Full-link data processing device for user operation and use method of full-link data processing device
By building a full-link data processing device, the problems of multi-channel data acquisition inconsistency and data abnormalities in user operations are solved, efficient data cleaning and analysis are realized, user portraits are generated, and the accuracy and effectiveness of user operations are improved.
Patent Information
- Application Number
- CN202510663795.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-22
- Publication Date
- 2025-08-22
AI Technical Summary
In the current user operation scenario, enterprises face inconsistency and complexity of multi-channel data collection, and there are duplication, missing and abnormalities in multi-source data, resulting in unreliable data cleaning results.
Build a full-link data processing device, collect data on mobile, non-mobile and third-party platforms, adopt HDFS storage technology and inverted indexing mechanism, combine K-means clustering, Markov chain and linear regression models for data cleaning and analysis, generate user portraits and formulate operation strategies.
It realizes unified, standardized storage and efficient and accurate preprocessing of data, improves data consistency and reliability, supports precise user grouping, personalized recommendations and precise marketing, and significantly improves user activity and conversion rate.
Smart Images
Figure CN120525567A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of full-link data processing, and specifically to a user-operated full-link data processing device and a method for using the same. Background Art
[0002] The full-link data processing device for user operations is a comprehensive tool used to support user operations. It aims to help operations teams achieve accurate user insights, efficient user management, and strategy optimization by collecting, storing, analyzing, and applying data on user behavior, characteristics, and feedback throughout their lifecycle. Through full-link data processing, the device can capture user dynamics in real time, predict user needs, optimize user experience, and provide data-driven support for user operation decisions, thereby improving user satisfaction, activity, and conversion rate, ultimately maximizing user value. In the current user operation scenario, enterprises are faced with the inconsistency and complexity of multi-channel data collection. They need to collect massive amounts of data with different formats, quality and update frequencies from mobile terminals, non-mobile terminals and third-party platforms. At the same time, multi-source data often have duplication, missing values and anomalies after collection. How to efficiently and accurately deduplicate the data, fill in missing values and detect outliers pre-process them to ensure the reliability of the data cleaning results? Therefore, a full-link data processing device for user operation and its use method are proposed to address the above problems. Summary of the Invention
[0003] The purpose of the present invention is to provide a full-link data processing device for user operations and a method for using the same, so as to solve the problems of inconsistency and complexity of multi-channel data collection faced by enterprises in current user operations scenarios. Enterprises need to collect massive amounts of data with different formats, quality and update frequencies from mobile terminals, non-mobile terminals and third-party platforms; at the same time, multi-source data often have duplication, missing values and anomalies after collection. How to efficiently and accurately deduplicate the data, fill in missing values and pre-process the outliers to ensure the reliability of the data cleaning results.
[0004] To achieve the above object, the present invention provides the following technical solutions: A user-operated full-link data processing device and a method for using the same, comprising the following steps: S1: Obtaining operation behavior data on the mobile terminal , non-mobile terminal and interactive information data with enterprise products on third-party platforms , generate the original data set ; S2: The original data set obtained Perform data cleaning and preprocessing to remove duplicate, erroneous and incomplete data records; S3: The operation behavior data obtained after cleaning and preprocessing in step S2 , non-mobile terminal and interactive information data with enterprise products on third-party platforms , using HDFS storage technology to store data in a structured or semi-structured format on multiple data nodes of HDFS, and at the same time establishing a data indexing mechanism based on inverted index to optimize query performance. The calculation formula of inverted index is:
[0005] Where, Represents an inverted index, Indicates keywords or features, Indicates one of the documents or data records containing the keyword or feature; S4: Deeply mine the stored data, analyze user behavior and preference data using the K-means clustering algorithm to generate user profiles, use the Markov chain method to analyze user behavior patterns and changing trends, and use the linear regression model to predict user needs; The objective function of the K-means algorithm is:
[0006] In the formula, k represents the number of clusters, According to business needs and data characteristics, it is set as an indicator variable indicating whether sample j belongs to cluster i. represents the center of cluster i, represents the jth user sample, including its operation behavior, browsing traces and third-party platform interaction information characteristics; The state transition probability formula of the Markov chain is:
[0007] Where, Indicates the slave state Transfer to state The probability of Represents the state at time t, corresponding to the user's behavior at different time points, and Represent state i and state j respectively, which are defined according to user behavior characteristics; The mathematical expression of the linear regression model predicting user demand is:
[0008] Where, Indicates user needs, Represents the regression coefficient, which is obtained through training of historical data; Represents the characteristic variables that affect user needs, represents the error term; S5: Develop operational strategies based on the analysis results generated in S4, including but not limited to user segmentation, personalized recommendations, and precision marketing; S6: Push the operational strategy formulated in S5 to users and monitor the execution effect of the strategy in real time. Based on user feedback and data analysis results, continuously optimize the operational strategy to form a closed-loop management.
