Intelligent marketing scene construction method and system based on user behavior track big data

Abstract

The invention discloses an intelligent marketing scene construction method and system based on user behavior track big data, and relates to the technical field of big data. The method comprises the following steps: collecting data of a user behavior track, and preprocessing the data; identifying the data information, and classifying and summarizing the behavior track data information; acquiring data distribution and rules according to data information of the data information base; constructing a user portrait according to the data distribution and rule; constructing an intelligent marketing scene model according to the user portrait, and recommending a predicted marketing scene to the user by the predicted marketing scene recommendation; performing real-time monitoring and analysis according to the received real-time data information, returning data to the intelligent marketing scene model, and optimizing and adjusting the intelligent marketing scene model; according to the method, the behavior track data information of the user can be obtained through big data, so that enough data support is provided for the user before shopping, and commodities meeting the requirements of the user are recommended for the user according to different requirements of the user.

Description

Technical Field

[0001] The present invention relates to the field of big data technology, and in particular to a method and system for constructing intelligent marketing scenarios based on user behavior trajectory big data. Background Art

[0002] In the past marketing environment, on the one hand, manufacturing enterprises manufactured and produced products, and the product inventory piled up and could not be sold, resulting in overcapacity production. On the other hand, due to information asymmetry, consumer demand was not fully met, resulting in a situation where sellers could not sell and buyers could not buy. With the rapid development of big data, the use of big data is becoming more and more indispensable in daily life, so the user's behavior trajectory data is needed to accurately recommend what the user needs in order to meet the needs of different users. Therefore, how to recommend what the user needs through user data is an urgent problem to be solved in this technical field. Summary of the invention

[0003] The present invention provides a method for constructing an intelligent marketing scenario based on user behavior trajectory big data, comprising:

[0004] Step S1: The cloud platform collects data on user behavior trajectories through a web crawler and pre-processes the data;

[0005] Step S2: The cloud platform identifies the data information, and classifies and aggregates different behavior trajectory data information to generate a data information database;

[0006] Step S3: The cloud platform obtains the distribution and regularity of the data through data analysis and summary based on the data information in the data information database;

[0007] Step S4: The cloud platform constructs a user profile based on the distribution and regularity of the data;

[0008] Step S5: The cloud platform constructs an intelligent marketing scenario model based on the user portrait through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user;

[0009] Step S6: The cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model, continuously optimizing and adjusting the intelligent marketing scenario model.

[0010] In the above-mentioned method for constructing an intelligent marketing scenario based on user behavior trajectory big data, the cloud platform collects user behavior trajectory data through a web crawler, and the sub-steps of preprocessing the data are as follows:

[0011] Step S11: The cloud platform sends an HTTP request to the target website through a web crawler to obtain web page content;

[0012] Step S12: The cloud platform extracts the required user behavior trajectory data through the acquired webpage content;

[0013] Step S13: The cloud platform stores the extracted user behavior track data in the MySQL database;

[0014] Step S14, the cloud platform calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data;

[0015] Step S15: The cloud platform converts the cleaned data into a form suitable for model training using normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain converted data;

[0016] Step S16: The cloud platform selects the main information data of the converted data through principal component analysis (PCA) and linear discriminant analysis (LDA) methods to obtain preprocessed data.

[0017] In the above-mentioned method for constructing an intelligent marketing scenario based on user behavior trajectory big data, the cloud platform identifies the data information, and classifies and aggregates different behavior trajectory data information, and the sub-steps of generating a data information database are as follows:

[0018] Step S21, the cloud platform uses direct recognition to directly determine whether the pre-processed data meets the conditions, and uses regular expressions to verify whether it meets the conditions;

[0019] Step S22: The cloud platform uses the keyword extraction method in indirect identification to extract all data that meet specific conditions from the pre-processed data to obtain identification data;

[0020] Step S23: The cloud platform classifies and aggregates the identified data through cluster analysis to obtain classified and aggregated data;

[0021] Step S24: The cloud platform transfers the classified and summarized data to the MySQL database to form a data information library.

[0022] In the above-mentioned intelligent marketing scenario construction method based on user behavior trajectory big data, the cloud platform performs statistics and summaries on the data information in the data information database through data analysis to obtain the distribution and regularity of the data in the following sub-steps:

[0023] Step S31: The cloud platform obtains the distribution of data through a histogram according to the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value;

[0024] Step S32: The cloud platform calculates the mean, median, standard deviation and other statistics based on the data in the data information database to describe the central tendency and dispersion of the obtained data, and obtains the central dispersion value;

[0025] Step S33: The cloud platform groups the data according to the variables through grouping and aggregation according to the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value;

[0026] Step S34, the cloud platform calculates the probability law value according to the relationship distribution value, the concentrated discrete value and the correlation value;

[0027] Step S35: The cloud platform obtains the distribution and regularity of the data through the probability regularity value.

[0028] In the above-mentioned method for constructing an intelligent marketing scenario based on user behavior trajectory big data, the cloud platform constructs the user portrait according to the distribution and rules of the data in the following sub-steps:

[0029] Step S41: The cloud platform establishes the user's information dimension according to the distribution and regularity of the target user's behavior trajectory data;

[0030] Step S42: The cloud platform extracts the user's purchasing interest and consumption ability indicators according to the distribution and regularity of the user's behavior trajectory data, and establishes the user's characteristic dimensions;

[0031] Step S43: The cloud platform constructs a user portrait based on the user behavior trajectory data information, the user's information dimension and the feature dimension.

[0032] In the above-mentioned method for constructing an intelligent marketing scenario based on user behavior trajectory big data, the cloud platform constructs an intelligent marketing scenario model based on user portraits through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user in the following sub-steps:

[0033] Step S51: The cloud platform uses machine learning to train user portraits and build an intelligent marketing scenario model;

[0034] Step S52: The cloud platform tests the performance of the intelligent marketing scenario model through a cross-validation method;

[0035] Step S53: The cloud platform determines whether the marketing scenario recommendation meets the recommendation condition. If so, the predicted marketing scenario is recommended to the user. Otherwise, step S6 is executed.

[0036] Step S54: The cloud platform uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

[0037] As described above, a method for constructing an intelligent marketing scenario based on user behavior trajectory big data is described, wherein the cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model. The sub-steps of continuously optimizing and adjusting the intelligent marketing scenario model are as follows:

[0038] Step S61: The cloud platform obtains real-time user behavior trajectory data information through steps S1, S2 and S3, and uses data stream processing to perform real-time monitoring, analysis and processing to obtain optimized data;

[0039] Step S62: The cloud platform transmits the optimized data to step S4, obtains the optimized user portrait, and uses the optimized user portrait to train the intelligent marketing scenario model again to achieve continuous optimization of the model.

[0040] The present invention also provides an intelligent marketing scenario construction system based on user behavior trajectory big data, including:

[0041] The collection module is used to collect data on user behavior trajectories through web crawlers and pre-process the data;

[0042] The classification and aggregation module identifies data information, classifies and aggregates different behavior trajectory data information, and generates a data information database;

[0043] The distribution pattern analysis module obtains the distribution and patterns of the data through statistics and summary based on the data information in the data information database.

