Terminal information intelligent publishing method and system based on user characteristics

Abstract

The present invention relates to the field of information release, and discloses a method and system for intelligent terminal information release based on user characteristics, including the following steps: collecting test user characteristic data and preprocessing the test user characteristic data, and training the preprocessed test user characteristic data based on a random forest algorithm to obtain a random forest training model; using the random forest training model to construct a user portrait, formulating a terminal information release strategy based on information in the user portrait, and updating the terminal information release strategy based on the feedback data after the terminal information release strategy is executed. The present invention can realize intelligent information release according to user characteristics through a terminal information intelligent release system, realize automatic and accurate information release, and improve the efficiency and effect of information transmission.

Description

Technical Field

[0001] The present invention relates to the field of information publishing, and in particular to a method and system for intelligently publishing terminal information based on user characteristics. Background Art

[0002] The terminal information intelligent publishing system is a system that uses modern information technology and intelligent algorithms to push specific information to the user's terminal devices, including but not limited to mobile phones, tablets, smart TVs, digital signage, etc. The system is designed to automatically and accurately publish information based on the characteristics and needs of users, thereby improving the efficiency and effectiveness of information transmission. Different users have different user characteristics and needs. For example, users prefer to read information at different times and in different types of information. The terminal information intelligent publishing system collects and manages user characteristic data, and publishes information to user terminal devices based on the characteristics of user characteristic data. It has the advantages of personalization, real-time, intelligence, and multi-channel, which significantly improves the information transmission effect and user satisfaction. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a method and system for intelligently publishing terminal information based on user characteristics.

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

[0005] The first aspect of the present invention provides a method for intelligently publishing terminal information based on user characteristics, comprising the following steps:

[0006] S102: Collecting test user feature data, performing data repetition processing on the test user feature data, and performing data modeling training on the repetition processed test user feature data based on a random forest algorithm;

[0007] S104: constructing a user profile through a random forest training model, determining the subject content to be released to the target user based on the user profile, and determining the time period for releasing the subject content to the target user;

[0008] S106: Analyze the target user's preference value for the subject content and the target user's reading frequency of the subject content in different time periods, and formulate a terminal information release strategy based on the analysis result;

[0009] S108: Acquire feedback data of the target terminal device through the terminal information intelligent publishing system, analyze the feedback data of the target terminal device, and update the terminal information publishing strategy based on the analysis result.

[0010] Furthermore, in a preferred embodiment of the present invention, the step S102 is specifically:

[0011] Acquire a terminal system capable of intelligent information release, mark it as a terminal information intelligent release system, acquire a test user, and acquire a terminal device of the test user, mark it as a test terminal device;

[0012] In the terminal information intelligent publishing system, a feature information collection request instruction is sent to the test terminal device, and it is determined whether the test terminal device confirms to accept the feature information collection request instruction;

[0013] If yes, the terminal information intelligent publishing system is controlled to perform device login identification on the test terminal device, wherein the device login identification is to identify and obtain all software registered and logged in on the test terminal device, and mark the software registered and logged in on the test terminal device as the user-used software;

[0014] Control the terminal information intelligent publishing system to obtain the basic information of the test user and the behavior data of the test user in all users' software, and mark the basic information of the test user and the behavior data of the test user as the test user characteristic data;

[0015] In the terminal information intelligent publishing system, the test user characteristic data is subjected to data duplication processing, wherein the data duplication processing includes data merging of the test user's basic information and character duplication recognition and merging of the test user's behavior data, to obtain the test user characteristic data after data duplication processing;

[0016] A random forest algorithm is introduced, and classification training is performed on the test user feature data after repeated data processing through the random forest algorithm to obtain a random forest model that can construct a user portrait.

[0017] Furthermore, in a preferred embodiment of the present invention, the test user feature data after repeated data processing by the random forest algorithm is subjected to classification training to obtain a random forest training model, specifically:

[0018] Introducing a random forest algorithm, and building a decision tree basic model based on the random forest algorithm;

[0019] Importing the test user feature data after repeated data processing into the decision tree basic model, and analyzing the test user feature data after repeated data processing in the decision tree basic model to determine the feature segmentation value;

[0020] Based on the feature segmentation value, a division process is performed in the decision tree basic model, and the divided nodes are marked as leaf nodes, a maximum number of divisions is preset, and when the number of divisions reaches the maximum number of divisions, the division process is stopped to obtain a decision tree training model;

[0021] In the decision tree training model, the test user feature data corresponding to the leaf node is obtained after repeated processing, and marked as the leaf node sample data;

[0022] Introducing a singular value decomposition algorithm, decomposing the leaf node sample data by the singular value decomposition algorithm to obtain a decomposed eigenvector, and calibrating the decomposed eigenvector with the highest occurrence frequency as an initial vector;

[0023] Introducing a cosine metric algorithm and a genetic algorithm, calculating a cosine metric value between an initial vector and other decomposed eigenvectors based on the cosine metric algorithm and the genetic algorithm, calibrating the initial vector to be a type of cosine metric value, and presetting a cosine threshold;

