WO2016054908A1 - Internet of things big data platform-based intelligent user profiling method and apparatus - Google Patents
Internet of things big data platform-based intelligent user profiling method and apparatus Download PDFInfo
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- WO2016054908A1 WO2016054908A1 PCT/CN2015/077320 CN2015077320W WO2016054908A1 WO 2016054908 A1 WO2016054908 A1 WO 2016054908A1 CN 2015077320 W CN2015077320 W CN 2015077320W WO 2016054908 A1 WO2016054908 A1 WO 2016054908A1
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- the invention relates to the field of data mining technology, in particular to a smart user portrait method and device based on the Internet of Things big data platform.
- User portraits are an effective tool for sketching target users, contacting users' appeals and design directions.
- User portrait is a kind of carrier that can combine qualitative and quantitative methods. Through quantitative preliminary research, we can get a more accurate understanding of the user group, and it can be good for users in the later establishment of user roles. Prioritize the rankings to highlight core, large-scale users.
- the data source of user portraits is relatively single and the classification is not clear, resulting in low efficiency and accuracy of data analysis and mining.
- the embodiment of the invention provides a smart user portrait method and device based on the Internet of Things big data platform, aiming at at least improving the accuracy and efficiency of data analysis of the user portrait, and further providing sufficient analysis for the consumer group analysis and marketing strategy formulation. Resources.
- an embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform, including:
- the big data platform is used to perform data mining on the bill data file to describe the user portrait.
- the step of processing the collected user data to generate the bill data file includes:
- the user data is processed based on the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, and the main dimensions include time, place, user, and item type;
- the bill is generated into a bill data file.
- the data mining is performed on the bill data file by using a big data platform, and the steps of describing the user image include:
- the first MR model is used to combine all the bill data files of a single user's bills into one user-related data block, and perform individual analysis of the data mining, and the steps of obtaining individual analysis parameters include:
- the method also includes:
- the user is analyzed for needs and strategic planning.
- the user data is obtained by the Internet of Things information center based on the RFID tag and the sensor network.
- the embodiment of the invention also provides a smart user portrait device based on the Internet of Things big data platform, comprising:
- the acquisition module is configured to collect user data from the Internet of Things information center;
- the processing module is configured to process the collected user data to generate a bill data file
- the mining module is configured to perform data mining on the bill data file by using a big data platform to describe a user image.
- the processing module is further configured to: establish a bill conversion table for the collected user data; process the user data according to the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item,
- the main dimensions include usage time, location, user, and item type; the bill is generated into the bill data file.
- the mining module is further configured to store the bill data file to the big data platform; on the big data platform, use the first MR model to merge all the bill data files of a single type of bill of the single user into one User-related data blocks, and individual analysis of data mining, to obtain individual analysis parameters; using the second MR model to calculate global parameters of the bill data file; using the third MR model, according to the individual analysis parameters and global parameters, Perform final data mining, describe user images based on mining results, and output user tags to the database.
- the mining module is further configured to use a first MR model to combine all the bill data files of a type of bill of a single user in multiple dimensions to generate a user-related data block;
- the new binary records are saved to the database; based on the merged data blocks, the user attributes are extracted, the behavior characteristics of the items are counted, various types of tags related to the individual are generated, and individual analysis parameters are obtained.
- the device also includes:
- the planning module is set to perform demand analysis and strategic planning for the user according to the user portrait.
- the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system according to the attribute of the user.
- the object behavior and other indicators are used to describe the user's portrait, thereby improving the accuracy and efficiency of the data analysis of the user's portrait, and providing sufficient resources for the user to analyze the consumer group, adjust the function plan, and formulate the marketing strategy.
- FIG. 1 is a schematic flow chart of an embodiment of a smart user portrait method based on the Internet of Things big data platform of the present invention
- FIG. 2 is a schematic diagram of a data processing flow of a big data platform in an embodiment of the present invention
- FIG. 3 is a schematic flow chart of another embodiment of a smart user portrait method based on the Internet of Things big data platform of the present invention.
- FIG. 4 is a schematic structural diagram of an embodiment of an intelligent user portrait device based on the Internet of Things big data platform of the present invention
- FIG. 5 is a schematic structural diagram of another embodiment of an intelligent user portrait device based on the Internet of Things big data platform of the present invention.
- the solution of the embodiment of the present invention is mainly: the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user, the behavior of the item, and the like, thereby improving
- the accuracy and efficiency of data analysis of user images provide users with sufficient resources for consumer group analysis, function planning adjustment and marketing strategy formulation.
- an embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform, including:
- Step S101 collecting user data from the Internet of Things information center
- the basis of the user's portrait is to have a preliminary understanding of the massive users through the data.
- the user data extraction analysis and the questionnaire survey are used to determine the dimensional indicators of the statistical analysis according to the product target.
- the dimension of analysis can be comprehensively analyzed according to population attributes and product behavior attributes.
- Population attributes generally include: region, age, gender, culture, occupation, income, living habits, consumption habits, etc.
- product behavior generally includes: product categories, frequency of activity, product preferences, product drivers, usage habits, product consumption, etc.
- the solution of the embodiment mainly completes the method of converting the use of the item by the user into a description of the user's portrait, including collecting the information of the Internet of Things, storing the information of the Internet of Things, mining the data, and describing the user's portrait, so that the generation of the user's portrait can be based on the collection.
- original user data is extracted from the Internet of Things information center, and the original user data is acquired by the Internet of Things information center according to the RFID tag and the sensor network.
- the Internet of Things can capture and store data based on RFID tags and sensor networks.
- Step S102 processing the collected user data to generate a bill data file
- Forming a bill conversion table for the collected user data processing the user data based on the bill conversion table to form a bill, the bill being set to describe the user's use behavior of the item, and the main dimensions include the use time, location, User, item type, etc.
- the bill data file is generated according to the bill.
- Step S103 Perform data mining on the bill data file by using a big data platform to describe a user portrait.
- the data file generated after processing is stored in the big data platform, and the data is mined by the big data platform system to obtain the distribution vector of different users on each subject, thereby describing according to the attributes of the user, the behavior of the item, the payment behavior and the like.