[0044] The user portrait construction module builds user portraits based on the distribution and rules of data.

[0045] The intelligent marketing scenario recommendation module builds an intelligent marketing scenario model based on user portraits through machine learning algorithms, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to users.

[0046] The optimization module performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits real-time data back to the intelligent marketing scenario model, continuously optimizing and adjusting the intelligent marketing scenario model.

[0047] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the collection module specifically includes:

[0048] The web page content acquisition submodule sends HTTP requests to the target website through a web crawler to obtain web page content;

[0049] The user behavior trajectory data extraction submodule extracts the required user behavior trajectory data through the acquired web page content;

[0050] The data storage submodule stores the extracted user behavior track data in the MySQL database;

[0051] The data cleaning submodule calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data;

[0052] The data conversion submodule converts the cleaned data into a form suitable for model training using the normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain the converted data;

[0053] The data preprocessing submodule selects the main information data of the transformed data through principal component analysis (PCA) and linear discriminant analysis (LDA) methods to obtain preprocessed data.

[0054] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the classification and aggregation module specifically includes:

[0055] The direct recognition submodule uses direct recognition to directly determine whether the preprocessed data meets the conditions and uses regular expressions to verify whether the conditions are met;

[0056] The indirect recognition submodule uses the keyword extraction method in indirect recognition to extract all data that meet specific conditions from the preprocessed data to obtain recognition data;

[0057] The analysis submodule classifies and summarizes the identified data through cluster analysis to obtain classified summary data;

[0058] The data transmission submodule transmits the classified and summarized data to the MySQL database to form a data information library.

[0059] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the distribution law analysis module specifically includes: a relationship distribution analysis submodule, which obtains the distribution of data through a histogram according to the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value;

[0060] The centralization and dispersion analysis submodule calculates the mean, median, standard deviation and other statistics based on the data in the database to describe the centralization trend and dispersion degree of the obtained data and obtain the centralization and dispersion values;

[0061] The correlation analysis submodule groups the data by variables through grouping and aggregation according to the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value;

[0062] The probability law calculation submodule calculates the probability law value according to the relationship distribution value, the concentrated discrete value and the correlation value;

[0063] The distribution law acquisition submodule obtains the distribution and law of data through the probability law value.

[0064] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the user portrait construction module specifically includes:

[0065] The information dimension establishment submodule establishes the user's information dimension based on the distribution and regularity of the target user's behavior trajectory data;

[0066] The feature dimension establishes a submodule, extracting the user's purchasing interest and consumption ability indicators based on the distribution and regularity of the user's behavior trajectory data, and establishing the user's feature dimension;

[0067] Construct a user portrait sub-module to construct a user portrait based on user behavior trajectory data information, user information dimensions and feature dimensions.

[0068] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the intelligent marketing scenario recommendation module specifically includes:

[0069] The training submodule uses machine learning to train user portraits and build intelligent marketing scenario models;

[0070] The testing submodule tests the performance of the intelligent marketing scenario model through the cross-validation method;

[0071] The recommendation judgment submodule judges whether the marketing scenario recommendation meets the recommendation conditions. If so, the predicted marketing scenario is recommended to the user. Otherwise, the optimization module is executed.

[0072] The push sub-module uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

[0073] In the above-mentioned intelligent marketing scenario construction system based on user behavior trajectory big data, the optimization module specifically includes:

[0074] Optimize the data transmission submodule, obtain real-time user behavior trajectory data information through the collection module, classification summary module and distribution law analysis module, and use data stream processing to perform real-time monitoring, analysis and processing to obtain optimized data;

[0075] Optimize the user data sub-module, transfer the optimized data to the user portrait construction module, obtain the optimized user portrait, and use the optimized user portrait to train the intelligent marketing scenario model again to achieve continuous optimization of the model.

[0076] The beneficial effects achieved by the present invention are as follows:

[0077] Compared with the above background technology, the present invention can obtain user behavior trajectory data information through big data so that there is sufficient data support before the user shops, and recommend products that meet their needs according to the different needs of each user, and upload the data of products purchased by the user to the cloud platform, so that the model can be continuously updated and optimized according to real-time data, and can recommend products that better meet their needs to users, thereby realizing customized and accurate recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0079] Figure 1 This is a flow chart of a method for constructing an intelligent marketing scenario based on user behavior trajectory big data provided in Example 1 of the present application;

[0080] Figure 2 This is a schematic diagram of an intelligent marketing scenario construction system based on user behavior trajectory big data provided in Example 2 of the present application. DETAILED DESCRIPTION

[0081] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0082] Embodiment 1

[0083] like Figure 1 As shown, the first embodiment of the present application provides a method for constructing an intelligent marketing scenario based on user behavior trajectory big data, and the method includes the following steps:

[0084] Step S1: The cloud platform collects data on user behavior trajectories through web crawlers and pre-processes the data.

[0085] Specifically, the data of user behavior trajectory includes time, location, person's age, person's gender, search keywords, browsing history, types of purchased goods and purchase amount, etc.

[0086] Furthermore, the cloud platform collects data on user behavior trajectories through web crawlers, and the sub-steps of preprocessing the data are as follows:

[0087] Step S11: The cloud platform sends an HTTP request to the target website through a web crawler to obtain web page content.

[0088] Specifically, use the Requests library of the programming tool Python to send a request and obtain the HTML code of the web page.

[0089] Step S12: The cloud platform extracts the required user behavior trajectory data through the acquired web page content.

[0090] Specifically, the HTML parsing library BeautifulSoup is used to parse the HTML document and extract data information.

[0091] Step S13: The cloud platform stores the extracted user behavior track data in the MySQL database.

[0092] Step S14, the cloud platform calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data.

[0093] Step S15: The cloud platform converts the cleaned data into a form suitable for model training using the normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain converted data.

[0094] Step S16: The cloud platform selects the main information data of the converted data through principal component analysis (PCA) and linear discriminant analysis (LDA) methods to obtain preprocessed data.

[0095] Step S2: The cloud platform identifies the data information, and classifies and summarizes different behavior trajectory data information to generate a data information database.

[0096] Furthermore, the cloud platform identifies the data information and classifies and aggregates different behavior trajectory data information. The sub-steps of generating a data information database are as follows:

[0097] Step S21: The cloud platform uses direct recognition to directly determine whether the preprocessed data meets the conditions, and uses regular expressions to verify whether the conditions are met.

[0098] Step S22: The cloud platform uses the keyword extraction method in indirect recognition to extract all data that meets specific conditions from the pre-processed data to obtain recognition data.