[0024] Determine whether there is a class of cosine metric values ​​greater than the cosine threshold. If so, mark the decomposed feature vector corresponding to the class of cosine metric values ​​greater than the cosine threshold as an outlier vector, and mark the leaf node corresponding to the outlier vector as an outlier leaf node;

[0025] Iteratively splitting the outlier leaf nodes by the genetic algorithm so that there are no outlier leaf nodes in the decision tree training model, thereby obtaining a decision tree training optimization model;

[0026] A standard number of decision trees is preset, and based on the standard number of decision trees, a number of decision tree training optimization models equal to the standard number of decision trees is obtained, and all decision tree training optimization models are combined to obtain a random forest training model, which can analyze the user's feature data and generate a user portrait.

[0027] Furthermore, in a preferred embodiment of the present invention, the step S104 is specifically:

[0028] Marking the user who needs to publish information as the target user, obtaining the terminal device of the target user, marking it as the target terminal device, and obtaining the target user feature data after repeated data processing based on the target terminal device and the terminal information intelligent publishing system;

[0029] Importing the target user feature data after the repeated data processing into the random forest training model for data training and user portrait construction, obtaining a user portrait of the target user, and marking it as the target user portrait, wherein the target user portrait includes the target user's preference value for different subject contents, and the frequency of the target user reading the subject contents in different time periods;

[0030] In the target user portrait, a preference threshold is preset, and the target user's preference values ​​for different subject contents are analyzed. If the target user's preference value for the subject content is greater than the preference threshold, the corresponding subject content is marked as a first-category subject content; if the target user's preference value for the subject content is less than the preference threshold, the corresponding subject content is marked as a second-category subject content;

[0031] In the target user portrait, the frequency of target users reading subject content in different time periods is analyzed, and a standard reading frequency is preset. The time period when the target users read subject content more frequently than the standard reading frequency is marked as a publishable time period, and the time period when the target users read subject content less frequently than the standard reading frequency is marked as a publish-pending time period.

[0032] Furthermore, in a preferred embodiment of the present invention, the step S106 is specifically as follows:

[0033] Connecting the terminal information intelligent publishing system to the Internet, and controlling the terminal information intelligent publishing system to search for all information matching a type of subject content on the Internet, and marking them as a type of subject content information;

[0034] Obtaining the update time of all the first-category subject content information, and based on the update time of all the first-category subject content information, filtering the first-category subject content information, selecting the first-category subject content information whose update time is today, and marking it as the latest first-category subject content information;

[0035] Convert all the latest first-category subject content information into text information, mark it as first-category text information, introduce the TextRank algorithm, and use the TextRank algorithm to analyze all the first-category text information to construct a word co-occurrence relationship graph of different first-category text information;

[0036] In a word co-occurrence relationship graph of a type of text information, the weights of all words are calculated, and a standard weight is preset. If the weight of a word is greater than the standard weight, the corresponding word is marked as a keyword;

[0037] Introducing a keyword matching algorithm, based on which the Jaccard similarity of keywords of different types of text information is calculated, and presetting a Jaccard standard similarity threshold;

[0038] Maintaining the Jaccard similarity of the keywords within the Jaccard standard similarity threshold, all the text information of the same category are collectively referred to as the same category of text information, and converting the same category of text information into the latest category of subject content information, and marking it as the same category of subject content information;

[0039] The terminal information intelligent publishing system obtains the reading volume of all the latest subject content information on the Internet, presets a standard reading volume, selects the latest subject content information with a reading volume greater than the standard reading volume, and marks it as high-reading subject content information;

[0040] Analyze the high-reading subject content information to obtain subject content information of the same type as the high-reading subject content information, and refer to the high-reading subject content information and subject content information of the same type as the high-reading subject content information as target release subject content information;

[0041] A terminal information publishing strategy is executed in the terminal information intelligent publishing system, wherein the terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the target publishing subject content information to the target terminal device within the publishable time period.

[0042] Furthermore, in a preferred embodiment of the present invention, the step S108 is specifically:

[0043] After executing a terminal information publishing strategy in the terminal information intelligent publishing system, feedback data of the target terminal device is obtained through the terminal information intelligent publishing system, wherein the feedback data of the target terminal device is the browsing time and browsing completeness of different target publishing subject content information on the target terminal device, and the browsing standard time and browsing minimum completeness are preset;

[0044] If the browsing time and browsing completeness of the target published subject content information on the target terminal device are respectively greater than the browsing standard time and the browsing minimum completeness, the corresponding target published subject content information is marked as a type of published subject content information, and the subject content corresponding to the type of published subject content information is marked as a frequently browsed subject content;

[0045] If the browsing completeness of the target published subject content information on the target terminal device is less than the minimum browsing completeness, or the browsing time is less than the browsing standard time, the corresponding target published subject content information is marked as the second-class published subject content information; and the subject content corresponding to the second-class published subject content information is marked as the infrequently browsed subject content.