- User portraits make it easy to segment users, find user needs, segment target markets, adjust function planning, and marketing strategies.
- the big data platform Hadoop is used to merge the bill data files, and the MapReduce model is cached and built in the Reduce instance, the data is mined, and the user tags are output to the HBASE database.
- the individual user image mining process is an analysis of the different types of CDRs associated with the user, and considers the distribution of the overall parameters of each type of CDR regardless of the individual.
- CDR data files need to be merged according to the MapReduce model using the Hadoop system.
- the merged file all the original CDR data files of one user type CDR are merged into one data block, and this data block is used as a binary new file. The record is saved to the HBASE database.
- the first MR model is used to combine all the bill data files of a single type of bill of a single user in multiple dimensions, and the top item such as frequency or time is used as a label to describe the user. It mainly includes extracting target user attributes, counting product behavior characteristics, generating various types of labels related to individual users, and obtaining Individual analysis parameters, including content preferences, frequent use of items, etc., in order to segment users, find core users, segment target markets, and adjust functional planning and marketing strategies.
- a user-related data block is generated by merging, and the data block is saved to the database as a binary new record.
- the user portrait is described according to the attributes of the user, the behavior of the item, and the payment behavior.
- the second MR model is used to calculate global parameters of the bill data file.
- the output is likely to be in a relational database depending on the access efficiency.
- the final data mining is performed based on the individual analysis parameters and global parameters obtained from the previous two MR models, the user images are described according to the mining results, and the user tags are output to the HBASE database. At this point, the description of the user's portrait is completed.
- the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user and the behavior of the item, thereby improving the user image.
- the accuracy and efficiency of data analysis provide users with sufficient resources for consumer group analysis, functional planning adjustment, and marketing strategy development.
- another embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform. Based on the foregoing embodiment, the method further includes:
- Step S104 performing demand analysis and strategy planning on the user according to the user portrait.
- the embodiment further includes a solution for performing requirement analysis and policy planning on the user.
- a relatively accurate understanding of the user group is obtained, and the user can be subdivided based on the user portrait, and the user role can be well established in the later generation of the user role.
- User prioritization is sorted to highlight core, large-scale users. Therefore, it provides resources and basis for finding user needs, subdividing target markets, adjusting function planning and marketing strategies.
- an embodiment of the present invention provides an intelligent user portrait device based on the Internet of Things big data platform, comprising: an acquisition module 201, a processing module 202, and a mining module 203, wherein:
- the collecting module 201 is configured to collect user data from the Internet of Things information center; the collecting module 201 is disposed between the access layer and the network layer.
- the sensing layer of the collection module 201 is mainly composed of an RFID and a sensor network, and the access layer mainly completes network access of various devices, such as a 3G/4G, Mesh network.
- the processing module 202 is configured to process the collected user data to generate a bill data file.
- the mining module 203 is configured to perform data mining on the bill data file by using a big data platform to describe a user image.
- the basis of the user's portrait is to have a preliminary understanding of the mass of users through the data, generally using user data extraction analysis and questionnaire research to determine the dimensional indicators of statistical analysis according to product goals.
- the dimension of analysis can be comprehensively analyzed according to population attributes and product behavior attributes.
- Population attributes generally include: region, age, gender, culture, occupation, income, living habits, consumption habits, etc.
- product behavior generally includes: product categories, frequency of activity, product preferences, product drivers, usage habits, product consumption, etc.
- the solution of the embodiment mainly completes the method of converting the use of the item by the user into a description of the user's portrait, including collecting the information of the Internet of Things, storing the information of the Internet of Things, mining the data, and describing the user's portrait, so that the generation of the user's portrait can be based on the collection.
- original user data is extracted from the Internet of Things information center, and the original user data is acquired by the Internet of Things information center according to the RFID tag and the sensor network.
- the Internet of Things can capture and store data based on RFID tags and sensor networks.
- the collected user data is processed to generate a bill data file.
- the method includes: establishing a bill conversion table for the collected user data; processing the user data according to the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, and the main dimension includes the use time. , location, user, item type, etc. After that, the bill data file is generated according to the bill.
- the CDR data file generated after processing is stored to the big data platform, and the data is mined by the big data platform system, and the distribution vectors of different users on each subject are obtained, thereby according to the attributes of the user, the behavior of the goods, the behavior of payment, and the like.
- To describe user portraits to facilitate segmentation of users, to find user needs, to segment target markets, to adjust functional planning and marketing strategies.
- the big data platform Hadoop is used to merge the bill data files, and the MapReduce model is cached and built in the Reduce instance, the data is mined, and the user tags are output to the HBASE database.
- the individual user image mining process is an analysis of the different types of CDRs associated with the user, and considers the distribution of the overall parameters of each type of CDR regardless of the individual.
- CDR data files need to be merged according to the MepReduce model using the Hadoop system.
- the merged file all the original CDR data files of one user type CDR are merged into one data block, and this data block is used as a binary new file. The record is saved to the HBASE database.
- the first MR model is used to combine all the bill data files of a single type of bill of a single user in multiple dimensions, and the top item such as frequency or time is used as a label to describe the user. It mainly includes extracting target user attributes, counting product behavior characteristics, generating various types of labels related to individual users, and obtaining individual analysis parameters, including content preferences and frequent use of items, so as to subdivide users, find core users, and subdivide targets. Market, adjustment function planning and marketing strategy.
- a user-related data block is generated by merging, and the data block is saved to the database as a binary new record.
- the user portrait is described according to the attributes of the user, the behavior of the item, and the payment behavior.
- the second MR model is used to calculate global parameters of the bill data file.
- the output is likely to be in a relational database depending on the access efficiency.
- the final data mining is performed based on the individual analysis parameters and global parameters obtained from the previous two MR models, the user images are described according to the mining results, and the user tags are output to the HBASE database. At this point, the description of the user's portrait is completed.
- the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user and the behavior of the item, thereby improving the user image.
- the accuracy and efficiency of data analysis provide users with sufficient resources for consumer group analysis, functional planning adjustment, and marketing strategy development.