[0099] Specifically, the pre-processed data is identified by direct identification and indirect identification, wherein firstly, direct identification is used to determine whether the data meets the specific conditions, then indirect identification is used to extract the data that meets the specific conditions, and then the data obtained by direct identification and the data obtained by indirect identification are compared and verified. If the verification data are the same, step S23 is executed, and if the verification data are different, the execution returns to step S1. For example: first, the clothing product data that the user has purchased is extracted from the pre-processed data using direct discriminants such as "coat equals coat" and "pants equals pants" and recorded as data 1, and then the clothing product data that the user has purchased is extracted from the pre-processed data using an indirect identification method through an overall query of "clothing category" equals "coat, pants, ..." and recorded as data 2, and then the two sets of data are compared. If data 1 is equal to data 2, step S23 is executed, and if data 1 is not equal to data 2, the execution returns to step S1.

[0100] Step S23: The cloud platform classifies and summarizes the identified data through cluster analysis to obtain classified and summarized data.

[0101] Specifically, the information is classified and summarized according to different types of time, place, person's age, person's gender, search keywords, browsing history, purchased goods type, purchased goods amount and payment method.

[0102] Step S24: The cloud platform transfers the classified and summarized data to the MySQL database to form a data information library.

[0103] Step S3: The cloud platform obtains the distribution and regularity of the data through data analysis and summary based on the data information in the data information database.

[0104] Furthermore, the cloud platform uses data analysis to perform statistics and summarize the distribution and regularity of data based on the data information in the data information database. The sub-steps are as follows:

[0105] Step S31: The cloud platform obtains the distribution of data through a histogram based on the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value.

[0106] Specifically, the data information in the database is directly queried based on the distribution and relationship of the data and a set is formed to obtain a relationship distribution value. The relationship distribution value is a data value set, including a search keyword set, a browsing history set, a purchase product category set, and a purchase product price set.

[0107] Step S32: The cloud platform calculates the mean, median, standard deviation and other statistics based on the data in the database to describe the central tendency and dispersion of the obtained data and obtain the central dispersion value.

[0108] Specifically, the concentrated discrete value is a data set, which includes a set of purchase times of commodities obtained from a data information database and a set of statistics of each commodity, wherein the set of statistics represents the purchase trend of each commodity by users.

[0109] Step S33: The cloud platform groups the data according to the variables through grouping and aggregation according to the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value.

[0110] Specifically, the correlation refers to whether the goods purchased by users are related to factors such as the users' income, needs, and hobbies. The data obtained through aggregation analysis is used to calculate the correlation value using the Pearson correlation coefficient method. The correlation value is used to describe the linear relationship between variables. The value range is from -1 to 1. Values ​​close to 1 or -1 indicate strong correlation, and values ​​close to 0 indicate no correlation.

[0111] Step S34: The cloud platform calculates the probability regularity value based on the relationship distribution value, the concentrated discrete value and the correlation value.

[0112] Specifically, the probability regularity values ​​of the commodities are integrated into a data set.

[0113] Furthermore, the expression of the probability law value is as follows:

[0114]

[0115] Among them, MTZ is the probability law value; FB is the relationship distribution value set, y represents the total price set of the goods purchased by the user, and x represents the single price of a certain product purchased by the user. It means that the price x of a certain product is traversed in the price set y in the relation distribution value set. represents the sum of all prices in the price set y in the relational distribution value set; LS is a concentrated discrete value set, b represents the number of times a user purchases all products, a represents the number of times a user purchases a certain product, represents the number of times a a certain product is purchased in the set b of product purchase times in the concentrated discrete value set; XG is the set of correlation values, c represents the set of all correlations purchased by the user, and d represents the correlation of a certain product purchased by the user. Indicates that the correlation value d of a certain product is traversed in the correlation value set c.

[0116] Specifically, the probability law value is the probability law value of a certain commodity, and then the obtained probability law values ​​of all commodities are combined into a probability law value set.

[0117] Step S35: The cloud platform obtains the distribution and regularity of the data through the probability regularity value.

[0118] Specifically, the cloud platform calculates the probability law value for all the goods purchased by the user according to the expression of the probability law value, and associates the user's basic information, purchase information and the probability law value to obtain the distribution and law of the user's behavior trajectory data information.

[0119] Step S4: The cloud platform constructs a user profile based on the distribution and rules of the data.

[0120] Specifically, user portrait is a labeled user model abstracted from the user's behavior trajectory data information and the distribution and regularity of the data. It affixes highly refined feature identifiers to users through mining massive data to form a comprehensive picture of user information. The core work of user portrait is to "label" users. These labels are refined descriptions of user dimensional characteristics, which together constitute an overall understanding of the user. The user portrait includes data on multiple aspects such as the customer's basic information, interests and hobbies, purchase history, and behavior data.

[0121] Furthermore, the cloud platform constructs the following sub-steps of user portrait based on the distribution and regularity of data:

[0122] Step S41: The cloud platform establishes the user's information dimension according to the distribution and regularity of the target user's behavior trajectory data.

[0123] Specifically, the information dimensions include demographic attributes (age, gender, location, occupation), behavioral attributes (search keywords, browsing history, purchase records, purchase frequency), and social attributes (consumption proportion, income); among them, the demographic attribute data is obtained through the user behavior trajectory information data; the search keyword browsing history and purchase records in the behavioral attributes are obtained through the relationship distribution value data set, and the purchase frequency in the behavioral attributes is obtained through the centralized discrete value; the total purchase amount of the user is obtained by summing the price set in the relationship distribution value and dividing it by the income to obtain the consumption proportion in the social attributes, among which the income is obtained from the user behavior trajectory information data.

[0124] Step S42: The cloud platform extracts the user's purchasing interest and consumption capacity indicators based on the distribution and regularity of the user's behavior trajectory data, and establishes the user's characteristic dimensions.

[0125] Specifically, purchasing interest mainly refers to the category preferences of users when shopping, such as clothing, luggage, home furnishing, maternal and child care, cleaning, food, etc. Consumption capacity refers to the purchasing power of users. The actual consumption level of users and the psychological consumption level of users in each category are distinguished, and characteristic dimensions are established respectively.

[0126] Step S43: The cloud platform constructs a user portrait based on the user behavior trajectory data information, the user's information dimension and the feature dimension.

[0127] Furthermore, the user portrait expression is as follows:

[0128]

