Methods, devices, and computer equipment for analyzing online e-commerce operational data

Abstract

The present invention provides a method, apparatus, and computer equipment for analyzing online e-commerce operational data, comprising: acquiring operational data of an e-commerce platform; wherein the operational data includes product browsing records, user transaction records, and user behavior logs; analyzing the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile; matching corresponding model difference parameters in a database based on the initial user profile; obtaining an initial data analysis model; adjusting the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model; and analyzing the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results. This invention enables more efficient operational decision-making and enhances personalized user experience, improving the efficiency and accuracy of data analysis.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, and computer equipment for analyzing online e-commerce operation data. Background Technology

[0002] Analyzing online operational data has become a crucial step for e-commerce platforms to enhance competitiveness, optimize user experience, and achieve precise marketing. With the surge in internet users and the diversification of consumer behavior, e-commerce platforms generate massive amounts of data daily, encompassing multiple dimensions such as user browsing habits, purchasing behavior, and changes in preferences. Effectively utilizing this data not only allows for insights into market trends but also provides a deeper understanding of user needs, offering strong data support for strategies such as personalized recommendations, inventory management, and advertising.

[0003] However, traditional data analysis methods often face several challenges: First, the data volume is massive and complex, making efficient processing and analysis a major challenge; second, user behavior is highly variable, and simple statistical analysis struggles to capture users' personalized characteristics, resulting in low accuracy of recommendation systems; third, general data analysis models may not fully consider the subtle differences between different user groups, affecting the accuracy and practicality of the analysis results; therefore, current analysis methods typically suffer from low accuracy. Summary of the Invention

[0004] The main objective of this invention is to provide a method, apparatus, and computer equipment for analyzing online e-commerce operational data, aiming to overcome the shortcomings of current analysis methods in terms of low accuracy.

[0005] To achieve the above objectives, the present invention provides a method for analyzing online e-commerce operation data, comprising the following steps:

[0006] Obtain operational data from e-commerce platforms; wherein, the operational data includes product browsing history, user transaction history, and user behavior logs;

[0007] The product browsing history and user transaction history are analyzed based on a preset user profile analysis model to obtain an initial user profile.

[0008] Based on the initial user profile, corresponding model difference parameters are matched in the database; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values;

[0009] Obtain an initial data analysis model, and adjust the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model;

[0010] The user behavior logs are analyzed based on the adjusted data analysis model to obtain the corresponding analysis results.

[0011] Furthermore, the step of analyzing the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile includes:

[0012] Based on the preset user profile analysis model, feature extraction is performed on the product browsing history and user transaction history to obtain browsing history features and transaction history features.

[0013] The browsing history features and transaction history features are combined to obtain profile features;

[0014] The user profile features are classified to obtain corresponding category labels, which are then used as the initial user profile.

[0015] Furthermore, after the step of analyzing the user behavior logs based on the adjusted data analysis model to obtain the corresponding analysis results, the method further includes:

[0016] The operational data and the analysis results are associated and stored in the database.

[0017] Furthermore, the step of differentially adjusting the model parameters of the initial data analysis model based on the model difference parameters includes:

[0018] Based on the model difference parameters, target model parameters with differences are matched from the initial data analysis model;

[0019] Based on the difference values ​​in the model difference parameters, the parameters of each target model are adjusted.

[0020] Furthermore, the step of obtaining the initial data analysis model includes:

[0021] Obtain the platform name information of the e-commerce platform, as well as the platform's latest update time;

[0022] A request code is generated based on the platform name information and the latest update time;

[0023] The request code is sent to the management terminal, where an initial data analysis model matching the request code is found. The management terminal pre-stores multiple mapping relationships between different request codes and data analysis models.

[0024] Furthermore, the step of generating a request code based on the platform name information and the latest update time includes:

[0025] Map each Chinese character in the platform name information to its corresponding encoded character; wherein, the platform name information is a Chinese name;

[0026] Create a blank data table with multiple rows and columns;

[0027] The encoded characters mapped to each Chinese character are added sequentially to the blank data table to obtain a character data table; only one character is added to each table.