- another embodiment of the present invention provides a smart user portrait device based on the Internet of Things big data platform. Based on the foregoing embodiment, the method further includes:
- the planning module 204 is configured to perform demand analysis and strategic planning on the user according to the user portrait.
- the embodiment further includes a solution for performing requirement analysis and policy planning on the user.
- a relatively accurate understanding of the user group is obtained, and the user can be subdivided based on the user portrait, and the user role can be well established in the later generation of the user role.
- User prioritization is sorted to highlight core, large-scale users. Therefore, it provides resources and basis for finding user needs, subdividing target markets, adjusting function planning and marketing strategies.
- the embodiment of the present invention is based on the intelligent user portrait method and device of the Internet of Things big data platform.
- the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system according to the attributes of the user, the behavior of the item, etc.
- the indicators describe the user's portrait, which improves the accuracy and efficiency of the data analysis of the user's portrait, and provides sufficient resources for the consumer to analyze the consumer group, adjust the function plan and formulate the marketing strategy.
- the smart user portrait method and apparatus based on the Internet of Things big data platform provided by the embodiments of the present invention have the following beneficial effects: the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the big data platform system The data is mined, and the user's portrait is described according to the attributes of the user, the behavior of the item, etc., thereby improving the accuracy and efficiency of data analysis of the user's portrait, and further providing sufficient analysis for the consumer group analysis, function planning adjustment, and marketing strategy formulation. Resources.
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Abstract
Disclosed are an Internet of Things big data platform-based intelligent user profiling method and apparatus. The method comprises: collecting user data from an Internet of Things information center; processing the collected user data to generate a bill data file; and performing data mining on the bill data file by using a big data platform, and depicting a user profile. By means of the present invention, data is acquired according to an RFID tag and a sensor network by an Internet of Things information center, data is mined by using a big data platform system, and a user profile is depicted according to indexes such as attributes and goods behaviors of a user, so that the accuracy and efficiency of data analysis on user profiling are improved, and sufficient resources are further provided for performing consumer group analysis, function planning and adjustment and marketing strategy making on the user.
Description
本发明涉及数据挖掘技术领域,尤其涉及一种基于物联网大数据平台的智能用户画像方法及装置。The invention relates to the field of data mining technology, in particular to a smart user portrait method and device based on the Internet of Things big data platform.
用户画像是一种勾画目标用户、联系用户诉求与设计方向的有效工具。用户画像是一种能将定性与定量方法很好结合在一起的载体,通过定量化的前期调研能获得一个对于用户群较为精准的认识,在后期的用户角色的建立中能很好地对用户优先顺序进行排序,将核心的、规模较大的用户着重突出出来。User portraits are an effective tool for sketching target users, contacting users' appeals and design directions. User portrait is a kind of carrier that can combine qualitative and quantitative methods. Through quantitative preliminary research, we can get a more accurate understanding of the user group, and it can be good for users in the later establishment of user roles. Prioritize the rankings to highlight core, large-scale users.
而目前用户画像的数据来源较为单一且分类不明确,造成数据分析与挖掘效率和准确率较低。At present, the data source of user portraits is relatively single and the classification is not clear, resulting in low efficiency and accuracy of data analysis and mining.
发明内容Summary of the invention
本发明实施例提供了一种基于物联网大数据平台的智能用户画像方法及装置,旨在至少提高用户画像的数据分析准确性和效率,进而为用户进行消费群体分析以及营销策略制定提供充分的资源。The embodiment of the invention provides a smart user portrait method and device based on the Internet of Things big data platform, aiming at at least improving the accuracy and efficiency of data analysis of the user portrait, and further providing sufficient analysis for the consumer group analysis and marketing strategy formulation. Resources.
为了达到上述目的,本发明实施例提出了一种基于物联网大数据平台的智能用户画像方法,包括:In order to achieve the above object, an embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform, including:
从物联网信息中心采集用户数据;Collect user data from the IoT Information Center;
对采集的用户数据进行处理,生成话单数据文件;Processing the collected user data to generate a bill data file;
采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。The big data platform is used to perform data mining on the bill data file to describe the user portrait.
所述对采集的用户数据进行处理,生成话单数据文件的步骤包括:The step of processing the collected user data to generate the bill data file includes:
对采集的用户数据建立话单转换表;Establishing a bill conversion table for the collected user data;
基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型;
The user data is processed based on the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, and the main dimensions include time, place, user, and item type;
将所述话单生成话单数据文件。The bill is generated into a bill data file.
所述采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像的步骤包括:The data mining is performed on the bill data file by using a big data platform, and the steps of describing the user image include:
将话单数据文件存储至大数据平台;Store the bill data file to the big data platform;
在所述大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数;On the big data platform, using the first MR model, all the bill data files of a single type of bill of a single user are merged into one user-related data block, and the individual analysis of the data mining is performed to obtain individual analysis parameters;
采用第二MR模型,计算话单数据文件的全局参数;Using the second MR model, calculating global parameters of the bill data file;
采用第三MR模型,根据所述个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,输出用户标签至数据库。Using the third MR model, final data mining is performed according to the individual analysis parameters and global parameters, the user portrait is described according to the mining result, and the user tag is output to the database.
所述采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数的步骤包括:The first MR model is used to combine all the bill data files of a single user's bills into one user-related data block, and perform individual analysis of the data mining, and the steps of obtaining individual analysis parameters include:
采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,生成一个用户相关的数据块;Using the first MR model, all the bill data files of a single type of bill of a single user are combined in multiple dimensions to generate a user-related data block;
将所述数据块作为一条二进制的新记录保存到数据库;Saving the data block as a binary new record to the database;
基于合并后的数据块,提取用户属性,统计物品行为特征,产生用户个体相关的各类标签,得到个体分析参数。Based on the merged data block, user attributes are extracted, and the behavior characteristics of the items are counted, and various types of tags related to the individual are generated, and individual analysis parameters are obtained.
该方法还包括:The method also includes:
根据用户画像,对用户进行需求分析和策略规划。According to the user's portrait, the user is analyzed for needs and strategic planning.
所述用户数据由所述物联网信息中心根据RFID标签和传感器网络获取。The user data is obtained by the Internet of Things information center based on the RFID tag and the sensor network.
