WO2020010570A1 - Big data automatic analysis system based on consumer habits - Google Patents

Big data automatic analysis system based on consumer habits Download PDF

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Publication number
WO2020010570A1
WO2020010570A1 PCT/CN2018/095417 CN2018095417W WO2020010570A1 WO 2020010570 A1 WO2020010570 A1 WO 2020010570A1 CN 2018095417 W CN2018095417 W CN 2018095417W WO 2020010570 A1 WO2020010570 A1 WO 2020010570A1
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user
knowledge
data
module
attribute
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PCT/CN2018/095417
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French (fr)
Chinese (zh)
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陈钦鹏
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深圳齐心集团股份有限公司
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Priority to PCT/CN2018/095417 priority Critical patent/WO2020010570A1/en
Publication of WO2020010570A1 publication Critical patent/WO2020010570A1/en

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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06QINFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00Commerce
    • G06Q30/06Buying, selling or leasing transactions

Definitions

  • the invention relates to a method for implementing big data analysis based on consumer habits.
  • big data has more comprehensive characteristics. From the data dimension, time dimension, spatial dimension, and cross-border data, they all converge. The value of big data is reflected in more accurate and personalized services for customers.
  • big data has great value potential in artificial intelligence such as language, vision, and prediction.For example, behavior analysis based on consumer habits can give Users bring many significant conveniences, which can provide customers with precise and extreme personalized services.
  • an automatic big data analysis system based on consumer habits includes a server and a plurality of mobile terminals connected to the server.
  • the server is provided with at least: a user for collecting mobile terminals.
  • a data collection module for habit data a data analysis module for analyzing user habit data, a reception request module for receiving a request from a mobile terminal, and a judgment module for determining a request from a mobile terminal based on the data analysis module.
  • the server performs the following operations:
  • the data collection module collects and stores mobile terminal user habits data in real time when the server interacts with the mobile terminal;
  • the data analysis module analyzes user habit data and forms a related knowledge set.
  • the related knowledge set is: classify the user habit data and perform deep mining for each category to form a knowledge set, and then perform an association calculation on each knowledge set to form a related knowledge set. ;
  • the receiving request module acquires a request sent by a mobile terminal; specifically, the receiving request module acquires multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set;
  • the judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result;
  • the feedback module feeds back the result of the judgment module to the mobile terminal.
  • the data analysis module analyzes user habit data and forms a related knowledge set.
  • the related knowledge set is: classify the user habit data and perform in-depth mining for each category to form a knowledge set, and then calculate each knowledge set through association calculation.
  • Related knowledge sets including:
  • the user habit data includes the user subjective attribute set S and the user objective attribute set O; the user subjective attribute set S is recorded as ⁇ S1, S2, ..., Sm ⁇ , and m is the number of subjective attributes of the user subjective attribute set S, Among them, the attribute characteristic value of Si is recorded as ⁇ si1, si2, ..., sit ⁇ , 1 ⁇ i ⁇ m, t is the user's subjective attribute set S is a natural number, which refers to the number of attribute characteristic values; the user's objective attribute set O Denote as ⁇ O1, O2, ..., On ⁇ , n is the number of objective attributes of the user objective attribute set O, where the attribute characteristic value of Oj is denoted as ⁇ oj1, oj2, ..., ojr ⁇ , 1 ⁇ j ⁇ n, r is the number of attribute characteristic values of the user's subjective attribute set O, which is a natural number; wherein the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs; the user
  • the correlation calculation is performed on the knowledge set Si 'and the knowledge set Oj' to obtain the related knowledge set R.
  • the related knowledge set R is denoted as ⁇ R1, R2, ..., Ra ⁇ , and a is the value of the related knowledge set. The number of associated attributes.
  • the knowledge set Si 'and the knowledge set Oj' are classified separately, and the knowledge sets describing the same class are associated.
  • the subjective weight value can be customized by the user, and the system sets the initial value of subjective weight value ⁇ to ⁇ 0, ⁇ 0 ⁇ [0,1], if the user defines it, the system setting value will be replaced by the delta ⁇ . If the user does not define it, it will be calculated by the system setting.
  • the obtaining of the request sent by the mobile terminal by the receiving request module is specifically that the receiving request module obtains multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set.
  • the judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result, specifically:
  • Classify the request (the classification rules are the same as the classification rules of the associated knowledge set) and get the classification results;
  • the judgment result may be a related knowledge set with the highest matching degree, or may be multiple related knowledge sets listed in order according to the matching degree from high to low.
  • the present invention has the following advantages:
  • user subjective attribute set S is used In describing the user's preferences, the user's psychological needs can be understood; the user's objective attribute set O is used to describe the data feedback of the user's application using the mobile terminal, and can understand the user's frequently occurring behavior;
  • the subjective knowledge set Si 'and the objective knowledge set Oj' after mining are subjected to correlation calculation to obtain a further related knowledge set.
  • the judgment module judges a user's request, it is easy to pass the knowledge set Retrieve results quickly and intelligently.
  • Retrieval in the prior art generally uses scattered data for retrieval or evaluation of retrieval formulas to obtain better results.
  • the present invention classifies back-end data as a knowledge set one by one, which can not only speed up Retrieval speed, and can also accurately retrieve the customized information required by users, has a very good human experience.
  • the invention provides a big data automatic analysis system based on consumer habits.
  • the system includes a server and a plurality of mobile terminals connected to the server.
  • the server is provided with at least a data collection module for collecting user habits data of the mobile terminal.
  • a data analysis module for analyzing user habits data, a reception request module for receiving a request from a mobile terminal, a determination module for determining a request from a mobile terminal based on the data analysis module, and a feedback for a result of the determination module Feedback module for mobile terminal.
  • the server performs the following operations:
  • the data collection module collects and stores mobile terminal user habits data in real time when the server interacts with the mobile terminal;
  • the data analysis module analyzes user habit data and forms a related knowledge set.
  • the related knowledge set is: classify the user habit data and perform deep mining for each category to form a knowledge set, and then perform an association calculation on each knowledge set to form a related knowledge set. ;
  • the receiving request module acquires a request sent by a mobile terminal; specifically, the receiving request module acquires multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set;
  • the judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result;
  • the feedback module feeds back the result of the judgment module to the mobile terminal.
