CN114254280A - Artificial intelligence big data analysis processing management method and middle station - Google Patents
Artificial intelligence big data analysis processing management method and middle station Download PDFInfo
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Abstract
The utility model relates to an artificial intelligence big data analysis processes management method and middling stage relates to network information processing technology field, has solved the login of the big data analysis processes management platform of present artificial intelligence only need fixed account number password can, if fixed account number password is known for non-staff, lead to the problem that the data of its big data analysis processes management middling stage of artificial intelligence reveals easily, it includes: predicting and analyzing the reading mode of the user login; comprehensively analyzing and determining a login password of the user according to the predicted reading mode of the user login, the user identity card information, the contact information, the current login time period and the total number information; and acquiring the login password of the user, and comparing the login password with the login password determined by analysis. The application has the following effects: the data safety of the large data analysis and processing management center station of the artificial intelligence is effectively guaranteed, and a user can conveniently and timely see the data in a current inclined mode after logging in.
Description
Technical Field
The application relates to the technical field of network information processing, in particular to an artificial intelligence big data analysis processing management method and a middlebox.
Background
The term "middle station," internet, generally applies to large enterprises. Generally, a framework which can flexibly and quickly cope with changes is built, the requirement of front-end lifting is quickly realized, repeated construction is avoided, and the purpose of improving the working efficiency is achieved.
The existing artificial intelligence big data analysis, processing and management middle desk can facilitate workers to check business data timely and effectively based on needs so as to know business conditions.
With respect to the related art in the above, the inventors consider that there are the following drawbacks: on one hand, the login of the current artificial intelligence big data analysis, processing and management platform only needs a fixed account password, if the fixed account password is known by non-working personnel, data leakage of the artificial intelligence big data analysis, processing and management platform is easily caused, and on the other hand, after the user enters the artificial intelligence big data analysis, processing and management platform, the user still needs to adjust the browsing mode of the user's tendency to browse the data, which is troublesome.
Disclosure of Invention
In order to effectively guarantee the data safety of the artificial intelligence big data analysis, processing and management center and facilitate users to see data in a current inclined mode after logging in, the application provides an artificial intelligence big data analysis, processing and management method and a center.
In a first aspect, the present application provides an artificial intelligence big data analysis processing management method, which adopts the following technical scheme:
an artificial intelligence big data analysis processing management method comprises the following steps:
acquiring a login account of a user;
predicting and analyzing the reading mode of the user login at this time based on the login account number of the user and the reading mode selected by the user history for viewing the channel information at different time periods;
comprehensively analyzing and determining a login password of the user according to the predicted reading mode of the user login, the user identity card information, the contact information, the current login time period and the total number information;
acquiring a login password of a user, and comparing the login password with the login password determined by analysis;
if the comparison is consistent, completing login and applying the reading mode confirmed by the predictive analysis in advance;
if the password is not consistent with the password, the user selects the browsing mode as the confirmed browsing mode; and if the password is completely inconsistent, clearing the login password, and waiting for the user to input again and compare again.
Optionally, the predictive analysis of the browsing mode of the user login at this time includes:
inquiring and acquiring the viewing mode selected by the information of the broadcasting station viewed by the user at the current time period by taking the user and the current time period as a common inquiry object from a preset database storing the viewing modes selected by the information of the broadcasting station viewed by the user at different time periods;
if the selected browsing mode is only one, taking the corresponding browsing mode as the browsing mode for the user to log in at this time;
if the selected browsing modes are 2 or more, acquiring time length ratio data and frequency ratio data of the user adopting different browsing modes at the current time period;
and taking the sum of the time length ratio data and the frequency ratio data of the same browsing mode as an effective value, and selecting the browsing mode with the largest effective value as the browsing mode which is logged in by the user at this time.
Optionally, the predictive analysis of the viewing mode that the user logs in at this time further includes a step after the viewing mode selected by the user for viewing the channel information at the current time is obtained by querying, which specifically includes the following steps:
if the selected browsing modes are 0, acquiring time length ratio data and frequency ratio data of different browsing modes adopted by the user in two time periods before and after the current time period;
and taking the sum of the time length ratio data and the frequency ratio data of the same browsing mode as an effective value, and selecting the browsing mode with the largest effective value as the browsing mode which is logged in by the user at this time.