[0046] Respectively searching and obtaining information corresponding to frequently browsed subject contents and infrequently browsed subject contents on the Internet, and marking them as frequently browsed subject content information and infrequently browsed subject content information;

[0047] The first-class terminal information publishing strategy is updated to obtain the second-class terminal information publishing strategy, wherein the second-class terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the frequently browsed subject content information to the target terminal device within the publishable time period, and simultaneously publish the infrequently browsed subject content information to the target terminal device within the pending publishing time period.

[0048] The second aspect of the present invention further provides a terminal information intelligent publishing system based on user characteristics, the terminal information intelligent publishing system includes a memory and a processor, the memory stores a terminal information intelligent publishing method, and when the terminal information intelligent publishing method is executed by the processor, the following steps are implemented:

[0049] Collecting test user feature data, performing data repetition processing on the test user feature data, and performing data modeling training on the data repetition processed test user feature data based on a random forest algorithm;

[0050] Build a user profile through the random forest training model, determine the subject content to be released to the target user based on the user profile, and determine the time period for releasing the subject content to the target user;

[0051] Analyze the target users' preferences for the subject content and the frequency of the target users reading the subject content in different time periods, and formulate a terminal information release strategy based on the analysis results;

[0052] The terminal information intelligent publishing system obtains feedback data from the target terminal device, analyzes the feedback data from the target terminal device, and updates the terminal information publishing strategy based on the analysis results.

[0053] The present invention solves the technical defects existing in the background technology, and the present invention has the following beneficial effects: collecting test user feature data and preprocessing the test user feature data, and training the preprocessed test user feature data based on the random forest algorithm to obtain a random forest training model; using the random forest training model to build a user portrait, formulate a terminal information release strategy based on the information in the user portrait, and update the terminal information release strategy based on the feedback data after the terminal information release strategy is executed. The present invention can realize the intelligent release of information according to user characteristics through the terminal information intelligent release system, realize automatic and accurate release of information, and improve the efficiency and effect of information transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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 of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flow chart of a method for intelligently publishing terminal information based on user characteristics is shown;

[0056] Figure 2 A flow chart of a method for constructing a random forest training model is shown;

[0057] Figure 3 The program view of the terminal information intelligent publishing system based on user characteristics is shown. DETAILED DESCRIPTION

[0058] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0060] Figure 1 A flow chart of a method for intelligently publishing terminal information based on user characteristics is shown, comprising the following steps:

[0061] S102: Collecting test user feature data, performing data repetition processing on the test user feature data, and performing data modeling training on the repetition processed test user feature data based on a random forest algorithm;

[0062] S104: constructing a user profile through a random forest training model, determining the subject content to be released to the target user based on the user profile, and determining the time period for releasing the subject content to the target user;

[0063] S106: Analyze the target user's preference value for the subject content and the target user's reading frequency of the subject content in different time periods, and formulate a terminal information release strategy based on the analysis result;

[0064] S108: Acquire feedback data of the target terminal device through the terminal information intelligent publishing system, analyze the feedback data of the target terminal device, and update the terminal information publishing strategy based on the analysis result.

[0065] Furthermore, in a preferred embodiment of the present invention, the step S104 is specifically:

[0066] Marking the user who needs to publish information as the target user, obtaining the terminal device of the target user, marking it as the target terminal device, and obtaining the target user feature data after repeated data processing based on the target terminal device and the terminal information intelligent publishing system;

[0067] Importing the target user feature data after the repeated data processing into the random forest training model for data training and user portrait construction, obtaining a user portrait of the target user, and marking it as the target user portrait, wherein the target user portrait includes the target user's preference value for different subject contents, and the frequency of the target user reading the subject contents in different time periods;

[0068] In the target user portrait, a preference threshold is preset, and the target user's preference values ​​for different subject contents are analyzed. If the target user's preference value for the subject content is greater than the preference threshold, the corresponding subject content is marked as a first-category subject content; if the target user's preference value for the subject content is less than the preference threshold, the corresponding subject content is marked as a second-category subject content;

[0069] In the target user portrait, the frequency of target users reading subject content in different time periods is analyzed, and a standard reading frequency is preset. The time period when the target users read subject content more frequently than the standard reading frequency is marked as a publishable time period, and the time period when the target users read subject content less frequently than the standard reading frequency is marked as a publish-pending time period.