[0129] Among them, XHZ is the user portrait; RK is the demographic attribute, n represents age, x represents gender, d represents location, z represents occupation, RK (n∪x∪d∪z) represents the query of age n, gender x, location d and occupation z from the user behavior trajectory information data and the collection of demographic attributes RK; XW is the behavior attribute, s represents the search keyword, l represents the browsing history, m represents the purchase record, p represents the purchase frequency, XW (s∪l∪m∪p) It means that the search keyword s, browsing history l, purchase record m and purchase frequency p are taken out from the relationship distribution value set to form the behavior attribute XW; SH is the social attribute, f represents the consumption proportion, sr represents the income, SH (f∪sr) Indicates that the calculated consumption proportion f and income sr are combined into the social attribute SH; [RK (n∪x∪d∪z) ∪XW (s∪l∪m∪p) ∪SH (f∪sr) ] represents the combination of demographic attributes RK, behavioral attributes XW and social attributes SH into user information dimensions; XG is a set of correlation values, c represents the set of all correlations purchased by the user, q represents the correlation of a certain type of product purchased by the user, It means that the correlation q of a user purchasing a certain type of goods is traversed in the user's purchase of all correlation sets c; LS is a concentrated discrete value set, b represents the number of times the user purchases all goods, and w represents the number of times a certain type of goods is purchased. It means that the number of times a user purchases all products, b, is used to traverse the number of times a user purchases a certain type of product, w. Indicates the relevance of users purchasing a certain type of goods and the number of purchases of a certain category of goods Multiplying them together gives the purchase interest; FB is the relationship distribution value set, e represents the actual purchase price of a certain type of goods, and y represents the total price set of the goods purchased by the user. It means to traverse the actual purchase price e of a certain type of goods in the total price set y of the goods purchased by the user, ll means the price of all the goods browsed, ml means the price of a certain type of goods browsed, It means that the price ml of a certain category of goods is traversed among the prices ll of all the goods browsed, mm means the number of goods browsed in a certain category, and as means the number of goods browsed in all categories. It means to traverse the number of browsed products of a certain category in the number of browsed products as. Indicates the user's spending power, using the actual purchase price of a certain type of product Subtract the average price of a certain category of products If the result is a negative number, it means that the user's consumption ability is weak, and if the result is a positive number, it means that the user's consumption ability is strong. The smaller the result, the weaker the user's consumption ability, and the larger the result, the stronger the user's consumption ability. It means that purchasing interest and consumption ability are combined into user characteristic dimensions.

[0130] Specifically, a user portrait is a collection of user information dimensions and feature dimensions.

[0131] Step S5: The cloud platform constructs an intelligent marketing scenario model based on the user portrait through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user.

[0132] Furthermore, the cloud platform constructs an intelligent marketing scenario model based on the user portrait through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user in the following sub-steps:

[0133] Step S51: The cloud platform uses machine learning to train user portraits and build an intelligent marketing scenario model.

[0134] Specifically, the intelligent marketing scenario model includes an existing data marketing scenario model and a predictive marketing scenario model. The existing data marketing scenario model is obtained by training based on user portrait data, and the predictive marketing scenario model is based on the results of the training of the existing data marketing scenario model. The process of training the intelligent marketing scenario model is the process of training the predictive marketing scenario model based on the existing data marketing scenario model, wherein the cloud platform inputs the user's known data into the predictive marketing scenario model to obtain the model output result, and then obtains the loss function based on the model output result and the result of manual annotation. Finally, the cloud platform corrects the data in the predictive model by back-propagating the loss function, thereby completing the training of the model.

[0135] Among them, the existing data marketing scenario model is obtained by training with a machine learning algorithm according to the user portrait, wherein age, gender, location, occupation, search keywords, browsing history, purchase records, purchase frequency, consumption proportion, income, purchase interest, and consumption capacity are used as known data for classification training, so that the training result is to obtain other marketing scenario data through a single known data, for example, other marketing scenario data is obtained through age, {"age"→"gender", "location", "occupation", "search keywords", "browsing history", "purchase records", "purchase frequency", "consumption proportion", "income", "purchase interest", "consumption capacity"}, and then other marketing scenario data are obtained by training according to different marketing scenario keywords. For example, search keywords, browsing history, purchase records, purchase frequency, purchase interest, and consumption capacity are obtained according to age, gender, location, occupation, consumption proportion, and income, {"age", "gender", "location", "occupation", "consumption proportion", "income"→"search keywords", "browsing history", "purchase records", "purchase frequency", "purchase interest", "consumption capacity"}, the more marketing scenario keywords are combined, the fewer the required marketing scenario keywords are obtained, and the more refined the classification is.

[0136] Among them, according to the training results of the existing data model, the data in the existing data model is used for prediction through forward propagation to train the prediction marketing scenario model. The prediction marketing scenario model is to predict the parameters obtained by training the existing data marketing scenario model according to the known user parameters and obtain the prediction data. For example, if the age, gender, location, occupation, consumption ratio, and income of a certain user are known, these parameters are input into the prediction marketing scenario model to obtain the search keywords, browsing history, purchase history, purchase frequency, purchase interest, and consumption capacity of the known data, and then prediction training is performed based on these known data to obtain the prediction data of this user. For example, according to age, gender, location, occupation, consumption ratio, and income, predict search keywords, predict browsing, predict purchase, predict purchase frequency, predict purchase interest, and predict consumption capacity are obtained, {"age", "gender", "location", "occupation", "consumption ratio", "income" → "predicted search keywords", "predicted browsing", "predicted purchase", "predicted purchase frequency", "predicted purchase interest", "predicted consumption capacity"}.

[0137] Step S52: The cloud platform tests the performance of the intelligent marketing scenario model through a cross-validation method.

[0138] Specifically, the holdout method in cross-validation is used to divide the existing data marketing scenario model into two non-overlapping parts, one as the training data set and the other as the test data set. The model parameters are further optimized through testing to ensure the accuracy and effectiveness of the model.

[0139] Step S53: The cloud platform determines whether the marketing scenario recommendation meets the recommendation conditions. If so, the predicted marketing scenario is recommended to the user. Otherwise, step S6 is executed.

[0140] Further, the cloud platform determines whether the marketing scenario recommendation meets the recommendation condition. If so, the predicted marketing scenario is recommended to the user. Otherwise, the sub-steps of step S6 are executed as follows:

[0141] Step S531: The cloud platform uses a machine learning algorithm to obtain the user's purchase habit trajectory data based on the user portrait, and calculates and predicts the predicted purchase value of the product recommended to the user.

[0142] Specifically, the predicted purchase value is to predict the products recommended to the user based on the user's browsing history, search keywords and other user purchasing habit trajectory data, and then calculate a numerical value based on the expression of the predicted purchase value to determine whether the predicted purchase value of the recommended product is consistent with the user's purchasing habits. If it is consistent, the predicted purchase value of the product recommended to the user is transmitted to the intelligent marketing scenario model. If it is not consistent, the predicted purchase value of the next product recommended to the user is calculated.

[0143] Furthermore, the expression for predicting purchase value is as follows:

[0144]

[0145] Among them, GMT is the predicted purchase value; YC is the predicted recommended product price, mc represents the set of all recommended products, and ym represents a selected predicted recommended product. It means selecting a predicted recommended product price ym from the total recommended product price set mc; FB is the relationship distribution value set, y represents the total price set of the products purchased by the user, e represents the actual purchase price of a certain type of product, It means to traverse and predict the actual purchase price e of the same category of recommended products in the total price set y of the products purchased by the user; LS is a concentrated discrete value set, b represents the number of times the user purchases all products, and w represents the number of times a certain category of products is purchased. It means that the number of times a user purchases all products b is used to traverse and predict the number of times w of products in the same category as the recommended product. It indicates that the predicted recommended product price is compared with the average price of the same category products that the user has purchased. The closer the value is to 0, the more it meets the user's consumption level. XG is the correlation set, yc is the correlation set of the predicted recommended products, and yy is the correlation of a selected predicted recommended product. It indicates that the correlation value yy of the selected predicted recommended product is traversed from the correlation value set yc of the predicted recommended product. The closer the value is to -1 or 1, the stronger the correlation is. as indicates the number of browsed products, mm indicates the number of browsed products of a certain category. It means that the number of products in the same category of the predicted recommended products is traversed from the number of all products browsed as, mm. The following means getting the number of all browsed products as, Indicates the number of products in the same category that are browsed for predicted recommended products Than the number of all products viewed The closer the value is to 1, the greater the user's interest in the product; the predicted purchase value GMT is calculated based on the expression. The closer its value is to 0, the greater the possibility that the user will purchase the predicted recommended product. Conversely, the user is less likely to purchase the predicted recommended product.