Optionally, the determining the login password of the user by the comprehensive analysis includes:
acquiring the times of the user logging in the middle station system at the end of the day;
taking the times of the user logging in the middle station system when the user is at the end of the current day and the number of current time periods as classification numbers;
if the classification number is odd, the last three digits of the ID card are selected as the first segment of password,
the first three digits of the mobile phone are used as a second section of password, and the digits corresponding to the reading mode are used as a third section of password;
if the classification tree is an even number, selecting the first three digits of the identity card and the last three digits of the mobile phone, selecting the first three digits of the identity card as a first section of password, the last three digits of the mobile phone as a second section of password, and selecting the corresponding digits of the reading mode as a third section of password;
and analyzing and confirming the arrangement sequence of the first section of password, the second section of password and the third section of password based on the number corresponding to the time section number.
Optionally, analyzing and confirming the arrangement sequence of the first section of password, the second section of password and the third section of password based on the number corresponding to the number of the time sections includes:
acquiring a number corresponding to the time segment number;
if the number corresponding to the time segment number is a multiple of 3, the password is a first segment password, a second segment password and a third segment password from front to back;
otherwise, the password is a third section password, a second section password and a first section password from front to back.
Optionally, the method further includes a step after the comparison is consistent and before the logging is completed and the browsing mode determined by the predictive analysis is applied in advance, specifically as follows:
acquiring a user account and a mark color which is inclined by a corresponding user;
marking the number corresponding to the browsing mode in the login password by the marking color preferred by the user so as to remind the user;
if the user does not feed back within the preset time, the login is automatically completed, and the reading mode of the predictive analysis confirmation is applied in advance.
Optionally, clearing the login password, and waiting for the user to input and compare again includes:
clearing the login password;
adding 1 to the last bit of the login password determined by analysis to be used as the correct login password;
and acquiring the password input by the user, and comparing the password input by the user with the correct login password again.
In a second aspect, the present application provides an artificial intelligence big data analysis processing management middle desk, which adopts the following technical scheme:
an artificial intelligence big data analysis processing management center comprises a memory, a processor and a program which is stored on the memory and can run on the processor, and the program can be loaded and executed by the processor to realize the artificial intelligence big data analysis processing management method in the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic diagram illustrating overall steps of a method for analyzing, processing and managing big data through artificial intelligence according to an embodiment of the present application.
Fig. 2 is a detailed step diagram of step S200 in fig. 1.
Fig. 3 is a detailed step diagram of step S300 in fig. 1.
Fig. 4 is a schematic diagram illustrating the detailed step of step S330 in fig. 3.
FIG. 5 is a schematic diagram of the steps following a match and prior to completion of the log-in and prior application of the predictive analysis confirmed viewing mode.
FIG. 6 is a diagram illustrating the detailed steps of step SB 00.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include a single feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The present application is described in further detail below with reference to the attached drawings.
Referring to fig. 1, a method for analyzing, processing and managing artificial intelligence big data disclosed by the present application includes:
and step S100, acquiring a login account of the user.
And step S200, predicting and analyzing the browsing mode of the user login at this time based on the login account number of the user and the browsing mode selected by the user history for viewing the channel information in different time periods.
The browsing mode in step S200 may be various, and may be an automatic browsing mode or a manual browsing mode.
Referring to fig. 2, wherein step S200 includes:
step S210, from a preset database storing viewing modes selected by the channel information viewed by the user in different periods, taking the user and the current period as a common query object, and querying and acquiring the viewing mode selected by the channel information viewed by the user in the current period.
In step S2a0, if only one viewing mode is selected, the corresponding viewing mode is used as the viewing mode that the user has logged in at this time.
For example, assuming that the staff member only browses in the automatic browsing mode at 11 o' clock, the login is directly browsing the data in the automatic browsing mode.
And step S2B0, if the selected browsing modes are 2 or more, acquiring the time length ratio data and the frequency ratio data of the user adopting different browsing modes in the current time period.
For example, suppose that the number of times of browsing by the staff in the automatic browsing mode at 11 points is 30%, the duration is 60%, the number of times of browsing in the manual browsing mode is 70%, and the duration is 40%.
Step S2C0, if the selected browsing mode is 0, acquiring duration ratio data and number ratio data of different browsing modes adopted by the user in two time periods before and after the current time period.