[0070] It should be noted that the target user feature data obtained in the target terminal device needs to be processed repeatedly like the user feature data of the test, so as to achieve data cleaning and remove duplicate values. After obtaining the target user feature data after data repetition processing, the target user feature data after data repetition processing can be imported into the random forest training model to obtain the target user portrait. User portrait is a systematic description that comprehensively displays user characteristics by collecting and analyzing user data. After the data is analyzed and processed by the random forest training model, the data is classified, and the target user portrait can be obtained through the classified data. The target user portrait records, including but not limited to, the user's behavioral characteristics, interests and hobbies, social characteristics, psychological characteristics, etc., wherein the behavioral characteristics include the user's consumption behavior, usage behavior, and browsing behavior, the social characteristics include the user's likes, comments, sharing and other social interaction records, etc., and the psychological characteristics include the user's values, lifestyle, etc. According to the above characteristics of the user, the user's preference value for different subject content can be obtained. The target user portrait also records the frequency of the user's reading information in different time periods of the day. Since there are many subject contents, and users have different preference values ​​for different subject contents, and when releasing information to users, it is necessary to select information that users prefer to release, so as to avoid releasing too much and messy information, which makes it impossible for users to read all the information. Therefore, analysis is performed based on the preference value to obtain a category of subject contents. The category of subject contents is the content with a high degree of user preference, and information of the category of subject contents is pushed preferentially. The frequency of users reading information in different time periods of a day is different. The higher the frequency, the more frequently the user reads information in the corresponding time period. Therefore, the publishable time period and the pending release time period are determined according to the frequency of users reading information in different time periods of a day. The publishable time period is the time period during which information is usually released, and the pending release time period is the time period during which information may be released.

[0071] Furthermore, in a preferred embodiment of the present invention, the step S106 is specifically as follows:

[0072] Connecting the terminal information intelligent publishing system to the Internet, and controlling the terminal information intelligent publishing system to search for all information matching a type of subject content on the Internet, and marking them as a type of subject content information;

[0073] Obtaining the update time of all the first-category subject content information, and based on the update time of all the first-category subject content information, filtering the first-category subject content information, selecting the first-category subject content information whose update time is today, and marking it as the latest first-category subject content information;

[0074] Convert all the latest first-category subject content information into text information, mark it as first-category text information, introduce the TextRank algorithm, and use the TextRank algorithm to analyze all the first-category text information to construct a word co-occurrence relationship graph of different first-category text information;

[0075] In a word co-occurrence relationship graph of a type of text information, the weights of all words are calculated, and a standard weight is preset. If the weight of a word is greater than the standard weight, the corresponding word is marked as a keyword;

[0076] Introducing a keyword matching algorithm, based on which the Jaccard similarity of keywords of different types of text information is calculated, and presetting a Jaccard standard similarity threshold;

[0077] Maintaining the Jaccard similarity of the keywords within the Jaccard standard similarity threshold, all the text information of the same category are collectively referred to as the same category of text information, and converting the same category of text information into the latest category of subject content information, and marking it as the same category of subject content information;

[0078] The terminal information intelligent publishing system obtains the reading volume of all the latest subject content information on the Internet, presets a standard reading volume, selects the latest subject content information with a reading volume greater than the standard reading volume, and marks it as high-reading subject content information;

[0079] Analyze the high-reading subject content information to obtain subject content information of the same type as the high-reading subject content information, and refer to the high-reading subject content information and subject content information of the same type as the high-reading subject content information as target release subject content information;

[0080] A terminal information publishing strategy is executed in the terminal information intelligent publishing system, wherein the terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the target publishing subject content information to the target terminal device within the publishable time period.

[0081] It should be noted that there are various types of information stored on the Internet, among which the subject contents of different information may be different. It is necessary to select information matching a type of subject content and publish it to the target user terminal, that is, a type of subject content information. Since the release time of various information stored on the Internet is different, in order to ensure that the information received by the target user is the latest first-hand information, it is necessary to screen the update time of a type of subject content information, select a type of subject content information with an update time of the day for output, that is, output the latest type of subject content information. Since there are many types of the latest type of subject content information, in order to improve the reading efficiency of the target user, it is necessary to select information with a high reading volume and publish it to the target user terminal, so as to make it convenient for the user to understand the information. However, the content contained in a piece of the latest type of subject content information may not be complete, for example, a piece of information may only contain half of the total information, so it is necessary to retrieve other information related to the information with a high reading volume when publishing the information with a high reading volume to the user, and publish them to the target user terminal together, so as to facilitate the user to understand the whole process of the information. The correlation between information can be determined by analyzing the similarity of keywords between information. The higher the similarity between keywords, the more it proves that the corresponding information is of the same type and needs to be published together, so that users can fully understand the subject content information. To determine the keywords of the subject content information, the subject content information needs to be converted into text information first. The text information directly describes the specific content of the subject content information, making it easier to obtain keywords. Keywords can be obtained through the TextRank algorithm, which is a graph-based sorting algorithm. It can construct a word co-occurrence relationship graph based on the subject content information, and calculate the weights of different words in the subject content information based on the word co-occurrence relationship graph. The higher the weight, the higher the criticality of the word in the information. Select words with weights greater than the standard weight and mark them as keywords. And introduce a keyword matching algorithm to calculate the intersection and union of keywords in different text information to obtain the keyword overlap rate. The keyword overlap rate indicator is the Jaccard similarity. When the Jaccard similarity is greater than the standard threshold, it proves that the higher the keyword overlap rate, the corresponding text information is of the same type of information and needs to be published together when publishing. After obtaining the high-reading subject content information, it is necessary to obtain the same type of information as the high-reading subject content information, and simultaneously publish the information to the target terminal device within the publishable time period through the terminal information intelligent publishing system.