[0028] Based on the year in the latest update time, the character data table is replaced to obtain a transformed data table; based on the month and date in the latest update time, a simulation curve is generated.

[0029] The simulated curve is superimposed onto the transformation data table according to a preset rule. All characters located below the simulated curve in the transformation data table are obtained and combined in sequence. The resulting character combination is used as the request code.

[0030] Further, the step of replacing the character data table based on the year in the latest update time to obtain a transformed data table; and generating a simulation curve based on the month and date in the latest update time, includes:

[0031] Obtain the year from the latest update time, and add the characters of the year sequentially to a two-row, two-column data table to obtain a year data table; wherein, the year is a four-digit number.

[0032] The center of the year data table and the character data table are superimposed, and the characters in the year data table are superimposed on the overlapping positions in the character data table to obtain the transformation data table;

[0033] Obtain the month and date from the latest update time, create a coordinate system, and use the number corresponding to the month as the x-coordinate of the feature point and the number corresponding to the date as the y-coordinate of the feature point.

[0034] Based on the feature points and the origin of the coordinate system, a straight line is constructed as the simulated curve.

[0035] Furthermore, the step of mapping each Chinese character in the platform name information to its corresponding encoded character includes:

[0036] Obtain the standard encoding table;

[0037] Keyword identification is performed on the platform name information to obtain the key name;

[0038] The encoding table of the standard is rearranged based on the key name to obtain a rearranged encoding table;

[0039] Based on the rearranged encoding table, each Chinese character in the platform name information is mapped to its corresponding encoded character.

[0040] The present invention also provides an analysis device for online e-commerce operation data, comprising:

[0041] The acquisition unit is used to acquire operational data of the e-commerce platform; wherein, the operational data includes product browsing records, user transaction records, and user behavior logs;

[0042] The first analysis unit is used to analyze the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile.

[0043] The matching unit is used to match corresponding model difference parameters in the database based on the initial user profile; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values;

[0044] An adjustment unit is used to obtain an initial data analysis model, and to perform differential adjustment on the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model.

[0045] The second analysis unit is used to analyze the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results.

[0046] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0047] The present invention provides a method, apparatus, and computer equipment for analyzing online e-commerce operational data, comprising: acquiring operational data of an e-commerce platform; wherein the operational data includes product browsing records, user transaction records, and user behavior logs; analyzing the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile; matching corresponding model difference parameters in a database based on the initial user profile; wherein the database pre-stores a mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values; acquiring an initial data analysis model; adjusting the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model; and analyzing the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results. In this invention, by integrating multi-source data such as user browsing records, transaction records, and behavior logs, a personalized analysis model is constructed using advanced user profiling technology. This method uses a preset user profile analysis model to deeply mine raw data and form an initial user profile. Building upon this foundation, the concept of model difference parameters is introduced. Based on subtle differences in user characteristics, the most suitable differential adjustment parameters are searched in the database to customize and optimize the basic analysis model, ensuring it better suits the characteristics of different user groups. Finally, the adjusted model is used to conduct in-depth analysis of user behavior logs, yielding more accurate and refined analytical results. This helps e-commerce platforms achieve more efficient operational decisions and enhance personalized user experiences, thereby improving the efficiency and accuracy of data analysis. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the steps of an e-commerce online operation data analysis method in one embodiment of the present invention;

[0049] Figure 2 This is a structural block diagram of an e-commerce online operation data analysis device according to an embodiment of the present invention;

[0050] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0053] Reference Figure 1 One embodiment of the present invention provides a method for analyzing online e-commerce operation data, comprising the following steps:

[0054] Step S1: Obtain operational data from the e-commerce platform; wherein, the operational data includes product browsing records, user transaction records, and user behavior logs;

[0055] Step S2: Analyze the product browsing history and user transaction history based on the preset user profile analysis model to obtain the initial user profile;

[0056] Step S3: Based on the initial user profile, match the corresponding model difference parameters in the database; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values;

[0057] Step S4: Obtain the initial data analysis model, and adjust the model parameters of the initial data analysis model based on the model difference parameters to obtain the adjusted data analysis model;

[0058] Step S5: Analyze the user behavior logs based on the adjusted data analysis model to obtain the corresponding analysis results.

