CN115098793B - User portrait analysis method and system based on big data - Google Patents

User portrait analysis method and system based on big data Download PDF

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CN115098793B
CN115098793B CN202210321101.3A CN202210321101A CN115098793B CN 115098793 B CN115098793 B CN 115098793B CN 202210321101 A CN202210321101 A CN 202210321101A CN 115098793 B CN115098793 B CN 115098793B
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陈应书
郭从仁
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Weimai Technology Co ltd
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Abstract

本发明提供的基于大数据的用户画像分析方法及系统,涉及大数据技术领域。在本发明中,针对每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息;针对多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度;基于多个设备用户中的每一个设备用户对应的用户画像匹配度,在多个设备用户中确定出目标设备用户,其中,确定出的每一个目标设备用户用于构建形成目标用户群体。基于上述方法,可以改善基于现有技术构建形成的用户群体的可靠度不佳的问题。

Figure 202210321101

The user portrait analysis method and system based on big data provided by the present invention relate to the technical field of big data. In the present invention, for each user terminal device, the user portrait information of the device user corresponding to the user terminal device is obtained; for each device user among the multiple device users corresponding to multiple user terminal devices, based on the device user corresponding The matching degree between the user portrait information and the predetermined target user portrait information is obtained to obtain the user portrait matching degree corresponding to the device user; based on the user portrait matching degree corresponding to each device user among multiple device users, in multiple Target device users are determined from the device users, wherein each determined target device user is used to construct a target user group. Based on the above method, the problem of poor reliability of the user group formed based on the prior art can be improved.

Figure 202210321101

Description

基于大数据的用户画像分析方法及系统User portrait analysis method and system based on big data

技术领域technical field

本发明涉及大数据技术领域,具体而言,涉及一种基于大数据的用户画像分析方法及系统。The present invention relates to the technical field of big data, in particular, to a user portrait analysis method and system based on big data.

背景技术Background technique

随着互联网技术和计算机技术的不断发展,使得用户的网络行为越来越大,如此,基于用户的网络行为数据来对用户进行分类已经成为了一种重要的应用,例如,可以基于用户的网络行为数据之间的相似性,来对不同的用户进行分类,形成不同的用户群体。但是,这是从用户之间的相似性的角度来进行群体的划分,因而,对于应用层面来说,可能存在构建形成的用户群体的可靠度不佳的问题,如与应用需求不匹配等。With the continuous development of Internet technology and computer technology, the user's network behavior is becoming more and more large. Therefore, it has become an important application to classify users based on the user's network behavior data. The similarity between behavioral data is used to classify different users and form different user groups. However, this is to divide groups from the perspective of similarity between users. Therefore, at the application level, there may be a problem of poor reliability of the constructed user groups, such as mismatching with application requirements.

发明内容Contents of the invention

有鉴于此,本发明的目的在于提供一种基于大数据的用户画像分析方法及系统,以改善基于现有技术构建形成的用户群体的可靠度不佳的问题。In view of this, the purpose of the present invention is to provide a user profile analysis method and system based on big data, so as to improve the problem of poor reliability of user groups formed based on existing technologies.

为实现上述目的,本发明实施例采用如下技术方案:In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:

一种基于大数据的用户画像分析方法,应用于用户数据分析服务器,所述基于大数据的用户画像分析方法包括:A user portrait analysis method based on big data, applied to a user data analysis server, the user portrait analysis method based on big data includes:

针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息构建形成;For each user terminal device among the plurality of user terminal devices connected by communication, obtain user portrait information of a device user corresponding to the user terminal device, wherein the user portrait information is based on user profile information obtained from data collection of the corresponding device user. The formation of characteristic information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成;For each of the multiple device users corresponding to the multiple user terminal devices, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the corresponding User portrait matching degree, wherein the target user portrait information is constructed and formed based on the user characteristic information of the target user group to be formed;

基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户,其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体。Based on the user portrait matching degree corresponding to each of the multiple device users, target device users are determined among the multiple device users, wherein each of the determined target device users is used to build a The target user group.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述针对所述多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, for each of the plurality of user terminal devices, the user portrait of the device user corresponding to the user terminal device is acquired Information steps, including:

判断是否获取到用户画像分析指令,并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;Judging whether the user portrait analysis instruction has been obtained, and after obtaining the user portrait analysis instruction, generating user portrait analysis notification information;

将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,每一个所述用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Send the user portrait analysis notification information to each user terminal device among the plurality of user terminal devices connected by communication, wherein each of the user terminal devices is configured to, after receiving the user portrait analysis notification information, send to The device user corresponding to the user terminal device displays the user portrait analysis notification information, and generates corresponding user portrait analysis confirmation information in response to the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information, and The user portrait analysis confirmation information is sent to the user data analysis server;

在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。After obtaining the user profile analysis confirmation information sent by each of the multiple user terminal devices, respectively obtain the user ID of the device user corresponding to each of the multiple user terminal devices portrait information.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, for each device user among the multiple device users corresponding to the multiple user terminal devices, based on the user corresponding to the device user The matching degree between the portrait information and the predetermined target user portrait information, and the steps of obtaining the matching degree of the user portrait corresponding to the device user include:

针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;For each device user among the multiple device users corresponding to the multiple user terminal devices, calculate each piece of user characteristic information included in the user portrait information corresponding to the device user and the corresponding user information included in the target user portrait information. The matching degree between the characteristic information is obtained to obtain the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理,得到该设备用户对应的用户画像匹配度。For each device user in the plurality of device users corresponding to the plurality of user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user characteristic information included in the user portrait information corresponding to the device user The corresponding feature matching degree is fused to obtain the user portrait matching degree corresponding to the device user.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, the step of determining the fusion coefficient corresponding to the user characteristic information for each piece of user characteristic information included in the predetermined target user portrait information, include:

获取在历史上构建形成的每一个历史目标用户群体,得到至少一个历史目标用户群体,其中,所述至少一个历史目标用户群体中的每一个所述历史目标用户群体包括至少一个历史设备用户;Obtaining each historical target user group constructed in history to obtain at least one historical target user group, wherein each of the at least one historical target user group includes at least one historical device user;

针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,确定该历史目标用户群体对应的历史用户画像信息,并对所述至少一个历史目标用户群体中的每一个历史目标用户群体对应的历史用户画像信息包括的历史用户特征信息进行去重筛选,得到对应的历史特征信息集合;For each historical target user group in the at least one historical target user group, determine historical user portrait information corresponding to the historical target user group, and correspond to each historical target user group in the at least one historical target user group The historical user profile information included in the historical user portrait information is deduplicated and filtered to obtain the corresponding historical feature information set;

针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information user, and determine the fusion coefficient corresponding to the historical user feature information based on the first historical device user;

针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息所属的历史用户特征信息,并将该历史用户特征信息对应的融合系数确定为该用户特征信息对应的融合系数。For each piece of user characteristic information included in the predetermined target user portrait information, determine the historical user characteristic information to which the user characteristic information belongs, and determine the fusion coefficient corresponding to the historical user characteristic information as the fusion coefficient corresponding to the user characteristic information.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, for each piece of historical user characteristic information in the historical characteristic information set, it is determined that the historical user characteristic information is included in the at least one history The corresponding historical device user in the target user group is used as the first historical device user corresponding to the historical user characteristic information, and the step of determining the fusion coefficient corresponding to the historical user characteristic information based on the first historical device user includes:

针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并统计该历史用户特征信息对应的第一历史设备用户的数量,得到该历史用户特征信息对应的第一用户统计数量,以及,基于该历史用户特征信息对应的第一用户统计数量确定该历史用户特征信息对应的第一系数,其中,所述第一系数和所述第一用户统计数量之间具有正相关关系;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information users, and count the number of first historical device users corresponding to the historical user characteristic information, obtain the first user statistical quantity corresponding to the historical user characteristic information, and determine the first user statistical quantity corresponding to the historical user characteristic information. A first coefficient corresponding to historical user characteristic information, wherein there is a positive correlation between the first coefficient and the first user statistics;

针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,分别确定该历史目标用户群体中的每一个历史设备用户对该历史目标用户群体对应的历史推荐信息的信息关注度,并基于每一个历史设备用户对应的信息关注度确定每一个历史设备用户在该历史目标用户群体中的群体贡献系数,其中,所述群体贡献系数与所述信息关注度之间具有正相关关系;For each historical target user group in the at least one historical target user group, respectively determine the information attention of each historical device user in the historical target user group to the historical recommendation information corresponding to the historical target user group, and based on The information attention corresponding to each historical device user determines the group contribution coefficient of each historical device user in the historical target user group, wherein the group contribution coefficient has a positive correlation with the information attention;

针对每一个所述群体贡献系数,确定该群体贡献系数对应的历史目标用户群体的历史形成时间,并基于该群体贡献系数和该历史形成时间构建形成对应的二维坐标,并确定该二维坐标对应的坐标向量;For each of the group contribution coefficients, determine the historical formation time of the historical target user group corresponding to the group contribution coefficient, and construct and form corresponding two-dimensional coordinates based on the group contribution coefficient and the historical formation time, and determine the two-dimensional coordinates Corresponding coordinate vector;

针对所述历史特征信息集合中的每一条历史用户特征信息,依次对该历史用户特征信息对应的每一个第一历史设备用户对应的一个坐标向量进行连接,得到该历史用户特征信息对应的一条连接路径,其中,所述针对所述历史特征信息集合中的每一条历史用户特征信息,依次对该历史用户特征信息对应的每一个第一历史设备用户对应的一个坐标向量进行连接,得到该历史用户特征信息对应的一条连接路径的步骤执行多次,得到对应的多条连接路径,其中,所述多条连接路径中的每两条连接路径不同;For each piece of historical user characteristic information in the historical characteristic information set, a coordinate vector corresponding to each first historical device user corresponding to the historical user characteristic information is sequentially connected to obtain a connection corresponding to the historical user characteristic information path, wherein, for each piece of historical user characteristic information in the historical characteristic information set, a coordinate vector corresponding to each first historical device user corresponding to the historical user characteristic information is sequentially connected to obtain the historical user The step of one connection path corresponding to the feature information is executed multiple times to obtain corresponding multiple connection paths, wherein every two connection paths in the multiple connection paths are different;