本发明实施例还提出了一种基于物联网大数据平台的智能用户画像装置,包括:The embodiment of the invention also provides a smart user portrait device based on the Internet of Things big data platform, comprising:
采集模块,设置为从物联网信息中心采集用户数据;The acquisition module is configured to collect user data from the Internet of Things information center;
处理模块,设置为对采集的用户数据进行处理,生成话单数据文件;The processing module is configured to process the collected user data to generate a bill data file;
挖掘模块,设置为采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。
The mining module is configured to perform data mining on the bill data file by using a big data platform to describe a user image.
所述处理模块,还设置为对采集的用户数据建立话单转换表;基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型;将所述话单生成话单数据文件。The processing module is further configured to: establish a bill conversion table for the collected user data; process the user data according to the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, The main dimensions include usage time, location, user, and item type; the bill is generated into the bill data file.
所述挖掘模块,还设置为将话单数据文件存储至大数据平台;在所述大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数;采用第二MR模型,计算话单数据文件的全局参数;采用第三MR模型,根据所述个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,输出用户标签至数据库。The mining module is further configured to store the bill data file to the big data platform; on the big data platform, use the first MR model to merge all the bill data files of a single type of bill of the single user into one User-related data blocks, and individual analysis of data mining, to obtain individual analysis parameters; using the second MR model to calculate global parameters of the bill data file; using the third MR model, according to the individual analysis parameters and global parameters, Perform final data mining, describe user images based on mining results, and output user tags to the database.
所述挖掘模块,还设置为采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,生成一个用户相关的数据块;将所述数据块作为一条二进制的新记录保存到数据库;基于合并后的数据块,提取用户属性,统计物品行为特征,产生用户个体相关的各类标签,得到个体分析参数。The mining module is further configured to use a first MR model to combine all the bill data files of a type of bill of a single user in multiple dimensions to generate a user-related data block; The new binary records are saved to the database; based on the merged data blocks, the user attributes are extracted, the behavior characteristics of the items are counted, various types of tags related to the individual are generated, and individual analysis parameters are obtained.
该装置还包括:The device also includes:
规划模块,设置为根据用户画像,对用户进行需求分析和策略规划。The planning module is set to perform demand analysis and strategic planning for the user according to the user portrait.
本发明实施例提出的一种基于物联网大数据平台的智能用户画像方法及装置,由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。An intelligent user portrait method and device based on the Internet of Things big data platform proposed by the embodiment of the present invention, the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system according to the attribute of the user. The object behavior and other indicators are used to describe the user's portrait, thereby improving the accuracy and efficiency of the data analysis of the user's portrait, and providing sufficient resources for the user to analyze the consumer group, adjust the function plan, and formulate the marketing strategy.
图1是本发明基于物联网大数据平台的智能用户画像方法一实施例的流程示意图;1 is a schematic flow chart of an embodiment of a smart user portrait method based on the Internet of Things big data platform of the present invention;
图2是本发明实施例中大数据平台的数据处理流程示意图;2 is a schematic diagram of a data processing flow of a big data platform in an embodiment of the present invention;
图3是本发明基于物联网大数据平台的智能用户画像方法另一实施例的流程示意图;3 is a schematic flow chart of another embodiment of a smart user portrait method based on the Internet of Things big data platform of the present invention;
图4是本发明基于物联网大数据平台的智能用户画像装置一实施例的结构示意图;
4 is a schematic structural diagram of an embodiment of an intelligent user portrait device based on the Internet of Things big data platform of the present invention;
图5是本发明基于物联网大数据平台的智能用户画像装置另一实施例的结构示意图。FIG. 5 is a schematic structural diagram of another embodiment of an intelligent user portrait device based on the Internet of Things big data platform of the present invention.
为了使本发明的技术方案更加清楚、明了,下面将结合附图作进一步详述。In order to make the technical solutions of the present invention clearer and clearer, the following will be further described in detail with reference to the accompanying drawings.
本发明实施例的解决方案主要是:由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。The solution of the embodiment of the present invention is mainly: the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user, the behavior of the item, and the like, thereby improving The accuracy and efficiency of data analysis of user images provide users with sufficient resources for consumer group analysis, function planning adjustment and marketing strategy formulation.
如图1所示,本发明一实施例提出一种基于物联网大数据平台的智能用户画像方法,包括:As shown in FIG. 1 , an embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform, including:
步骤S101,从物联网信息中心采集用户数据;Step S101, collecting user data from the Internet of Things information center;
用户画像的基础,是通过数据对海量用户有一个初步的了解,一般采用用户数据提取分析与问卷调研两种方式进行,根据产品目标确定统计分析的维度指标。其中,分析的维度,可以按照人口属性和产品行为属性进行综合分析。人口属性一般包括:地域、年龄、性别、文化、职业、收入、生活习惯、消费习惯等;产品行为一般包括:产品类别、活跃频率、产品喜好、产品驱动、使用习惯、产品消费等。The basis of the user's portrait is to have a preliminary understanding of the massive users through the data. Generally, the user data extraction analysis and the questionnaire survey are used to determine the dimensional indicators of the statistical analysis according to the product target. Among them, the dimension of analysis can be comprehensively analyzed according to population attributes and product behavior attributes. Population attributes generally include: region, age, gender, culture, occupation, income, living habits, consumption habits, etc.; product behavior generally includes: product categories, frequency of activity, product preferences, product drivers, usage habits, product consumption, etc.
本实施例方案主要完成将用户对物品的使用转化成描述用户画像的方式,包括对物联网信息的采集、物联网信息的存储、挖掘数据以及描述用户画像,实现用户画像的产生可以基于以采集自物联网的大数据的支持和挖掘,采取将定量化和定性化方法相结合来创建用户画像。The solution of the embodiment mainly completes the method of converting the use of the item by the user into a description of the user's portrait, including collecting the information of the Internet of Things, storing the information of the Internet of Things, mining the data, and describing the user's portrait, so that the generation of the user's portrait can be based on the collection. Support and mining of big data from the Internet of Things, combined with quantitative and qualitative methods to create user portraits.