[0059] In this embodiment, it specifically includes:

[0060] Step S1: Obtain operational data from the e-commerce platform

[0061] Data Sources: First, data is collected from the backend systems of e-commerce platforms. This includes, but is not limited to, product browsing history (product details, categories, times viewed by users, etc.), user transaction history (purchased products, quantities, payment methods, transaction times, etc.), and user behavior logs (such as search keywords, page dwell time, click-through rates, and adding items to cart). This data is typically stored in databases or big data warehouses and needs to be obtained through API interfaces or data extraction tools.

[0062] Step S2: Initial User Profile Analysis

[0063] Analysis Model: Utilizing pre-defined user profiling analysis models, such as collaborative filtering, clustering algorithms, or deep learning models, the system analyzes product browsing history and user transaction records. These models can identify user behavior patterns, preferences, and purchasing habits.

[0064] User profile generation: Through model analysis, preliminary user characteristics can be summarized, such as age range, gender preference, spending level, and areas of interest, forming an initial user profile. This step is the foundation for subsequent personalized analysis.

[0065] Step S3: Match model difference parameters

[0066] Database mapping: A pre-built database stores the mapping relationship between different user profile features and corresponding model adjustment parameters. These differential parameters are designed to adapt to different user groups and can reflect the special needs or behavioral deviations of specific user groups.

[0067] Matching process: Based on the initial user profile, query the database for the optimal model difference parameters. These parameters can be adjusted by modifying model sensitivity, weight allocation, or other hyperparameters in the algorithm to better match the specificity of the user group.

[0068] Step S4: Adjust the data analysis model

[0069] Initial Model: Select one or more initial data analysis models suitable for e-commerce analysis, such as time series analysis, association rule mining, or predictive models.

[0070] Parameter tuning: Using the model differentiation parameters obtained from step S3, the parameters of the initial model are differentiated and tuned. This involves adjusting the threshold of the prediction algorithm, optimizing the decision boundary of the classifier, or changing the feature selection criteria to more accurately reflect the behavior of the target user group.

[0071] Step S5: Analyze user behavior logs

[0072] Applying the adjusted model: The adjusted data analysis model is applied to user behavior logs for in-depth analysis. This step aims to discover potential patterns in user behavior, predict future trends, or identify abnormal behavior.

[0073] Analysis Results: Through model analysis, a series of analytical results can be output, such as user group segmentation, purchase intention prediction, identification of potential high-value users, and product recommendation optimization suggestions. The above information has direct guiding significance for e-commerce marketing, inventory management, user experience optimization, and other aspects.

[0074] In summary, this technical solution integrates multi-source data such as user browsing history, transaction records, and behavior logs, and utilizes advanced user profiling technology to construct a personalized analysis model. This method uses a pre-set user profiling analysis model to deeply mine raw data, forming an initial user profile. Building upon this, the concept of model difference parameters is introduced. Based on subtle differences in user characteristics, the most matching differential adjustment parameters are searched in the database to customize and optimize the basic analysis model, ensuring it better suits the characteristics of different user groups. Finally, the adjusted model is used to conduct in-depth analysis of user behavior logs, obtaining more accurate and refined analysis results. This helps e-commerce platforms achieve more efficient operational decisions and improve personalized user experience, thereby enhancing the efficiency and accuracy of data analysis.

[0075] In one embodiment, the step of analyzing the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile includes:

[0076] Based on the preset user profile analysis model, feature extraction is performed on the product browsing history and user transaction history to obtain browsing history features and transaction history features.

[0077] The browsing history features and transaction history features are combined to obtain profile features;

[0078] The user profile features are classified to obtain corresponding category labels, which are then used as the initial user profile.

[0079] In this embodiment, it specifically includes:

[0080] Product browsing history feature extraction:

[0081] Browsing frequency: This counts how often users browse each product category, revealing areas of user interest.

[0082] Dwell time: Records the average time users spend on each product page, reflecting the level of user attention to the product.

[0083] Browsing time distribution: Analyze users' active time periods to understand their online behavior habits.