[0146] Step S532: The cloud platform determines whether the predicted recommended product meets the recommendation conditions based on the predicted purchase value. If so, the predicted purchase product is transmitted to the intelligent marketing scenario model. If not, step S6 is executed.

[0147] Step S54: The cloud platform uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

[0148] Specifically, intelligent delivery technology monitors the effectiveness of advertising in real time and analyzes users' behavioral data such as clicks, browsing, and conversions. Intelligent delivery technology can automatically adjust the time, frequency, channel and other strategies of advertising according to the intelligent marketing scenario model to ensure that advertising reaches target users in the best way.

[0149] Step S6: The cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model, continuously optimizing and adjusting the intelligent marketing scenario model.

[0150] Furthermore, the cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model. The sub-steps of continuously optimizing and adjusting the intelligent marketing scenario model are as follows:

[0151] Step S61: The cloud platform obtains real-time user behavior trajectory data information through steps S1, S2 and S3, and uses data stream processing to perform real-time monitoring, analysis and processing to obtain optimized data.

[0152] Specifically, the purpose of real-time monitoring and analysis is to monitor abnormal data, issue early warnings for abnormal data, and eliminate abnormal data.

[0153] Step S62: The cloud platform transmits the optimized data to step S4, obtains the optimized user portrait, and uses the optimized user portrait to train the intelligent marketing scenario model again to achieve continuous optimization of the model.

[0154] Specifically, optimizing user images is to continuously fill in data for user portraits based on real-time data information, so that user portraits are always practical, thereby continuing to train the intelligent marketing scenario model, and continuously optimizing the intelligent marketing scenario model, thereby achieving precise marketing and personalized services.

[0155] Embodiment 2

[0156] like Figure 2 As shown, the first embodiment of the present application provides an intelligent marketing scenario construction system based on user behavior trajectory big data, including:

[0157] The collection module 21 is used to collect the data of user behavior trajectory through a web crawler and pre-process the data.

[0158] Specifically, the data of user behavior trajectory includes time, location, person's age, person's gender, search keywords, browsing history, types of purchased goods and purchase amount, etc.

[0159] Furthermore, the acquisition module 21 includes the following submodules:

[0160] The web page content acquisition submodule sends HTTP requests to the target website through a web crawler to obtain web page content.

[0161] Specifically, use the Requests library of the programming tool Python to send a request and obtain the HTML code of the web page.

[0162] The user behavior trajectory data extraction submodule extracts the required user behavior trajectory data through the acquired web page content.

[0163] Specifically, the HTML parsing library BeautifulSoup is used to parse the HTML document and extract data information.

[0164] The data storage submodule stores the data extracted from the user behavior trajectory in the MySQL database.

[0165] The data cleaning submodule calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data.

[0166] The data conversion submodule converts the cleaned data into a form suitable for model training using the normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain the converted data.

[0167] The data preprocessing submodule selects the main information data of the transformed data through principal component analysis (PCA) and linear discriminant analysis (LDA) methods to obtain preprocessed data.

[0168] The classification and aggregation module 22 identifies the data information, and classifies and aggregates different behavior trajectory data information to generate a data information library.

[0169] Furthermore, the classification and summary module includes the following submodules:

[0170] The direct recognition submodule uses direct recognition to directly determine whether the preprocessed data meets the conditions and uses regular expressions to verify whether the conditions are met.

[0171] The indirect recognition submodule uses the keyword extraction method in indirect recognition to extract all data that meet specific conditions from the preprocessed data to obtain the recognition data.

[0172] Specifically, the pre-processed data is identified by direct identification and indirect identification, wherein firstly, direct identification is used to determine whether the data meets the specific conditions, and then indirect identification is used to extract the data that meets the specific conditions, and then the data obtained by direct identification and the data obtained by indirect identification are compared and verified. If the verification data are the same, the analysis submodule is executed, and if the verification data are different, the collection module 21 is returned to execute. For example: first, the clothing product data that the user has purchased is extracted from the pre-processed data by direct identification through direct discriminants such as "coat equals coat" and "pants equals pants" and recorded as data 1, and then the clothing product data that the user has purchased is extracted from the pre-processed data by indirect identification through the overall query of "clothing category" equals "coat, pants, ..." and recorded as data 2, and then the two groups of data are compared. If data 1 is equal to data 2, the analysis submodule is executed, and if data 1 is not equal to data 2, the collection module 21 is returned to execute.

[0173] The analysis submodule classifies and summarizes the identified data through cluster analysis to obtain classified and summarized data.

[0174] Specifically, the information is classified and summarized according to different types of time, place, person's age, person's gender, search keywords, browsing history, purchased goods type, purchased goods amount and payment method.

[0175] The data transmission submodule transmits the classified and summarized data to the MySQL database to form a data information library.

[0176] The distribution law analysis module 23 obtains the distribution and law of the data through data analysis and summary based on the data information in the data information base.

[0177] Furthermore, the distribution law analysis module 23 includes the following submodules:

[0178] The relationship distribution analysis submodule obtains the distribution of data through a histogram based on the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value.

[0179] Specifically, the data information in the database is directly queried based on the distribution and relationship of the data and a set is formed to obtain a relationship distribution value. The relationship distribution value is a data value set, including a search keyword set, a browsing history set, a purchase product category set, and a purchase product price set.

[0180] The centralization-dispersion analysis submodule calculates the mean, median, standard deviation and other statistics based on the data in the database to describe the centralization trend and dispersion of the data and obtain the centralization-dispersion value.

[0181] Specifically, the concentrated discrete value is a data set, which includes a set of purchase times of commodities obtained from a data information database and a set of statistics of each commodity, wherein the set of statistics represents the purchase trend of each commodity by users.

[0182] The correlation analysis submodule groups the data according to the variables through grouping and aggregation based on the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value.

[0183] Specifically, the correlation refers to whether the goods purchased by users are related to factors such as the users' income, needs, and hobbies. The data obtained through aggregation analysis is used to calculate the correlation value using the Pearson correlation coefficient method. The correlation value is used to describe the linear relationship between variables. The value range is from -1 to 1. Values ​​close to 1 or -1 indicate strong correlation, and values ​​close to 0 indicate no correlation.

[0184] The probability law calculation submodule calculates the probability law value based on the relationship distribution value, the concentrated discrete value and the correlation value.