针对所述历史特征信息集合中的每一条历史用户特征信息,分别计算该历史用户特征信息对应的每一条连接路径中相邻两个坐标向量之间的向量距离,并分别计算每一条连接路径中相邻两个坐标向量之间的向量距离的和值,得到每一条连接路径对应的向量距离和值,以及,确定出具有最小值的向量距离和值对应的连接路径作为该历史用户特征信息对应的目标连接路径,在融合该目标连接路径对应的每一个群体贡献系数,得到该历史用户特征信息对应的贡献系数融合值;For each piece of historical user feature information in the historical feature information set, calculate the vector distance between two adjacent coordinate vectors in each connection path corresponding to the historical user feature information, and calculate the vector distance between two adjacent coordinate vectors in each connection path respectively. The sum of the vector distances between two adjacent coordinate vectors is used to obtain the vector distance and value corresponding to each connection path, and the connection path corresponding to the vector distance and value with the minimum value is determined as the corresponding historical user characteristic information The target connection path of the target connection path is fused with each group contribution coefficient corresponding to the target connection path to obtain the fusion value of the contribution coefficient corresponding to the historical user characteristic information;

针对所述历史特征信息集合中的每一条历史用户特征信息,基于该历史用户特征系信息对应的所述贡献系数融合值和所述第一系数,确定该历史用户特征系信息对应的融合系数。For each piece of historical user characteristic information in the historical characteristic information set, the fusion coefficient corresponding to the historical user characteristic information is determined based on the contribution coefficient fusion value and the first coefficient corresponding to the historical user characteristic information.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, based on the user portrait matching degree corresponding to each of the multiple device users, among the multiple device users Steps to identify target device users include:

针对所述多个设备用户中的每一个设备用户,确定该设备用户对应的用户画像匹配度与预先配置的画像匹配度阈值之间的相对大小关系;For each device user in the plurality of device users, determine the relative size relationship between the user portrait matching degree corresponding to the device user and a pre-configured portrait matching degree threshold;

针对所述多个设备用户中的每一个设备用户,若该设备用户对应的用户画像匹配度大于或等于所述画像匹配度阈值,则将该设备用户确定为目标设备用户,若该设备用户对应的用户画像匹配度小于所述画像匹配度阈值,则将该设备用户确定为非目标设备用户。For each device user among the plurality of device users, if the user portrait matching degree corresponding to the device user is greater than or equal to the portrait matching degree threshold, determine the device user as the target device user, and if the device user corresponds to If the matching degree of the user portrait is less than the threshold matching degree of the portrait, the device user is determined to be a non-target device user.

在一些优选的实施例中,在上述基于大数据的用户画像分析方法中,所述基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户的步骤,包括:In some preferred embodiments, in the above-mentioned user portrait analysis method based on big data, based on the user portrait matching degree corresponding to each of the multiple device users, among the multiple device users Steps to identify target device users include:

基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,对所述设备用户进行排序处理,得到所述多个设备用户对应的用户排序序列,其中,对所述设备用户进行排序处理时,按照所述设备用户对应的用户画像匹配度的先大后小或先小后大的顺序进行排序;Based on the user portrait matching degree corresponding to each of the multiple device users, sort the device users to obtain a user sorting sequence corresponding to the multiple device users, wherein the device users are sorted During the sorting process, sorting is performed in the order of first large and then small or first small and then large according to the matching degree of the user portrait corresponding to the device user;

获取预先针对所述目标用户群体配置的群体数量范围信息,并基于所述群体数量范围信息在所述用户排序序列中选择出用户画像匹配度最大的对应数量范围的设备用户作为目标设备用户。Acquiring group number range information pre-configured for the target user group, and selecting device users in the corresponding number range with the highest matching degree of user portraits in the user sorting sequence as target device users based on the group number range information.

本发明实施例还提供一种基于大数据的用户画像分析系统,应用于用户数据分析服务器,所述基于大数据的用户画像分析系统包括:The embodiment of the present invention also provides a user portrait analysis system based on big data, which is applied to a user data analysis server, and the user portrait analysis system based on big data includes:

用户画像获取模块,用于针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息构建形成;A user portrait acquisition module, configured to acquire user portrait information of a device user corresponding to the user terminal device for each user terminal device in a plurality of user terminal devices connected by communication, wherein the user portrait information is based on the corresponding device The user characteristic information is constructed and formed through data collection by the user;

画像匹配度确定模块,用于针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成;The portrait matching degree determination module is configured to, for each of the multiple device users corresponding to the multiple user terminal devices, based on the matching between the user portrait information corresponding to the device user and the predetermined target user portrait information degree, to obtain the user portrait matching degree corresponding to the device user, wherein the target user portrait information is constructed and formed based on the user characteristic information of the target user group to be formed;

目标用户确定模块,用于基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户,其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体。The target user determination module is configured to determine the target device user among the multiple device users based on the user portrait matching degree corresponding to each of the multiple device users, wherein each of the determined Target device users are used to construct and form the target user group.

在一些优选的实施例中,在上述基于大数据的用户画像分析系统中,所述用户画像获取模块具体用于:In some preferred embodiments, in the above-mentioned user portrait analysis system based on big data, the user portrait acquisition module is specifically used for:

判断是否获取到用户画像分析指令,并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;Judging whether the user portrait analysis instruction has been obtained, and after obtaining the user portrait analysis instruction, generating user portrait analysis notification information;

将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,每一个所述用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Send the user portrait analysis notification information to each user terminal device among the plurality of user terminal devices connected by communication, wherein each of the user terminal devices is configured to, after receiving the user portrait analysis notification information, send to The device user corresponding to the user terminal device displays the user portrait analysis notification information, and generates corresponding user portrait analysis confirmation information in response to the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information, and The user portrait analysis confirmation information is sent to the user data analysis server;

在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。After obtaining the user profile analysis confirmation information sent by each of the multiple user terminal devices, respectively obtain the user ID of the device user corresponding to each of the multiple user terminal devices portrait information.

在一些优选的实施例中,在上述基于大数据的用户画像分析系统中,所述画像匹配度确定模块具体用于:In some preferred embodiments, in the above-mentioned user portrait analysis system based on big data, the portrait matching degree determination module is specifically used for:

针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;For each device user among the multiple device users corresponding to the multiple user terminal devices, calculate each piece of user characteristic information included in the user portrait information corresponding to the device user and the corresponding user information included in the target user portrait information. The matching degree between the characteristic information is obtained to obtain the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理,得到该设备用户对应的用户画像匹配度。For each device user in the plurality of device users corresponding to the plurality of user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user characteristic information included in the user portrait information corresponding to the device user The corresponding feature matching degree is fused to obtain the user portrait matching degree corresponding to the device user.

本发明实施例提供的基于大数据的用户画像分析方法及系统,可以先针对每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,然后,可以针对多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,使得可以基于多个设备用户中的每一个设备用户对应的用户画像匹配度,在多个设备用户中确定出目标设备用户,如此,可以保障确定出的目标设备用户与表征应用需求的目标用户画像信息(即需要的用户的特征)之间具有较高的匹配度,从而保障基于确定出的目标设备用户构建的目标用户群体的可靠度,从而改善基于现有技术构建形成的用户群体的可靠度不佳的问题。The user portrait analysis method and system based on big data provided by the embodiments of the present invention can first obtain the user portrait information of the device user corresponding to the user terminal device for each user terminal device, and then can correspond to multiple user terminal devices. For each device user among the multiple device users, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the user portrait matching degree corresponding to the device user is obtained, so that the user portrait matching degree corresponding to the device user can be obtained based on multiple The user portrait matching degree corresponding to each device user among multiple device users determines the target device user among multiple device users. In this way, it can be guaranteed that the determined target device user and the target user portrait information representing the application requirements (that is, the required There is a high degree of matching between the user's characteristics), so as to ensure the reliability of the target user group constructed based on the determined target device user, thereby improving the problem of poor reliability of the user group formed based on the existing technology .

为使本发明的上述目的、特征和优点能更明显易懂,下文特举较佳实施例,并配合所附附图,作详细说明如下。In order to make the above-mentioned objects, features and advantages of the present invention more comprehensible, preferred embodiments will be described in detail below together with the accompanying drawings.

附图说明Description of drawings

图1为本发明实施例提供的用户数据分析服务器的应用框图。FIG. 1 is an application block diagram of a user data analysis server provided by an embodiment of the present invention.

图2为本发明实施例提供的基于大数据的用户画像分析方法包括的各步骤的流程示意图。FIG. 2 is a schematic flowchart of steps included in the method for analyzing user portraits based on big data provided by an embodiment of the present invention.

图3为本发明实施例提供的基于大数据的用户画像分析系统包括的各模块的示意图。Fig. 3 is a schematic diagram of various modules included in the user portrait analysis system based on big data provided by the embodiment of the present invention.

具体实施方式Detailed ways

为使本发明实施例的目的、技术方案和优点更加清楚,下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例只是本发明的一部分实施例,而不是全部的实施例。通常在此处附图中描述和示出的本发明实施例的组件可以以各种不同的配置来布置和设计。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is only a part of embodiments of the present invention, but not all embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations.

因此,以下对在附图中提供的本发明的实施例的详细描述并非旨在限制要求保护的本发明的范围,而是仅仅表示本发明的选定实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

如图1所示,本发明实施例提供了一种用户数据分析服务器。其中,所述用户数据分析服务器可以包括存储器和处理器。As shown in FIG. 1 , an embodiment of the present invention provides a user data analysis server. Wherein, the user data analysis server may include a memory and a processor.