[0084] Browsing path: Tracking the sequence of products a user browses to explore user shopping behavior patterns.

[0085] User transaction record feature extraction:

[0086] Purchase frequency: Calculates the number of times a user makes a purchase within a specific time period.

[0087] Consumption Amount: Summarizes the user's total consumption amount to assess their purchasing power.

[0088] Product preferences: Based on purchase history, determine the categories of products that users frequently purchase.

[0089] Repurchase rate: Analyzes users' repeat purchase behavior for specific products or brands.

[0090] Combined features and profile construction:

[0091] Feature fusion: The browsing history features extracted above are combined with transaction history features to form a comprehensive feature vector. For example, various features can be simply quantified and concatenated, or more complex feature engineering techniques such as feature crossing and dimensionality reduction can be used to enhance the correlation and expressiveness between features.

[0092] Classification operations:

[0093] Model Training: Using machine learning or deep learning algorithms (such as logistic regression, random forest, neural networks, etc.), the above comprehensive feature vector is trained based on existing user classification labels (such as age group, consumption level, interest groups, etc.). This requires a labeled training set containing historical user data and their corresponding manually or algorithmically generated user profile labels.

[0094] Classification operation: After training, the feature vector of a new user's profile is input into the model, and classification operation is used to predict the user's category label. These labels constitute the initial user profile, such as "young and fashionable woman" or "high-end technology enthusiast," providing the foundation for subsequent personalized recommendations and services.

[0095] The entire process is completed automatically through algorithms, which not only efficiently processes massive amounts of data, but also achieves a deep understanding and precise characterization of user behavior, making it an important cornerstone of personalized services in modern e-commerce.

[0096] In one embodiment, after the step of analyzing the user behavior logs based on the adjusted data analysis model to obtain the corresponding analysis results, the method further includes:

[0097] The operational data and the analysis results are associated and stored in the database.

[0098] In this embodiment, it specifically includes:

[0099] Data integration: This involves correlating and matching the two sets of data mentioned above—operational data and user behavior analysis results. This correlation is based on timestamps (ensuring that operational activities and user behavior occur within similar timeframes), user IDs (ensuring that the analysis focuses on a specific user group), or other common identifiers. The purpose of this correlation is to establish causal or correlational relationships between the data to gain a more comprehensive understanding of how operational strategies influence user behavior, and vice versa.

[0100] Storage strategy: The integrated data is stored in a database, which is a structured storage system such as a relational database (MySQL, PostgreSQL, etc.). Alternatively, a non-relational database (NoSQL, such as MongoDB, Cassandra) can be used to adapt to complex and ever-changing data structures. During storage, appropriate table structures or document schemas are designed to ensure data consistency, integrity, and efficient query capabilities.

[0101] Purpose and Application: The purpose of storing the above-mentioned data in a linked manner is to enable efficient retrieval and further analysis. Enterprises can utilize this data for more refined operational decision support, market trend forecasting, user experience optimization, and improvements to personalized recommendation systems. The data in the database can be accessed by BI tools, data science teams, or automated reporting systems to quickly generate reports, dashboards, or trigger automated workflows, thereby improving the efficiency and quality of data-driven decision-making.

[0102] In summary, this technical solution provides a solid foundation for enterprise data insights and strategy formulation by integrating interrelated data from different sources and storing it in a structured manner.

[0103] In one embodiment, the step of differentially adjusting the model parameters of the initial data analysis model based on the model difference parameters includes:

[0104] Based on the model difference parameters, target model parameters with differences are matched from the initial data analysis model;

[0105] Based on the difference values ​​in the model difference parameters, the parameters of each target model are adjusted.

[0106] In this embodiment, it specifically includes:

[0107] Model discrepancy parameters: First, it is necessary to identify the specific parameters that create a gap between the existing data analysis model and the expected performance or specific application scenario. These parameters involve model complexity, feature selection, weight allocation, learning rate, regularization strength, etc., which directly affect the model's prediction accuracy, generalization ability, or sensitivity to specific data characteristics.