[0009] As a further optimized content of the present invention, wherein: the operation behavior data By embedding SDK code snippets in the mobile terminal to collect data, including but not limited to click event data, purchase behavior data, search keyword data, the non-mobile terminal Obtained through browser plug-ins and server log recording technology, including but not limited to page visit duration data and browsing path data, interaction information with the enterprise's products on the third-party platform Utilize the API interface provided by third-party platforms to pull data, including but not limited to social media comment data and sharing data.
[0010] As a further optimization of the present invention, in which: in S2, the data set The process of data cleaning and preprocessing is as follows: S21: Deduplication: Calculate the hash value of the data in D to remove duplicate data. The hash value calculation formula is:
[0011] Where, Representation data If the hash values of different data are the same, they are considered duplicate data and removed; S22: Missing value filling: For continuous data fields with missing values in D, the mean filling method is used to fill them. The mean calculation formula is:
[0012] Where, Indicates the mean of the field, used to fill missing values. Indicates the number of non-missing samples in this field. Represents the value of the i-th non-missing sample; S23: Outlier detection and processing: Use the Z score method to detect outliers in D. The Z score calculation formula is:
[0013] Where, represents the Z score of the i-th sample, represents the mean of the data, represents the standard deviation of the data, if , then the sample is judged as an outlier and deleted.
[0014] As a further optimization of the present invention, the specific process of performing user grouping based on the user portrait and behavior pattern analysis results generated in step S4 is as follows: Input data: user profile data, including interests, purchase history, and spending power; The processing method is to use the K-means clustering algorithm to group users into groups with high spending power and a love of fashion, groups with medium spending power and a focus on cost-effectiveness, and groups with low spending power and a focus on practicality. At the same time, Markov chains are used to analyze user behavior patterns to identify active users, potential churn users, and dormant users; The output result is: the user set after grouping, including the group with high consumption ability and fashion preference, the group with medium consumption ability and cost-effectiveness, the group with low consumption ability and practicality, active users, potential churn users, and dormant users.
[0015] As a further optimization of the present invention, the process of performing personalized recommendation based on the user portrait data and association rule mining results generated in step S4 is as follows: The input data is: user portrait data and association rule mining results; The solution is to recommend new product information and exclusive discounts from high-end fashion brands to users with high spending power and a love for fashion. For users with medium spending power who are concerned about cost-effectiveness, we recommend products with large discounts but good quality. For users with low spending power who value practicality, we recommend practical and reasonably priced products. Use association rule mining algorithms to discover the correlation between user purchase behaviors and recommend other products related to the products they have purchased; The output result is a personalized recommendation list, which contains recommended content for different user groups.
[0016] As a further optimization of the present invention, the process of precision marketing based on the demand forecasting model results and user portrait data generated in step S4 is as follows: The input data is: demand forecast model results and user portrait data; The processing method is: Combine the user purchase probability predicted by the linear regression model and select users with a purchase probability above a certain threshold as target marketing objects; Choose the right marketing opportunity based on user behavior patterns and demand forecasts, such as pushing promotional information during user active hours. The output is: precise marketing plan, including target user set, marketing timing and marketing content.