[0082] Furthermore, in a preferred embodiment of the present invention, the step S108 is specifically:

[0083] After executing a terminal information publishing strategy in the terminal information intelligent publishing system, feedback data of the target terminal device is obtained through the terminal information intelligent publishing system, wherein the feedback data of the target terminal device is the browsing time and browsing completeness of different target publishing subject content information on the target terminal device, and the browsing standard time and browsing minimum completeness are preset;

[0084] If the browsing time and browsing completeness of the target published subject content information on the target terminal device are respectively greater than the browsing standard time and the browsing minimum completeness, the corresponding target published subject content information is marked as a type of published subject content information, and the subject content corresponding to the type of published subject content information is marked as a frequently browsed subject content;

[0085] If the browsing completeness of the target published subject content information on the target terminal device is less than the minimum browsing completeness, or the browsing time is less than the browsing standard time, the corresponding target published subject content information is marked as the second-class published subject content information; and the subject content corresponding to the second-class published subject content information is marked as the infrequently browsed subject content.

[0086] Respectively searching and obtaining information corresponding to frequently browsed subject contents and infrequently browsed subject contents on the Internet, and marking them as frequently browsed subject content information and infrequently browsed subject content information;

[0087] The first-class terminal information publishing strategy is updated to obtain the second-class terminal information publishing strategy, wherein the second-class terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the frequently browsed subject content information to the target terminal device within the publishable time period, and simultaneously publish the infrequently browsed subject content information to the target terminal device within the pending publishing time period.

[0088] It should be noted that after executing the first-class terminal information release strategy in the terminal information intelligent release system, the user will receive the target release subject content information at the target user terminal, but the user may not click on the target release subject content information and browse and read it. If the user does not click on it for a long time, it is not necessary to release this type of information to the user in the future, and it is necessary to release information that the user clicks on and reads more times. After obtaining the feedback data of the target terminal device, it is necessary to analyze the feedback data and update the release strategy based on the analysis results of the feedback data. When the time and completeness of the target user browsing the target release subject content information on the target terminal device are greater than the standard value, the corresponding target release subject content information is the information that the user likes to browse and read, and is marked as the first-class release subject content information. Otherwise, it is the second-class release subject content information. Based on the first-class release subject content information and the second-class release subject content information, it can be known that the subject content with a higher degree of preference of the target user is the frequently browsed subject content, and vice versa, the infrequently browsed subject content is obtained. The information corresponding to the frequently browsed subject content is released to the target terminal device within the release time, which can improve the click-through rate and reading rate of the information and ensure that the user reads and browses more seriously and carefully. The information corresponding to the seldom-browsed subject content can be published to the target terminal device within the publishing pending time period to improve the information delivery rate, so that users can browse the corresponding information when they suddenly need to browse.

[0089] Figure 2 A flow chart of a method for constructing a random forest training model is shown, comprising the following steps:

[0090] S202: Acquire user characteristic data and perform data duplication processing on the user characteristic data to obtain test user characteristic data after data duplication processing;

[0091] S204: Introducing a random forest algorithm, and building a decision tree training model based on the random forest algorithm;

[0092] S206: Introduce a singular value decomposition algorithm, a cosine metric algorithm, and a genetic algorithm to process data of the decision tree training model to obtain a random forest training model.

[0093] Furthermore, in a preferred embodiment of the present invention, the step S202 is specifically:

[0094] Acquire a terminal system capable of intelligent information release, mark it as a terminal information intelligent release system, acquire a test user, and acquire a terminal device of the test user, mark it as a test terminal device;

[0095] In the terminal information intelligent publishing system, a feature information collection request instruction is sent to the test terminal device, and it is determined whether the test terminal device confirms to accept the feature information collection request instruction;

[0096] If yes, the terminal information intelligent publishing system is controlled to perform device login identification on the test terminal device, wherein the device login identification is to identify and obtain all software registered and logged in on the test terminal device, and mark the software registered and logged in on the test terminal device as the user-used software;

[0097] Control the terminal information intelligent publishing system to obtain the basic information of the test user and the behavior data of the test user in all users' software, and mark the basic information of the test user and the behavior data of the test user as the test user characteristic data;

[0098] In the terminal information intelligent publishing system, the test user characteristic data is subjected to data duplication processing, wherein the data duplication processing includes data merging of the test user's basic information and character duplication recognition merging of the test user's behavior data to obtain the test user characteristic data after data duplication processing.