[0108] Matching target model parameters: Based on the identified model discrepancies, identify those parameters from the initial data analysis model that require special attention and adjustment. This matching process may involve algorithmic knowledge and an understanding of the model's internal mechanisms to ensure that the adjustments are made to the critical parameters that will bring about the expected changes.

[0109] Adjustment based on variance values: Once the target model parameters are determined, the next step is to adjust these parameters according to the specific values ​​(or guidelines) in the model variance parameters. Adjustments can be made by directly setting new values, increasing or decreasing them proportionally, or applying more complex adjustment rules. For example, if the model is found to be overfitting the training set, the model complexity might be reduced or the regularization strength increased; if the model's generalization ability is insufficient, the learning rate needs to be adjusted to promote better convergence, or more features need to be introduced to capture patterns in the data.

[0110] Such differentiated adjustments can effectively optimize model performance on specific tasks or datasets, improving prediction accuracy, reducing errors, and enhancing generalization ability. Model parameters are flexibly adjusted to better suit different application scenarios and needs, making the model more closely aligned with real-world problems and meeting specific business or research requirements. This process is often not completed in one go, but requires multiple iterations, evaluations, and adjustments to gradually approach the ideal model performance. After each adjustment, the model is typically retrained and its performance evaluated to ensure the adjustments are correct and effective.

[0111] In summary, this technical solution aims to overcome the limitations of the initial model by identifying and specifically adjusting the key parameters of the model, thereby promoting the model's performance to a better level and better serving specific analytical objectives or business scenarios.

[0112] In one embodiment, the step of obtaining the initial data analysis model includes:

[0113] Obtain the platform name information of the e-commerce platform, as well as the platform's latest update time;

[0114] A request code is generated based on the platform name information and the latest update time;

[0115] The request code is sent to the management terminal, where an initial data analysis model matching the request code is found. The management terminal pre-stores multiple mapping relationships between different request codes and data analysis models.

[0116] In this embodiment, it specifically includes:

[0117] Platform Name Information: First, you need to know which e-commerce platform you are analyzing. Each platform has different requirements for its data analysis model due to differences in its architecture, data structure, and business logic.

[0118] Last Update Time: This information identifies the latest state of the platform's data or structure. Because e-commerce platforms update regularly or irregularly, these updates can affect the effectiveness and accuracy of data analysis. Therefore, obtaining the latest update time is crucial for selecting the most suitable model.

[0119] Request code generation: Based on the obtained platform name information and the latest update time, a unique request code is generated. This request code is highly customized, encoding key information about the platform identity and data status to ensure the uniqueness and specificity of the request.

[0120] Request code transmission: The generated request code is sent to a centrally managed server or cloud platform (management terminal). This management terminal maintains a database of all available data analysis models and their applicable conditions.

[0121] Mapping Matching: After receiving a request code, the management system searches for a matching initial data analysis model in a pre-stored mapping table. This mapping table associates different request codes with corresponding data analysis models, each of which is pre-optimized or customized for a specific platform state.

[0122] Model selection: Once a match is found, the management system will select the appropriate initial data analysis model for deployment or provide it directly to the requester. If no direct match is found, the closest model will be selected according to a certain strategy (such as the most recently updated model, the default model, etc.).

[0123] Beneficial effects:

[0124] Flexibility and adaptability: This solution allows for rapid adaptation to changes in e-commerce platforms, ensuring that data analysis is always based on the most suitable model, thus improving the accuracy and efficiency of the analysis.

[0125] Resource optimization: Centralized management of model mapping relationships reduces the work of repeatedly developing and maintaining similar models, while also simplifying the model deployment process and facilitating large-scale management and updates.

[0126] Standardization and Automation: By standardizing the request code generation and matching process, the model acquisition is automated, reducing the need for manual intervention and improving the system's response speed and processing capacity.

[0127] In summary, this technical solution, through a sophisticated request code mechanism and centralized management, achieves efficient and accurate allocation of data analysis models across different e-commerce platforms and under different update states, making it an effective means to improve the quality of data analysis services.

