CN112052270A - Method and system for carrying out user portrait depth analysis through big data - Google Patents
Method and system for carrying out user portrait depth analysis through big data Download PDFInfo
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Abstract
The invention discloses a method and a system for carrying out user portrait depth analysis through big data, relates to the technical field of user portrait, and aims to solve the problem that the user portrait models of employees are not synchronized after internal personnel changes of a company due to the fact that the user portrait models are not used for building identity recognition association in the prior user portrait. The employee work management client side is provided with collected user data at one end, the user data collecting and storing end is provided with offline data collection and storage, the user data collecting and storing end is provided with an uploading background server, the company big data server background is arranged at the uploading background server end, background analysis data is arranged at the company big data server background end, a user portrait building model is arranged at the background analysis data end, and a deep learning module is arranged at the user portrait building model end.
Description
Technical Field
The invention relates to the technical field of user portrait, in particular to a method and a system for carrying out user portrait depth analysis through big data.
Background
User portrayal is widely applied in various fields as an effective tool for outlining target users and associating user appeal and design direction. User portrayal is originally applied in the E-commerce field, and in the background of the big data era, user information is flooded in a network, each piece of concrete information of a user is abstracted into labels, and the labels are utilized to concretize the user image, so that targeted services are provided for the user. As an effective tool for sketching the appeal and the design direction of target users and related users, user portrayal is widely applied to various fields. In the practical operation process, the attributes and behaviors of the user are often combined with expected data conversion by the utterances with the most shallow and close to life. As a virtual representation of an actual user, the user roles formed by user portrayal are not constructed outside products and markets, and the formed user roles need to represent the main audience and target groups of the products.
However, in the existing user portrait, the problem that the user portrait model of the staff is not synchronized after the personnel in the company change due to the fact that the user portrait model is not related to identity identification; therefore, the existing requirements are not met, and a method and a system for performing user portrait depth analysis through big data are provided.
Disclosure of Invention
The invention aims to provide a method and a system for carrying out user portrait depth analysis through big data, which aim to solve the problem that the user portrait model of staff is not synchronized after internal personnel changes of a company due to the fact that the user portrait model is not related to identity identification.
In order to achieve the purpose, the invention provides the following technical scheme: a system for carrying out user portrait depth analysis through big data comprises a staff work management client, wherein one end of the staff work management client is provided with collected user data, one end of the collected user data is provided with offline data acquisition and storage, one ends of the collected user data and the offline data acquisition and storage are both provided with an uploading background server, one end of the uploading background server is provided with a company big data server background, one end of the company big data server background is provided with background analysis data, one end of the background analysis data is provided with a constructed user portrait model, one end of the constructed user portrait model is provided with a depth learning module, one end of the depth learning module is provided with a tag for a target user, one end of the tag for the target user is provided with a user tag tracking module, and the user tag tracking module is in bidirectional data transmission with the big data database, and the user tag tracking module is in bidirectional data transmission with the uploading background server, and one end of the uploading background server is provided with staff information management.
Preferably, the user tag tracking module is respectively provided with a card punching machine login verification and a company employee management client login verification.
Preferably, the constructed user portrait model comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, employee income conditions, employee ratings, colleague evaluation feedback, search keywords, network environment detection and user information.
Preferably, the deep learning module includes a tensor, tensor-based operations, automatic microtools, a computation graph, a BLAS, a cuBLAS, a cuDNN, and other development packages.
Preferably, the tagging of the target user includes a statistic class tag, a rule class tag, and a machine learning mining class tag.
Preferably, the tagging of the target user uses a calculation engine such as Hive, Spark, etc., wherein the individual storage of the tag takes Hive as a main storage mode.
Preferably, the user feedback mechanism is provided with questionnaire feedback.
Preferably, one end of the user client is provided with a user feedback mechanism, and the user feedback mechanism and the background analysis perform data transmission.
