit scores based on transaction records, and impose restrictions on low-credit users. Specific details include:

[0086] Determine credit score indicators based on the user's transaction records, evaluations and behavioral performance;

[0087] Set scoring rules, assign different weights to each credit scoring indicator, and score users based on their performance on each indicator;

[0088] According to the set scoring rules, a credit score is calculated for each user, where the credit score calculation formula is:

[0089] CS=∑(IS×W)

[0090] In the formula, CS represents the credit score, IS represents the indicator score, and W represents the weight;

[0091] For users with lower credit scores, different restriction measures are taken according to their credit level.

[0092] The merchant information management stage includes reviewing merchant qualification documents, setting up an audit team, determining the review cycle, notifying rectification and restricting product sales, monitoring and analyzing transactions, and investigating abnormal merchants. The big data analysis and decision-making stage includes extracting information, selecting algorithm technology, mining and analyzing data, selecting analysis models, evaluating and optimizing models, determining risk factors, and formulating preventive measures. The continuous optimization and improvement stage includes determining evaluation indicators, setting feedback portals, sending survey emails, sorting out feedback, formulating adjustment plans, and monitoring and evaluating. It also includes data encryption, establishing access control mechanisms, and recommending products based on user behavior models and credit scores and establishing feedback mechanisms.

[0093] The use process of the present invention is as follows: in the data collection and preprocessing stage, multi-source data is collected to ensure that the data is complete, accurate, and in a unified format, and duplications and missing values ​​are removed; in the user information management stage, a variety of verification methods are adopted to verify information, analyze behavior, build models, evaluate credit, and restrict low-credit users; in the merchant information management stage, qualifications are strictly reviewed, rechecked, transaction data is monitored, abnormal behavior is identified, an evaluation mechanism is established, and measures are taken against merchants with poor reviews; in the big data analysis and decision-making stage, data mining technology is used to extract information, build models, warn of risks, and provide decision-making support; in the continuous optimization and improvement stage, the effects are regularly evaluated, feedback is collected, and strategies are adjusted.

[0094] In summary, the advantages of the present invention are: improving the accuracy and efficiency of data management, multi-source data collection and preprocessing, ensuring data integrity, accuracy and unified format, removing duplicate data and processing missing values, and enhancing the security and reliability of user information management. Register with multiple verification methods, verify information regularly, analyze behavioral models to detect anomalies, establish a credit evaluation system to restrict low-credit users, and optimize the merchant information management process. Strictly review qualifications and recheck, monitor transaction data to identify abnormal behavior, protect consumer interests, and provide big data analysis and decision support. Mining valuable information to establish models, early warning risks to reduce operational risks, and achieve continuous optimization and improvement. Regularly evaluate and collect feedback to adjust strategies, strengthen interactive communication between the platform and users and merchants, ensure data security, encrypt and process information, use encrypted channels for transmission, and establish a strict access control mechanism. This patent provides a comprehensive and effective solution for big data software management on e-commerce platforms.

[0095] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for managing big data software, characterized in that: include: Data collection and preprocessing stage, user information management stage, merchant information management stage, big data analysis and decision-making stage, and continuous optimization and improvement stage; In the data collection and preprocessing stage, multi-source data collection includes user registration information, product information, transaction records, and evaluation information. Data collection tools are used to ensure that the data is complete, accurate, and in a unified format, and duplicate data is removed and missing values ​​are processed. In the user information management stage, multiple verification methods are used when users register, user information is verified regularly, user browsing, purchasing and evaluation behaviors are analyzed, behavioral models are established, abnormal behaviors are detected and investigated and verified, a credit evaluation system is established, credit scores are given based on transaction records, etc., and low-credit users are restricted; During the merchant information management phase, strictly review merchant qualification documents, conduct regular reviews, monitor merchant transaction data in real time, use data analysis to identify abnormal transaction behaviors and investigate, establish an evaluation review mechanism, remove false and malicious reviews, encourage true and detailed reviews and reward users, warn merchants with poor reviews to make corrections, and take measures such as removing them from shelves and suspending their operations; In the big data analysis and decision-making stage, data mining technology is used to extract valuable information, establish analysis models, establish risk warning mechanisms based on analysis results, discover potential risk factors and formulate preventive measures, and provide decision support for e-commerce platform operation and management based on big data analysis results; During the continuous optimization and improvement stage, management results are regularly evaluated, user and merchant feedback is collected, and management strategies and methods are adjusted based on the results.