[0128] In one embodiment, the step of generating a request code based on the platform name information and the latest update time includes:

[0129] Map each Chinese character in the platform name information to its corresponding encoded character; wherein, the platform name information is a Chinese name;

[0130] Create a blank data table with multiple rows and columns;

[0131] The encoded characters mapped to each Chinese character are added sequentially to the blank data table to obtain a character data table; only one character is added to each table.

[0132] Based on the year in the latest update time, the character data table is replaced to obtain a transformed data table; based on the month and date in the latest update time, a simulation curve is generated.

[0133] The simulated curve is superimposed onto the transformation data table according to a preset rule. All characters located below the simulated curve in the transformation data table are obtained and combined in sequence. The resulting character combination is used as the request code.

[0134] In this embodiment, it specifically includes:

[0135] Platform Name Encoding: First, map each Chinese character in the platform name to an encoded character. This step can be based on Unicode or other encoding standards to ensure that each Chinese character can be uniquely identified and converted into a computer-processable form.

[0136] Creating a data table: Next, construct a blank data table with multiple rows and columns. This data table can be understood as a matrix, which will be used to store information for subsequent processing.

[0137] Fill in the encoded characters: Fill the data table one by one with the encoded Chinese characters, with each cell storing only one character. This is actually a spatial representation of the platform name information, which facilitates the influence of subsequent time information on operations.

[0138] Year-driven data table changes: Based on the year of the latest update, the character data table is "replaced" in some form. This "replacement" can be operations such as rearranging, rotating, or replacing. The specific transformation rules vary depending on the year, and the purpose is to introduce changes in the time dimension, causing the request code to evolve over time.

[0139] Month- and day-based simulation curve: A simulation curve is generated using the month and date of the latest update. The shape, direction, and other characteristics of this curve are related to the specific values ​​of the month and date, further reflecting the dynamic influence of time.

[0140] Overlay and Filter Curves: The simulated curves are overlaid onto the transformed data table according to preset rules. Here, "overlay" can refer to selecting characters in the data table based on the curve's position. Specifically, characters located below the simulated curves are selected; these characters represent the final request code.

[0141] Character Combination: Finally, all the characters located below the simulation curve are combined sequentially to form a string, which is the final request code. This request code not only contains direct information about the platform but also embeds dynamic elements reflecting the platform's update time, ensuring that each generated request code is unique and closely related to the current platform state.

[0142] In summary, this technical solution creatively integrates platform name and time information through a series of carefully designed steps to generate a request code with high specificity and time-varying characteristics, thereby enhancing the security and effectiveness of data requests.

[0143] In one embodiment, the steps of replacing the character data table based on the year in the latest update time to obtain a transformed data table, and generating a simulation curve based on the month and date in the latest update time, include:

[0144] Obtain the year from the latest update time, and add the characters of the year sequentially to a two-row, two-column data table to obtain a year data table; wherein, the year is a four-digit number.

[0145] The center of the year data table and the character data table are superimposed, and the characters in the year data table are superimposed on the overlapping positions in the character data table to obtain the transformation data table;

[0146] Obtain the month and date from the latest update time, create a coordinate system, and use the number corresponding to the month as the x-coordinate of the feature point and the number corresponding to the date as the y-coordinate of the feature point.

[0147] Based on the feature points and the origin of the coordinate system, a straight line is constructed as the simulated curve.

[0148] In this embodiment, it specifically includes:

[0149] Year characterization and layout: First, extract the four-digit year from the latest update time (e.g., 2023), and then place these four digits into a two-row, two-column data table. This layout means that each digit of the year occupies one cell in the data table, forming a compact structure.

[0150] The year data table is then overlaid with the character data table: Next, the year data table is aligned and overlaid onto the center of the character data table previously composed of platform name encoded characters. If there are overlapping cells, the numbers in the year data table will be combined with the existing characters in the character data table. For example, in overlapping cells, if the original character data table contains the character 'a' and the year data table contains the character '2', they can be combined to form 'a2'. This process, through a "center overlay" strategy, ensures that the year information significantly influences the final request code structure, while also introducing dynamic time characteristics.

[0151] Coordinate system and feature point definition: Subsequently, the month and date are extracted from the same time information and used as the basis for constructing the simulation curve. In the two-dimensional coordinate system, the month is designated as the X-axis (horizontal axis), and the date is designated as the Y-axis (vertical axis). In this way, a specific month and date are transformed into a feature point in the coordinate system.