  • the data analysis module analyzes user habits data and forms a related knowledge set.
  • the related knowledge set is: classify the user habits data and perform deep mining for each category to form a knowledge set, and then calculate the association of each knowledge set to form a relationship.
  • Knowledge set including:
  • the user habit data includes the user subjective attribute set S and the user objective attribute set O; the user subjective attribute set S is recorded as ⁇ S1, S2, ..., Sm ⁇ , and m is the number of subjective attributes of the user subjective attribute set S, Among them, the attribute characteristic value of Si is recorded as ⁇ si1, si2, ..., sit ⁇ , 1 ⁇ i ⁇ m, t is the user's subjective attribute set S is a natural number, which refers to the number of attribute characteristic values; the user's objective attribute set O Denote as ⁇ O1, O2, ..., On ⁇ , n is the number of objective attributes of the user's objective attribute set O, where the attribute characteristic value of Oj is recorded as ⁇ oj1, oj2, ..., ojr ⁇ , 1 ⁇ j ⁇ n, r is the number of attribute characteristic values of the user's subjective attribute set O, which is a natural number; where the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs; the user'
  • the correlation calculation is performed on the knowledge set Si 'and the knowledge set Oj' to obtain the related knowledge set R.
  • the related knowledge set R is denoted as ⁇ R1, R2, ..., Ra ⁇ , and a is the value of the related knowledge set. The number of associated attributes.
  • the knowledge set Si 'and the knowledge set Oj' are classified separately, and the knowledge sets describing the same class are associated.
  • the subjective weight value can be customized by the user, and the system sets the initial value of subjective weight value ⁇ to ⁇ 0, ⁇ 0 ⁇ [0,1], if the user defines it, the system setting value will be replaced by the delta ⁇ . If the user does not define it, it will be calculated by the system setting.
  • the judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result, specifically:
  • Classify the request (the classification rules are the same as the classification rules of the associated knowledge set) and get the classification results;
  • the judgment result may be a related knowledge set with the highest matching degree, or may be multiple related knowledge sets listed in order according to the matching degree from high to low.
  • the calculation of the matching degree between the related knowledge set and the request is specifically: extracting the first w-th related knowledge set Rw, and matching the characteristic feature values of each attribute of the request Ck (1 ⁇ k ⁇ z) with Rw to obtain
  • the matching values are ⁇ match_ck1, match_ck2, ..., match_ckz ⁇
  • the comprehensive matching value match_w1 is the weighted average of each matching value.
  • the second w-type associated knowledge set Rw is extracted and the above operation is repeated to obtain match_w2 ... match_wz.
  • the comprehensive matching values match_w1, match_w2, ... match_wz are arranged according to the numerical value, and the corresponding related knowledge set is arranged in order from high to low as the judgment result.
  • User habits refer to a user's recurring behavior. Essentially, user habits include the frequency with which the user uses the product, and the user's determination of the utility of the product (perceivedutility).
  • the invention creatively divides the user habits into subjective and objective aspects, that is, the user's subjective attribute set S and the user's objective attribute set O, which can provide accurate personalized services for customer data mining, where subjective refers to the user's preference (Data that reflects the user ’s personality and preferences can be classified as subjective data. For example, when users browse news or articles or books, they often click pessimistic or full of righteousness, that is, subjective data.) Subjective data can understand the user's psychological needs.
  • Objective refers to the data feedback of the user's application using the mobile terminal (for example, the user often uses the application store APPStore to download the APP application, then the application store's data feedback is objective data), and the objective data can understand the user's recurring behavior;
  • the subjective knowledge set Si 'and the objective knowledge set Oj' after mining are subjected to correlation calculation to obtain a further related knowledge set.
  • the judgment module judges a user's request, it is easy to pass the knowledge set. Retrieve results quickly and intelligently.
  • the search in the prior art is generally decentralized data. For example, when a user searches for a "mobile phone", the retrieved result is generally a mobile phone encyclopedia and a mobile-related hot news.
  • a user search for a "mobile phone” is based on the user. Preference directly recommends a certain mobile phone and the related knowledge list of the mobile phone (the mobile phone's sales, praise rate, repair rate and other user data feedback), so as to provide customers with more accurate personalized services.
  • the invention not only improves the user experience mentioned above, but also improves the intelligent characteristics: when users search for problems, they no longer need to summarize their knowledge points by themselves (the search results in the prior art are one-to-one knowledge points that are not to be controlled. ), But directly gives the related knowledge set after induction, which has very good intelligent characteristics.
  • the method of the present invention is not limited to being performed in the chronological order described in the specification, but may also be performed in other chronological order, in parallel, or independently. Therefore, the execution order of the methods described in this specification does not limit the technical scope of the present invention.

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Abstract

A big data automatic analysis system based on consumer habits, the system comprising a server and a plurality of mobile terminals connected to the server; the server executes the following operations: a data collection module collects habit data of a mobile terminal user and stores said data in real time when the server interacts with a mobile terminal; a data analyzing module analyzes the habit data of the user and forms an associated knowledge set, the associated knowledge set being formed by: classifying the user habit data and deep mining each category to form knowledge sets, and then performing association calculation on each knowledge set to form the associated knowledge set; a request receiving module obtains a request sent by the mobile terminal; a determining module determines, according to the request received by the request receiving module, the knowledge set formed by the data analyzing module as well as a preset threshold condition, and forms a determination result; a feedback module feeds back the result of the determining module to the mobile terminal. By means of the described manner, more accurate personalized services may be provided to customers.

Description

一种基于消费者习惯的大数据自动分析系统Big data automatic analysis system based on consumer habits 技术领域Technical field
本发明涉及一种基于消费者习惯的大数据分析的实现方法。The invention relates to a method for implementing big data analysis based on consumer habits.