[0017] As a further optimization of the present invention, it includes: a data collection module for acquiring user data from multiple channels, including user operation behavior data on the APP, interaction information data with enterprise products on non-mobile terminals and third-party platforms, and the module comprehensively collects user behavior data and integrates it into a structured data set by embedding SDK code snippets in the APP, using browser plug-ins and server logging technology, and utilizing API interface docking provided by third-party platforms; The data cleaning and preprocessing module is used to purify the collected raw data to ensure its accuracy and availability. This module includes a deduplication processing unit that removes duplicate data by calculating the hash value of the data; a missing value filling unit that uses the mean filling method to handle missing values in continuous data fields; and an outlier detection and processing unit that uses the Z-score method to detect and process outliers. The data storage module uses HDFS storage technology to store cleaned and preprocessed data in a structured or semi-structured format on multiple data nodes. This module also establishes a data indexing mechanism based on inverted indexes to optimize data query performance. The inverted index enables fast data retrieval by associating keywords or features with the documents or data records containing them. The data modeling and analysis module is used to deeply mine stored data and extract valuable information. This module includes a user profile construction unit that uses the K-means clustering algorithm to analyze user behavior and preference data to generate detailed user profiles; a behavior pattern analysis unit that uses the Markov chain method to analyze user behavior patterns and changing trends; and a demand forecasting unit that uses a linear regression model to predict user demand. The operational strategy formulation module formulates targeted operational strategies based on the analysis results generated by the data modeling and analysis module. This module includes a user segmentation unit, which divides users into different groups based on user profiles and behavioral pattern analysis, such as those with high spending power and a preference for fashion, and those with medium spending power and a focus on cost-effectiveness; a personalized recommendation unit, which provides personalized recommendations for different user groups based on user profile data and association rule mining results; and a precision marketing unit, which uses demand forecasting model results and user profile data to formulate precision marketing plans, including target user screening, marketing opportunity selection, and marketing content design. The closed-loop management module is responsible for pushing the formulated operational strategies to users and monitoring the execution effects of the strategies in real time. This module includes a strategy execution unit, which pushes strategy content to users through multiple channels; an effect monitoring unit, which monitors the execution effects of strategies in real time and establishes a multi-dimensional effect evaluation indicator system; a data feedback collection unit, which collects user feedback data on strategy execution; and a data analysis and optimization unit, which conducts in-depth analysis of feedback data, proposes strategy optimization suggestions, and forms a closed-loop management.
[0018] As a further optimization of the present invention, a computer program is stored, which, when executed by a processor, can implement the entire process of data collection, preprocessing, storage, modeling, strategy formulation and closed-loop management according to the steps of any one of the methods in claims 1 to 6.
[0019] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention builds a full-link data processing device that integrates data collection, data cleaning and preprocessing, massive data storage, and data modeling and analysis. This effectively solves the data duplication, missing, and anomaly problems that exist in the multi-channel data collection process, achieves unified and standardized data storage, uses K-means clustering to deeply mine user behavior data and preference data, combines Markov chains to analyze user behavior patterns and changing trends, and uses linear regression models to predict user needs, thereby building a high-precision user profile. 2. In this invention, by performing automated pre-processing on massive data collected from multiple channels, including using a hash algorithm to remove duplicates, a mean-filling method to handle missing values, and a Z-score detection method to eliminate abnormal data, the data cleaning process is made efficient and accurate, significantly improving data consistency and reliability, and laying a solid foundation for subsequent data analysis and modeling; 3. In the present invention, the system not only supports accurate user grouping, but also generates personalized recommendations and precise marketing strategies for different user groups, and then realizes real-time monitoring and feedback of the strategy execution effect through the closed-loop management module, significantly improving user activity, retention rate and conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 This is a flow chart of a method for using a user-operated full-link data processing device of the present invention; Figure 2 This is a system block diagram of a user-operated full-link data processing device of the present invention. DETAILED DESCRIPTION
[0021] See also Figure 1-2 , the present invention provides a technical solution: A user-operated full-link data processing device and a method for using the same, comprising the following steps: S1: Obtaining operation behavior data on the mobile terminal , non-mobile terminal and interactive information data with enterprise products on third-party platforms , generate the original data set ; S2: The original data set obtained Perform data cleaning and preprocessing to remove duplicate, erroneous and incomplete data records; S3: The operation behavior data obtained after cleaning and preprocessing in step S2 , non-mobile terminal and interactive information data with enterprise products on third-party platforms , using HDFS storage technology to store data in a structured or semi-structured format on multiple data nodes of HDFS, and at the same time establishing a data indexing mechanism based on inverted index to optimize query performance. The calculation formula of inverted index is:
[0022] Where, Represents an inverted index, Indicates keywords or features, Indicates one of the documents or data records containing the keyword or feature; S4: Deeply mine the stored data, analyze user behavior and preference data using the K-means clustering algorithm to generate user profiles, use the Markov chain method to analyze user behavior patterns and changing trends, and use the linear regression model to predict user needs; The objective function of the K-means algorithm is:
[0023] In the formula, k represents the number of clusters, According to business needs and data characteristics, it is set as an indicator variable indicating whether sample j belongs to cluster i. represents the center of cluster i, represents the jth user sample, including its operation behavior, browsing traces and third-party platform interaction information characteristics; The state transition probability formula of the Markov chain is:
[0024] Where, Indicates the slave state Transfer to state The probability of Represents the state at time t, corresponding to the user's behavior at different time points, and Represent state i and state j respectively, which are defined according to user behavior characteristics; The mathematical expression of the linear regression model predicting user demand is:
[0025] Where, Indicates user needs, Represents the regression coefficient, which is obtained through training of historical data; Represents the characteristic variables that affect user needs, represents the error term; S5: Develop operational strategies based on the analysis results generated in S4, including but not limited to user segmentation, personalized recommendations, and precision marketing; S6: Push the operational strategy formulated in S5 to users and monitor the execution effect of the strategy in real time. Based on user feedback and data analysis results, continuously optimize the operational strategy to form a closed-loop management, effectively integrate raw data from different channels, ensure the consistency and accuracy of the basic data for subsequent data processing, automate data cleaning, preprocessing, structured storage and index construction, and provide guarantees for the efficient management of massive data. Through advanced data modeling and deep mining methods, achieve accurate construction of user portraits and behavior patterns, and provide data support for the company's precise operational strategy formulation.
[0026] As a technical solution for further implementation of this solution, the operation behavior data By embedding SDK code snippets in the mobile terminal to collect data, including but not limited to click event data, purchase behavior data, search keyword data, the non-mobile terminal Obtained through browser plug-ins and server log recording technology, including but not limited to page visit duration data and browsing path data, interaction information with the enterprise's products on the third-party platform Leverage APIs provided by third-party platforms to pull data, including but not limited to social media comment data and share count data. Multiple data collection methods complement each other, comprehensively capturing user behavior information and improving the richness and coverage of data collection. Standardized interfaces and collection technologies are used to achieve real-time and efficient data collection, providing support for subsequent unified data processing. As a technical solution for further implementation of this solution, in S2, the data set The process of data cleaning and preprocessing is as follows: S21: Deduplication: Calculate the hash value of the data in D to remove duplicate data. The hash value calculation formula is:
[0027] Where, Representation data If the hash values of different data are the same, they are considered duplicate data and removed; S22: Missing value filling: For continuous data fields with missing values in D, the mean filling method is used to fill them. The mean calculation formula is:
[0028] Where, Indicates the mean of the field, used to fill missing values. Indicates the number of non-missing samples in this field. Represents the value of the i-th non-missing sample; S23: Outlier detection and processing: Use the Z score method to detect outliers in D. The Z score calculation formula is:
[0029] Where, represents the Z score of the i-th sample, represents the mean of the data, represents the standard deviation of the data, if , then the sample is determined to be an outlier and deleted. The clear data cleaning process effectively eliminates data duplication, missingness and abnormal interference, ensures data quality, and adopts standard algorithm methods to improve the automation and accuracy of data preprocessing, laying a solid foundation for the next step of data storage and analysis; As a technical solution for further implementing this solution, the specific process of grouping users based on the user portrait and behavior pattern analysis results generated in step S4 is as follows: Input data: user profile data, including interests, purchase history, and spending power; The processing method is to use the K-means clustering algorithm to group users into groups with high spending power and a love of fashion, groups with medium spending power and a focus on cost-effectiveness, and groups with low spending power and a focus on practicality. At the same time, Markov chains are used to analyze user behavior patterns to identify active users, potential churn users, and dormant users; The output is: a clustered user set, including those with high spending power and a love of fashion, those with medium spending power and a focus on cost-effectiveness, those with low spending power and a focus on practicality, active users, potential churners, and dormant users. This enables precise clustering based on multi-dimensional user characteristics and behavior patterns, facilitating the implementation of customized operational strategies for different user groups. It also identifies user activity status, provides data support for precision marketing and customer relationship management, and optimizes resource allocation. As a technical solution for further implementing this solution, the process of performing personalized recommendation based on the user portrait data and association rule mining results generated in step S4 is as follows: The input data is: user portrait data and association rule mining results; The solution is to recommend new product information and exclusive