[0152] A straight line simulates a curve: Based on this feature point (month, date) and the origin of the coordinate system (0,0), a straight line is drawn as a simulated curve. This straight line simply and intuitively represents a specific moment in time. Although it is called a "simulated curve," in this context it is essentially a straight line because it is based only on two discrete time variables (month and date).

[0153] Through the above steps, this technical solution cleverly integrates platform name and time information (especially year, month, and date) to generate a transformation data table and a simulation curve that both identify the platform and reflect the dynamic nature of time. The transformation data table, through the direct overlay of years, ensures the macroscopic impact of time, while the simulation curve (although in linear form) captures the immediate state of time in a more abstract way. This combination not only enhances the uniqueness of the request code but also provides an additional time dimension for security verification, representing an innovative time-sensitive security measure.

[0154] In one embodiment, the step of mapping each Chinese character in the platform name information to its corresponding encoded character includes:

[0155] Obtain the standard encoding table;

[0156] Keyword identification is performed on the platform name information to obtain the key name;

[0157] The encoding table of the standard is rearranged based on the key name to obtain a rearranged encoding table;

[0158] Based on the rearranged encoding table, each Chinese character in the platform name information is mapped to its corresponding encoded character.

[0159] In this embodiment, it specifically includes:

[0160] Obtaining a standard encoding table: First, a pre-defined standard encoding table will be obtained. This table typically contains all potentially used Chinese characters and their corresponding encoded characters. Encoded characters can be numbers, letters, symbols, etc., and they correspond one-to-one with the original Chinese characters according to certain rules. For example, common encoding methods include Unicode, GBK, or other custom mapping rules.

[0161] Keyword Identification: Next, the platform name information is analyzed to identify key names. Key names can be words that best represent the characteristics of the platform name, or parts with higher security requirements. The purpose of identifying key names is to give these characters higher weight or special treatment, thereby highlighting their role in subsequent steps.

[0162] Reordering the encoding table: Based on the identified key names, the standard encoding table is rearranged. The purpose of rearrangement can be to increase the difficulty of decoding or to optimize the encoding rules according to the characteristics of the key names. The logic of rearrangement can be varied, such as moving characters related to the key name to the beginning of the encoding table, using certain attributes of the key name (such as the first letter or the number of strokes) to guide the selection order of encoded characters, or even constructing the encoding table entirely according to a new set of rules customized based on the key names.

[0163] Mapping Encoded Characters: Finally, using the rearranged encoding table, each Chinese character in the platform name information is mapped to a new encoded character. This process ensures that every part of the original platform name, especially the key names, is transformed into a form that is not easily recognizable directly, improving the security level of the information. Because the encoding table has been personalized according to the key names, even the same Chinese characters may be mapped to different encoded characters under different platform names or at different times, increasing complexity and security.

[0164] In summary, this technical solution achieves a flexible and targeted information encryption method through keyword recognition and dynamic adjustment of the encoding table. It not only protects platform name information but also optimizes the encoding process according to specific needs, improving security while also taking into account information processing efficiency and practicality.

[0165] Reference Figure 2 In one embodiment of the present invention, an analysis device for online e-commerce operation data is also provided, comprising:

[0166] The acquisition unit is used to acquire operational data of the e-commerce platform; wherein, the operational data includes product browsing records, user transaction records, and user behavior logs;

[0167] The first analysis unit is used to analyze the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile.

[0168] The matching unit is used to match corresponding model difference parameters in the database based on the initial user profile; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values;

[0169] An adjustment unit is used to obtain an initial data analysis model, and to perform differential adjustment on the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model.

[0170] The second analysis unit is used to analyze the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results.

[0171] In this embodiment, the specific implementation of each unit in the above device embodiment is described in the above method embodiment, and will not be repeated here.