背景技术Background technique
随着科技、社会经济的迅猛发展,引发了大数据时代的到来,大数据的价值来源具有更加全面化的特点,从数据维度、时间维度、空间维度以及跨界的各种数据交汇在一起,大数据的价值体现在更为极致的为客户提供精准的个性化服务,此外,大数据在语言、视觉、预测等人工智能方面拥有巨大的价值潜力,例如针对消费者习惯进行行为分析,可给使用者带来诸多显著便利,可以为客户提供精准的极致的个性化服务。With the rapid development of science and technology, society and economy, the era of big data has begun. The value source of big data has more comprehensive characteristics. From the data dimension, time dimension, spatial dimension, and cross-border data, they all converge. The value of big data is reflected in more accurate and personalized services for customers.In addition, big data has great value potential in artificial intelligence such as language, vision, and prediction.For example, behavior analysis based on consumer habits can give Users bring many significant conveniences, which can provide customers with precise and extreme personalized services.
另一方面,随着社会的不断进步,商品化社会程度不断提高,居民的消费能力不断提升,而信息社会的不断进步导致居民的消费方式的多样性和多选择性,这样对于商贸行业的挑战将更激烈。如何判断终端消费者的消费习惯,从而制订出自己的商品销售策略成了当务之急。On the other hand, with the continuous progress of society, the degree of a commoditized society has continued to increase, and the consumption power of residents has continued to increase, while the continuous progress of the information society has led to the diversity and multi-selection of residents' consumption patterns, which poses a challenge to the commerce industry Will be more intense. How to judge the consumption habits of end consumers, and thus formulate their own product sales strategies has become a top priority.
发明内容Summary of the invention
在下文中给出了关于本发明实施例的简要概述,以便提供关于本发明的某些方面的基本理解。应当理解,以下概述并不是关于本发明的穷举性概述。它并不是意图确定本发明的关键或重要部分,也不是意图限定本发明的范围。其目的仅仅是以简化的形式给出某些概念,以此作为稍后论述的更详细描述的前序。A brief overview of embodiments of the invention is given below in order to provide a basic understanding of certain aspects of the invention. It should be understood that the following summary is not an exhaustive overview of the invention. It is not intended to identify key or important parts of the invention, nor is it intended to limit the scope of the invention. Its purpose is merely to present some concepts in a simplified form as a prelude to the more detailed description that is discussed later.
根据本申请的一个方面,提供一种基于消费者习惯的大数据自动分析系统,该系统包括服务器以及与服务器连接的多个移动终端;所述服务器上至少 设有:用于收集移动终端的用户习惯数据的数据收集模块、用于对用户习惯数据进行分析的数据分析模块、用于接收移动终端的请求的接收请求模块、用于依据数据分析模块对移动终端的请求进行判断的判断模块,用于将判断模块的结果反馈给移动终端的反馈模块;所述服务器执行如下操作:According to one aspect of the present application, an automatic big data analysis system based on consumer habits is provided. The system includes a server and a plurality of mobile terminals connected to the server. The server is provided with at least: a user for collecting mobile terminals. A data collection module for habit data, a data analysis module for analyzing user habit data, a reception request module for receiving a request from a mobile terminal, and a judgment module for determining a request from a mobile terminal based on the data analysis module. To feedback the result of the judgment module to the feedback module of the mobile terminal; the server performs the following operations:
数据收集模块在服务器与移动终端交互时,实时收集移动终端用户习惯数据并存储;The data collection module collects and stores mobile terminal user habits data in real time when the server interacts with the mobile terminal;
数据分析模块对用户习惯数据进行分析并形成关联知识集,关联知识集是:将用户习惯数据分类并分别针对每一类别进行深度挖掘形成知识集合,然后将各知识集合进行关联计算形成关联知识集;The data analysis module analyzes user habit data and forms a related knowledge set. The related knowledge set is: classify the user habit data and perform deep mining for each category to form a knowledge set, and then perform an association calculation on each knowledge set to form a related knowledge set. ;
接收请求模块获取移动终端发送的请求;具体是,接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集;The receiving request module acquires a request sent by a mobile terminal; specifically, the receiving request module acquires multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set;
判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及预设的阈值条件进行判断,并形成判断结果;The judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result;
反馈模块将判断模块的结果反馈给移动终端。The feedback module feeds back the result of the judgment module to the mobile terminal.
进一步的,数据分析模块对用户习惯数据进行分析并形成关联知识集,关联知识集是:将用户习惯数据分类并分别针对每一类别进行深度挖掘形成知识集合,然后将各知识集合进行关联计算形成关联知识集,具体包括:Further, the data analysis module analyzes user habit data and forms a related knowledge set. The related knowledge set is: classify the user habit data and perform in-depth mining for each category to form a knowledge set, and then calculate each knowledge set through association calculation. Related knowledge sets, including:
将用户习惯数据包括用户主观属性集S和用户客观属性集O;用户主观属性集S记为{S1,S2,...,Sm},m为用户主观属性集S的主观属性的个数,其中,Si的属性特征值记为{si1,si2,...,sit},1<i<m,t为用户主观属性集S为自然数,指属性特征值的个数;用户客观属性集O记为{O1,O2,..., On},n为用户客观属性集O的客观属性的个数,其中,Oj的属性特征值记为{oj1,oj2,...,ojr},1<j<n,r为用户主观属性集O的属性特征值的个数,为自然数;其中,所述用户主观属性集S用于描述用户的偏好,了解用户的心理需求;用户客观属性集O是指用户使用移动终端的应用的数据反馈,了解用户经常性发生的行为。The user habit data includes the user subjective attribute set S and the user objective attribute set O; the user subjective attribute set S is recorded as {S1, S2, ..., Sm}, and m is the number of subjective attributes of the user subjective attribute set S, Among them, the attribute characteristic value of Si is recorded as {si1, si2, ..., sit}, 1 <i <m, t is the user's subjective attribute set S is a natural number, which refers to the number of attribute characteristic values; the user's objective attribute set O Denote as {O1, O2, ..., On}, n is the number of objective attributes of the user objective attribute set O, where the attribute characteristic value of Oj is denoted as {oj1, oj2, ..., ojr}, 1 <j <n, r is the number of attribute characteristic values of the user's subjective attribute set O, which is a natural number; wherein the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs; the user's objective attribute set O It refers to the data feedback of the user using the application of the mobile terminal to understand the user's frequently occurring behavior.