[0099] It should be noted that the terminal information intelligent publishing system can publish and push information suitable for the user's interests, hobbies, age group and other characteristics to the user according to the user's characteristics, and the user's terminal device is the device that receives the information published by the terminal information intelligent publishing system. The terminal information intelligent publishing system cannot publish information directly to the terminal device, and it needs to obtain confirmation from the terminal device before publishing information. Before publishing information, it is necessary to determine the user's own characteristic data, including interests, hobbies, age group, etc. A user portrait is constructed based on the above characteristic data, and appropriate information is pushed to the user based on the user portrait. The construction of the user portrait requires the use of a random forest model, and the construction of the model requires a large amount of data support, so it is necessary to collect the characteristic information of the test user for training. After obtaining the test user, send an instruction to request the collection of characteristic information to the test terminal device to ensure that the terminal information intelligent publishing system can publish information. After receiving the instruction, the terminal information intelligent publishing system obtains all the software that the user has logged in and registered in the test terminal device, that is, the user uses the software. The user's use of the software contains the user's basic information and behavior data. The basic information includes the user's age, household registration, name, etc., and the behavior data includes but is not limited to the videos and articles that the user likes online, and the comments posted by the user. The user's basic information and behavior data are marked as user feature data, and the user feature data needs to be processed for data duplication. Since the user's information and behavior data in the software may be repeated, the user feature data needs to be processed for data duplication, including identifying and merging repeated information, realizing information deduplication, and finally obtaining the test user feature data after data duplication processing.

[0100] Furthermore, in a preferred embodiment of the present invention, the step S204 is specifically:

[0101] Introducing a random forest algorithm, and building a decision tree basic model based on the random forest algorithm;

[0102] Importing the test user feature data after repeated data processing into the decision tree basic model, and analyzing the test user feature data after repeated data processing in the decision tree basic model to determine the feature segmentation value;

[0103] Based on the feature segmentation value, a division process is performed in the decision tree basic model, and the divided nodes are marked as leaf nodes. A maximum number of divisions is preset. When the number of divisions reaches the maximum number of divisions, the division process is stopped to obtain a decision tree training model.

[0104] It should be noted that the random forest model is a model that can classify feature data. In this article, the test user feature data after repeated data processing can be classified and a user portrait can be generated. Model training is required before building the random forest model to be used, and model training requires a basic model for training. The random forest model is composed of multiple decision tree models, so it is necessary to obtain a decision tree basic model. After importing the test user feature data after repeated data processing into the decision tree basic model, the feature segmentation value can be obtained. The feature segmentation value is the decision point for dividing the data set into smaller subsets. In the decision tree model, a tree structure is constructed according to the corresponding feature segmentation, and the data set is divided into different parts in a recursive manner until the stopping condition is met. The stopping condition is that the number of divisions reaches the maximum number of times and the divided nodes are leaf nodes. After division, a decision tree training model is obtained.

[0105] Furthermore, in a preferred embodiment of the present invention, the step S206 is specifically:

[0106] In the decision tree training model, the test user feature data corresponding to the leaf node is obtained after repeated processing, and marked as the leaf node sample data;

[0107] A singular value decomposition algorithm is introduced to decompose the leaf node sample data to obtain a decomposed eigenvector, and the decomposed eigenvector with the highest occurrence frequency is calibrated as an initial vector;

[0108] Introducing a cosine metric algorithm and a genetic algorithm, calculating a cosine metric value between an initial vector and other decomposed eigenvectors based on the cosine metric algorithm and the genetic algorithm, calibrating the value as a type of cosine metric value, and presetting a cosine threshold;

[0109] Determine whether there is a class of cosine metric values ​​greater than the cosine threshold. If so, mark the decomposed feature vector corresponding to the class of cosine metric values ​​greater than the cosine threshold as an outlier vector, and mark the leaf node corresponding to the outlier vector as an outlier leaf node;

[0110] Iteratively splitting the outlier leaf nodes by the genetic algorithm so that there are no outlier leaf nodes in the decision tree training model, thereby obtaining a decision tree training optimization model;

[0111] A standard number of decision trees is preset, and based on the standard number of decision trees, a number of decision tree training optimization models equal to the standard number of decision trees is obtained, and all decision tree training optimization models are combined to obtain a random forest training model, which can analyze the user's feature data and generate a user portrait.

[0112] It should be noted that in the decision tree training model, there may be outlier leaf nodes. The existence of outlier leaf nodes will cause anomalies in the process of constructing user portraits in the decision tree training model, such as disorderly distribution of user interests and hobbies, errors in personal information, etc., so it is necessary to remove outlier leaf nodes. Introducing the singular value decomposition algorithm to singularly decompose the sample data in the leaf node can reduce the complexity of the calculation and help find outlier leaf nodes. The cosine metric algorithm can be used to judge the cosine metric value between the feature vector and the initial vector of the leaf node. If the cosine metric value is not within the preset range, it proves that the similarity between the feature vector of the leaf node and the initial vector is low, and it is an outlier vector, so the leaf node is an outlier leaf node. The outlier leaf node can be iteratively split by the genetic algorithm until there is no outlier leaf node, and a decision tree optimization model is obtained. Combined with multiple decision tree optimization models, a random forest training model can be obtained, and the random forest training model can be used to construct a user portrait.