And step S220, taking the sum of the time length ratio data and the frequency ratio data of the same browsing mode as an effective value, and selecting the browsing mode with the largest effective value as the browsing mode which is logged in by the user at this time.
Assuming the case illustrated in step S2B0, the manual browsing mode is selected as the browsing mode in which the user logs in this time.
And step S300, comprehensively analyzing and determining the login password of the user according to the predicted browsing mode of the user login, the user identity card information, the contact information, the current login time period and the total number information.
Referring to fig. 3, wherein step S300 includes:
step S310, the number of times that the user logs in the middle station system at the end of the day is obtained.
Step S320, the number of times that the user logs in the middling system when the user is down on the same day and the number of sessions when the user is down are used as the classification number.
And S3A0, if the classification number is an odd number, selecting the last three digits of the ID card as a first section of password, the first three digits of the mobile phone as a second section of password, and the corresponding digits of the browsing mode as a third section of password.
And step S3B0, if the classification tree is an even number, selecting the first three digits of the identity card and the last three digits of the mobile phone, selecting the first three digits of the identity card as a first section of password, the last three digits of the mobile phone as a second section of password, and selecting the corresponding digits of the browsing mode as a third section of password.
And step S330, analyzing and confirming the arrangement sequence of the first section of password, the second section of password and the third section of password based on the number corresponding to the time section number.
For example, assume that the number of times that the user logs in the central station system is 3 times since the day, and when the current period is 7, the classification number is 10, i.e., an even number, the first three digits of the id card are selected as the first password, the last three digits of the mobile phone are selected as the second password, and the number corresponding to the browsing mode is selected as the third password.
Referring to fig. 4, wherein step S330 includes:
in step S331, a number corresponding to the number of time slots is obtained.
In step S33A, if the number corresponding to the time segment number is a multiple of 3, the password is the first password, the second password, and the third password in the order from front to back.
And step S33B, otherwise, the password is a third section password, a second section password and a first section password according to the sequence from front to back.
Assuming that the number of the time segments is 7, the passwords are a third-segment password, a second-segment password and a first-segment password from front to back.
Step S400, the login password of the user is obtained and compared with the login password determined by analysis.
In step SA00, if the comparison matches, the registration is completed and the predicted analysis confirmed viewing mode is applied in advance.
Referring to fig. 5, the method for analyzing, processing and managing artificial intelligence big data further includes the following steps after the comparison is consistent and before the login is completed and the browsing mode of the predictive analysis confirmation is applied in advance:
and step Sa00, acquiring the user account and the mark color inclined by the corresponding user.
In step Sb00, the number corresponding to the viewing mode in the login password is marked with a mark color preferred by the user to remind the user.
And step Sc00, if the user does not feed back within the preset time, automatically completing login and applying the browsing mode of the predictive analysis confirmation in advance.
Step SB00, otherwise, further comparing and analyzing whether the codes are completely inconsistent, if only the reading modes are inconsistent, using the reading mode selected by the user at the current password as the confirmed reading mode; and if the password is completely inconsistent, clearing the login password, and waiting for the user to input again and compare again.
Referring to FIG. 6, wherein the clearing of the login password in step SB00, waiting for the user to input again and comparing again includes:
step SB10, the login password is cleared.
And step SB20, adding 1 to the last digit of the analyzed and determined login password as the correct login password.
Step SB30, the password input by the user is obtained, and the password input by the user is compared with the correct login password again.
Based on the same inventive concept, an embodiment of the present invention provides an artificial intelligence big data analysis processing management center, which includes a memory and a processor, wherein the memory stores a program that can run on the processor to implement any one of the methods shown in fig. 1 to fig. 6.
The embodiments of the present invention are preferred embodiments of the present application, and the scope of protection of the present application is not limited by the embodiments, so: all equivalent changes made according to the structure, shape and principle of the present application shall be covered by the protection scope of the present application.
Claims (8)
1. An artificial intelligence big data analysis processing management method is characterized by comprising the following steps:
acquiring a login account of a user;
predicting and analyzing the reading mode of the user login at this time based on the login account number of the user and the reading mode selected by the user history for viewing the channel information at different time periods;
comprehensively analyzing and determining a login password of the user according to the predicted reading mode of the user login, the user identity card information, the contact information, the current login time period and the total number information;
acquiring a login password of a user, and comparing the login password with the login password determined by analysis;
if the comparison is consistent, completing login and applying the reading mode confirmed by the predictive analysis in advance;
if the password is not consistent with the password, the user selects the browsing mode as the confirmed browsing mode; and if the password is completely inconsistent, clearing the login password, and waiting for the user to input again and compare again.