详细地,所述存储器和处理器之间直接或间接地电性连接,以实现数据的传输或交互。例如,相互之间可通过一条或多条通讯总线或信号线实现电性连接。所述存储器中可以存储有至少一个可以以软件或固件(firmware)的形式,存在的软件功能模块(计算机程序)。所述处理器可以用于执行所述存储器中存储的可执行的计算机程序,从而实现本发明实施例(如后文所述)提供的基于大数据的用户画像分析方法。In detail, the memory and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, they can be electrically connected to each other through one or more communication buses or signal lines. The memory may store at least one software function module (computer program) in the form of software or firmware (firmware). The processor can be used to execute the executable computer program stored in the memory, so as to realize the user profile analysis method based on big data provided by the embodiment of the present invention (as described later).

举例来说,在一种可能的实施方式中,所述存储器可以是,但不限于,随机存取存储器(Random Access Memory,RAM),只读存储器(Read Only Memory,ROM),可编程只读存储器(Programmable Read-Only Memory,PROM),可擦除只读存储器(ErasableProgrammable Read-Only Memory,EPROM),电可擦除只读存储器(Electric ErasableProgrammable Read-Only Memory,EEPROM)等。For example, in a possible implementation manner, the memory may be, but not limited to, random access memory (Random Access Memory, RAM), read only memory (Read Only Memory, ROM), programmable read-only Memory (Programmable Read-Only Memory, PROM), Erasable Programmable Read-Only Memory (EPROM), Electric Erasable Programmable Read-Only Memory (EEPROM), etc.

举例来说,在一种可能的实施方式中,所述处理器可以是一种通用处理器,包括中央处理器(Central Processing Unit,CPU)、网络处理器(Network Processor,NP)、片上系统(System on Chip,SoC)等;还可以是数字信号处理器(DSP)、专用集成电路(ASIC)、现场可编程门阵列(FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。For example, in a possible implementation manner, the processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), a system on a chip ( System on Chip, SoC), etc.; it can also be digital signal processor (DSP), application specific integrated circuit (ASIC), field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

并且,图1所示的结构仅为示意,所述用户数据分析服务器还可包括比图1中所示更多或者更少的组件,或具有与图1所示不同的配置,例如,可以包括用于与其它设备(如用户终端设备等,其中,用户终端设备可以包括,但不限于手机、电脑等)进行信息交互的通信单元。Moreover, the structure shown in FIG. 1 is only for illustration, and the user data analysis server may also include more or fewer components than those shown in FIG. 1, or have a configuration different from that shown in FIG. 1, for example, may include A communication unit for information interaction with other devices (such as user terminal equipment, etc., where the user terminal equipment may include, but not limited to, mobile phones, computers, etc.).

结合图2,本发明实施例还提供一种基于大数据的用户画像分析方法,可应用于上述用户数据分析服务器。其中,所述基于大数据的用户画像分析方法有关的流程所定义的方法步骤,可以由所述用户数据分析服务器实现。下面将对图2所示的具体流程,进行详细阐述。With reference to FIG. 2 , an embodiment of the present invention also provides a method for analyzing user portraits based on big data, which can be applied to the above-mentioned user data analysis server. Wherein, the method steps defined in the process related to the big data-based user portrait analysis method can be implemented by the user data analysis server. The specific process shown in FIG. 2 will be described in detail below.

步骤S110,针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息。Step S110, for each user terminal device among the plurality of user terminal devices connected by communication, acquire user portrait information of a device user corresponding to the user terminal device.

在本发明实施例中,所述用户数据分析服务器可以针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息。其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息(如性别、年龄、收入等)构建形成。In the embodiment of the present invention, the user data analysis server may obtain user portrait information of a device user corresponding to the user terminal device for each user terminal device among a plurality of user terminal devices connected by communication. Wherein, the user portrait information is constructed based on user characteristic information (such as gender, age, income, etc.) obtained through data collection of corresponding device users.

步骤S120,针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度。Step S120, for each of the multiple device users corresponding to the multiple user terminal devices, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, obtain the device User portrait matching degree corresponding to the user.

在本发明实施例中,所述用户数据分析服务器可以针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度。其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成。In the embodiment of the present invention, the user data analysis server may, for each device user among the multiple device users corresponding to the multiple user terminal devices, based on the user portrait information corresponding to the device user and the predetermined target user The matching degree between the portrait information is used to obtain the matching degree of the user portrait corresponding to the device user. Wherein, the target user profile information is constructed based on the user characteristic information of the target user group to be constructed.

步骤S130,基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户。Step S130, based on the user portrait matching degree corresponding to each of the multiple device users, determine a target device user among the multiple device users.

在本发明实施例中,所述用户数据分析服务器可以基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户。其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体(如此,可以将所述目标用户画像信息对应的待推荐信息推送给所述目标用户群体中的每一个目标设备用户)。In the embodiment of the present invention, the user data analysis server may determine the target device user among the multiple device users based on the user profile matching degree corresponding to each of the multiple device users. Wherein, each determined target device user is used to construct and form the target user group (in this way, the information to be recommended corresponding to the target user portrait information can be pushed to each target device in the target user group user).

基于上述的基于大数据的用户画像分析方法,可以先针对每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,然后,可以针对多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,使得可以基于多个设备用户中的每一个设备用户对应的用户画像匹配度,在多个设备用户中确定出目标设备用户,如此,可以保障确定出的目标设备用户与表征应用需求的目标用户画像信息(即需要的用户的特征)之间具有较高的匹配度,从而保障基于确定出的目标设备用户构建的目标用户群体的可靠度,从而改善基于现有技术构建形成的用户群体的可靠度不佳的问题。Based on the above-mentioned user portrait analysis method based on big data, the user portrait information of the device user corresponding to the user terminal device can be obtained for each user terminal device first, and then the multiple device users corresponding to multiple user terminal devices can be obtained For each device user in the device user, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the user portrait matching degree corresponding to the device user is obtained, so that it can be based on multiple device users. The user portrait matching degree corresponding to each device user determines the target device user among multiple device users, so that the determined target device user and the target user portrait information (that is, the required user characteristics) that represent the application requirements can be guaranteed. There is a high degree of matching between them, so as to ensure the reliability of the target user group constructed based on the determined target device users, thereby improving the problem of poor reliability of the user group formed based on the existing technology.

举例来说,在一种可能的实施方式中,上述实施方式中的步骤S110可以进一步包括以下的各步骤:For example, in a possible implementation manner, step S110 in the above implementation manner may further include the following steps:

首先,判断是否获取到用户画像分析指令(如在接收到如上述的待推荐信息之后,可以认为获取到所述用户画像分析指令),并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;First, determine whether the user portrait analysis instruction has been obtained (for example, after receiving the above-mentioned information to be recommended, it can be considered that the user portrait analysis instruction has been obtained), and after obtaining the user portrait analysis instruction, generate a user portrait analyze notification information;

其次,将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,多个用户终端设备中的每一个用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Secondly, the user portrait analysis notification information is sent to each user terminal device in a plurality of user terminal devices connected by communication, wherein each user terminal device in a plurality of user terminal devices is configured to receive the user After the portrait analysis notification information, display the user portrait analysis notification information to the device user corresponding to the user terminal device, and generate a corresponding user portrait in response to the operation of the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information Analyzing the confirmation information, and sending the user portrait analysis confirmation information to the user data analysis server;

然后,在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。Then, after obtaining the user profile analysis confirmation information sent by each of the plurality of user terminal devices, respectively obtaining the device user corresponding to each of the plurality of user terminal devices user profile information.

举例来说,在一种可能的实施方式中,上述实施方式中的步骤S120可以进一步包括以下的各步骤:For example, in a possible implementation manner, step S120 in the above implementation manner may further include the following steps:

首先,针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;First, for each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information;

其次,针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;Secondly, for each of the multiple device users corresponding to the multiple user terminal devices, respectively calculate the correspondence between each piece of user characteristic information included in the user portrait information corresponding to the device user and the target user portrait information. The matching degree between the user characteristic information of the user profile information is obtained, and the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information is obtained;

然后,针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理(如加权求和计算等),得到该设备用户对应的用户画像匹配度。Then, for each of the multiple device users corresponding to the multiple user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user information included in the user portrait information corresponding to the device user The feature matching degree corresponding to the feature information is fused (such as weighted sum calculation, etc.) to obtain the user portrait matching degree corresponding to the device user.

举例来说,在一种可能的实施方式中,上述实施方式中的所述针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数的步骤,可以进一步包括以下的各步骤:For example, in a possible implementation manner, the step of determining the fusion coefficient corresponding to the user characteristic information for each piece of user characteristic information included in the predetermined target user portrait information in the above implementation manner may be further Include the following steps:

首先,获取在历史上构建形成的每一个历史目标用户群体,得到至少一个历史目标用户群体,其中,所述至少一个历史目标用户群体中的每一个所述历史目标用户群体包括至少一个历史设备用户;Firstly, acquire each historical target user group formed historically, and obtain at least one historical target user group, wherein each of the historical target user groups in the at least one historical target user group includes at least one historical device user ;

其次,针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,确定该历史目标用户群体对应的历史用户画像信息,并对所述至少一个历史目标用户群体中的每一个历史目标用户群体对应的历史用户画像信息包括的历史用户特征信息进行去重筛选(即相同的历史用户特征信息,仅保留其中一条),得到对应的历史特征信息集合;Secondly, for each historical target user group in the at least one historical target user group, determine the historical user portrait information corresponding to the historical target user group, and for each historical target user group in the at least one historical target user group The historical user feature information included in the historical user portrait information corresponding to the group is deduplicated and filtered (that is, only one of the same historical user feature information is retained), and the corresponding historical feature information set is obtained;

然后,针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数;Then, for each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first user corresponding to the historical user characteristic information. Historical device users, and determine the fusion coefficient corresponding to the historical user feature information based on the first historical device user;

最后,针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息所属(如相同)的历史用户特征信息,并将该历史用户特征信息对应的融合系数确定为该用户特征信息对应的融合系数。Finally, for each piece of user characteristic information included in the predetermined target user portrait information, determine the historical user characteristic information to which the user characteristic information belongs (if the same), and determine the fusion coefficient corresponding to the historical user characteristic information as the user characteristic The fusion coefficient corresponding to the information.