针对用户主观属性集S的每一个属性Si进行深度挖掘,将Si的属性特征值{si1,si2,...,sit}按照预设的规则进行整合,形成知识集Si’,Si’={si1’,si2’,...,sit’};Deeply mine each attribute Si of the user's subjective attribute set S, and integrate the attribute characteristic values of Si {si1, si2, ..., sit} according to preset rules to form a knowledge set Si ', Si' = { si1 ', si2', ..., sit '};
针对用户客观属性集O的每一个属性Oj进行深度挖掘,将Oj的属性特征值{oj1,oj2,...,ojr}按照预设的规则进行整合,形成知识集Oj’,Oj’={oj1’,oj2’,...,ojt’};Perform deep mining for each attribute Oj of the user's objective attribute set O, and integrate the attribute characteristic values {oj1, oj2, ..., ojr} of Oj according to preset rules to form a knowledge set Oj ', Oj' = { oj1 ', oj2', ..., ojt '};
根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算,获得关联知识集R,关联知识集R记为{R1,R2,...,Ra},a为关联知识集的关联属性的个数。According to the preset knowledge rules, the correlation calculation is performed on the knowledge set Si 'and the knowledge set Oj' to obtain the related knowledge set R. The related knowledge set R is denoted as {R1, R2, ..., Ra}, and a is the value of the related knowledge set. The number of associated attributes.
其中,根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算具体是:The calculation of the correlation between the knowledge set Si ′ and the knowledge set Oj ’according to a preset knowledge rule is specifically:
将知识集Si’和知识集Oj’分别进行分类,将描述同一类的知识集进行关联,其关联算法是:预设知识集Si’的主观权重值为δ,δ∈[0,1],则知识集Oj’的客观权重值为1-δ,则第i类关联知识集Ri(1<i<a)=δ∑Si’+(1-δ)∑Oj’,∑Si’为同属于第i类主观知识集的求和,∑Oj’为同属于第i类客观知识集的求和;此外,主观权重值可由用户自定义,系统设定主观权重值δ的初始值为δ0,δ0∈[0,1],如果用户自定义则由顶以后的δ替换系统设定值,如果用户没有自定义,则由系统设定的来计算。The knowledge set Si 'and the knowledge set Oj' are classified separately, and the knowledge sets describing the same class are associated. The association algorithm is: preset the subjective weight value of the knowledge set Si 'is δ, δ ∈ [0,1], Then the objective weight value of the knowledge set Oj 'is 1-δ, then the i-type associated knowledge set Ri (1 <i <a) = δΣSi' + (1-δ) ΣOj ', ΣSi' belong to the same category Sum of category i subjective knowledge set, ΣOj 'is the sum of category i objective knowledge set; In addition, the subjective weight value can be customized by the user, and the system sets the initial value of subjective weight value δ to δ0, δ0 ∈ [0,1], if the user defines it, the system setting value will be replaced by the delta δ. If the user does not define it, it will be calculated by the system setting.
进一步的,接收请求模块获取移动终端发送的请求具体是,接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳为,组合成关联请求集。Further, the obtaining of the request sent by the mobile terminal by the receiving request module is specifically that the receiving request module obtains multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set.
判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及预设的阈值条件进行判断,并形成判断结果,具体是:The judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result, specifically:
接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集A,A={A1,A2,…,Az};Ck的属性特征值记为{ck1,ck2,…,ckz},1<k<z,z为Ck的属性特征值的个数;The receiving request module obtains multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set A, A = {A1, A2,…, Az}; the attribute characteristic value of Ck is recorded {Ck1, ck2,…, ckz}, 1 <k <z, where z is the number of attribute eigenvalues of Ck;
将请求进行分类(分类规则与关联知识集的分类规则相同),得到分类结果;Classify the request (the classification rules are the same as the classification rules of the associated knowledge set) and get the classification results;
获得关联知识集R中与请求的分类结果相同的多个关联知识集;Obtain multiple related knowledge sets in the related knowledge set R that are the same as the requested classification result;
分别计算多个关联知识集与请求的匹配度,得到判断结果。其中,判断结果可以是匹配度最高的一个关联知识集,也可以是按匹配度从高到低依次排列得到的多个顺序罗列的关联知识集。Calculate the matching degree between multiple related knowledge sets and the request, and get the judgment result. Among them, the judgment result may be a related knowledge set with the highest matching degree, or may be multiple related knowledge sets listed in order according to the matching degree from high to low.
本发明与现有技术相比,具有如下优点:Compared with the prior art, the present invention has the following advantages:
1、创造性的将用户习惯分为主观和客观两方面,也就是用户主观属性集S和用户客观属性集O,可为客户的数据挖掘提供精准的个性化服务,其中,用户主观属性集S用于描述用户的偏好,可了解用户的心理需求;用户客观属性集O用于描述用户使用移动终端的应用的数据反馈,可了解用户经常性发生的行为;1. Creatively divide user habits into subjective and objective aspects, that is, user subjective attribute set S and user objective attribute set O, which can provide accurate personalized services for customer data mining. Among them, user subjective attribute set S is used In describing the user's preferences, the user's psychological needs can be understood; the user's objective attribute set O is used to describe the data feedback of the user's application using the mobile terminal, and can understand the user's frequently occurring behavior;
2、本发明将挖掘后的主观知识集Si’和客观知识集Oj’进行关联计算,可得到进一步关联的知识集的集合,当判断模块对用户的请求进行判断时,则很 容易通过知识集的检索快速而智能的获得结果。现有技术中的检索,一般为分散的数据进行检索,或者是对检索式进行评判来获得更佳的结果,而本发明是将后端的数据进行分类作为一个一个的知识集存在,不仅可以加快检索速度,而且还能精确检索到用户所需要的定制化信息,具有非常好的人性化体验。2. According to the present invention, the subjective knowledge set Si 'and the objective knowledge set Oj' after mining are subjected to correlation calculation to obtain a further related knowledge set. When the judgment module judges a user's request, it is easy to pass the knowledge set Retrieve results quickly and intelligently. Retrieval in the prior art generally uses scattered data for retrieval or evaluation of retrieval formulas to obtain better results. The present invention classifies back-end data as a knowledge set one by one, which can not only speed up Retrieval speed, and can also accurately retrieve the customized information required by users, has a very good human experience.