2. The method for managing big data software according to claim 1, characterized in that: When registering a user, a variety of verification methods are used, and user information is regularly verified, including: Identify the specific channels for obtaining data on the e-commerce platform, including: user registration page, product details page, transaction order system and user evaluation module; Select professional data acquisition software and configure the parameters of the acquisition tool; Conduct preliminary verification of the collected data to check whether the data format meets expectations, compare the same type of data collected from different channels to ensure data consistency, establish a unified data format standard, and convert the collected data in different formats to make them meet the unified standard; Analyze each field in the data set, determine the key attributes used to judge duplicate data, traverse the data set, and compare the key attribute values ​​of each data with other data. If the key attribute values ​​of two or more data are found to be exactly the same, then these data are considered to be duplicates. Once duplicate data is identified, select one of the data to be retained based on the data's timestamp and integrity, and delete the other duplicate data; Traverse the data set and check whether the value of each field is empty or has a specific missing value identifier. For numeric fields, check whether it is a special value. For character fields, check whether it is an empty string or a specific string. For numeric fields, if the data distribution is relatively concentrated, the missing values ​​are filled by calculating the mean of the field. The calculation formula for the mean is: In the formula, Mean represents the mean, SOANV represents the sum of all non-missing values, and NONV represents the number of non-missing values; If the data distribution is skewed, fill in the missing values ​​by calculating the median of the field, sort the data from small to large, and the value in the middle is the median. If the number of data is an even number, the median is the average of the two middle numbers.

3. The method for managing big data software according to claim 1, characterized in that: The user information management stage specifically includes: The application of multiple verification methods during user registration, including: mobile verification code verification, email verification and real-name authentication, and setting of information verification and maintenance cycles; Mobile verification code: When a user registers, the system sends a random verification code to the mobile device entered by the user. The user enters the received verification code on the registration page, and the system verifies it. If the verification code is correct and within the valid time, the verification is passed; Email verification: a verification email is sent to the email address provided by the user. The email contains a unique verification link. After the user clicks the link, the system confirms the validity of the email address and completes the verification. Real-name authentication: Users are required to provide real-name information and the identity information submitted by users is verified by connecting with a third-party identity authentication agency.

4. The method for managing big data software according to claim 1, characterized in that: Analyze user browsing, purchasing and evaluation behaviors, establish behavioral models, detect abnormal behaviors and investigate and verify them. Specifically include: Analyze user browsing behavior, record the product categories, browsing time and number of browsing times that users browse on e-commerce platforms, and understand user interests and preferences by analyzing these data; Analyze user purchasing behavior, record user purchase history, analyze user purchase frequency and consumption amount distribution, and understand user spending capacity and consumption habits; Record the user's evaluation content, evaluation time, and evaluation level of the purchased goods, and understand the user's satisfaction with the goods and services by analyzing the evaluation behavior; Integrate users' browsing, purchasing and evaluation behavior data, clean and pre-process the data, extract features from the processed data, and establish a user behavior model; Based on the constructed user behavior model, the re-acquired user data is judged to detect whether the user has abnormal behavior, and the user is marked for further investigation and verification; Among them, the expression of the user behavior model is: In the formula, Y represents the predicted value of the user's purchase behavior, X is the user behavior feature, i represents the feature of the i-th type, n represents the total number of feature types, β0,β1,β2,...,β n Represents the parameters of the model.