Preferably, the method for performing user portrait depth analysis through big data is characterized by comprising the following steps:
the method comprises the following steps: the employee work management client side firstly extracts and collects user data, then the extracted and collected user data are uploaded to the background server when the network environment is good, the offline data acquisition and storage can firstly store the collected user characteristic information when the network environment is poor, the collected user characteristic information is continuously uploaded after the network environment is good, the system is more energy-saving, the problem of frequent uploading and outputting in places with poor network environments is solved, the user experience and the fluency are improved, the user front-end experience is good, the background server accesses the collected user data into the background of the company big data server, the background analysis data is extracted, analyzed and sorted, then a user portrait model is established according to the user data, and the user portrait model is established and comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, Employee income condition, employee rating, colleague evaluation feedback, search keywords, network environment detection and user information;
step two: the user portrait model is put in a deep learning module for deep analysis so as to form the purpose of labeling the user, the labeling of the target user comprises statistical labels, rule labels and machine learning mining labels, the labeling of the target user uses a calculation engine such as Hive, Spark and the like, wherein the independent storage of the labels takes Hive as a main storage mode, the labeled data is uploaded to a big data database for storage, the labeling of the client portrait is finally output, the user labels in a company big data server background are tracked and synchronized by a tracking module, the user label tracking module aims to identify the identity of the employee user by the background when the employee user changes through the internal personnel, and the corresponding user portrait labels stored in the company big data server background are verified and synchronized with the front end used by the employee, the user portrait label tracking module uses two user identity verification channels, and employee user identity verification is respectively login verification in a card punching machine; the method comprises the steps that a company employee management client side performs verification and data synchronization in a login verification mode, after login is successful, a front end sends related information to a background so as to determine a user portrait label formed by related employee identities in a company big data server background, so that the time for establishing a model and deep learning can be greatly reduced, the error of labeling is reduced, and the existing employee user portrait labels are directly used synchronously;
step three: finally, a user feedback mechanism of the user at the front end changes and perfects submitted data of the whole system, the user feedback mechanism comprises a questionnaire feedback mode for feedback, questionnaire investigation is pushed to the user front end, employees can confirm feedback problems such as whether the pushed content meets requirements and interests of various employees and the like, and accordingly, the establishment of employee user portrait models is modified and perfected, the user portrait models are closer to the real situation, updating of user labels in a big data database is facilitated, the users can accurately and truly reflect current requirements and interests of the users after internal personnel change, and data are more reliable, the system has the advantages that the employees in different departments can be verified and identified, model training cost is greatly reduced, the identification rate of the system for user identities is improved, and the user portrait labels in a backstage of a big data server of a company are synchronously identified, the aim of accurately identifying the real identity of the employee is achieved.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention sets the user label tracking module, the user label tracking module aims to identify the user in the background when the user logs in different platforms, and the corresponding user portrait label stored in the company big data server background is verified and synchronized with the front end used by the client, so as to achieve the function of identity recognition by the personnel change in each department of the company staff, so that the user portrait label is more accurate and has a use value, the problem of the prior user portrait is solved, because the user portrait model is lack of identity identification association, the problem that the user portrait model of the staff is lack of synchronization after the personnel in the company change is caused, the user label tracking module uses two channels of user identity authentication, and the user identity authentication is respectively logged in by using a card punching machine login authentication method when the user logs in; the method for logging in and verifying the company employee management client is used for verification and data synchronization, after logging in successfully, the front end sends related information to the background so as to determine the logging in of the related employee identity information, and a user portrait label is formed in the background of a company big data server, so that the time for establishing a model and deep learning can be greatly reduced, the error of labeling is reduced, and the existing user portrait label is directly used synchronously.
2. Through the setting of the user feedback mechanism, the user feedback mechanism is questionnaire feedback, questionnaire investigation is pushed to the front end of a staff user, the staff can confirm whether the feedback problems such as the needs and interests of various staff are met or not according to the pushed content, and therefore the user portrait model is modified and perfected, the user portrait model is closer to the real situation, the updating of a user label in a large data server background of a company is facilitated, the user can correctly and truly reflect the current needs and interests of the user after internal personnel change is carried out, and the data are more reliable.
3. Through the setting of off-line data acquisition storage, in the relatively poor place of network environment, the staff user characteristic information storage of off-line data acquisition storage collection earlier can be collected, the work of continuing to upload again after waiting for network environment is good for this system is more energy-conserving, prevents to frequently upload the problem of output in the relatively poor place of network environment, improves staff user's use and experiences and smoothness nature, makes staff user front end experience comparatively good.