[0185] Specifically, the probability regularity values ​​of the commodities are integrated into a data set.

[0186] Furthermore, the expression of the probability law value is as follows:

[0187]

[0188] Among them, MTZ is the probability law value; FB is the relationship distribution value set, y represents the total price set of the goods purchased by the user, and x represents the single price of a certain product purchased by the user. It means that the price x of a certain product is traversed in the price set y in the relation distribution value set. represents the sum of all prices in the price set y in the relational distribution value set; LS is a concentrated discrete value set, b represents the number of times a user purchases all products, a represents the number of times a user purchases a certain product, represents the number of times a a certain product is purchased in the set b of product purchase times in the concentrated discrete value set; XG is the set of correlation values, c represents the set of all correlations purchased by the user, and d represents the correlation of a certain product purchased by the user. Indicates that the correlation value d of a certain product is traversed in the correlation value set c.

[0189] Specifically, the probability law value is the probability law value of a certain commodity, and then the obtained probability law values ​​of all commodities are combined into a probability law value set.

[0190] The distribution law acquisition submodule obtains the distribution and law of data through the probability law value.

[0191] Specifically, all commodities purchased by the user are calculated according to the expression of the probability law value to obtain the probability law value, and the user's basic information, purchase information and the probability law value are data-associated to obtain the distribution and law of the user's behavior trajectory data information.

[0192] The user portrait building module 24 builds a user portrait according to the distribution and regularity of the data.

[0193] Specifically, user portrait is a labeled user model abstracted from the user's behavior trajectory data information and the distribution and regularity of the data. It affixes highly refined feature identifiers to users through mining massive data to form a comprehensive picture of user information. The core work of user portrait is to "label" users. These labels are refined descriptions of user dimensional characteristics, which together constitute an overall understanding of the user. The user portrait includes data on multiple aspects such as the customer's basic information, interests and hobbies, purchase history, and behavior data.

[0194] Furthermore, the user portrait construction module 24 includes the following submodules:

[0195] The information dimension establishment submodule establishes the user's information dimension based on the distribution and regularity of the target user's behavior trajectory data.

[0196] Specifically, the information dimensions include demographic attributes (age, gender, location, occupation), behavioral attributes (search keywords, browsing history, purchase records, purchase frequency), and social attributes (consumption proportion, income); among them, the demographic attribute data is obtained through the user behavior trajectory information data; the search keyword browsing history and purchase records in the behavioral attributes are obtained through the relationship distribution value data set, and the purchase frequency in the behavioral attributes is obtained through the centralized discrete value; the total purchase amount of the user is obtained by summing the price set in the relationship distribution value and dividing it by the income to obtain the consumption proportion in the social attributes, among which the income is obtained from the user behavior trajectory information data.

[0197] The feature dimension establishes a sub-module, extracts the user's purchasing interest and consumption ability indicators based on the distribution and rules of the user's behavior trajectory data, and establishes the user's feature dimension.

[0198] Specifically, purchasing interest mainly refers to the category preferences of users when shopping, such as clothing, luggage, home furnishing, maternal and child care, cleaning, food, etc. Consumption capacity refers to the purchasing power of users. The actual consumption level of users and the psychological consumption level of users in each category are distinguished, and characteristic dimensions are established respectively.

[0199] Construct a user portrait sub-module to construct a user portrait based on user behavior trajectory data information, user information dimensions and feature dimensions.

[0200] Furthermore, the user portrait expression is as follows:

[0201]

[0202] Among them, XHZ is the user portrait; RK is the demographic attribute, n represents age, x represents gender, d represents location, z represents occupation, RK (n∪x∪d∪z) represents the query of age n, gender x, location d and occupation z from the user behavior trajectory information data and the collection of demographic attributes RK; XW is the behavior attribute, s represents the search keyword, l represents the browsing history, m represents the purchase record, p represents the purchase frequency, XW (s∪l∪m∪p) It means that the search keyword s, browsing history l, purchase record m and purchase frequency p are taken out from the relationship distribution value set to form the behavior attribute XW; SH is the social attribute, f represents the consumption proportion, sr represents the income, SH (f∪sr) Indicates that the calculated consumption proportion f and income sr are combined into the social attribute SH; [RK (n∪x∪d∪z) ∪XW (s∪l∪m∪p) ∪SH (f∪sr) ] represents the combination of demographic attributes RK, behavioral attributes XW and social attributes SH into user information dimensions; XG is a set of correlation values, c represents the set of all correlations purchased by the user, q represents the correlation of a certain type of product purchased by the user, It means that the correlation q of a user purchasing a certain type of goods is traversed in the user's purchase of all correlation sets c; LS is a concentrated discrete value set, b represents the number of times the user purchases all goods, and w represents the number of times a certain type of goods is purchased. It means that the number of times a user purchases all products, b, is used to traverse the number of times a user purchases a certain type of product, w. Indicates the relevance of users purchasing a certain type of goods and the number of purchases of a certain category of goods Multiplying them together gives the purchase interest; FB is the relationship distribution value set, e represents the actual purchase price of a certain type of goods, and y represents the total price set of the goods purchased by the user. It means to traverse the actual purchase price e of a certain type of goods in the total price set y of the goods purchased by the user, ll means the price of all the goods browsed, ml means the price of a certain type of goods browsed, It means that the price ml of a certain category of goods is traversed among the prices ll of all the goods browsed, mm means the number of goods browsed in a certain category, and as means the number of goods browsed in all categories. It means to traverse the number of browsed products of a certain category in the number of browsed products as. Indicates the user's spending power, using the actual purchase price of a certain type of product Subtract the average price of a certain category of products If the result is a negative number, it means that the user's consumption ability is weak, and if the result is a positive number, it means that the user's consumption ability is strong. The smaller the result, the weaker the user's consumption ability, and the larger the result, the stronger the user's consumption ability. It means that purchasing interest and consumption ability are combined into user characteristic dimensions.

[0203] Specifically, a user portrait is a collection of user information dimensions and feature dimensions.

[0204] The intelligent marketing scenario recommendation module 25 constructs an intelligent marketing scenario model based on the user portrait through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user.

[0205] Furthermore, the intelligent marketing scenario recommendation module 25 includes the following submodules:

[0206] The training sub-module uses machine learning to train user portraits and build intelligent marketing scenario models.

[0207] Specifically, the intelligent marketing scenario model includes an existing data marketing scenario model and a predictive marketing scenario model. The existing data marketing scenario model is obtained by training based on user portrait data, and the predictive marketing scenario model is based on the training results of the existing data marketing scenario model. The process of training the intelligent marketing scenario model is the process of training the predictive marketing scenario model based on the existing data marketing scenario model, wherein the user's known data is input into the predictive marketing scenario model to obtain the model output result, and then the loss function is obtained based on the model output result and the result of manual annotation, and finally the loss function is back-propagated to correct the data in the predictive model, thereby completing the training of the model.