举例来说,在一种可能的实施方式中,上述实施方式中的所述针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数的步骤,进一步包括以下的各步骤:For example, in a possible implementation manner, in the above implementation manner, for each piece of historical user characteristic information in the historical characteristic information set, it is determined that the historical user characteristic information is included in the at least one historical target user. The corresponding historical device user in the group is used as the first historical device user corresponding to the historical user characteristic information, and the step of determining the fusion coefficient corresponding to the historical user characteristic information based on the first historical device user further includes the following steps:

首先,针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并统计该历史用户特征信息对应的第一历史设备用户的数量,得到该历史用户特征信息对应的第一用户统计数量,以及,基于该历史用户特征信息对应的第一用户统计数量确定该历史用户特征信息对应的第一系数,其中,所述第一系数和所述第一用户统计数量之间具有正相关关系;First, for each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first user corresponding to the historical user characteristic information. Historical device users, and count the number of first historical device users corresponding to the historical user characteristic information, obtain the first user statistical quantity corresponding to the historical user characteristic information, and, based on the first user statistical quantity corresponding to the historical user characteristic information determining a first coefficient corresponding to the historical user feature information, wherein there is a positive correlation between the first coefficient and the first user statistics;

其次,针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,分别确定该历史目标用户群体中的每一个历史设备用户对该历史目标用户群体对应的历史推荐信息的信息关注度,并基于每一个历史设备用户对应的信息关注度确定每一个历史设备用户在该历史目标用户群体中的群体贡献系数,其中,所述群体贡献系数与所述信息关注度之间具有正相关关系(如可以直接将所述信息关注度作为所述群体贡献系数);Secondly, for each historical target user group in the at least one historical target user group, respectively determine the information attention of each historical device user in the historical target user group to the historical recommendation information corresponding to the historical target user group, And determine the group contribution coefficient of each historical device user in the historical target user group based on the information attention corresponding to each historical device user, wherein there is a positive correlation between the group contribution coefficient and the information attention ( For example, the information attention can be directly used as the group contribution coefficient);

然后,针对每一个所述群体贡献系数,确定该群体贡献系数对应的历史目标用户群体的历史形成时间,并基于该群体贡献系数和该历史形成时间构建形成对应的二维坐标,并确定该二维坐标对应的坐标向量;Then, for each of the group contribution coefficients, determine the historical formation time of the historical target user group corresponding to the group contribution coefficient, and construct and form corresponding two-dimensional coordinates based on the group contribution coefficient and the historical formation time, and determine the two The coordinate vector corresponding to the dimension coordinate;

之后,针对所述历史特征信息集合中的每一条历史用户特征信息,依次对该历史用户特征信息对应的每一个第一历史设备用户对应的一个坐标向量进行连接,得到该历史用户特征信息对应的一条连接路径,其中,所述针对所述历史特征信息集合中的每一条历史用户特征信息,依次对该历史用户特征信息对应的每一个第一历史设备用户对应的一个坐标向量进行连接,得到该历史用户特征信息对应的一条连接路径的步骤执行多次,得到对应的多条连接路径,所述多条连接路径中的每两条连接路径不同;Afterwards, for each piece of historical user characteristic information in the historical characteristic information set, a coordinate vector corresponding to each first historical device user corresponding to the historical user characteristic information is sequentially connected to obtain the corresponding historical user characteristic information. A connection path, wherein, for each piece of historical user characteristic information in the historical characteristic information set, a coordinate vector corresponding to each first historical device user corresponding to the historical user characteristic information is sequentially connected to obtain the The step of one connection path corresponding to the historical user characteristic information is executed multiple times to obtain corresponding multiple connection paths, and every two connection paths in the multiple connection paths are different;

进一步,针对所述历史特征信息集合中的每一条历史用户特征信息,分别计算该历史用户特征信息对应的每一条连接路径中相邻两个坐标向量之间的向量距离,并分别计算每一条连接路径中相邻两个坐标向量之间的向量距离的和值,得到每一条连接路径对应的向量距离和值,以及,确定出具有最小值的向量距离和值对应的连接路径作为该历史用户特征信息对应的目标连接路径,在融合该目标连接路径对应的每一个群体贡献系数,得到该历史用户特征信息对应的贡献系数融合值;Further, for each piece of historical user feature information in the set of historical feature information, calculate the vector distance between two adjacent coordinate vectors in each connection path corresponding to the historical user feature information, and calculate the distance of each connection The sum of the vector distances between two adjacent coordinate vectors in the path obtains the vector distance and value corresponding to each connection path, and determines the connection path corresponding to the vector distance and value with the minimum value as the historical user feature The target connection path corresponding to the information is fused with each group contribution coefficient corresponding to the target connection path to obtain the fusion value of the contribution coefficient corresponding to the historical user characteristic information;

最后,针对所述历史特征信息集合中的每一条历史用户特征信息,基于该历史用户特征系信息对应的所述贡献系数融合值和所述第一系数(如计算乘积或平均值等),确定该历史用户特征系信息对应的融合系数。Finally, for each piece of historical user feature information in the historical feature information set, determine The fusion coefficient corresponding to the historical user characteristic information.

举例来说,在一种可能的实施方式中,上述实施方式中的所述针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数的步骤,进一步包括以下的各步骤:For example, in a possible implementation manner, in the above implementation manner, for each piece of historical user characteristic information in the historical characteristic information set, it is determined that the historical user characteristic information is included in the at least one historical target user. The corresponding historical device user in the group is used as the first historical device user corresponding to the historical user characteristic information, and the step of determining the fusion coefficient corresponding to the historical user characteristic information based on the first historical device user further includes the following steps:

首先,针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并统计该历史用户特征信息对应的第一历史设备用户的数量,得到该历史用户特征信息对应的第一用户统计数量,以及,基于该历史用户特征信息对应的第一用户统计数量确定该历史用户特征信息对应的第一系数,其中,所述第一系数和所述第一用户统计数量之间具有正相关关系;First, for each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first user corresponding to the historical user characteristic information. Historical device users, and count the number of first historical device users corresponding to the historical user characteristic information, obtain the first user statistical quantity corresponding to the historical user characteristic information, and, based on the first user statistical quantity corresponding to the historical user characteristic information determining a first coefficient corresponding to the historical user characteristic information, wherein there is a positive correlation between the first coefficient and the first user statistics;

其次,针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,分别确定该历史目标用户群体中的每一个历史设备用户对该历史目标用户群体对应的历史推荐信息的信息关注度,并基于每一个历史设备用户对应的信息关注度确定每一个历史设备用户在该历史目标用户群体中的群体贡献系数,其中,所述群体贡献系数与所述信息关注度之间具有正相关关系,所述历史目标用户群体为多个;Secondly, for each historical target user group in the at least one historical target user group, respectively determine the information attention of each historical device user in the historical target user group to the historical recommendation information corresponding to the historical target user group, And determine the group contribution coefficient of each historical device user in the historical target user group based on the information attention corresponding to each historical device user, wherein there is a positive correlation between the group contribution coefficient and the information attention, There are multiple historical target user groups;

然后,针对每一个所述历史设备用户,统计该历史设备用户对应的群体贡献系数的数量,得到该历史设备用于对应的系数统计数量,并确定该系数统计数量与预先配置的统计数量阈值之间的相对大小关系,以及,在该系数统计数量大于所述统计数量阈值时,将该历史设备用户确定为第二历史设备用户,或者,在该系数统计数量小于或等于所述统计数量阈值时,将该历史设备用户确定为第三历史设备用户,其中,每一个所述历史设备用户对应的多个群体贡献系数基于每一个群体贡献系数对应的历史目标用户群体的历史形成时间进行依次排列;Then, for each historical device user, count the number of group contribution coefficients corresponding to the historical device user, obtain the statistical quantity of the coefficient corresponding to the historical device, and determine the difference between the coefficient statistical quantity and the pre-configured statistical quantity threshold The relative size relationship among them, and, when the statistical quantity of the coefficient is greater than the threshold of the statistical quantity, determine the historical equipment user as the second historical equipment user, or, when the statistical quantity of the coefficient is less than or equal to the threshold of the statistical quantity , determining the historical device user as a third historical device user, wherein the multiple group contribution coefficients corresponding to each historical device user are arranged in order based on the historical formation time of the historical target user group corresponding to each group contribution coefficient;

之后,在每一个所述第二历史设备用户对应的系数统计数量中确定出具有最小值的系数统计数量,作为第一数量参考值,并针对每一个所述第三历史设备用户,计算该第三历史设备用户对应的群体贡献系数的平均值,以及,基于该平均值和每一个所述第二历史设备用户对应的群体贡献系数的平均值,确定出具有相关关系的一个第二历史设备用户,并基于该第二历史设备用户对应的群体贡献系数,对该第三历史设备用户对应的群体贡献系数进行插值处理,得到该第三历史设备用户对应的新的群体贡献系数,其中,每一个所述第三历史设备用户对应的新的群体贡献系数的数量与具有相关关系的一个第二历史设备用户对应的群体贡献系数的数量相同;Afterwards, among the coefficient statistical quantities corresponding to each of the second historical device users, the coefficient statistical quantity with the smallest value is determined as the first quantity reference value, and for each of the third historical device users, the first statistical quantity is calculated. The average value of the group contribution coefficients corresponding to the three historical device users, and, based on the average value and the average value of the group contribution coefficients corresponding to each of the second historical device users, determine a second historical device user with a correlation , and based on the group contribution coefficient corresponding to the second historical device user, the group contribution coefficient corresponding to the third historical device user is interpolated to obtain a new group contribution coefficient corresponding to the third historical device user, wherein each The number of new group contribution coefficients corresponding to the third historical device user is the same as the number of group contribution coefficients corresponding to a second historical device user having a correlation;