discounts from high-end fashion brands to users with high spending power and a love for fashion. For users with medium spending power who are concerned about cost-effectiveness, we recommend products with large discounts but good quality. For users with low spending power who value practicality, we recommend practical and reasonably priced products. Use association rule mining algorithms to discover the correlation between user purchase behaviors and recommend other products related to the products they have purchased; The output is a personalized recommendation list. This list targets different user groups and uses user profiles and association rule mining to achieve more accurate personalized recommendations, significantly improving user satisfaction. Customized recommendation strategies better meet the needs of different user groups, promoting cross-selling and repeat purchases, and improving marketing effectiveness. As a technical solution for further implementing this solution, the process of using the demand forecasting model results and user portrait data generated in step S4 for precision marketing is as follows: The input data is: demand forecast model results and user portrait data; The processing method is: Combine the user purchase probability predicted by the linear regression model and select users with a purchase probability above a certain threshold as target marketing objects; Choose the right marketing opportunity based on user behavior patterns and demand forecasts, such as pushing promotional information during user active hours. The output is a precision marketing plan, including target user groups, marketing timing, and marketing content. This accurately identifies target users and implements targeted marketing strategies by combining demand forecasting models with user profile data. This uses reasonable marketing timing and customized promotional plans to effectively increase conversion rates and reduce marketing costs. As a technical solution for further implementing this solution, it includes a data collection module for obtaining user data from multiple channels, including user operation behavior data on the APP, interaction information data with enterprise products on non-mobile terminals and third-party platforms. The module comprehensively collects user behavior data and integrates it into a structured data set by embedding SDK code snippets in the APP, using browser plug-ins and server logging technology, and utilizing API interface docking provided by third-party platforms; The data cleaning and preprocessing module is used to purify the collected raw data to ensure its accuracy and availability. This module includes a deduplication processing unit that removes duplicate data by calculating the hash value of the data; a missing value filling unit that uses the mean filling method to handle missing values in continuous data fields; and an outlier detection and processing unit that uses the Z-score method to detect and process outliers. The data storage module uses HDFS storage technology to store cleaned and preprocessed data in a structured or semi-structured format on multiple data nodes. This module also establishes a data indexing mechanism based on inverted indexes to optimize data query performance. The inverted index enables fast data retrieval by associating keywords or features with the documents or data records containing them. The data modeling and analysis module is used to deeply mine stored data and extract valuable information. This module includes a user profile construction unit that uses the K-means clustering algorithm to analyze user behavior and preference data to generate detailed user profiles; a behavior pattern analysis unit that uses the Markov chain method to analyze user behavior patterns and changing trends; and a demand forecasting unit that uses a linear regression model to predict user demand. The operational strategy formulation module formulates targeted operational strategies based on the analysis results generated by the data modeling and analysis module. This module includes a user segmentation unit, which divides users into different groups based on user profiles and behavioral pattern analysis, such as those with high spending power and a preference for fashion, and those with medium spending power and a focus on cost-effectiveness; a personalized recommendation unit, which provides personalized recommendations for different user groups based on user profile data and association rule mining results; and a precision marketing unit, which uses demand forecasting model results and user profile data to formulate precision marketing plans, including target user screening, marketing opportunity selection, and marketing content design. The closed-loop management module is responsible for pushing the formulated operational strategies to users and monitoring the execution effects of the strategies in real time. This module includes a strategy execution unit, which pushes strategy content to users through various channels; an effect monitoring unit, which monitors the execution effects of the strategies in real time and establishes a multi-dimensional effect evaluation indicator system; a data feedback collection unit, which collects user feedback data on strategy execution; and a data analysis and optimization unit, which conducts in-depth analysis of feedback data and proposes strategy optimization suggestions, forming a closed-loop management. The integrated module design realizes full-link data management. The modules work together to improve the processing efficiency and response speed of the overall system. The combination of multiple data collection and processing technologies ensures data quality and efficient query, while providing timely and accurate data support for operational strategies. As a technical solution for further implementing this solution, a computer program is stored. When the computer program is executed by the processor, it can implement the entire process of data collection, preprocessing, storage, modeling, strategy formulation and closed-loop management according to the steps of any one of the methods in claims 1 to 6. The software program provides a flexible and modular implementation method, so that the present invention can be quickly deployed and customized in a variety of practical application scenarios, and the entire process is automated through program control, ensuring that the system can operate efficiently in all aspects of data operation, thereby improving the overall operation effect and service quality.