具体地,首先,从物联网信息中心提取原始用户数据,该原始用户数据由物联网信息中心根据RFID标签和传感器网络获取。Specifically, first, original user data is extracted from the Internet of Things information center, and the original user data is acquired by the Internet of Things information center according to the RFID tag and the sensor network.
物联网基于云计算平台和智能网络,可以依据RFID标签和传感器网络获取数据,并将之存储。Based on cloud computing platforms and intelligent networks, the Internet of Things can capture and store data based on RFID tags and sensor networks.
步骤S102,对采集的用户数据进行处理,生成话单数据文件;
Step S102, processing the collected user data to generate a bill data file;
对采集的用户数据建立话单转换表;基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型等。Forming a bill conversion table for the collected user data; processing the user data based on the bill conversion table to form a bill, the bill being set to describe the user's use behavior of the item, and the main dimensions include the use time, location, User, item type, etc.
之后,根据话单生成话单数据文件。After that, the bill data file is generated according to the bill.
每天不同类型的话单,都会产生多个话单数据文件。Multiple bill data files are generated for different types of bills every day.
步骤S103,采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。Step S103: Perform data mining on the bill data file by using a big data platform to describe a user portrait.
将处理后生成的话单数据文件存储至大数据平台,以大数据平台系统对数据进行挖掘,获得不同用户在各个主体上的分布向量,从而根据用户的属性、物品行为、付费行为等指标来描述用户画像,便于对用户进行细分,寻找用户需求,细分目标市场,调整功能规划以及营销策略等。The data file generated after processing is stored in the big data platform, and the data is mined by the big data platform system to obtain the distribution vector of different users on each subject, thereby describing according to the attributes of the user, the behavior of the item, the payment behavior and the like. User portraits make it easy to segment users, find user needs, segment target markets, adjust function planning, and marketing strategies.
具体地,结合图2所示,在大数据平台上,使用大数据平台Hadoop合并话单数据文件,并在Reduce实例中缓存和建立MapReduce模型,挖掘数据,把用户标签输出到HBASE数据库。Specifically, as shown in FIG. 2, on the big data platform, the big data platform Hadoop is used to merge the bill data files, and the MapReduce model is cached and built in the Reduce instance, the data is mined, and the user tags are output to the HBASE database.
其中,个体用户画像挖掘过程,是对用户相联的不同类型话单总体的一个分析,同时考虑每类话单不分个体的各项总体参数分布情况。Among them, the individual user image mining process is an analysis of the different types of CDRs associated with the user, and considers the distribution of the overall parameters of each type of CDR regardless of the individual.
因为每天不同类型的话单,都会产生多个话单数据文件。需要对这些话单数据文件使用Hadoop系统根据MapReduce模型进行合并,合并后的文件中,一个用户一类话单所有的原始话单数据文件,合并为一个数据块,此数据块作为一条二进制的新记录保存到HBASE数据库。Because of the different types of bills every day, multiple bill data files are generated. These CDR data files need to be merged according to the MapReduce model using the Hadoop system. In the merged file, all the original CDR data files of one user type CDR are merged into one data block, and this data block is used as a binary new file. The record is saved to the HBASE database.
具体数据挖掘过程如下:The specific data mining process is as follows:
首先,在大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数。对应MR模型中的二进制类型记录,通过此种方式,保证单个用户的数据在一个数据节点上。First, on the big data platform, using the first MR model, all the bill data files of a single type of bill of a single user are merged into one user-related data block, and the individual analysis of the data mining is performed to obtain individual analysis parameters. Corresponding to the binary type record in the MR model, in this way, the data of a single user is guaranteed to be on one data node.
其中,在合并时,采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,使用频率或时间等靠前的物品即作为标签用来描述用户。主要包括提取目标用户属性,统计产品行为特征、产生用户个体相关的各类标签,得到
个体分析参数,包括内容偏好、物品使用频繁度等,以便对用户进行细分,寻找核心用户,细分目标市场,调整功能规划及营销策略。In the merge, the first MR model is used to combine all the bill data files of a single type of bill of a single user in multiple dimensions, and the top item such as frequency or time is used as a label to describe the user. It mainly includes extracting target user attributes, counting product behavior characteristics, generating various types of labels related to individual users, and obtaining
Individual analysis parameters, including content preferences, frequent use of items, etc., in order to segment users, find core users, segment target markets, and adjust functional planning and marketing strategies.
通过合并生成一个用户相关的数据块,将所述数据块作为一条二进制的新记录保存到数据库。A user-related data block is generated by merging, and the data block is saved to the database as a binary new record.
之后,以合并后的话单数据文件为依据,根据用户的属性、物品行为、付费行为等指标来描述用户画像。采用第二MR模型,计算话单数据文件的全局参数。输出结果根据存取效率有可能在关系型数据库中。Then, based on the combined bill data file, the user portrait is described according to the attributes of the user, the behavior of the item, and the payment behavior. The second MR model is used to calculate global parameters of the bill data file. The output is likely to be in a relational database depending on the access efficiency.
最后,采用第三MR模型,根据前面两个MR模型得到的个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,并输出用户标签至HBASE数据库。至此,完成对用户画像的描述。Finally, using the third MR model, the final data mining is performed based on the individual analysis parameters and global parameters obtained from the previous two MR models, the user images are described according to the mining results, and the user tags are output to the HBASE database. At this point, the description of the user's portrait is completed.
本实施例通过上述方案,由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。In the embodiment, the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user and the behavior of the item, thereby improving the user image. The accuracy and efficiency of data analysis provide users with sufficient resources for consumer group analysis, functional planning adjustment, and marketing strategy development.
如图3所示,本发明另一实施例提出一种基于物联网大数据平台的智能用户画像方法,基于上述实施例,还包括:As shown in FIG. 3, another embodiment of the present invention provides a smart user portrait method based on the Internet of Things big data platform. Based on the foregoing embodiment, the method further includes:
步骤S104,根据用户画像,对用户进行需求分析和策略规划。Step S104, performing demand analysis and strategy planning on the user according to the user portrait.
相比上述实施例,本实施例还包括对用户进行需求分析和策略规划的方案。Compared with the foregoing embodiment, the embodiment further includes a solution for performing requirement analysis and policy planning on the user.