[0113] like Figure 3 As shown, the second aspect of the present invention further provides a terminal information intelligent publishing system based on user characteristics, the terminal information intelligent publishing system includes a memory 31 and a processor 32, the memory 31 stores a terminal information intelligent publishing method, and when the terminal information intelligent publishing method is executed by the processor 32, the following steps are implemented:

[0114] Collecting test user feature data, performing data repetition processing on the test user feature data, and performing data modeling training on the data repetition processed test user feature data based on a random forest algorithm;

[0115] Build a user profile through the random forest training model, determine the subject content to be released to the target user based on the user profile, and determine the time period for releasing the subject content to the target user;

[0116] Analyze the target users' preferences for the subject content and the frequency of the target users reading the subject content in different time periods, and formulate a terminal information release strategy based on the analysis results;

[0117] The terminal information intelligent publishing system obtains feedback data from the target terminal device, analyzes the feedback data from the target terminal device, and updates the terminal information publishing strategy based on the analysis results.

[0118] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for intelligently publishing terminal information based on user characteristics, characterized in that: The following steps are involved: S102: Collect test user feature data, perform data repetition processing on the test user feature data, and perform data modeling training on the test user feature data after data repetition processing based on a random forest algorithm to obtain a random forest training model, specifically: Introducing a random forest algorithm, and building a decision tree basic model based on the random forest algorithm; Importing the test user feature data after repeated data processing into the decision tree basic model, and analyzing the test user feature data after repeated data processing in the decision tree basic model to determine the feature segmentation value; Based on the feature segmentation value, a division process is performed in the decision tree basic model, and the divided nodes are marked as leaf nodes, a maximum number of divisions is preset, and when the number of divisions reaches the maximum number of divisions, the division process is stopped to obtain a decision tree training model; In the decision tree training model, the test user feature data corresponding to the leaf node is obtained after repeated processing, and marked as the leaf node sample data; A singular value decomposition algorithm is introduced to decompose the leaf node sample data to obtain a decomposed eigenvector, and the decomposed eigenvector with the highest occurrence frequency is calibrated as an initial vector; Introducing a cosine metric algorithm and a genetic algorithm, calculating a cosine metric value between an initial vector and other decomposed eigenvectors based on the cosine metric algorithm and the genetic algorithm, calibrating the value as a type of cosine metric value, and presetting a cosine threshold; Determine whether there is a class of cosine metric values ​​greater than the cosine threshold. If so, mark the decomposed feature vector corresponding to the class of cosine metric values ​​greater than the cosine threshold as an outlier vector, and mark the leaf node corresponding to the outlier vector as an outlier leaf node; Iteratively splitting the outlier leaf nodes by the genetic algorithm so that there are no outlier leaf nodes in the decision tree training model, thereby obtaining a decision tree training optimization model; A standard number of decision trees is preset, and based on the standard number of decision trees, a number of decision tree training optimization models equal to the standard number of decision trees is obtained, and all decision tree training optimization models are combined to obtain a random forest training model, wherein the random forest training model can analyze the characteristic data of the user and generate a user portrait; S104: constructing a user profile through a random forest training model, determining the subject content to be released to the target user based on the user profile, and determining the time period for releasing the subject content to the target user; S106: Analyze the target user's preference value for the subject content and the target user's reading frequency of the subject content in different time periods, and formulate a terminal information release strategy based on the analysis result; S108: Acquire feedback data of the target terminal device through the terminal information intelligent publishing system, analyze the feedback data of the target terminal device, and update the terminal information publishing strategy based on the analysis result.

2. The method for intelligently publishing terminal information based on user characteristics according to claim 1, characterized in that: The S102 is specifically: Acquire a terminal system capable of intelligent information release, mark it as a terminal information intelligent release system, acquire a test user, and acquire a terminal device of the test user, mark it as a test terminal device; In the terminal information intelligent publishing system, a feature information collection request instruction is sent to the test terminal device, and it is determined whether the test terminal device confirms to accept the feature information collection request instruction; If yes, the terminal information intelligent publishing system is controlled to perform device login identification on the test terminal device, wherein the device login identification is to identify and obtain all software registered and logged in on the test terminal device, and mark the software registered and logged in on the test terminal device as the user-used software; Control the terminal information intelligent publishing system to obtain the basic information of the test user and the behavior data of the test user in all users' software, and mark the basic information of the test user and the behavior data of the test user as the test user characteristic data; In the terminal information intelligent publishing system, the test user characteristic data is subjected to data duplication processing, wherein the data duplication processing includes data merging of the test user's basic information and character duplication recognition and merging of the test user's behavior data, to obtain the test user characteristic data after data duplication processing; A random forest algorithm is introduced, and classification training is performed on the test user feature data after repeated data processing through the random forest algorithm to obtain a random forest model that can construct a user portrait.