Drawings
FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a schematic diagram of a user portrait model construction according to the present invention;
FIG. 3 is a schematic diagram of a user tag tracking module according to the present invention;
FIG. 4 is a schematic structural diagram of a deep learning module according to the present invention;
FIG. 5 is a schematic diagram of a tagging structure for a target user according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1-5, an embodiment of the present invention is shown: a system for carrying out user portrait depth analysis through big data comprises a staff work management client, wherein one end of the staff work management client is provided with collected user data, one end of the collected user data is provided with offline data acquisition and storage, one ends of the collected user data and the offline data acquisition and storage are both provided with an uploading background server, one end of the uploading background server is provided with a company big data server background, one end of the company big data server background is provided with background analysis data, one end of the background analysis data is provided with a constructed user portrait model, one end of the constructed user portrait model is provided with a depth learning module, one end of the depth learning module is provided with a tag for a target user, one end of the tag for the target user is provided with a user tag tracking module, and the user tag tracking module is in bidirectional data transmission with the big data database, and the user tag tracking module is in bidirectional data transmission with the uploading background server, and one end of the uploading background server is provided with staff information management.
Further, the user tag tracking module is respectively provided with a card punching machine login verification and a company employee management client login verification.
Further, the constructed user portrait model comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, employee income conditions, employee ratings, colleague evaluation feedback, search keywords, network environment detection and user information.
Further, the deep learning module includes a tensor, various tensor-based operations, automatic microtools, a computation graph, a BLAS, a cuBLAS, a cuDNN, and other development packages.
Further, the labeling of the target user includes a statistic class label, a rule class label and a machine learning mining class label.
Further, the labeling of the target user uses a Hive, Spark and other calculation engines, wherein the individual storage of the labels takes the Hive as a main storage mode.
Further, the user feedback mechanism is provided with questionnaire feedback.
Further, a user feedback mechanism is arranged at one end of the user client, and the user feedback mechanism and the background analysis perform data transmission.
Further, the method for performing user portrait depth analysis through big data is characterized by comprising the following steps:
the method comprises the following steps: the employee work management client side firstly extracts and collects user data, then the extracted and collected user data are uploaded to the background server when the network environment is good, the offline data acquisition and storage can firstly store the collected user characteristic information when the network environment is poor, the collected user characteristic information is continuously uploaded after the network environment is good, the system is more energy-saving, the problem of frequent uploading and outputting in places with poor network environments is solved, the user experience and the fluency are improved, the user front-end experience is good, the background server accesses the collected user data into the background of the company big data server, the background analysis data is extracted, analyzed and sorted, then a user portrait model is established according to the user data, and the user portrait model is established and comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, Employee income condition, employee rating, colleague evaluation feedback, search keywords, network environment detection and user information;
step two: the user portrait model is put in a deep learning module for deep analysis so as to form the purpose of labeling the user, the labeling of the target user comprises statistical labels, rule labels and machine learning mining labels, the labeling of the target user uses a calculation engine such as Hive, Spark and the like, wherein the independent storage of the labels takes Hive as a main storage mode, the labeled data is uploaded to a big data database for storage, the labeling of the client portrait is finally output, the user labels in a company big data server background are tracked and synchronized by a tracking module, the user label tracking module aims to identify the identity of the employee user by the background when the employee user changes through the internal personnel, and the corresponding user portrait labels stored in the company big data server background are verified and synchronized with the front end used by the employee, the user portrait label tracking module uses two user identity verification channels, and employee user identity verification is respectively login verification in a card punching machine; the method comprises the steps that a company employee management client side performs verification and data synchronization in a login verification mode, after login is successful, a front end sends related information to a background so as to determine a user portrait label formed by related employee identities in a company big data server background, so that the time for establishing a model and deep learning can be greatly reduced, the error of labeling is reduced, and the existing employee user portrait labels are directly used synchronously;
step three: finally, a user feedback mechanism of the user at the front end changes and perfects submitted data of the whole system, the user feedback mechanism comprises a questionnaire feedback mode for feedback, questionnaire investigation is pushed to the user front end, employees can confirm feedback problems such as whether the pushed content meets requirements and interests of various employees and the like, and accordingly, the establishment of employee user portrait models is modified and perfected, the user portrait models are closer to the real situation, updating of user labels in a big data database is facilitated, the users can accurately and truly reflect current requirements and interests of the users after internal personnel change, and data are more reliable, the system has the advantages that the employees in different departments can be verified and identified, model training cost is greatly reduced, the identification rate of the system for user identities is improved, and the user portrait labels in a backstage of a big data server of a company are synchronously identified, the aim of accurately identifying the real identity of the employee is achieved.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. A system for carrying out user portrait depth analysis through big data comprises an employee work management client and is characterized in that: one end of the staff work management client is provided with collected user data, one end of the collected user data is provided with offline data collection and storage, one ends of the collected user data and the offline data collection and storage are both provided with an uploading background server, one end of the uploading background server is provided with a company big data server background, one end of the company big data server background is provided with background analysis data, one end of the background analysis data is provided with a constructed user portrait model, one end of the constructed user portrait model is provided with a deep learning module, one end of the deep learning module is provided with a tag for a target user, one end of the tag for the target user is provided with a user tag tracking module, the user tag tracking module is in bidirectional data transmission with a big data database, and the user tag tracking module is in bidirectional data transmission with the uploading background server, and one end of the uploading background server is provided with staff information management.
2. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the user tag tracking module is respectively provided with a card punching machine login verification and a company employee management client login verification.
3. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the constructed user portrait model comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, employee income conditions, employee ratings, colleague evaluation feedback, search keywords, network environment detection and user information.
4. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the deep learning module comprises a tensor, various tensor-based operations, automatic microtools, a computation graph, a BLAS, a cubLAS, a cuDNN and other expansion packages.
5. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the labeling of the target user comprises a statistic label, a rule label and a machine learning mining label.
6. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the tagging of the target user uses a Hive, Spark and other computing engines, wherein the Hive is used as a main storage mode for the individual storage of the tags.
7. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: the user feedback mechanism is provided with questionnaire feedback.
8. A system for user portrait depth analysis via big data as claimed in claim 1, wherein: and a user feedback mechanism is arranged at one end of the user client, and the user feedback mechanism and the background analysis perform data transmission.
9. A method for user portrait depth analysis with big data according to any of claims 1-8, comprising the steps of:
the method comprises the following steps: the employee work management client side firstly extracts and collects user data, then the extracted and collected user data are uploaded to the background server when the network environment is good, the offline data acquisition and storage can firstly store the collected user characteristic information when the network environment is poor, the collected user characteristic information is continuously uploaded after the network environment is good, the system is more energy-saving, the problem of frequent uploading and outputting in places with poor network environments is solved, the user experience and the fluency are improved, the user front-end experience is good, the background server accesses the collected user data into the background of the company big data server, the background analysis data is extracted, analyzed and sorted, then a user portrait model is established according to the user data, and the user portrait model is established and comprises employee family addresses, employee recruitment information, personal preferences, employee attendance information, Employee income condition, employee rating, colleague evaluation feedback, search keywords, network environment detection and user information;
step two: the user portrait model is put in a deep learning module for deep analysis so as to form the purpose of labeling the user, the labeling of the target user comprises statistical labels, rule labels and machine learning mining labels, the labeling of the target user uses a calculation engine such as Hive, Spark and the like, wherein the independent storage of the labels takes Hive as a main storage mode, the labeled data is uploaded to a big data database for storage, the labeling of the client portrait is finally output, the user labels in a company big data server background are tracked and synchronized by a tracking module, the user label tracking module aims to identify the identity of the employee user by the background when the employee user changes through the internal personnel, and the corresponding user portrait labels stored in the company big data server background are verified and synchronized with the front end used by the employee, the user portrait label tracking module uses two user identity verification channels, and employee user identity verification is respectively login verification in a card punching machine; the method comprises the steps that a company employee management client side performs verification and data synchronization in a login verification mode, after login is successful, a front end sends related information to a background so as to determine a user portrait label formed by related employee identities in a company big data server background, so that the time for establishing a model and deep learning can be greatly reduced, the error of labeling is reduced, and the existing employee user portrait labels are directly used synchronously;
step three: finally, a user feedback mechanism of the user at the front end changes and perfects submitted data of the whole system, the user feedback mechanism comprises a questionnaire feedback mode for feedback, questionnaire investigation is pushed to the user front end, employees can confirm feedback problems such as whether the pushed content meets requirements and interests of various employees and the like, and accordingly, the establishment of employee user portrait models is modified and perfected, the user portrait models are closer to the real situation, updating of user labels in a big data database is facilitated, the users can accurately and truly reflect current requirements and interests of the users after internal personnel change, and data are more reliable, the system has the advantages that the employees in different departments can be verified and identified, model training cost is greatly reduced, the identification rate of the system for user identities is improved, and the user portrait labels in a backstage of a big data server of a company are synchronously identified, the aim of accurately identifying the real identity of the employee is achieved.
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