进一步,针对每一个所述历史设备用户,基于所述统计数量阈值,对该历史设备用户当前具有的多个群体贡献系数进行滑窗处理,得到该历史设备用户对应的多个系数滑窗序列,并计算该多个系数滑窗序列中的每两个系数滑窗序列之间的序列相似度(如分别计算对应序列位置的群体贡献系数的系数相似度,再计算系数相似度的平均值等),以及,针对该历史设备用户对应的每一个系数滑窗序列,计算该系数滑窗序列与每一个其它系数滑窗序列之间的序列相似度的平均值,得到该系数滑窗序列对应的相似度均值,再在该多个系数滑窗序列中确定出对应的相似度均值最大的一个系数滑窗序列,作为该历史设备用户对应的目标系数滑窗序列;Further, for each historical device user, based on the statistical quantity threshold, perform sliding window processing on the plurality of group contribution coefficients currently owned by the historical device user to obtain a plurality of coefficient sliding window sequences corresponding to the historical device user, And calculate the sequence similarity between every two coefficient sliding window sequences in the multiple coefficient sliding window sequences (such as calculating the coefficient similarity of the group contribution coefficient corresponding to the sequence position respectively, and then calculating the average value of the coefficient similarity, etc.) , and, for each coefficient sliding window sequence corresponding to the historical device user, calculate the average value of the sequence similarity between this coefficient sliding window sequence and every other coefficient sliding window sequence, and obtain the corresponding similarity of the coefficient sliding window sequence Degree mean value, and then determine a corresponding coefficient sliding window sequence with the largest similarity mean value in the plurality of coefficient sliding window sequences, as the target coefficient sliding window sequence corresponding to the historical device user;

再进一步,针对每一个所述历史设备用户,基于该历史设备用户对应的所述目标系数滑窗序列包括的多个群体贡献系数,确定出该历史设备用户对应的目标群体贡献系数(如计算所述目标系数滑窗序列包括的多个群体贡献系数的平均值或中位值,再将该平均值或中位值作为目标群体贡献系数),并针对所述历史特征信息集合中的每一条历史用户特征信息,对该历史用户特征信息对应的每一个历史设备用户对应的目标群体贡献系数进行融合处理,得到历史用户特征系信息对应的贡献系数融合值;Still further, for each historical device user, based on the plurality of group contribution coefficients included in the target coefficient sliding window sequence corresponding to the historical device user, determine the target group contribution coefficient corresponding to the historical device user (as calculated The average or median value of the multiple group contribution coefficients included in the target coefficient sliding window sequence, and then use the average or median value as the target group contribution coefficient), and for each piece of history in the historical feature information set For the user characteristic information, fusion processing is performed on the target group contribution coefficient corresponding to each historical device user corresponding to the historical user characteristic information, to obtain the fusion value of the contribution coefficient corresponding to the historical user characteristic information;

最后,针对所述历史特征信息集合中的每一条历史用户特征信息,基于该历史用户特征系信息对应的所述贡献系数融合值和所述第一系数,确定该历史用户特征系信息对应的融合系数。Finally, for each piece of historical user characteristic information in the historical characteristic information set, based on the fusion value of the contribution coefficient corresponding to the historical user characteristic information and the first coefficient, determine the fusion value corresponding to the historical user characteristic information. coefficient.

举例来说,在一种可能的实施方式中,上述实施方式中的步骤S130可以进一步包括以下的各步骤:For example, in a possible implementation manner, step S130 in the above implementation manner may further include the following steps:

首先,针对所述多个设备用户中的每一个设备用户,确定该设备用户对应的用户画像匹配度与预先配置的画像匹配度阈值之间的相对大小关系(如所述用户画像匹配度是否大于或等于所述画像匹配度阈值);First, for each device user among the plurality of device users, determine the relative size relationship between the user portrait matching degree corresponding to the device user and a pre-configured portrait matching degree threshold (for example, whether the user portrait matching degree is greater than or equal to the portrait matching degree threshold);

其次,针对所述多个设备用户中的每一个设备用户,若该设备用户对应的用户画像匹配度大于或等于所述画像匹配度阈值,则将该设备用户确定为目标设备用户,若该设备用户对应的用户画像匹配度小于所述画像匹配度阈值,则将该设备用户确定为非目标设备用户。Secondly, for each device user among the plurality of device users, if the user portrait matching degree corresponding to the device user is greater than or equal to the portrait matching degree threshold, the device user is determined as the target device user, and if the device user If the user portrait matching degree corresponding to the user is less than the portrait matching degree threshold, the device user is determined to be a non-target device user.

举例来说,在一种可能的实施方式中,上述实施方式中的步骤S130可以进一步包括以下的各步骤:For example, in a possible implementation manner, step S130 in the above implementation manner may further include the following steps:

首先,基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,对所述设备用户进行排序处理,得到所述多个设备用户对应的用户排序序列,其中,对所述设备用户进行排序处理时,按照所述设备用户对应的用户画像匹配度的先大后小或先小后大的顺序进行排序;First, based on the matching degree of user portraits corresponding to each of the multiple device users, the device users are sorted to obtain a user sorting sequence corresponding to the multiple device users, wherein the device users When the user performs sorting processing, the user portrait matching degree corresponding to the device user is sorted in the order of first large and then small or first small and then large;

其次,获取预先针对所述目标用户群体配置的群体数量范围信息,并基于所述群体数量范围信息在所述用户排序序列中选择出用户画像匹配度最大的对应数量范围的设备用户作为目标设备用户。Secondly, obtain the group number range information pre-configured for the target user group, and select the device users in the corresponding number range with the highest matching degree of user portraits in the user ranking sequence as target device users based on the group number range information .

结合图3,本发明实施例还提供一种基于大数据的用户画像分析系统,可应用于上述用户数据分析服务器。其中,所述用户画像分析系统可以包括用户画像获取模块、画像匹配度确定模块和目标用户确定模块。With reference to FIG. 3 , an embodiment of the present invention also provides a user profile analysis system based on big data, which can be applied to the above user data analysis server. Wherein, the user portrait analysis system may include a user portrait acquisition module, a portrait matching degree determination module, and a target user determination module.

所述用户画像获取模块,用于针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息构建形成。The user portrait acquisition module is configured to acquire user portrait information of a device user corresponding to the user terminal equipment for each user terminal equipment among a plurality of user terminal equipment connected by communication, wherein the user portrait information is based on a corresponding The user characteristic information obtained from data collection by device users is constructed and formed.

所述画像匹配度确定模块,用于针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成。The portrait matching degree determination module is configured to, for each of the multiple device users corresponding to the multiple user terminal devices, based on the difference between the user portrait information corresponding to the device user and the predetermined target user portrait information The matching degree of the user portrait corresponding to the device user is obtained, wherein the target user portrait information is constructed based on the user characteristic information of the target user group to be formed.

所述目标用户确定模块,用于基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户,其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体。The target user determination module is configured to determine target device users among the multiple device users based on the matching degree of user portraits corresponding to each of the multiple device users, wherein each determined The target device users are used to construct and form the target user group.

举例来说,在一种可能的实施方式中,所述用户画像获取模块具体用于(可以参照上述的步骤S110的相关描述):For example, in a possible implementation manner, the user portrait acquisition module is specifically used for (refer to the relevant description of the above-mentioned step S110):

判断是否获取到用户画像分析指令,并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;Judging whether the user portrait analysis instruction has been obtained, and after obtaining the user portrait analysis instruction, generating user portrait analysis notification information;

将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,每一个所述用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Send the user portrait analysis notification information to each user terminal device among the plurality of user terminal devices connected by communication, wherein each of the user terminal devices is configured to, after receiving the user portrait analysis notification information, send to The device user corresponding to the user terminal device displays the user portrait analysis notification information, and generates corresponding user portrait analysis confirmation information in response to the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information, and The user portrait analysis confirmation information is sent to the user data analysis server;

在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。After obtaining the user profile analysis confirmation information sent by each of the multiple user terminal devices, respectively obtain the user ID of the device user corresponding to each of the multiple user terminal devices portrait information.

举例来说,在一种可能的实施方式中,所述画像匹配度确定模块具体用于(可以参照上述的步骤S120的相关描述):For example, in a possible implementation manner, the portrait matching degree determination module is specifically used for (refer to the relevant description of the above-mentioned step S120):

针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;For each device user among the multiple device users corresponding to the multiple user terminal devices, calculate each piece of user characteristic information included in the user portrait information corresponding to the device user and the corresponding user information included in the target user portrait information. The matching degree between the characteristic information is obtained to obtain the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information;

针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理,得到该设备用户对应的用户画像匹配度。For each device user in the plurality of device users corresponding to the plurality of user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user characteristic information included in the user portrait information corresponding to the device user The corresponding feature matching degree is fused to obtain the user portrait matching degree corresponding to the device user.