具体实施方式detailed description
下面将说明本发明的实施例。Embodiments of the present invention will be described below.
本发明提供一种基于消费者习惯的大数据自动分析系统,该系统包括服务器以及与服务器连接的多个移动终端;服务器上至少设有:用于收集移动终端的用户习惯数据的数据收集模块、用于对用户习惯数据进行分析的数据分析模块、用于接收移动终端的请求的接收请求模块、用于依据数据分析模块对移动终端的请求进行判断的判断模块,用于将判断模块的结果反馈给移动终端的反馈模块。The invention provides a big data automatic analysis system based on consumer habits. The system includes a server and a plurality of mobile terminals connected to the server. The server is provided with at least a data collection module for collecting user habits data of the mobile terminal. A data analysis module for analyzing user habits data, a reception request module for receiving a request from a mobile terminal, a determination module for determining a request from a mobile terminal based on the data analysis module, and a feedback for a result of the determination module Feedback module for mobile terminal.
其中,服务器执行如下操作:The server performs the following operations:
数据收集模块在服务器与移动终端交互时,实时收集移动终端用户习惯数据并存储;The data collection module collects and stores mobile terminal user habits data in real time when the server interacts with the mobile terminal;
数据分析模块对用户习惯数据进行分析并形成关联知识集,关联知识集是:将用户习惯数据分类并分别针对每一类别进行深度挖掘形成知识集合,然后将各知识集合进行关联计算形成关联知识集;The data analysis module analyzes user habit data and forms a related knowledge set. The related knowledge set is: classify the user habit data and perform deep mining for each category to form a knowledge set, and then perform an association calculation on each knowledge set to form a related knowledge set. ;
接收请求模块获取移动终端发送的请求;具体是,接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集;The receiving request module acquires a request sent by a mobile terminal; specifically, the receiving request module acquires multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set;
判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及 预设的阈值条件进行判断,并形成判断结果;The judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result;
反馈模块将判断模块的结果反馈给移动终端。The feedback module feeds back the result of the judgment module to the mobile terminal.
其中,数据分析模块对用户习惯数据进行分析并形成关联知识集,关联知识集是:将用户习惯数据分类并分别针对每一类别进行深度挖掘形成知识集合,然后将各知识集合进行关联计算形成关联知识集,具体包括:Among them, the data analysis module analyzes user habits data and forms a related knowledge set. The related knowledge set is: classify the user habits data and perform deep mining for each category to form a knowledge set, and then calculate the association of each knowledge set to form a relationship. Knowledge set, including:
将用户习惯数据包括用户主观属性集S和用户客观属性集O;用户主观属性集S记为{S1,S2,...,Sm},m为用户主观属性集S的主观属性的个数,其中,Si的属性特征值记为{si1,si2,...,sit},1<i<m,t为用户主观属性集S为自然数,指属性特征值的个数;用户客观属性集O记为{O1,O2,...,On},n为用户客观属性集O的客观属性的个数,其中,Oj的属性特征值记为{oj1,oj2,...,ojr},1<j<n,r为用户主观属性集O的属性特征值的个数,为自然数;其中,用户主观属性集S用于描述用户的偏好,了解用户的心理需求;用户客观属性集O是指用户使用移动终端的应用的数据反馈,了解用户经常性发生的行为。The user habit data includes the user subjective attribute set S and the user objective attribute set O; the user subjective attribute set S is recorded as {S1, S2, ..., Sm}, and m is the number of subjective attributes of the user subjective attribute set S, Among them, the attribute characteristic value of Si is recorded as {si1, si2, ..., sit}, 1 <i <m, t is the user's subjective attribute set S is a natural number, which refers to the number of attribute characteristic values; the user's objective attribute set O Denote as {O1, O2, ..., On}, n is the number of objective attributes of the user's objective attribute set O, where the attribute characteristic value of Oj is recorded as {oj1, oj2, ..., ojr}, 1 <j <n, r is the number of attribute characteristic values of the user's subjective attribute set O, which is a natural number; where the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs; the user's objective attribute set O refers to The user uses the data feedback of the mobile terminal application to understand the user's recurring behavior.
针对用户主观属性集S的每一个属性Si进行深度挖掘,将Si的属性特征值{si1,si2,...,sit}按照预设的规则进行整合,形成知识集Si’,Si’={si1’,si2’,...,sit’};Deeply mine each attribute Si of the user's subjective attribute set S, and integrate the attribute characteristic values of Si {si1, si2, ..., sit} according to preset rules to form a knowledge set Si ', Si' = { si1 ', si2', ..., sit '};
针对用户客观属性集O的每一个属性Oj进行深度挖掘,将Oj的属性特征值{oj1,oj2,...,ojr}按照预设的规则进行整合,形成知识集Oj’,Oj’={oj1’,oj2’,...,ojt’};Perform deep mining for each attribute Oj of the user's objective attribute set O, and integrate the attribute characteristic values {oj1, oj2, ..., ojr} of Oj according to preset rules to form a knowledge set Oj ', Oj' = { oj1 ', oj2', ..., ojt '};
根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算,获得关联知识集R,关联知识集R记为{R1,R2,...,Ra},a为关联知识集的关联属性 的个数。According to the preset knowledge rules, the correlation calculation is performed on the knowledge set Si 'and the knowledge set Oj' to obtain the related knowledge set R. The related knowledge set R is denoted as {R1, R2, ..., Ra}, and a is the value of the related knowledge set. The number of associated attributes.