具体地,在得到创建的用户画像后,由此获得一个对于用户群较为精准的认识,后续基于该用户画像,则可以对用户进行细分,在后期的用户角色的建立中能很好地对用户优先顺序进行排序,将核心的、规模较大的用户着重突出出来。从而为寻找用户需求,细分目标市场,调整功能规划以及营销策略提供资源和依据。Specifically, after obtaining the created user portrait, a relatively accurate understanding of the user group is obtained, and the user can be subdivided based on the user portrait, and the user role can be well established in the later generation of the user role. User prioritization is sorted to highlight core, large-scale users. Therefore, it provides resources and basis for finding user needs, subdividing target markets, adjusting function planning and marketing strategies.
如图4所示,本发明一实施例提出一种基于物联网大数据平台的智能用户画像装置,包括:采集模块201、处理模块202以及挖掘模块203,其中:As shown in FIG. 4, an embodiment of the present invention provides an intelligent user portrait device based on the Internet of Things big data platform, comprising: an acquisition module 201, a processing module 202, and a mining module 203, wherein:
采集模块201,设置为从物联网信息中心采集用户数据;该采集模块201部署在接入层和网络层之间。采集模块201所在感知层主要由RFID和传感器网络组成,接入层主要完成各类设备的网络接入,如3G/4G、Mesh网络等。The collecting module 201 is configured to collect user data from the Internet of Things information center; the collecting module 201 is disposed between the access layer and the network layer. The sensing layer of the collection module 201 is mainly composed of an RFID and a sensor network, and the access layer mainly completes network access of various devices, such as a 3G/4G, Mesh network.
处理模块202,设置为对采集的用户数据进行处理,生成话单数据文件;
The processing module 202 is configured to process the collected user data to generate a bill data file.
挖掘模块203,设置为采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。The mining module 203 is configured to perform data mining on the bill data file by using a big data platform to describe a user image.
具体地,用户画像的基础,是通过数据对海量用户有一个初步的了解,一般采用用户数据提取分析与问卷调研两种方式进行,根据产品目标确定统计分析的维度指标。其中,分析的维度,可以按照人口属性和产品行为属性进行综合分析。人口属性一般包括:地域、年龄、性别、文化、职业、收入、生活习惯、消费习惯等;产品行为一般包括:产品类别、活跃频率、产品喜好、产品驱动、使用习惯、产品消费等。Specifically, the basis of the user's portrait is to have a preliminary understanding of the mass of users through the data, generally using user data extraction analysis and questionnaire research to determine the dimensional indicators of statistical analysis according to product goals. Among them, the dimension of analysis can be comprehensively analyzed according to population attributes and product behavior attributes. Population attributes generally include: region, age, gender, culture, occupation, income, living habits, consumption habits, etc.; product behavior generally includes: product categories, frequency of activity, product preferences, product drivers, usage habits, product consumption, etc.
本实施例方案主要完成将用户对物品的使用转化成描述用户画像的方式,包括对物联网信息的采集、物联网信息的存储、挖掘数据以及描述用户画像,实现用户画像的产生可以基于以采集自物联网的大数据的支持和挖掘,采取将定量化和定性化方法相结合来创建用户画像。The solution of the embodiment mainly completes the method of converting the use of the item by the user into a description of the user's portrait, including collecting the information of the Internet of Things, storing the information of the Internet of Things, mining the data, and describing the user's portrait, so that the generation of the user's portrait can be based on the collection. Support and mining of big data from the Internet of Things, combined with quantitative and qualitative methods to create user portraits.
具体地,首先,从物联网信息中心提取原始用户数据,该原始用户数据由物联网信息中心根据RFID标签和传感器网络获取。Specifically, first, original user data is extracted from the Internet of Things information center, and the original user data is acquired by the Internet of Things information center according to the RFID tag and the sensor network.
物联网基于云计算平台和智能网络,可以依据RFID标签和传感器网络获取数据,并将之存储。Based on cloud computing platforms and intelligent networks, the Internet of Things can capture and store data based on RFID tags and sensor networks.
之后,对采集的用户数据进行处理,生成话单数据文件。After that, the collected user data is processed to generate a bill data file.
具体包括:对采集的用户数据建立话单转换表;基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型等。之后,根据话单生成话单数据文件。Specifically, the method includes: establishing a bill conversion table for the collected user data; processing the user data according to the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, and the main dimension includes the use time. , location, user, item type, etc. After that, the bill data file is generated according to the bill.
每天不同类型的话单,都会产生多个话单数据文件。Multiple bill data files are generated for different types of bills every day.
然后,将处理后生成的话单数据文件存储至大数据平台,以大数据平台系统对数据进行挖掘,获得不同用户在各个主体上的分布向量,从而根据用户的属性、物品行为、付费行为等指标来描述用户画像,便于对用户进行细分,寻找用户需求,细分目标市场,调整功能规划以及营销策略等。Then, the CDR data file generated after processing is stored to the big data platform, and the data is mined by the big data platform system, and the distribution vectors of different users on each subject are obtained, thereby according to the attributes of the user, the behavior of the goods, the behavior of payment, and the like. To describe user portraits, to facilitate segmentation of users, to find user needs, to segment target markets, to adjust functional planning and marketing strategies.
具体地,结合图2所示,在大数据平台上,使用大数据平台Hadoop合并话单数据文件,并在Reduce实例中缓存和建立MapReduce模型,挖掘数据,把用户标签输出到HBASE数据库。
Specifically, as shown in FIG. 2, on the big data platform, the big data platform Hadoop is used to merge the bill data files, and the MapReduce model is cached and built in the Reduce instance, the data is mined, and the user tags are output to the HBASE database.
其中,个体用户画像挖掘过程,是对用户相联的不同类型话单总体的一个分析,同时考虑每类话单不分个体的各项总体参数分布情况。Among them, the individual user image mining process is an analysis of the different types of CDRs associated with the user, and considers the distribution of the overall parameters of each type of CDR regardless of the individual.