综上所述,本发明提供的基于大数据的用户画像分析方法及系统,可以先针对每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,然后,可以针对多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,使得可以基于每一个设备用户对应的用户画像匹配度,在多个设备用户中确定出目标设备用户,如此,可以保障确定出的目标设备用户与表征应用需求的目标用户画像信息(即需要的用户的特征)之间具有较高的匹配度,从而保障基于确定出的目标设备用户构建的目标用户群体的可靠度,从而改善基于现有技术构建形成的用户群体的可靠度不佳的问题。To sum up, the user portrait analysis method and system based on big data provided by the present invention can first obtain the user portrait information of the device user corresponding to the user terminal device for each user terminal device, and then can target multiple user For each of the multiple device users corresponding to the terminal device, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the matching degree of the user portrait corresponding to the device user is obtained, so that Based on the user profile matching degree corresponding to each device user, the target device user can be determined among multiple device users. In this way, it can be guaranteed that the determined target device user and the target user profile information representing the application requirements (that is, the required user profile information) Features) have a high matching degree, thereby ensuring the reliability of the target user group constructed based on the determined target device users, thereby improving the problem of poor reliability of the user group formed based on the existing technology.

在本发明实施例所提供的几个实施例中,应该理解到,所揭露的装置和方法,也可以通过其它的方式实现。以上所描述的装置和方法实施例仅仅是示意性的,例如,附图中的流程图和框图显示了根据本发明的多个实施例的装置、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上,流程图或框图中的每个方框可以代表一个模块、程序段或代码的一部分,所述模块、程序段或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意,在有些作为替换的实现方式中,方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如,两个连续的方框实际上可以基本并行地执行,它们有时也可以按相反的顺序执行,这依所涉及的功能而定。也要注意的是,框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合,可以用执行规定的功能或动作的专用的基于硬件的系统来实现,或者可以用专用硬件与计算机指令的组合来实现。In the several embodiments provided by the embodiments of the present invention, it should be understood that the disclosed devices and methods may also be implemented in other ways. The device and method embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show possible implementation architectures of devices, methods and computer program products according to multiple embodiments of the present invention, function and operation. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or part of code that includes one or more Executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks in succession may, in fact, be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by a dedicated hardware-based system that performs the specified function or action , or may be implemented by a combination of dedicated hardware and computer instructions.

另外,在本发明各个实施例中的各功能模块可以集成在一起形成一个独立的部分,也可以是各个模块单独存在,也可以两个或两个以上模块集成形成一个独立的部分。In addition, each functional module in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。基于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,电子设备,或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。需要说明的是,在本文中,术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含,从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素,而且还包括没有明确列出的其他要素,或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下,由语句“包括一个……”限定的要素,并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, an electronic device, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes. . It should be noted that, in this document, the terms "comprising", "comprising" or any other variation thereof are intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims (6)