3. The method for intelligently publishing terminal information based on user characteristics according to claim 1, characterized in that: The S104 is specifically: Marking the user who needs to publish information as the target user, obtaining the terminal device of the target user, marking it as the target terminal device, and obtaining the target user feature data after repeated data processing based on the target terminal device and the terminal information intelligent publishing system; Importing the target user feature data after the repeated data processing into the random forest training model for data training and user portrait construction, obtaining a user portrait of the target user, and marking it as the target user portrait, wherein the target user portrait includes the target user's preference value for different subject contents, and the frequency of the target user reading the subject contents in different time periods; In the target user portrait, a preference threshold is preset, and the target user's preference values ​​for different subject contents are analyzed. If the target user's preference value for the subject content is greater than the preference threshold, the corresponding subject content is marked as a first-category subject content; if the target user's preference value for the subject content is less than the preference threshold, the corresponding subject content is marked as a second-category subject content; In the target user portrait, the frequency of target users reading subject content in different time periods is analyzed, and a standard reading frequency is preset. The time period when the target users read subject content more frequently than the standard reading frequency is marked as a publishable time period, and the time period when the target users read subject content less frequently than the standard reading frequency is marked as a publish-pending time period.

4. The method for intelligently publishing terminal information based on user characteristics according to claim 1, characterized in that: The S106 is specifically: Connecting the terminal information intelligent publishing system to the Internet, and controlling the terminal information intelligent publishing system to search for all information matching a type of subject content on the Internet, and marking them as a type of subject content information; Obtaining the update time of all the first-category subject content information, and based on the update time of all the first-category subject content information, filtering the first-category subject content information, selecting the first-category subject content information whose update time is today, and marking it as the latest first-category subject content information; Convert all the latest first-category subject content information into text information, mark it as first-category text information, introduce the TextRank algorithm, and use the TextRank algorithm to analyze all the first-category text information to construct a word co-occurrence relationship graph of different first-category text information; In a word co-occurrence relationship graph of a type of text information, the weights of all words are calculated, and a standard weight is preset. If the weight of a word is greater than the standard weight, the corresponding word is marked as a keyword; Introducing a keyword matching algorithm, based on which the Jaccard similarity of keywords of different types of text information is calculated, and presetting a Jaccard standard similarity threshold; Maintaining the Jaccard similarity of the keywords within the Jaccard standard similarity threshold, all the text information of the same category are collectively referred to as the same category of text information, and converting the same category of text information into the latest category of subject content information, and marking it as the same category of subject content information; The terminal information intelligent publishing system obtains the reading volume of all the latest subject content information on the Internet, presets a standard reading volume, selects the latest subject content information with a reading volume greater than the standard reading volume, and marks it as high-reading subject content information; Analyze the high-reading subject content information to obtain subject content information of the same type as the high-reading subject content information, and refer to the high-reading subject content information and subject content information of the same type as the high-reading subject content information as target release subject content information; A terminal information publishing strategy is executed in the terminal information intelligent publishing system, wherein the terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the target publishing subject content information to the target terminal device within the publishable time period.

5. The method for intelligently publishing terminal information based on user characteristics according to claim 1, characterized in that: The S108 is specifically: After executing a terminal information publishing strategy in the terminal information intelligent publishing system, feedback data of the target terminal device is obtained through the terminal information intelligent publishing system, wherein the feedback data of the target terminal device is the browsing time and browsing completeness of different target publishing subject content information on the target terminal device, and the browsing standard time and browsing minimum completeness are preset; If the browsing time and browsing completeness of the target published subject content information on the target terminal device are respectively greater than the browsing standard time and the browsing minimum completeness, the corresponding target published subject content information is marked as a type of published subject content information, and the subject content corresponding to the type of published subject content information is marked as a frequently browsed subject content; If the browsing completeness of the target published subject content information on the target terminal device is less than the minimum browsing completeness, or the browsing time is less than the browsing standard time, the corresponding target published subject content information is marked as the second-class published subject content information; and the subject content corresponding to the second-class published subject content information is marked as the infrequently browsed subject content. Respectively searching and obtaining information corresponding to frequently browsed subject contents and infrequently browsed subject contents on the Internet, and marking them as frequently browsed subject content information and infrequently browsed subject content information; The first-class terminal information publishing strategy is updated to obtain the second-class terminal information publishing strategy, wherein the second-class terminal information publishing strategy controls the terminal information intelligent publishing system to simultaneously publish the frequently browsed subject content information to the target terminal device within the publishable time period, and simultaneously publish the infrequently browsed subject content information to the target terminal device within the pending publishing time period.

6. The terminal information intelligent publishing system based on user characteristics is characterized by: The terminal information intelligent publishing system includes a memory and a processor, wherein the memory stores a terminal information intelligent publishing method program, and when the terminal information intelligent publishing method program is executed by the processor, the terminal information intelligent publishing method steps as described in any one of claims 1-5 are implemented.

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