因为每天不同类型的话单,都会产生多个话单数据文件。需要对这些话单数据文件使用hadoop系统根据MepReduce模型进行合并,合并后的文件中,一个用户一类话单所有的原始话单数据文件,合并为一个数据块,此数据块作为一条二进制的新记录保存到HBASE数据库。Because of the different types of bills every day, multiple bill data files are generated. These CDR data files need to be merged according to the MepReduce model using the Hadoop system. In the merged file, all the original CDR data files of one user type CDR are merged into one data block, and this data block is used as a binary new file. The record is saved to the HBASE database.
具体数据挖掘过程如下:The specific data mining process is as follows:
首先,在大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数。对应MR模型中的二进制类型记录,通过此种方式,保证单个用户的数据在一个数据节点上。First, on the big data platform, using the first MR model, all the bill data files of a single type of bill of a single user are merged into one user-related data block, and the individual analysis of the data mining is performed to obtain individual analysis parameters. Corresponding to the binary type record in the MR model, in this way, the data of a single user is guaranteed to be on one data node.
其中,在合并时,采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,使用频率或时间等靠前的物品即作为标签用来描述用户。主要包括提取目标用户属性,统计产品行为特征、产生用户个体相关的各类标签,得到个体分析参数,包括内容偏好、物品使用频繁度等,以便对用户进行细分,寻找核心用户,细分目标市场,调整功能规划及营销策略。In the merge, the first MR model is used to combine all the bill data files of a single type of bill of a single user in multiple dimensions, and the top item such as frequency or time is used as a label to describe the user. It mainly includes extracting target user attributes, counting product behavior characteristics, generating various types of labels related to individual users, and obtaining individual analysis parameters, including content preferences and frequent use of items, so as to subdivide users, find core users, and subdivide targets. Market, adjustment function planning and marketing strategy.
通过合并生成一个用户相关的数据块,将所述数据块作为一条二进制的新记录保存到数据库。A user-related data block is generated by merging, and the data block is saved to the database as a binary new record.
之后,以合并后的话单数据文件为依据,根据用户的属性、物品行为、付费行为等指标来描述用户画像。采用第二MR模型,计算话单数据文件的全局参数。输出结果根据存取效率有可能在关系型数据库中。Then, based on the combined bill data file, the user portrait is described according to the attributes of the user, the behavior of the item, and the payment behavior. The second MR model is used to calculate global parameters of the bill data file. The output is likely to be in a relational database depending on the access efficiency.
最后,采用第三MR模型,根据前面两个MR模型得到的个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,并输出用户标签至HBASE数据库。至此,完成对用户画像的描述。Finally, using the third MR model, the final data mining is performed based on the individual analysis parameters and global parameters obtained from the previous two MR models, the user images are described according to the mining results, and the user tags are output to the HBASE database. At this point, the description of the user's portrait is completed.
本实施例通过上述方案,由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。
In the embodiment, the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system, and the user portrait is described according to the attributes of the user and the behavior of the item, thereby improving the user image. The accuracy and efficiency of data analysis provide users with sufficient resources for consumer group analysis, functional planning adjustment, and marketing strategy development.
如图5所示,本发明另一实施例提出一种基于物联网大数据平台的智能用户画像装置,基于上述实施例,还包括:As shown in FIG. 5, another embodiment of the present invention provides a smart user portrait device based on the Internet of Things big data platform. Based on the foregoing embodiment, the method further includes:
规划模块204,设置为根据用户画像,对用户进行需求分析和策略规划。The planning module 204 is configured to perform demand analysis and strategic planning on the user according to the user portrait.
相比上述实施例,本实施例还包括对用户进行需求分析和策略规划的方案。Compared with the foregoing embodiment, the embodiment further includes a solution for performing requirement analysis and policy planning on the user.
具体地,在得到创建的用户画像后,由此获得一个对于用户群较为精准的认识,后续基于该用户画像,则可以对用户进行细分,在后期的用户角色的建立中能很好地对用户优先顺序进行排序,将核心的、规模较大的用户着重突出出来。从而为寻找用户需求,细分目标市场,调整功能规划以及营销策略提供资源和依据。Specifically, after obtaining the created user portrait, a relatively accurate understanding of the user group is obtained, and the user can be subdivided based on the user portrait, and the user role can be well established in the later generation of the user role. User prioritization is sorted to highlight core, large-scale users. Therefore, it provides resources and basis for finding user needs, subdividing target markets, adjusting function planning and marketing strategies.
本发明实施例基于物联网大数据平台的智能用户画像方法及装置,由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。The embodiment of the present invention is based on the intelligent user portrait method and device of the Internet of Things big data platform. The Internet of Things information center acquires data according to the RFID tag and the sensor network, and the data is mined by the big data platform system according to the attributes of the user, the behavior of the item, etc. The indicators describe the user's portrait, which improves the accuracy and efficiency of the data analysis of the user's portrait, and provides sufficient resources for the consumer to analyze the consumer group, adjust the function plan and formulate the marketing strategy.
以上所述仅为本发明的优选实施例,并非因此限制本发明的专利范围,凡是利用本发明说明书及附图内容所作的等效结构或流程变换,或直接或间接运用在其它相关的技术领域,均同理包括在本发明的专利保护范围内。The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the invention, and the equivalent structure or process changes made by the specification and the drawings of the present invention may be directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of the present invention.
如上所述,本发明实施例提供的一种基于物联网大数据平台的智能用户画像方法及装置,具有以下有益效果:由物联网信息中心根据RFID标签和传感器网络获取数据,以大数据平台系统对数据进行挖掘,根据用户的属性、物品行为等指标来描述用户画像,从而提高了用户画像的数据分析准确性和效率,进而为用户进行消费群体分析、功能规划调整以及营销策略制定提供充分的资源。
As described above, the smart user portrait method and apparatus based on the Internet of Things big data platform provided by the embodiments of the present invention have the following beneficial effects: the Internet of Things information center acquires data according to the RFID tag and the sensor network, and the big data platform system The data is mined, and the user's portrait is described according to the attributes of the user, the behavior of the item, etc., thereby improving the accuracy and efficiency of data analysis of the user's portrait, and further providing sufficient analysis for the consumer group analysis, function planning adjustment, and marketing strategy formulation. Resources.