1.一种基于大数据的用户画像分析方法,其特征在于,应用于用户数据分析服务器,所述基于大数据的用户画像分析方法包括:1. A user portrait analysis method based on big data, characterized in that it is applied to a user data analysis server, and the user portrait analysis method based on big data comprises: 针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息构建形成;For each user terminal device among the plurality of user terminal devices connected by communication, obtain user portrait information of a device user corresponding to the user terminal device, wherein the user portrait information is based on user profile information obtained from data collection of the corresponding device user. The formation of characteristic information; 针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成;For each of the multiple device users corresponding to the multiple user terminal devices, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the corresponding User portrait matching degree, wherein the target user portrait information is constructed and formed based on the user characteristic information of the target user group to be formed; 基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户,其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体;Based on the user portrait matching degree corresponding to each of the multiple device users, target device users are determined among the multiple device users, wherein each of the determined target device users is used to build a the target user group; 其中,所述针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度的步骤,包括:Wherein, for each of the multiple device users corresponding to the multiple user terminal devices, based on the matching degree between the user portrait information corresponding to the device user and the predetermined target user portrait information, the The steps of user profile matching degree corresponding to the device user include: 针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information; 针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;For each device user among the multiple device users corresponding to the multiple user terminal devices, calculate each piece of user characteristic information included in the user portrait information corresponding to the device user and the corresponding user information included in the target user portrait information. The matching degree between the characteristic information is obtained to obtain the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information; 针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理,得到该设备用户对应的用户画像匹配度;For each device user in the plurality of device users corresponding to the plurality of user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user characteristic information included in the user portrait information corresponding to the device user The corresponding feature matching degree is fused to obtain the user portrait matching degree corresponding to the device user; 其中,所述针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数的步骤,包括:Wherein, the step of determining the fusion coefficient corresponding to the user characteristic information for each piece of user characteristic information included in the predetermined target user portrait information includes: 获取在历史上构建形成的每一个历史目标用户群体,得到至少一个历史目标用户群体,其中,所述至少一个历史目标用户群体中的每一个所述历史目标用户群体包括至少一个历史设备用户;Obtaining each historical target user group constructed in history to obtain at least one historical target user group, wherein each of the at least one historical target user group includes at least one historical device user; 针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,确定该历史目标用户群体对应的历史用户画像信息,并对所述至少一个历史目标用户群体中的每一个历史目标用户群体对应的历史用户画像信息包括的历史用户特征信息进行去重筛选,得到对应的历史特征信息集合;For each historical target user group in the at least one historical target user group, determine historical user portrait information corresponding to the historical target user group, and correspond to each historical target user group in the at least one historical target user group The historical user profile information included in the historical user portrait information is deduplicated and filtered to obtain the corresponding historical feature information set; 针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information user, and determine the fusion coefficient corresponding to the historical user feature information based on the first historical device user; 针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息所属的历史用户特征信息,并将该历史用户特征信息对应的融合系数确定为该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the historical user characteristic information to which the user characteristic information belongs, and determine the fusion coefficient corresponding to the historical user characteristic information as the fusion coefficient corresponding to the user characteristic information; 其中,所述针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数的步骤,包括:Wherein, for each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group, as the historical user characteristic information corresponding The first historical device user, and the step of determining the fusion coefficient corresponding to the historical user feature information based on the first historical device user includes: 针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并统计该历史用户特征信息对应的第一历史设备用户的数量,得到该历史用户特征信息对应的第一用户统计数量,以及,基于该历史用户特征信息对应的第一用户统计数量确定该历史用户特征信息对应的第一系数,其中,所述第一系数和所述第一用户统计数量之间具有正相关关系;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information users, and count the number of first historical device users corresponding to the historical user characteristic information, obtain the first user statistical quantity corresponding to the historical user characteristic information, and determine the first user statistical quantity corresponding to the historical user characteristic information. A first coefficient corresponding to historical user characteristic information, wherein there is a positive correlation between the first coefficient and the first user statistics; 针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,分别确定该历史目标用户群体中的每一个历史设备用户对该历史目标用户群体对应的历史推荐信息的信息关注度,并基于每一个历史设备用户对应的信息关注度确定每一个历史设备用户在该历史目标用户群体中的群体贡献系数,其中,所述群体贡献系数与所述信息关注度之间具有正相关关系,所述历史目标用户群体为多个;For each historical target user group in the at least one historical target user group, respectively determine the information attention of each historical device user in the historical target user group to the historical recommendation information corresponding to the historical target user group, and based on The information attention corresponding to each historical equipment user determines the group contribution coefficient of each historical equipment user in the historical target user group, wherein the group contribution coefficient has a positive correlation with the information attention, and the There are multiple historical target user groups; 针对每一个所述历史设备用户,统计该历史设备用户对应的群体贡献系数的数量,得到该历史设备用于对应的系数统计数量,并确定该系数统计数量与预先配置的统计数量阈值之间的相对大小关系,以及,在该系数统计数量大于所述统计数量阈值时,将该历史设备用户确定为第二历史设备用户,或者,在该系数统计数量小于或等于所述统计数量阈值时,将该历史设备用户确定为第三历史设备用户,其中,每一个所述历史设备用户对应的多个群体贡献系数基于每一个群体贡献系数对应的历史目标用户群体的历史形成时间进行依次排列;For each historical device user, count the number of group contribution coefficients corresponding to the historical device user, obtain the corresponding coefficient statistics for the historical device, and determine the coefficient between the coefficient statistics and the pre-configured statistical threshold Relative size relationship, and, when the statistical quantity of the coefficient is greater than the statistical quantity threshold, determine the historical device user as the second historical device user, or, when the statistical quantity of the coefficient is less than or equal to the statistical quantity threshold, determine The historical device user is determined as the third historical device user, wherein the multiple group contribution coefficients corresponding to each historical device user are arranged in order based on the historical formation time of the historical target user group corresponding to each group contribution coefficient; 在每一个所述第二历史设备用户对应的系数统计数量中确定出具有最小值的系数统计数量,作为第一数量参考值,并针对每一个所述第三历史设备用户,计算该第三历史设备用户对应的群体贡献系数的平均值,以及,基于该平均值和每一个所述第二历史设备用户对应的群体贡献系数的平均值,确定出具有相关关系的一个第二历史设备用户,并基于该第二历史设备用户对应的群体贡献系数,对该第三历史设备用户对应的群体贡献系数进行插值处理,得到该第三历史设备用户对应的新的群体贡献系数,其中,每一个所述第三历史设备用户对应的新的群体贡献系数的数量与具有相关关系的一个第二历史设备用户对应的群体贡献系数的数量相同;Determine the coefficient statistic quantity with the smallest value among the coefficient statistic quantities corresponding to each of the second historical device users as the first quantity reference value, and calculate the third history for each of the third historical device users The average value of the group contribution coefficient corresponding to the device user, and, based on the average value and the average value of the group contribution coefficient corresponding to each of the second historical device users, determine a second historical device user with a correlation, and Based on the group contribution coefficient corresponding to the second historical device user, the group contribution coefficient corresponding to the third historical device user is interpolated to obtain a new group contribution coefficient corresponding to the third historical device user, wherein each of the The number of new group contribution coefficients corresponding to the third historical device user is the same as the number of group contribution coefficients corresponding to a second historical device user with a correlation; 针对每一个所述历史设备用户,基于所述统计数量阈值,对该历史设备用户当前具有的多个群体贡献系数进行滑窗处理,得到该历史设备用户对应的多个系数滑窗序列,并计算该多个系数滑窗序列中的每两个系数滑窗序列之间的序列相似度,以及,针对该历史设备用户对应的每一个系数滑窗序列,计算该系数滑窗序列与每一个其它系数滑窗序列之间的序列相似度的平均值,得到该系数滑窗序列对应的相似度均值,再在该多个系数滑窗序列中确定出对应的相似度均值最大的一个系数滑窗序列,作为该历史设备用户对应的目标系数滑窗序列;For each historical device user, based on the statistical quantity threshold, perform sliding window processing on multiple group contribution coefficients currently owned by the historical device user, obtain a plurality of coefficient sliding window sequences corresponding to the historical device user, and calculate The sequence similarity between every two coefficient sliding window sequences in the plurality of coefficient sliding window sequences, and, for each coefficient sliding window sequence corresponding to the historical device user, calculate the coefficient sliding window sequence and each other coefficient The average value of the sequence similarity between the sliding window sequences is obtained to obtain the corresponding similarity average value of the coefficient sliding window sequence, and then determine a coefficient sliding window sequence with the largest corresponding similarity average value in the plurality of coefficient sliding window sequences, As the target coefficient sliding window sequence corresponding to the historical device user; 针对每一个所述历史设备用户,基于该历史设备用户对应的所述目标系数滑窗序列包括的多个群体贡献系数,确定出该历史设备用户对应的目标群体贡献系数,并针对所述历史特征信息集合中的每一条历史用户特征信息,对该历史用户特征信息对应的每一个历史设备用户对应的目标群体贡献系数进行融合处理,得到历史用户特征系信息对应的贡献系数融合值;For each historical device user, based on the plurality of group contribution coefficients included in the target coefficient sliding window sequence corresponding to the historical device user, determine the target group contribution coefficient corresponding to the historical device user, and for the historical feature For each piece of historical user feature information in the information collection, the target group contribution coefficient corresponding to each historical device user corresponding to the historical user feature information is fused to obtain the contribution coefficient fusion value corresponding to the historical user feature system information; 针对所述历史特征信息集合中的每一条历史用户特征信息,基于该历史用户特征系信息对应的所述贡献系数融合值和所述第一系数,确定该历史用户特征系信息对应的融合系数。For each piece of historical user characteristic information in the historical characteristic information set, the fusion coefficient corresponding to the historical user characteristic information is determined based on the contribution coefficient fusion value and the first coefficient corresponding to the historical user characteristic information. 2.如权利要求1所述的基于大数据的用户画像分析方法,其特征在于,所述针对所述多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息的步骤,包括:2. The user portrait analysis method based on big data as claimed in claim 1, wherein, for each user terminal device in the plurality of user terminal devices, the information of the device user corresponding to the user terminal device is obtained. The steps of user profile information include: 判断是否获取到用户画像分析指令,并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;Judging whether the user portrait analysis instruction has been obtained, and after obtaining the user portrait analysis instruction, generating user portrait analysis notification information; 将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,每一个所述用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Send the user portrait analysis notification information to each user terminal device among the plurality of user terminal devices connected by communication, wherein each of the user terminal devices is configured to, after receiving the user portrait analysis notification information, send to The device user corresponding to the user terminal device displays the user portrait analysis notification information, and generates corresponding user portrait analysis confirmation information in response to the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information, and The user portrait analysis confirmation information is sent to the user data analysis server; 在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。After obtaining the user profile analysis confirmation information sent by each of the multiple user terminal devices, respectively obtain the user ID of the device user corresponding to each of the multiple user terminal devices portrait information. 3.如权利要求1或2所述的基于大数据的用户画像分析方法,其特征在于,所述基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户的步骤,包括:3. The user portrait analysis method based on big data as claimed in claim 1 or 2, wherein, based on the user portrait matching degree corresponding to each device user in the multiple device users, in the multiple The steps of determining the target device user among the device users include: 针对所述多个设备用户中的每一个设备用户,确定该设备用户对应的用户画像匹配度与预先配置的画像匹配度阈值之间的相对大小关系;For each device user in the plurality of device users, determine the relative size relationship between the user portrait matching degree corresponding to the device user and a pre-configured portrait matching degree threshold; 针对所述多个设备用户中的每一个设备用户,若该设备用户对应的用户画像匹配度大于或等于所述画像匹配度阈值,则将该设备用户确定为目标设备用户,若该设备用户对应的用户画像匹配度小于所述画像匹配度阈值,则将该设备用户确定为非目标设备用户。For each device user among the plurality of device users, if the user portrait matching degree corresponding to the device user is greater than or equal to the portrait matching degree threshold, determine the device user as the target device user, and if the device user corresponds to If the matching degree of the user portrait is less than the threshold matching degree of the portrait, the device user is determined to be a non-target device user. 4.