[0172] Reference Figure 3 This invention also provides a computer device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0173] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0174] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0175] In summary, the e-commerce online operation data analysis method, apparatus, and computer equipment provided in this embodiment of the invention include: acquiring e-commerce platform operation data; wherein the operation data includes product browsing records, user transaction records, and user behavior logs; analyzing the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile; matching corresponding model difference parameters in a database based on the initial user profile; wherein the database pre-stores a mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values; acquiring an initial data analysis model; adjusting the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model; and analyzing the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results. In this invention, by integrating multi-source data such as user browsing records, transaction records, and behavior logs, a personalized analysis model is constructed using advanced user profiling technology. This method uses a preset user profile analysis model to deeply mine raw data and form an initial user profile. Building upon this foundation, the concept of model difference parameters is introduced. Based on subtle differences in user characteristics, the most suitable differential adjustment parameters are searched in the database to customize and optimize the basic analysis model, ensuring it better suits the characteristics of different user groups. Finally, the adjusted model is used to conduct in-depth analysis of user behavior logs, yielding more accurate and refined analytical results. This helps e-commerce platforms achieve more efficient operational decisions and enhance personalized user experiences, thereby improving the efficiency and accuracy of data analysis.

[0176] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0177] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0178] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for analyzing online e-commerce operation data, characterized in that, Includes the following steps: Obtain operational data from e-commerce platforms; wherein, the operational data includes product browsing history, user transaction history, and user behavior logs; The product browsing history and user transaction history are analyzed based on a preset user profile analysis model to obtain an initial user profile. Based on the initial user profile, corresponding model difference parameters are matched in the database; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values; Obtain an initial data analysis model, and adjust the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model; The user behavior logs are analyzed based on the adjusted data analysis model to obtain the corresponding analysis results; The steps for obtaining the initial data analysis model include: Obtain the platform name information of the e-commerce platform, as well as the platform's latest update time; A request code is generated based on the platform name information and the latest update time; The request code is sent to the management terminal, where an initial data analysis model matching the request code is found. The management terminal pre-stores multiple mapping relationships between different request codes and data analysis models.

2. The method for analyzing online e-commerce operation data according to claim 1, characterized in that, The step of analyzing the product browsing history and user transaction history based on a preset user profile analysis model to obtain an initial user profile includes: Based on the preset user profile analysis model, feature extraction is performed on the product browsing history and user transaction history to obtain browsing history features and transaction history features. The browsing history features and transaction history features are combined to obtain profile features; The user profile features are classified to obtain corresponding category labels, which are then used as the initial user profile.

3. The method for analyzing online e-commerce operation data according to claim 1, characterized in that, After the step of analyzing the user behavior logs based on the adjusted data analysis model to obtain the corresponding analysis results, the method further includes: The operational data and the analysis results are associated and stored in the database.

4. The method for analyzing online e-commerce operation data according to claim 1, characterized in that, The step of differentially adjusting the model parameters of the initial data analysis model based on the model difference parameters includes: Based on the model difference parameters, target model parameters with differences are matched from the initial data analysis model; Based on the difference values ​​in the model difference parameters, the parameters of each target model are adjusted.

5. An analysis device for online e-commerce operation data, characterized in that, include: The acquisition unit is used to acquire operational data of the e-commerce platform; wherein, the operational data includes product browsing records, user transaction records, and user behavior logs; The first analysis unit is used to analyze the product browsing records and user transaction records based on a preset user profile analysis model to obtain an initial user profile. The matching unit is used to match corresponding model difference parameters in the database based on the initial user profile; wherein, the database pre-stores the mapping relationship between user profiles and model difference parameters; the model difference parameters include differentiated model parameters and their corresponding difference values; An adjustment unit is used to obtain an initial data analysis model, and to perform differential adjustment on the model parameters of the initial data analysis model based on the model difference parameters to obtain an adjusted data analysis model. The second analysis unit is used to analyze the user behavior logs based on the adjusted data analysis model to obtain corresponding analysis results; The process of obtaining the initial data analysis model includes: Obtain the platform name information of the e-commerce platform, as well as the platform's latest update time; A request code is generated based on the platform name information and the latest update time; The request code is sent to the management terminal, where an initial data analysis model matching the request code is found. The management terminal pre-stores multiple mapping relationships between different request codes and data analysis models.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

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