[0208] Among them, the existing data marketing scenario model is obtained by training with a machine learning algorithm according to the user portrait, wherein age, gender, location, occupation, search keywords, browsing history, purchase records, purchase frequency, consumption proportion, income, purchase interest, and consumption capacity are used as known data for classification training, so that the training result is to obtain other marketing scenario data through a single known data, for example, other marketing scenario data is obtained through age, {"age"→"gender", "location", "occupation", "search keywords", "browsing history", "purchase records", "purchase frequency", "consumption proportion", "income", "purchase interest", "consumption capacity"}, and then other marketing scenario data are obtained by training according to different marketing scenario keywords. For example, search keywords, browsing history, purchase records, purchase frequency, purchase interest, and consumption capacity are obtained according to age, gender, location, occupation, consumption proportion, and income, {"age", "gender", "location", "occupation", "consumption proportion", "income"→"search keywords", "browsing history", "purchase records", "purchase frequency", "purchase interest", "consumption capacity"}, the more marketing scenario keywords are combined, the fewer the required marketing scenario keywords are obtained, and the more refined the classification is.

[0209] Among them, according to the training results of the existing data model, the data in the existing data model is used for prediction through forward propagation to train the prediction marketing scenario model. The prediction marketing scenario model is to predict the parameters obtained by training the existing data marketing scenario model according to the known user parameters and obtain the prediction data. For example, if the age, gender, location, occupation, consumption ratio, and income of a certain user are known, these parameters are input into the prediction marketing scenario model to obtain the search keywords, browsing history, purchase history, purchase frequency, purchase interest, and consumption capacity of the known data, and then prediction training is performed based on these known data to obtain the prediction data of this user. For example, according to age, gender, location, occupation, consumption ratio, and income, predict search keywords, predict browsing, predict purchase, predict purchase frequency, predict purchase interest, and predict consumption capacity are obtained, {"age", "gender", "location", "occupation", "consumption ratio", "income" → "predicted search keywords", "predicted browsing", "predicted purchase", "predicted purchase frequency", "predicted purchase interest", "predicted consumption capacity"}.

[0210] The testing submodule tests the performance of the intelligent marketing scenario model through the cross-validation method.

[0211] Specifically, the holdout method in cross-validation is used to divide the existing data marketing scenario model into two non-overlapping parts, one as the training data set and the other as the test data set. The model parameters are further optimized through testing to ensure the accuracy and effectiveness of the model.

[0212] The recommendation judgment submodule judges whether the marketing scenario recommendation meets the recommendation conditions. If so, the predicted marketing scenario is recommended to the user. Otherwise, the optimization module 26 is executed.

[0213] Furthermore, the recommendation judgment submodule includes the following submodules:

[0214] The purchase prediction submodule uses a machine learning algorithm to obtain the user's purchase habit trajectory data based on the user portrait, and calculates and predicts the predicted purchase value of the products recommended to the user.

[0215] Specifically, the predicted purchase value is to predict the products recommended to the user based on the user's browsing history, search keywords and other user purchasing habit trajectory data, and then calculate a numerical value based on the expression of the predicted purchase value to determine whether the predicted purchase value of the recommended product is consistent with the user's purchasing habits. If it is consistent, the predicted purchase value of the product recommended to the user is transmitted to the intelligent marketing scenario model. If it is not consistent, the predicted purchase value of the next product recommended to the user is calculated.

[0216] Furthermore, the expression for predicting purchase value is as follows:

[0217]

[0218] Among them, GMT is the predicted purchase value; YC is the predicted recommended product price, mc represents the set of all recommended products, and ym represents a selected predicted recommended product. It means selecting a predicted recommended product price ym from the total recommended product price set mc; FB is the relationship distribution value set, y represents the total price set of the products purchased by the user, e represents the actual purchase price of a certain type of product, It means to traverse and predict the actual purchase price e of the same category of recommended products in the total price set y of the products purchased by the user; LS is a concentrated discrete value set, b represents the number of times the user purchases all products, and w represents the number of times a certain category of products is purchased. It means that the number of times a user purchases all products b is used to traverse and predict the number of times w of products in the same category as the recommended product. It indicates that the predicted recommended product price is compared with the average price of the same category products that the user has purchased. The closer the value is to 0, the more it meets the user's consumption level. XG is the correlation set, yc is the correlation set of the predicted recommended products, and yy is the correlation of a selected predicted recommended product. It indicates that the correlation value yy of the selected predicted recommended product is traversed from the correlation value set yc of the predicted recommended product. The closer the value is to -1 or 1, the stronger the correlation is. as indicates the number of browsed products, mm indicates the number of browsed products of a certain category. It means that the number of products in the same category of the predicted recommended products is traversed from the number of all products browsed as, mm. The following means getting the number of all browsed products as, Indicates the number of products in the same category that are browsed for predicted recommended products Than the number of all products viewed The closer the value is to 1, the greater the user's interest in the product; the predicted purchase value GMT is calculated based on the expression. The closer its value is to 0, the greater the possibility that the user will purchase the predicted recommended product. Conversely, the user is less likely to purchase the predicted recommended product.

[0219] The judgment submodule judges whether the predicted recommended product meets the recommendation conditions according to the predicted purchase value. If so, the predicted purchase product is transmitted to the intelligent marketing scenario model. If not, the optimization module 26 is executed.

[0220] The push sub-module uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

[0221] Specifically, intelligent delivery technology monitors the effectiveness of advertising in real time and analyzes users' behavioral data such as clicks, browsing, and conversions. Intelligent delivery technology can automatically adjust the time, frequency, channel and other strategies of advertising according to the intelligent marketing scenario model to ensure that advertising reaches target users in the best way.

[0222] The optimization module 26 performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model, thereby continuously optimizing and adjusting the intelligent marketing scenario model.

[0223] Furthermore, the cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model. The sub-steps of continuously optimizing and adjusting the intelligent marketing scenario model are as follows:

[0224] The data optimization submodule obtains real-time user behavior trajectory data information through the collection module 21, the classification and summary module 22 and the distribution law analysis module 23, and uses data stream processing to perform real-time monitoring, analysis and processing to obtain optimized data.

[0225] Specifically, the purpose of real-time monitoring and analysis is to monitor abnormal data, issue early warnings for abnormal data, and eliminate abnormal data.

[0226] The user data submodule is optimized, and the optimized data is transmitted to the user portrait construction module 24 to obtain the optimized user portrait, and the optimized user portrait is used to train the intelligent marketing scenario model again to achieve continuous optimization of the model.

[0227] Specifically, optimizing user images is to continuously fill in data for user portraits based on real-time data information, so that user portraits are always practical, thereby continuing to train the intelligent marketing scenario model, and continuously optimizing the intelligent marketing scenario model, thereby achieving precise marketing and personalized services.