[0030] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method of the present invention and its core ideas. The above is only a preferred implementation method of the present invention. It should be pointed out that due to the limitations of textual expression, there are objectively infinite specific structures. For ordinary technicians in this technical field, without departing from the principles of the present invention, they can make several improvements, modifications or changes, and can also combine the above technical features in an appropriate manner; these improvements, modifications, changes or combinations, or the direct application of the inventive concept and technical solution to other occasions without improvement, should be regarded as the scope of protection of the present invention.
Claims
1. A method for using a user-operated full-link data processing device, characterized in that: The following steps are involved: S1: Obtaining operation behavior data on the mobile terminal , non-mobile terminal and interactive information data with enterprise products on third-party platforms , generate the original data set ; S2: The original data set obtained Perform data cleaning and preprocessing to remove duplicate, erroneous and incomplete data records; S3: The operation behavior data obtained after cleaning and preprocessing in step S2 , non-mobile terminal and interactive information data with enterprise products on third-party platforms , using HDFS storage technology to store data in a structured or semi-structured format on multiple data nodes of HDFS, and at the same time establishing a data indexing mechanism based on inverted index to optimize query performance. The calculation formula of inverted index is: Where, Represents an inverted index, Indicates keywords or features, Indicates one of the documents or data records containing the keyword or feature; S4: Deeply mine the stored data, analyze user behavior and preference data using the K-means clustering algorithm to generate user profiles, use the Markov chain method to analyze user behavior patterns and changing trends, and use the linear regression model to predict user needs; The objective function of the K-means algorithm is: In the formula, k represents the number of clusters, According to business needs and data characteristics, it is set as an indicator variable indicating whether sample j belongs to cluster i. represents the center of cluster i, represents the jth user sample, including its operation behavior, browsing traces and third-party platform interaction information characteristics; The state transition probability formula of the Markov chain is: Where, Indicates the slave state Transfer to state The probability of Represents the state at time t, corresponding to the user's behavior at different time points, and Represent state i and state j respectively, which are defined according to user behavior characteristics; The mathematical expression of the linear regression model predicting user demand is: Where, Indicates user needs, Represents the regression coefficient, which is obtained through training of historical data; Represents the characteristic variables that affect user needs, represents the error term; S5: Develop operational strategies based on the analysis results generated in S4, including but not limited to user segmentation, personalized recommendations, and precision marketing; S6: Push the operational strategy formulated in S5 to users and monitor the execution effect of the strategy in real time. Based on user feedback and data analysis results, continuously optimize the operational strategy to form a closed-loop management.
2. The method for using a user-operated full-link data processing device according to claim 1, characterized in that: The operation behavior data By embedding SDK code snippets in the APP to collect data, including but not limited to click event data, purchase behavior data, search keyword data, the non-mobile terminal Obtained through browser plug-ins and server log recording technology, including but not limited to page visit duration data and browsing path data, interaction information with the enterprise's products on the third-party platform Utilize the API interface provided by third-party platforms to pull data, including but not limited to social media comment data and sharing data.
3. The method for using a user-operated full-link data processing device according to claim 1, characterized in that: In S2, the data set The process of data cleaning and preprocessing is as follows: S21: Deduplication: Calculate the hash value of the data in D to remove duplicate data. The hash value calculation formula is: Where, Representation data If the hash values of different data are the same, they are considered duplicate data and removed; S22: Missing value filling: For continuous data fields with missing values in D, the mean filling method is used to fill them. The mean calculation formula is: Where, Indicates the mean of the field, used to fill missing values. Indicates the number of non-missing samples in this field. Represents the value of the i-th non-missing sample; S23: Outlier detection and processing: Use the Z score method to detect outliers in D. The Z score calculation formula is: Where, represents the Z score of the i-th sample, represents the mean of the data, represents the standard deviation of the data, if , then the sample is judged as an outlier and deleted.