如权利要求1或2所述的基于大数据的用户画像分析方法,其特征在于,所述基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户的步骤,包括:4. The user portrait analysis method based on big data as claimed in claim 1 or 2, wherein, based on the user portrait matching degree corresponding to each device user in the multiple device users, in the multiple The steps of determining the target device user among the device users include: 基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,对所述设备用户进行排序处理,得到所述多个设备用户对应的用户排序序列,其中,对所述设备用户进行排序处理时,按照所述设备用户对应的用户画像匹配度的先大后小或先小后大的顺序进行排序;Based on the user portrait matching degree corresponding to each of the multiple device users, sort the device users to obtain a user sorting sequence corresponding to the multiple device users, wherein the device users are sorted During the sorting process, sorting is performed in the order of first large and then small or first small and then large according to the matching degree of the user portrait corresponding to the device user; 获取预先针对所述目标用户群体配置的群体数量范围信息,并基于所述群体数量范围信息在所述用户排序序列中选择出用户画像匹配度最大的对应数量范围的设备用户作为目标设备用户。Acquiring group number range information pre-configured for the target user group, and selecting device users in the corresponding number range with the highest matching degree of user portraits in the user sorting sequence as target device users based on the group number range information. 5.一种基于大数据的用户画像分析系统,其特征在于,应用于用户数据分析服务器,所述基于大数据的用户画像分析系统包括:5. A user portrait analysis system based on big data, characterized in that it is applied to a user data analysis server, and the user portrait analysis system based on big data includes: 用户画像获取模块,用于针对通信连接的多个用户终端设备中的每一个用户终端设备,获取该用户终端设备对应的设备用户的用户画像信息,其中,所述用户画像信息基于对对应的设备用户进行数据采集得到的用户特征信息构建形成;A user portrait acquisition module, configured to acquire user portrait information of a device user corresponding to the user terminal device for each user terminal device in a plurality of user terminal devices connected by communication, wherein the user portrait information is based on the corresponding device The user characteristic information is constructed and formed through data collection by the user; 画像匹配度确定模块,用于针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于该设备用户对应的用户画像信息与预先确定的目标用户画像信息之间的匹配度,得到该设备用户对应的用户画像匹配度,其中,所述目标用户画像信息基于待构建形成的目标用户群体的用户特征信息构建形成;The portrait matching degree determination module is configured to, for each of the multiple device users corresponding to the multiple user terminal devices, based on the matching between the user portrait information corresponding to the device user and the predetermined target user portrait information degree, to obtain the user portrait matching degree corresponding to the device user, wherein the target user portrait information is constructed and formed based on the user characteristic information of the target user group to be formed; 目标用户确定模块,用于基于所述多个设备用户中的每一个设备用户对应的用户画像匹配度,在所述多个设备用户中确定出目标设备用户,其中,确定出的每一个所述目标设备用户用于构建形成所述目标用户群体;The target user determination module is configured to determine the target device user among the multiple device users based on the user portrait matching degree corresponding to each of the multiple device users, wherein each of the determined Target device users are used to construct and form the target user group; 其中,所述画像匹配度确定模块具体用于:Wherein, the image matching degree determination module is specifically used for: 针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the fusion coefficient corresponding to the user characteristic information; 针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,分别计算该设备用户对应的用户画像信息包括的每一条用户特征信息与所述目标用户画像信息包括的对应的用户特征信息之间的匹配度,得到该用户画像信息包括的每一条用户特征信息对应的特征匹配度;For each device user among the multiple device users corresponding to the multiple user terminal devices, calculate each piece of user characteristic information included in the user portrait information corresponding to the device user and the corresponding user information included in the target user portrait information. The matching degree between the characteristic information is obtained to obtain the characteristic matching degree corresponding to each piece of user characteristic information included in the user portrait information; 针对所述多个用户终端设备对应的多个设备用户中的每一个设备用户,基于每一条所述用户特征信息对应的融合系数,对该设备用户对应的用户画像信息包括的每一条用户特征信息对应的特征匹配度进行融合处理,得到该设备用户对应的用户画像匹配度;For each device user in the plurality of device users corresponding to the plurality of user terminal devices, based on the fusion coefficient corresponding to each piece of user characteristic information, each piece of user characteristic information included in the user portrait information corresponding to the device user The corresponding feature matching degree is fused to obtain the user portrait matching degree corresponding to the device user; 其中,所述针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息对应的融合系数,包括:Wherein, for each piece of user characteristic information included in the predetermined target user portrait information, determining the fusion coefficient corresponding to the user characteristic information includes: 获取在历史上构建形成的每一个历史目标用户群体,得到至少一个历史目标用户群体,其中,所述至少一个历史目标用户群体中的每一个所述历史目标用户群体包括至少一个历史设备用户;Obtaining each historical target user group constructed in history to obtain at least one historical target user group, wherein each of the at least one historical target user group includes at least one historical device user; 针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,确定该历史目标用户群体对应的历史用户画像信息,并对所述至少一个历史目标用户群体中的每一个历史目标用户群体对应的历史用户画像信息包括的历史用户特征信息进行去重筛选,得到对应的历史特征信息集合;For each historical target user group in the at least one historical target user group, determine historical user portrait information corresponding to the historical target user group, and correspond to each historical target user group in the at least one historical target user group The historical user profile information included in the historical user portrait information is deduplicated and filtered to obtain the corresponding historical feature information set; 针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information user, and determine the fusion coefficient corresponding to the historical user feature information based on the first historical device user; 针对预先确定的目标用户画像信息包括的每一条用户特征信息,确定该用户特征信息所属的历史用户特征信息,并将该历史用户特征信息对应的融合系数确定为该用户特征信息对应的融合系数;For each piece of user characteristic information included in the predetermined target user portrait information, determine the historical user characteristic information to which the user characteristic information belongs, and determine the fusion coefficient corresponding to the historical user characteristic information as the fusion coefficient corresponding to the user characteristic information; 其中,所述针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并基于该第一历史设备用户确定该历史用户特征信息对应的融合系数,包括:Wherein, for each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group, as the historical user characteristic information corresponding The first historical device user, and determining the fusion coefficient corresponding to the historical user feature information based on the first historical device user, including: 针对所述历史特征信息集合中的每一条历史用户特征信息,确定该历史用户特征信息在所述至少一个历史目标用户群体中对应的历史设备用户,作为该历史用户特征信息对应的第一历史设备用户,并统计该历史用户特征信息对应的第一历史设备用户的数量,得到该历史用户特征信息对应的第一用户统计数量,以及,基于该历史用户特征信息对应的第一用户统计数量确定该历史用户特征信息对应的第一系数,其中,所述第一系数和所述第一用户统计数量之间具有正相关关系;For each piece of historical user characteristic information in the historical characteristic information set, determine the historical device user corresponding to the historical user characteristic information in the at least one historical target user group as the first historical device corresponding to the historical user characteristic information users, and count the number of first historical device users corresponding to the historical user characteristic information, obtain the first user statistical quantity corresponding to the historical user characteristic information, and determine the first user statistical quantity corresponding to the historical user characteristic information. A first coefficient corresponding to historical user characteristic information, wherein there is a positive correlation between the first coefficient and the first user statistics; 针对所述至少一个历史目标用户群体中的每一个历史目标用户群体,分别确定该历史目标用户群体中的每一个历史设备用户对该历史目标用户群体对应的历史推荐信息的信息关注度,并基于每一个历史设备用户对应的信息关注度确定每一个历史设备用户在该历史目标用户群体中的群体贡献系数,其中,所述群体贡献系数与所述信息关注度之间具有正相关关系,所述历史目标用户群体为多个;For each historical target user group in the at least one historical target user group, respectively determine the information attention of each historical device user in the historical target user group to the historical recommendation information corresponding to the historical target user group, and based on The information attention corresponding to each historical equipment user determines the group contribution coefficient of each historical equipment user in the historical target user group, wherein the group contribution coefficient has a positive correlation with the information attention, and the There are multiple historical target user groups; 针对每一个所述历史设备用户,统计该历史设备用户对应的群体贡献系数的数量,得到该历史设备用于对应的系数统计数量,并确定该系数统计数量与预先配置的统计数量阈值之间的相对大小关系,以及,在该系数统计数量大于所述统计数量阈值时,将该历史设备用户确定为第二历史设备用户,或者,在该系数统计数量小于或等于所述统计数量阈值时,将该历史设备用户确定为第三历史设备用户,其中,每一个所述历史设备用户对应的多个群体贡献系数基于每一个群体贡献系数对应的历史目标用户群体的历史形成时间进行依次排列;For each historical device user, count the number of group contribution coefficients corresponding to the historical device user, obtain the corresponding coefficient statistics for the historical device, and determine the coefficient between the coefficient statistics and the pre-configured statistical threshold Relative size relationship, and, when the statistical quantity of the coefficient is greater than the statistical quantity threshold, determine the historical device user as the second historical device user, or, when the statistical quantity of the coefficient is less than or equal to the statistical quantity threshold, determine The historical device user is determined as the third historical device user, wherein the multiple group contribution coefficients corresponding to each historical device user are arranged in order based on the historical formation time of the historical target user group corresponding to each group contribution coefficient; 在每一个所述第二历史设备用户对应的系数统计数量中确定出具有最小值的系数统计数量,作为第一数量参考值,并针对每一个所述第三历史设备用户,计算该第三历史设备用户对应的群体贡献系数的平均值,以及,基于该平均值和每一个所述第二历史设备用户对应的群体贡献系数的平均值,确定出具有相关关系的一个第二历史设备用户,并基于该第二历史设备用户对应的群体贡献系数,对该第三历史设备用户对应的群体贡献系数进行插值处理,得到该第三历史设备用户对应的新的群体贡献系数,其中,每一个所述第三历史设备用户对应的新的群体贡献系数的数量与具有相关关系的一个第二历史设备用户对应的群体贡献系数的数量相同;Determine the coefficient statistic quantity with the smallest value among the coefficient statistic quantities corresponding to each of the second historical device users as the first quantity reference value, and calculate the third history for each of the third historical device users The average value of the group contribution coefficient corresponding to the device user, and, based on the average value and the average value of the group contribution coefficient corresponding to each of the second historical device users, determine a second historical device user with a correlation, and Based on the group contribution coefficient corresponding to the second historical device user, the group contribution coefficient corresponding to the third historical device user is interpolated to obtain a new group contribution coefficient corresponding to the third historical device user, wherein each of the The number of new group contribution coefficients corresponding to the third historical device user is the same as the number of group contribution coefficients corresponding to a second historical device user having a correlation relationship; 针对每一个所述历史设备用户,基于所述统计数量阈值,对该历史设备用户当前具有的多个群体贡献系数进行滑窗处理,得到该历史设备用户对应的多个系数滑窗序列,并计算该多个系数滑窗序列中的每两个系数滑窗序列之间的序列相似度,以及,针对该历史设备用户对应的每一个系数滑窗序列,计算该系数滑窗序列与每一个其它系数滑窗序列之间的序列相似度的平均值,得到该系数滑窗序列对应的相似度均值,再在该多个系数滑窗序列中确定出对应的相似度均值最大的一个系数滑窗序列,作为该历史设备用户对应的目标系数滑窗序列;For each historical device user, based on the statistical quantity threshold, perform sliding window processing on multiple group contribution coefficients currently owned by the historical device user, obtain a plurality of coefficient sliding window sequences corresponding to the historical device user, and calculate The sequence similarity between every two coefficient sliding window sequences in the plurality of coefficient sliding window sequences, and, for each coefficient sliding window sequence corresponding to the historical device user, calculate the coefficient sliding window sequence and each other coefficient The average value of the sequence similarity between the sliding window sequences is obtained to obtain the corresponding similarity average value of the coefficient sliding window sequence, and then determine a coefficient sliding window sequence with the largest corresponding similarity average value among the plurality of coefficient sliding window sequences, As the target coefficient sliding window sequence corresponding to the historical device user; 针对每一个所述历史设备用户,基于该历史设备用户对应的所述目标系数滑窗序列包括的多个群体贡献系数,确定出该历史设备用户对应的目标群体贡献系数,并针对所述历史特征信息集合中的每一条历史用户特征信息,对该历史用户特征信息对应的每一个历史设备用户对应的目标群体贡献系数进行融合处理,得到历史用户特征系信息对应的贡献系数融合值;For each historical device user, based on the plurality of group contribution coefficients included in the target coefficient sliding window sequence corresponding to the historical device user, determine the target group contribution coefficient corresponding to the historical device user, and for the historical feature For each piece of historical user feature information in the information collection, the target group contribution coefficient corresponding to each historical device user corresponding to the historical user feature information is fused to obtain the contribution coefficient fusion value corresponding to the historical user feature system information; 针对所述历史特征信息集合中的每一条历史用户特征信息,基于该历史用户特征系信息对应的所述贡献系数融合值和所述第一系数,确定该历史用户特征系信息对应的融合系数。For each piece of historical user characteristic information in the historical characteristic information set, the fusion coefficient corresponding to the historical user characteristic information is determined based on the contribution coefficient fusion value and the first coefficient corresponding to the historical user characteristic information. 6.如权利要求5所述的基于大数据的用户画像分析系统,其特征在于,所述用户画像获取模块具体用于:6. The user portrait analysis system based on big data as claimed in claim 5, wherein the user portrait acquisition module is specifically used for: 判断是否获取到用户画像分析指令,并在获取到所述用户画像分析指令之后,生成用户画像分析通知信息;Judging whether the user portrait analysis instruction has been obtained, and after obtaining the user portrait analysis instruction, generating user portrait analysis notification information; 将所述用户画像分析通知信息发送给通信连接的多个用户终端设备中的每一个用户终端设备,其中,每一个所述用户终端设备用于在接收到所述用户画像分析通知信息之后,向所述用户终端设备对应的设备用户显示所述用户画像分析通知信息,并响应该设备用户基于所述用户画像分析通知信息同意进行用户画像分析的操作生成对应的用户画像分析确认信息,以及,将所述用户画像分析确认信息发送给所述用户数据分析服务器;Sending the user portrait analysis notification information to each user terminal device among a plurality of user terminal devices connected by communication, wherein each of the user terminal devices is configured to, after receiving the user portrait analysis notification information, send to The device user corresponding to the user terminal device displays the user portrait analysis notification information, and generates corresponding user portrait analysis confirmation information in response to the device user agreeing to perform user portrait analysis based on the user portrait analysis notification information, and The user portrait analysis confirmation information is sent to the user data analysis server; 在获取到所述多个用户终端设备中的每一个用户终端设备发送的所述用户画像分析确认信息之后,分别获取所述多个用户终端设备中的每一个用户终端设备对应的设备用户的用户画像信息。After obtaining the user profile analysis confirmation information sent by each of the multiple user terminal devices, respectively obtain the user ID of the device user corresponding to each of the multiple user terminal devices portrait information.
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