其中,根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算具体是:The calculation of the correlation between the knowledge set Si ′ and the knowledge set Oj ’according to a preset knowledge rule is specifically:
将知识集Si’和知识集Oj’分别进行分类,将描述同一类的知识集进行关联,其关联算法是:预设知识集Si’的主观权重值为δ,δ∈[0,1],则知识集Oj’的客观权重值为1-δ,则第i类关联知识集Ri(1<i<a)=δ∑Si’+(1-δ)∑Oj’,∑Si’为同属于第i类主观知识集的求和,∑Oj’为同属于第i类客观知识集的求和;此外,主观权重值可由用户自定义,系统设定主观权重值δ的初始值为δ0,δ0∈[0,1],如果用户自定义则由顶以后的δ替换系统设定值,如果用户没有自定义,则由系统设定的来计算。The knowledge set Si 'and the knowledge set Oj' are classified separately, and the knowledge sets describing the same class are associated. The association algorithm is: preset the subjective weight value of the knowledge set Si 'is δ, δ ∈ [0,1], Then the objective weight value of the knowledge set Oj 'is 1-δ, then the i-type associated knowledge set Ri (1 <i <a) = δΣSi' + (1-δ) ΣOj ', ΣSi' belong to the same category Sum of category i subjective knowledge set, ΣOj 'is the sum of category i objective knowledge set; In addition, the subjective weight value can be customized by the user, and the system sets the initial value of subjective weight value δ to δ0, δ0 ∈ [0,1], if the user defines it, the system setting value will be replaced by the delta δ. If the user does not define it, it will be calculated by the system setting.
判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及预设的阈值条件进行判断,并形成判断结果,具体是:The judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result, specifically:
接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集A,A={A1,A2,…,Az};Ck的属性特征值记为{ck1,ck2,…,ckz},1<k<z,z为Ck的属性特征值的个数;The receiving request module obtains multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set A, A = {A1, A2,…, Az}; the attribute characteristic value of Ck is recorded {Ck1, ck2,…, ckz}, 1 <k <z, where z is the number of attribute eigenvalues of Ck;
将请求进行分类(分类规则与关联知识集的分类规则相同),得到分类结果;Classify the request (the classification rules are the same as the classification rules of the associated knowledge set) and get the classification results;
获得关联知识集R中与请求的分类结果相同的多个关联知识集;Obtain multiple related knowledge sets in the related knowledge set R that are the same as the requested classification result;
分别计算多个关联知识集与请求的匹配度,得到判断结果。其中,判断结果可以是匹配度最高的一个关联知识集,也可以是按匹配度从高到低依次排列得到的多个顺序罗列的关联知识集。Calculate the matching degree between multiple related knowledge sets and the request, and get the judgment result. Among them, the judgment result may be a related knowledge set with the highest matching degree, or may be multiple related knowledge sets listed in order according to the matching degree from high to low.
其中,计算关联知识集与请求的匹配度具体是:将第一个第w类关联知识集Rw提取出来,将请求Ck(1<k<z)的各属性特征值分别与Rw进行匹配, 获得分别匹配值为{match_ck1,match_ck2,…,match_ckz},计算综合匹配值match_w1为各匹配值的加权平均值;提取第二个第w类关联知识集Rw重复上述运算,得到match_w2……match_wz,将综合匹配值match_w1、match_w2……match_wz按照数值大小排列,将其对应的关联知识集按按匹配度从高到低依次排列作为判断结果。The calculation of the matching degree between the related knowledge set and the request is specifically: extracting the first w-th related knowledge set Rw, and matching the characteristic feature values of each attribute of the request Ck (1 <k <z) with Rw to obtain The matching values are {match_ck1, match_ck2, ..., match_ckz}, and the comprehensive matching value match_w1 is the weighted average of each matching value. The second w-type associated knowledge set Rw is extracted and the above operation is repeated to obtain match_w2 ... match_wz. The comprehensive matching values match_w1, match_w2, ... match_wz are arranged according to the numerical value, and the corresponding related knowledge set is arranged in order from high to low as the judgment result.
用户习惯是指用户的一种经常性发生的行为,从本质上用户习惯涉及的要素包括用户使用产品的频率(frequency)、用户对产品实用性的认定(perceivedutility)。本发明创造性的将用户习惯分为主观和客观两方面,也就是用户主观属性集S和用户客观属性集O,可为客户的数据挖掘提供精准的个性化服务,其中,主观是指用户的偏好(反应用户的性格、喜好的数据均可归类为主观数据,例如用户在浏览新闻或者文章或者图书时经常点击悲观类或者充满正气类,即为主观数据),主观数据可了解用户的心理需求;客观是指用户使用移动终端的应用的数据反馈(例如用户经常使用应用商店APPStore下载APP应用,那么应用商店APPStore的数据反馈则为客观数据),由客观数据可了解用户经常性发生的行为;同时本发明将挖掘后的主观知识集Si’和客观知识集Oj’进行关联计算,可得到进一步关联的知识集的集合,当判断模块对用户的请求进行判断时,则很容易通过知识集的检索快速而智能的获得结果。现有技术中的检索,一般为分散的数据,例如用户检索“手机”,则检索出来的结果一般是手机百科,手机相关的热点新闻,而本发明中,用户检索“手机”则会根据用户的偏好直接推荐某某款手机以及相关手机的知识集列表(该手机的销量、好评率、报修率等等用户数据反馈),从而为客户提供更加精确的个性化服务。User habits refer to a user's recurring behavior. Essentially, user habits include the frequency with which the user uses the product, and the user's determination of the utility of the product (perceivedutility). The invention creatively divides the user habits into subjective and objective aspects, that is, the user's subjective attribute set S and the user's objective attribute set O, which can provide accurate personalized services for customer data mining, where subjective refers to the user's preference (Data that reflects the user ’s personality and preferences can be classified as subjective data. For example, when users browse news or articles or books, they often click pessimistic or full of righteousness, that is, subjective data.) Subjective data can understand the user's psychological needs. ; Objective refers to the data feedback of the user's application using the mobile terminal (for example, the user often uses the application store APPStore to download the APP application, then the application store's data feedback is objective data), and the objective data can understand the user's recurring behavior; At the same time, according to the present invention, the subjective knowledge set Si 'and the objective knowledge set Oj' after mining are subjected to correlation calculation to obtain a further related knowledge set. When the judgment module judges a user's request, it is easy to pass the knowledge set. Retrieve results quickly and intelligently. The search in the prior art is generally decentralized data. For example, when a user searches for a "mobile phone", the retrieved result is generally a mobile phone encyclopedia and a mobile-related hot news. In the present invention, a user search for a "mobile phone" is based on the user. Preference directly recommends a certain mobile phone and the related knowledge list of the mobile phone (the mobile phone's sales, praise rate, repair rate and other user data feedback), so as to provide customers with more accurate personalized services.