4. The method for using a user-operated full-link data processing device according to claim 1, characterized in that: The specific process of grouping users based on the user portrait and behavior pattern analysis results generated in step S4 is as follows: Input data: user profile data, including interests, purchase history, and spending power; The processing method is to use the K-means clustering algorithm to group users into groups with high spending power and a love of fashion, groups with medium spending power and a focus on cost-effectiveness, and groups with low spending power and a focus on practicality. At the same time, Markov chains are used to analyze user behavior patterns to identify active users, potential churn users, and dormant users; The output result is: the user set after grouping, including the group with high spending power and fashion preference, the group with medium spending power and cost-effectiveness, the group with low spending power and practicality, active users, potential churn users, and dormant users.
5. The method for using a user-operated full-link data processing device according to claim 1, characterized in that: The process of making personalized recommendations based on the user portrait data and association rule mining results generated in step S4 is as follows: The input data is: user portrait data and association rule mining results; The solution is to recommend new product information and exclusive discounts from high-end fashion brands to users with high spending power and a love for fashion. For users with medium spending power who are concerned about cost-effectiveness, we recommend products with large discounts but good quality. For users with low spending power who value practicality, we recommend practical and reasonably priced products. Use association rule mining algorithms to discover the correlation between user purchase behaviors and recommend other products related to the products they have purchased; The output result is a personalized recommendation list, which contains recommended content for different user groups.
6. The method for using a user-operated full-link data processing device according to claim 1, characterized in that: The process of precision marketing based on the demand forecast model results and user portrait data generated in step S4 is as follows: The input data is: demand forecast model results and user portrait data; The processing method is: Combine the user purchase probability predicted by the linear regression model and select users with a purchase probability above a certain threshold as target marketing objects; Choose the right marketing opportunity based on user behavior patterns and demand forecasts, such as pushing promotional information during user active hours. The output is: precise marketing plan, including target user set, marketing timing and marketing content.
7. A user-operated full-link data processing device according to any one of claims 1 to 6, characterized in that: It includes a data collection module for acquiring user data from multiple channels, including user operation behavior data on the APP, interaction information data with enterprise products on non-mobile terminals and third-party platforms. The module comprehensively collects user behavior data and integrates it into a structured data set by embedding SDK code snippets in the mobile terminal, using browser plug-ins and server logging technology, and utilizing API interface docking provided by third-party platforms; The data cleaning and preprocessing module is used to purify the collected raw data to ensure the accuracy and availability of the data. This module includes a deduplication processing unit that removes duplicate data by calculating the hash value of the data; Missing value filling unit, which uses mean filling method to handle missing values in continuous data fields; and outlier detection and processing unit, which uses Z score method to detect and handle outliers; The data storage module uses HDFS storage technology to store cleaned and preprocessed data in a structured or semi-structured format on multiple data nodes. This module also establishes a data indexing mechanism based on inverted indexes to optimize data query performance. The inverted index enables fast data retrieval by associating keywords or features with the documents or data records containing them. The data modeling and analysis module is used to conduct in-depth mining of stored data and extract valuable information. This module includes a user profile construction unit that uses the K-means clustering algorithm to analyze user behavior data and preference data to generate detailed user profiles. Behavior pattern analysis unit, which uses Markov chain method to analyze user behavior patterns and changing trends; and a demand forecasting unit that uses a linear regression model to predict user demand; The operation strategy formulation module formulates targeted operation strategies based on the analysis results generated by the data modeling and analysis module. This module includes a user grouping unit, which divides users into different groups based on the analysis results of user portraits and behavior patterns, such as those with high spending power and a love of fashion, and those with medium spending power and a focus on cost-effectiveness. The recommendation unit provides personalized recommendations for different user groups based on user portrait data and association rule mining results; The precision marketing unit uses demand forecasting model results and user profile data to develop precision marketing plans, including target user screening, marketing timing selection, and marketing content design; The closed-loop management module is responsible for pushing the formulated operational strategies to users and monitoring the execution effects of the strategies in real time. This module includes a strategy execution unit, which pushes strategy content to users through various channels; an effect monitoring unit, which monitors the execution effects of the strategies in real time and establishes a multi-dimensional effect evaluation indicator system. The data feedback collection unit collects user feedback data on strategy execution; and the data analysis and optimization unit conducts in-depth analysis of the feedback data, puts forward strategy optimization suggestions, and forms a closed-loop management.
8. A user-operated full-link data processing device according to claim 7, characterized in that: A computer program is stored, which, when executed by a processor, can implement the entire process of data collection, preprocessing, storage, modeling, strategy formulation and closed-loop management according to the steps of any one of the methods in claims 1 to 6.