Claims (11)
- 一种基于物联网大数据平台的智能用户画像方法,包括:A smart user portrait method based on the Internet of Things big data platform, comprising:从物联网信息中心采集用户数据;Collect user data from the IoT Information Center;对采集的用户数据进行处理,生成话单数据文件;Processing the collected user data to generate a bill data file;采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。The big data platform is used to perform data mining on the bill data file to describe the user portrait.
- 根据权利要求1所述的方法,其中,所述对采集的用户数据进行处理,生成话单数据文件的步骤包括:The method of claim 1, wherein the processing of the collected user data to generate a bill data file comprises:对采集的用户数据建立话单转换表;Establishing a bill conversion table for the collected user data;基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型;The user data is processed based on the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, and the main dimensions include time, place, user, and item type;将所述话单生成话单数据文件。The bill is generated into a bill data file.
- 根据权利要求1所述的方法,其中,所述采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像的步骤包括:The method of claim 1, wherein the step of data mining the CDR data file by using a big data platform, the step of describing a user image comprises:将话单数据文件存储至大数据平台;Store the bill data file to the big data platform;在所述大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数;On the big data platform, using the first MR model, all the bill data files of a single type of bill of a single user are merged into one user-related data block, and the individual analysis of the data mining is performed to obtain individual analysis parameters;采用第二MR模型,计算话单数据文件的全局参数;Using the second MR model, calculating global parameters of the bill data file;采用第三MR模型,根据所述个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,输出用户标签至数据库。Using the third MR model, final data mining is performed according to the individual analysis parameters and global parameters, the user portrait is described according to the mining result, and the user tag is output to the database.
- 根据权利要求3所述的方法,其中,所述采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数的步骤包括:The method according to claim 3, wherein the first MR model is used to merge all the bill data files of a single type of bill of a single user into one user-related data block, and perform individual analysis of data mining. The steps to obtain individual analysis parameters include:采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,生成一个用户相关的数据块;Using the first MR model, all the bill data files of a single type of bill of a single user are combined in multiple dimensions to generate a user-related data block;将所述数据块作为一条二进制的新记录保存到数据库; Saving the data block as a binary new record to the database;基于合并后的数据块,提取用户属性,统计物品行为特征,产生用户个体相关的各类标签,得到个体分析参数。Based on the merged data block, user attributes are extracted, and the behavior characteristics of the items are counted, and various types of tags related to the individual are generated, and individual analysis parameters are obtained.
- 根据权利要求1-4中任一项所述的方法,其中,还包括:The method of any one of claims 1 to 4, further comprising:根据用户画像,对用户进行需求分析和策略规划。According to the user's portrait, the user is analyzed for needs and strategic planning.
- 根据权利要求1-4中任一项所述的方法,其中,所述用户数据由所述物联网信息中心根据RFID标签和传感器网络获取。The method of any of claims 1-4, wherein the user data is obtained by the IoT information center based on an RFID tag and a sensor network.
- 一种基于物联网大数据平台的智能用户画像装置,包括:A smart user portrait device based on the Internet of Things big data platform, comprising:采集模块,设置为从物联网信息中心采集用户数据;The acquisition module is configured to collect user data from the Internet of Things information center;处理模块,设置为对采集的用户数据进行处理,生成话单数据文件;The processing module is configured to process the collected user data to generate a bill data file;挖掘模块,设置为采用大数据平台对所述话单数据文件进行数据挖掘,描述用户画像。The mining module is configured to perform data mining on the bill data file by using a big data platform to describe a user image.
- 根据权利要求7所述的装置,其中,The apparatus according to claim 7, wherein所述处理模块,还设置为对采集的用户数据建立话单转换表;基于所述话单转换表对用户数据进行处理,形成话单,所述话单设置为描述用户对物品的使用行为,主要维度包括使用时间、地点、使用者、物品类型;将所述话单生成话单数据文件。The processing module is further configured to: establish a bill conversion table for the collected user data; process the user data according to the bill conversion table to form a bill, and the bill is set to describe the user's use behavior of the item, The main dimensions include usage time, location, user, and item type; the bill is generated into the bill data file.
- 根据权利要求7所述的装置,其中,The apparatus according to claim 7, wherein所述挖掘模块,还设置为将话单数据文件存储至大数据平台;在所述大数据平台上,采用第一MR模型,将单个用户的一类话单的所有话单数据文件合并成一个用户相关的数据块,并进行数据挖掘的个体分析,得到个体分析参数;采用第二MR模型,计算话单数据文件的全局参数;采用第三MR模型,根据所述个体分析参数和全局参数,进行最终数据挖掘,根据挖掘结果描述用户画像,输出用户标签至数据库。The mining module is further configured to store the bill data file to the big data platform; on the big data platform, use the first MR model to merge all the bill data files of a single type of bill of the single user into one User-related data blocks, and individual analysis of data mining, to obtain individual analysis parameters; using the second MR model to calculate global parameters of the bill data file; using the third MR model, according to the individual analysis parameters and global parameters, Perform final data mining, describe user images based on mining results, and output user tags to the database.
- 根据权利要求9所述的装置,其中,The apparatus according to claim 9, wherein所述挖掘模块,还设置为采用第一MR模型,将单个用户的一类话单的所有话单数据文件以多个维度进行合并,生成一个用户相关的数据块;将所述数据块作为一条二进制的新记录保存到数据库;基于合并后的数据块,提取用户属性,统计物品行为特征,产生用户个体相关的各类标签,得到个体分析参数。 The mining module is further configured to use a first MR model to combine all the bill data files of a type of bill of a single user in multiple dimensions to generate a user-related data block; The new binary records are saved to the database; based on the merged data blocks, the user attributes are extracted, the behavior characteristics of the items are counted, various types of tags related to the individual are generated, and individual analysis parameters are obtained.
- 根据权利要求7-10中任一项所述的装置,其中,还包括:The apparatus according to any one of claims 7 to 10, further comprising:规划模块,设置为根据用户画像,对用户进行需求分析和策略规划。 The planning module is set to perform demand analysis and strategic planning for the user according to the user portrait.
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