本发明不仅提高了上述提到的用户体验,还提高了智能化特性:当用户在 检索问题时不再需要自己将知识点归纳总结(现有技术中的检索结果为一个个不想管的知识点),而是直接给出经过归纳后的关联知识集,具有非常好的智能化特点。The invention not only improves the user experience mentioned above, but also improves the intelligent characteristics: when users search for problems, they no longer need to summarize their knowledge points by themselves (the search results in the prior art are one-to-one knowledge points that are not to be controlled. ), But directly gives the related knowledge set after induction, which has very good intelligent characteristics.
应该强调,术语“包括/包含”在本文使用时指特征、要素、步骤或组件的存在,但并不排除一个或更多个其它特征、要素、步骤或组件的存在或附加。It should be emphasized that the term "including / comprising" as used herein refers to the presence of a feature, element, step or component, but does not exclude the presence or addition of one or more other features, elements, steps or components.
此外,本发明的方法不限于按照说明书中描述的时间顺序来执行,也可以按照其他的时间顺序地、并行地或独立地执行。因此,本说明书中描述的方法的执行顺序不对本发明的技术范围构成限制。In addition, the method of the present invention is not limited to being performed in the chronological order described in the specification, but may also be performed in other chronological order, in parallel, or independently. Therefore, the execution order of the methods described in this specification does not limit the technical scope of the present invention.
尽管上面已经通过对本发明的具体实施例的描述对本发明进行了披露,但是,应该理解,上述的所有实施例和示例均是示例性的,而非限制性的。本领域的技术人员可在所附权利要求的精神和范围内设计对本发明的各种修改、改进或者等同物。这些修改、改进或者等同物也应当被认为包括在本发明的保护范围内。Although the present invention has been disclosed above by describing specific embodiments of the present invention, it should be understood that all the embodiments and examples described above are exemplary and not restrictive. Those skilled in the art may design various modifications, improvements, or equivalents to the present invention within the spirit and scope of the appended claims. These modifications, improvements or equivalents should also be considered to be included in the protection scope of the present invention.

Claims (8)

  1. 一种基于消费者习惯的大数据自动分析系统,其特征在于:该系统包括服务器以及与服务器连接的多个移动终端;An automatic big data analysis system based on consumer habits, which is characterized in that the system includes a server and a plurality of mobile terminals connected to the server;
    所述服务器上至少设有:用于收集移动终端的用户习惯数据的数据收集模块、用于对用户习惯数据进行分析的数据分析模块、用于接收移动终端的请求的接收请求模块、用于依据数据分析模块对移动终端的请求进行判断的判断模块,用于将判断模块的结果反馈给移动终端的反馈模块;The server is provided with at least: a data collection module for collecting user habits data of the mobile terminal, a data analysis module for analyzing user habits data, a receiving request module for receiving requests from the mobile terminal, and A data analysis module, a judgment module that judges a request of a mobile terminal, and is configured to feed back the result of the judgment module to a feedback module of the mobile terminal;
    所述服务器执行如下操作:The server performs the following operations:
    数据收集模块在服务器与移动终端交互时,实时收集移动终端用户习惯数据并存储;The data collection module collects and stores mobile terminal user habits data in real time when the server interacts with the mobile terminal;
    数据分析模块对用户习惯数据进行分析并形成关联知识集,关联知识集是:将用户习惯数据分类并分别针对每一类别进行深度挖掘形成知识集合,然后将各知识集合进行关联计算形成关联知识集;The data analysis module analyzes user habit data and forms a related knowledge set. The related knowledge set is: classify the user habit data and perform deep mining for each category to form a knowledge set, and then perform an association calculation on each knowledge set to form a related knowledge set. ;
    接收请求模块获取移动终端发送的请求;The receiving request module obtains a request sent by a mobile terminal;
    判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及预设的阈值条件进行判断,并形成判断结果;The judgment module makes a judgment according to the request received by the reception request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms a judgment result;
    反馈模块将判断模块的结果反馈给移动终端。The feedback module feeds back the result of the judgment module to the mobile terminal.
  2. 根据权利要求1所述的基于消费者习惯的大数据自动分析系统,其特征在于:The big data automatic analysis system based on consumer habits according to claim 1, characterized in that:
    所述用户习惯数据包括用户主观属性集S和用户客观属性集O;The user habit data includes a user subjective attribute set S and a user objective attribute set O;
    用户主观属性集S记为{S1,S2,...,Sm},m为用户主观属性集S的主观属性的个数,其中,Si的属性特征值记为{si1,si2,...,sit},1<i<m,t为用户 主观属性集S为自然数,指属性特征值的个数;The user subjective attribute set S is recorded as {S1, S2, ..., Sm}, and m is the number of subjective attributes of the user subjective attribute set S. Among them, the attribute characteristic value of Si is recorded as {si1, si2, ... , Sit}, 1 <i <m, t is the user's subjective attribute set S is a natural number, which refers to the number of attribute characteristic values;
    用户客观属性集O记为{O1,O2,...,On},n为用户客观属性集O的客观属性的个数,其中,Oj的属性特征值记为{oj1,oj2,...,ojr},1<j<n,r为用户主观属性集O的属性特征值的个数,为自然数。The user objective attribute set O is recorded as {O1, O2, ..., On}, and n is the number of objective attributes of the user objective attribute set O. Among them, the attribute characteristic value of Oj is recorded as {oj1, oj2, ... , Ojr}, 1 <j <n, r is the number of attribute characteristic values of the user's subjective attribute set O, and is a natural number.