[0228] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for constructing intelligent marketing scenarios based on user behavior trajectory big data, characterized in that: include: Step S1: The cloud platform collects user behavior trajectory data through a web crawler and pre-processes the data; Step S2: The cloud platform identifies the data information, and classifies and aggregates different behavior trajectory data information to generate a data information database; Step S3: The cloud platform obtains the distribution and regularity of the data through data analysis and summary based on the data information in the data information database; Step S4: The cloud platform constructs a user profile based on the distribution and regularity of the data; Step S5: The cloud platform constructs an intelligent marketing scenario model based on the user portrait through a machine learning algorithm, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to the user; Step S6: The cloud platform performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits the real-time data back to the intelligent marketing scenario model, continuously optimizing and adjusting the intelligent marketing scenario model.

2. The method for constructing an intelligent marketing scenario based on user behavior trajectory big data according to claim 1, characterized in that: The cloud platform collects data on user behavior trajectories through web crawlers, and the sub-steps of preprocessing the data are as follows: Step S11: The cloud platform sends an HTTP request to the target website through a web crawler to obtain web page content; Step S12: The cloud platform extracts the required user behavior trajectory data through the acquired webpage content; Step S13: The cloud platform stores the extracted user behavior trajectory data in a database; Step S14, the cloud platform calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data; Step S15: The cloud platform converts the cleaned data into a form suitable for model training using normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain converted data; Step S16: The cloud platform selects the main information data of the converted data through principal component analysis and linear discriminant analysis methods to obtain pre-processed data.

3. The method for constructing an intelligent marketing scenario based on user behavior trajectory big data according to claim 1, characterized in that: The cloud platform uses data analysis to collect statistics and summarize the data distribution and regularity based on the data information in the database. The sub-steps are as follows: Step S31: The cloud platform obtains the distribution of data through a histogram according to the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value; Step S32: The cloud platform calculates the mean, median, and standard deviation statistics based on the data in the data information database to describe the central tendency and dispersion of the obtained data, and obtains the central dispersion value; Step S33: The cloud platform groups the data according to the variables through grouping and aggregation according to the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value; Step S34, the cloud platform calculates the probability law value according to the relationship distribution value, the concentrated discrete value and the correlation value; Step S35: The cloud platform obtains the distribution and regularity of the data through the probability regularity value.

4. The method for constructing an intelligent marketing scenario based on user behavior trajectory big data according to claim 1, characterized in that: The cloud platform constructs user portraits based on the distribution and rules of data in the following sub-steps: Step S41: The cloud platform establishes the user's information dimension according to the distribution and regularity of the target user's behavior trajectory data; Step S42: The cloud platform extracts the user's purchasing interest and consumption ability indicators according to the distribution and regularity of the user's behavior trajectory data, and establishes the user's characteristic dimensions; Step S43: The cloud platform constructs a user portrait based on the user behavior trajectory data information, the user's information dimension and the feature dimension.

5. The method for constructing an intelligent marketing scenario based on user behavior trajectory big data according to claim 1, characterized in that: The cloud platform builds an intelligent marketing scenario model based on user portraits through machine learning algorithms, predicts marketing scenarios based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to users in the following sub-steps: Step S51: The cloud platform uses machine learning to train user portraits and build an intelligent marketing scenario model; Step S52: The cloud platform tests the performance of the intelligent marketing scenario model through a cross-validation method; Step S53: The cloud platform determines whether the marketing scenario recommendation meets the recommendation condition. If so, the predicted marketing scenario is recommended to the user. Otherwise, step S6 is executed. Step S54: The cloud platform uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

6. An intelligent marketing scenario construction system based on user behavior trajectory big data, characterized in that: include: The collection module is used to collect data on user behavior trajectories through web crawlers and pre-process the data; The classification and aggregation module identifies data information, classifies and aggregates different behavior trajectory data information, and generates a data information database; The distribution law analysis module obtains the distribution and law of data through statistics and summary based on the data information in the data information database; User portrait construction module, which constructs user portraits based on the distribution and rules of data; The intelligent marketing scenario recommendation module builds an intelligent marketing scenario model based on user portraits through machine learning algorithms, predicts marketing scenario recommendations based on the intelligent marketing scenario model, and recommends the predicted marketing scenario to users; The optimization module performs real-time monitoring and analysis based on the received real-time user behavior trajectory data information and transmits real-time data back to the intelligent marketing scenario model, continuously optimizing and adjusting the intelligent marketing scenario model.

7. The intelligent marketing scenario construction system based on user behavior trajectory big data according to claim 6, characterized in that: The acquisition module includes: The web page content acquisition submodule sends HTTP requests to the target website through a web crawler to obtain web page content; The user behavior trajectory data extraction submodule extracts the required user behavior trajectory data through the acquired web page content; The data storage submodule stores the extracted user behavior trajectory data in the database; The data cleaning submodule calls the database data, processes the missing values ​​by deleting the missing values, identifies and replaces the outliers through the box plot, deletes the duplicate data in the data by querying the data, and obtains the cleaned data; The data conversion submodule converts the cleaned data into a form suitable for model training using the normalization, standardization and logarithmic transformation methods in numerical data conversion to obtain the converted data; The data preprocessing submodule selects the main information data of the converted data through principal component analysis and linear discriminant analysis methods to obtain preprocessed data.

8. The intelligent marketing scenario construction system based on user behavior trajectory big data according to claim 6, characterized in that: Distribution law analysis module, specifically including: The relationship distribution analysis submodule obtains the distribution of data through a histogram according to the data information in the data information database, obtains the relationship between the data through a scatter plot and a box plot, and obtains the relationship distribution value; The centralization and dispersion analysis submodule calculates the mean, median, and standard deviation statistics based on the data in the data information database to describe the centralization trend and dispersion degree of the obtained data and obtain the centralization and dispersion values; The correlation analysis submodule groups the data by variables through grouping and aggregation according to the data information in the data information database, and performs aggregation analysis on each group of data to obtain the correlation between the variables and obtain the correlation value; The probability law calculation submodule calculates the probability law value according to the relationship distribution value, the concentrated discrete value and the correlation value; The distribution law acquisition submodule obtains the distribution and law of data through the probability law value.

9. The intelligent marketing scenario construction system based on user behavior trajectory big data according to claim 6, characterized in that: User portrait building module, including: The information dimension establishment submodule establishes the user's information dimension based on the distribution and regularity of the target user's behavior trajectory data; The feature dimension establishes a submodule, extracting the user's purchasing interest and consumption ability indicators based on the distribution and regularity of the user's behavior trajectory data, and establishing the user's feature dimension; Construct a user portrait sub-module to construct a user portrait based on user behavior trajectory data information, user information dimensions and feature dimensions.

10. The intelligent marketing scenario construction system based on user behavior trajectory big data according to claim 6, characterized in that: Intelligent marketing scenario recommendation module, including: The training submodule uses machine learning to train user portraits and build intelligent marketing scenario models; The testing submodule tests the performance of the intelligent marketing scenario model through the cross-validation method; The recommendation judgment submodule judges whether the marketing scenario recommendation meets the recommendation conditions. If so, the predicted marketing scenario is recommended to the user. Otherwise, the optimization module is executed. The push sub-module uses intelligent delivery technology to accurately push advertisements and promotional information to users based on the intelligent marketing scenario model.

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