2. The method for analyzing, processing and managing artificial intelligence big data as claimed in claim 1, wherein the predictive analysis of the browsing mode of the user login comprises:
inquiring and acquiring the viewing mode selected by the information of the broadcasting station viewed by the user at the current time period by taking the user and the current time period as a common inquiry object from a preset database storing the viewing modes selected by the information of the broadcasting station viewed by the user at different time periods;
if the selected browsing mode is only one, taking the corresponding browsing mode as the browsing mode for the user to log in at this time;
if the selected browsing modes are 2 or more, acquiring time length ratio data and frequency ratio data of the user adopting different browsing modes at the current time period;
and taking the sum of the time length ratio data and the frequency ratio data of the same browsing mode as an effective value, and selecting the browsing mode with the largest effective value as the browsing mode which is logged in by the user at this time.
3. The method for analyzing, processing and managing artificial intelligence big data as claimed in claim 2, wherein the predictive analysis of the viewing mode of the user login at this time further includes a step after querying and acquiring the viewing mode selected by the user viewing the channel information at the current time, specifically as follows:
if the selected browsing modes are 0, acquiring time length ratio data and frequency ratio data of different browsing modes adopted by the user in two time periods before and after the current time period;
and taking the sum of the time length ratio data and the frequency ratio data of the same browsing mode as an effective value, and selecting the browsing mode with the largest effective value as the browsing mode which is logged in by the user at this time.
4. The artificial intelligence big data analysis processing management method according to claim 1, wherein: the comprehensive analysis for determining the login password of the user comprises the following steps:
acquiring the times of the user logging in the middle station system at the end of the day;
taking the times of the user logging in the middle station system when the user is at the end of the current day and the number of current time periods as classification numbers;
if the classification number is odd, the last three digits of the ID card are selected as the first segment of password,
the first three digits of the mobile phone are used as a second section of password, and the digits corresponding to the reading mode are used as a third section of password;
if the classification tree is an even number, selecting the first three digits of the identity card and the last three digits of the mobile phone, selecting the first three digits of the identity card as a first section of password, the last three digits of the mobile phone as a second section of password, and selecting the corresponding digits of the reading mode as a third section of password;
and analyzing and confirming the arrangement sequence of the first section of password, the second section of password and the third section of password based on the number corresponding to the time section number.
5. The method for analyzing, processing and managing the artificial intelligence big data as claimed in claim 3, wherein the analyzing and confirming the arrangement sequence of the first section of the password, the second section of the password and the third section of the password based on the number corresponding to the number of the time sections comprises:
acquiring a number corresponding to the time segment number;
if the number corresponding to the time segment number is a multiple of 3, the password is a first segment password, a second segment password and a third segment password from front to back;
otherwise, the password is a third section password, a second section password and a first section password from front to back.
6. The method for analyzing, processing and managing big data of artificial intelligence according to any one of claims 1 to 5, further comprising a step of, after matching and before completing login and applying a browsing mode of predictive analysis confirmation in advance, as follows:
acquiring a user account and a mark color which is inclined by a corresponding user;
marking the number corresponding to the browsing mode in the login password by the marking color preferred by the user so as to remind the user;
if the user does not feed back within the preset time, the login is automatically completed, and the reading mode of the predictive analysis confirmation is applied in advance.
7. The method for big data analysis, processing and management according to claim 1, wherein clearing the login password, waiting for the user to re-enter and re-compare comprises:
clearing the login password;
adding 1 to the last bit of the login password determined by analysis to be used as the correct login password;
and acquiring the password input by the user, and comparing the password input by the user with the correct login password again.
8. The utility model provides a big data analysis of artificial intelligence handles management zhongtai which characterized in that: the large data analysis and processing management system comprises a memory, a processor and a program which is stored on the memory and can run on the processor, wherein the program can be loaded and executed by the processor to realize the large data analysis and processing management method of artificial intelligence according to any one of claims 1 to 7.
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