  3. 根据权利要求2所述的基于消费者习惯的大数据自动分析系统,其特征在于:The big data automatic analysis system based on consumer habits according to claim 2, characterized in that:
    数据分析模块对用户习惯数据进行分析并形成关联知识集,具体包括:The data analysis module analyzes user habits data and forms related knowledge sets, including:
    针对用户主观属性集S的每一个属性Si进行深度挖掘,将Si的属性特征值{si1,si2,...,sit}按照预设的规则进行整合,形成知识集Si’,Si’={si1’,si2’,...,sit’};Deeply mine each attribute Si of the user's subjective attribute set S, and integrate the attribute characteristic values of Si {si1, si2, ..., sit} according to preset rules to form a knowledge set Si ', Si' = { si1 ', si2', ..., sit '};
    针对用户客观属性集O的每一个属性Oj进行深度挖掘,将Oj的属性特征值{oj1,oj2,...,ojr}按照预设的规则进行整合,形成知识集Oj’,Oj’={oj1’,oj2’,...,ojt’};Perform deep mining for each attribute Oj of the user's objective attribute set O, and integrate the attribute characteristic values {oj1, oj2, ..., ojr} of Oj according to preset rules to form a knowledge set Oj ', Oj' = { oj1 ', oj2', ..., ojt '};
    根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算,获得关联知识集R,关联知识集R记为{R1,R2,...,Ra},a为关联知识集的关联属性的个数。According to the preset knowledge rules, the correlation calculation is performed on the knowledge set Si 'and the knowledge set Oj' to obtain the related knowledge set R. The related knowledge set R is denoted as {R1, R2, ..., Ra}, and a is the value of the related knowledge set. The number of associated attributes.
  4. 根据权利要求3所述的基于消费者习惯的大数据自动分析系统,其特征在于:所述用户主观属性集S用于描述用户的偏好,了解用户的心理需求;用户客观属性集O是指用户使用移动终端的应用的数据反馈,了解用户经常性发生的行为。The big data automatic analysis system based on consumer habits according to claim 3, characterized in that: the user's subjective attribute set S is used to describe the user's preferences and understand the user's psychological needs; the user's objective attribute set O refers to the user Use data feedback from mobile terminal applications to understand the recurring behaviors of users.
  5. 根据权利要求3或4所述的基于消费者习惯的大数据自动分析系统,其特征在于:根据预设的知识规则对知识集Si’和知识集Oj’进行关联计算具体是: 将知识集Si’和知识集Oj’分别进行分类,将描述同一类的知识集进行关联,其关联算法是:预设知识集Si’的主观权重值为δ,δ∈[0,1],则知识集Oj’的客观权重值为1-δ,则第i类关联知识集Ri(1<i<a)=δ∑Si’+(1-δ)∑Oj’,∑Si’为同属于第i类主观知识集的求和,∑Oj’为同属于第i类客观知识集的求和。The big data automatic analysis system based on consumer habits according to claim 3 or 4, characterized in that the correlation calculation of the knowledge set Si 'and the knowledge set Oj' according to a preset knowledge rule is specifically: 'And the knowledge set Oj' are classified separately, and the knowledge sets describing the same class are associated. The association algorithm is: preset the subjective weight value of the knowledge set Si 'δ, δ ∈ [0,1], then the knowledge set Oj 'The objective weight value is 1-δ, then the i-type related knowledge set Ri (1 <i <a) = δΣSi' + (1-δ) ΣOj ', ∑Si' is also subjective of the i-th category The sum of knowledge sets, ΣOj 'is the sum of objective knowledge sets belonging to the i category.
  6. 根据权利要求5所述的基于消费者习惯的大数据自动分析系统,其特征在于:接收请求模块获取移动终端发送的请求;具体是,接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集。The big data automatic analysis system based on consumer habits according to claim 5, characterized in that: the receiving request module acquires a request sent by the mobile terminal; and specifically, the receiving request module acquires a plurality of data sent by the mobile terminal within a preset time period. Requests and summarize the multiple requests into a set of associated requests.
  7. 根据权利要求6所述的基于消费者习惯的大数据自动分析系统,其特征在于:判断模块根据接收请求模块接收的请求、数据分析模块形成的知识集以及预设的阈值条件进行判断,并形成判断结果,具体是:The big data automatic analysis system based on consumer habits according to claim 6, characterized in that the judgment module judges according to the request received by the receiving request module, the knowledge set formed by the data analysis module, and a preset threshold condition, and forms The judgment results are:
    接收请求模块获取预设时间段内的移动终端发送的多个请求,并将该多个请求归纳组合成关联请求集A,A={A1,A2,…,Az};Ck的属性特征值记为{ck1,ck2,…,ckz},1<k<z,z为Ck的属性特征值的个数;The receiving request module obtains multiple requests sent by the mobile terminal within a preset time period, and summarizes the multiple requests into an association request set A, A = {A1, A2,…, Az}; the attribute characteristic value of Ck is recorded {Ck1, ck2,…, ckz}, 1 <k <z, where z is the number of attribute eigenvalues of Ck;
    将请求进行分类,得到分类结果;Classify the request and get the classification result;
    获得关联知识集R中与请求的分类结果相同的多个关联知识集;Obtain multiple related knowledge sets in the related knowledge set R that are the same as the requested classification result;
    分别计算多个关联知识集与请求的匹配度,得到判断结果。Calculate the matching degree between multiple related knowledge sets and the request, and get the judgment result.
  8. 根据权利要求7所述的基于消费者习惯的大数据自动分析系统,其特征在于:计算关联知识集与请求的匹配度具体是:The big data automatic analysis system based on consumer habits according to claim 7, characterized in that: calculating the matching degree between the associated knowledge set and the request is specifically:
    将第一个第w类关联知识集Rw提取出来,将请求Ck(1<k<z)的各属性特征值分别与Rw进行匹配,获得分别匹配值为{match_ck1,match_ck2,…,match_ckz},计算综合匹配值match_w1为各匹配 值的加权平均值;Extract the first w-th related knowledge set Rw, and match the characteristic feature values of each request Ck (1 <k <z) with Rw to obtain the respective matching values {match_ck1, match_ck2, ..., match_ckz}, Calculate the comprehensive matching value match_w1 as the weighted average of each matching value;
    提取第二个第w类关联知识集Rw重复上述运算,得到match_w2……match_wz;Extract the second w-type associated knowledge set Rw and repeat the above operation to obtain match_w2 ... match_wz;
    将综合匹配值match_w1、match_w2……match_wz按照数值大小排列。The comprehensive matching values match_w1, match_w2, ... match_wz are arranged according to the numerical value.
PCT/CN2018/095417 2018-07-12 2018-07-12 Big data automatic analysis system based on consumer habits WO2020010570A1 (en)

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