5. The method for managing big data software according to claim 1, characterized in that: Establish a credit evaluation system, assign credit scores based on transaction records, and impose restrictions on low-credit users. Specific details include: Determine credit score indicators based on the user's transaction records, evaluations and behavioral performance; Set scoring rules, assign different weights to each credit scoring indicator, and score users based on their performance on each indicator; According to the set scoring rules, a credit score is calculated for each user, where the credit score calculation formula is: CS=∑(IS×W) In the formula, CS represents the credit score, IS represents the indicator score, and W represents the weight; For users with lower credit scores, different restriction measures are taken according to their credit level.

6. The method for managing big data software according to claim 1, characterized in that: The business information management stage specifically includes: Identify the merchant qualification documents that need to be reviewed, including business license, tax registration certificate and organization code certificate, and check whether the merchant's business scope is consistent with the goods sold on the e-commerce platform; Establish a dedicated review team or department to review merchant qualifications and require merchants to submit clear and complete qualification documents; Determine the period for merchant qualification review based on actual conditions, check whether the merchant's qualification documents are still valid, and verify whether there are any major changes in the merchant's business conditions; If it is found that the merchant's qualifications have expired or there are problems, the merchant will be notified in a timely manner to make corrections. During the correction period, the merchant's product sales will be restricted; For transaction monitoring and analysis, we use the transaction system of the e-commerce platform to record merchant transaction data in real time, establish a data monitoring platform, monitor transaction data in real time, set judgment criteria for abnormal transaction behavior, use data analysis technology to analyze transaction data, and identify merchants with abnormal transaction behavior; Investigate merchants marked as having abnormal transaction behaviors, require them to provide transaction receipts, verify the authenticity and legality of the transactions, and check whether there are any violations.

7. The method for managing big data software according to claim 1, characterized in that: The big data analysis and decision-making stage specifically includes: Extract user preferences, product sales trends and merchant reputation information from the data, select appropriate data mining algorithms and techniques according to the mining objectives, clean and preprocess the collected data to ensure the quality and availability of the data, use the selected data mining techniques to mine the preprocessed data, analyze and interpret the results of data mining, and extract valuable information; Clarify the operation status of the e-commerce platform to be analyzed, select the appropriate analysis model according to the analysis objectives, input the pre-processed data into the analysis model for training, improve the accuracy and reliability of the model by adjusting the model parameters, evaluate the trained model, and optimize the model based on the evaluation results; Based on the results of data analysis, potential risk factors are identified, risk warning indicators and thresholds are set, and the operational data of the e-commerce platform is continuously monitored. When the risk warning indicators reach or exceed the thresholds, potential risk factors are discovered in a timely manner, and corresponding preventive measures are formulated for different risk factors.

8. The method for managing big data software according to claim 1, characterized in that: The continuous optimization and improvement phase specifically includes: Determine specific evaluation indicators for different management aspects. For the data collection and preprocessing stage, evaluate the integrity, accuracy and deduplication effect of the data; for the user information management stage, evaluate the user verification pass rate and the rationality of the credit score; for the merchant information management stage, evaluate the merchant qualification review pass rate and the detection rate of abnormal transaction behavior; for the big data analysis and decision-making stage, evaluate the accuracy of the analysis model and the timeliness of risk warning; Set up user feedback portals on e-commerce platforms and establish communication channels with merchants; Regularly send satisfaction survey emails or text messages to users, inviting them to evaluate the platform's services and make suggestions; Classify and organize the feedback collected from users and merchants, extract the main issues and suggestions, conduct in-depth analysis of the evaluation results, find out the problems and shortcomings in management, formulate specific adjustment plans based on the analysis results, implement the formulated adjustment plans, monitor and evaluate the implementation process to ensure the effectiveness of the adjustment plans.

9. The method for managing big data software according to claim 1, characterized in that: User registration information, product information, transaction records and evaluation information are encrypted. During data transmission, encrypted channels are used to prevent data from being stolen or tampered with. A strict data access permission control mechanism is established so that only authorized personnel can access specific data.

10. The method for managing big data software according to claim 1, characterized in that: Based on the user's behavioral model and credit score, we recommend products, services and promotions that are in line with his or her interests, preferences and spending power. We establish a user feedback mechanism to promptly collect user feedback on personalized service recommendations.

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