CN114254280B - Artificial intelligence big data analysis processing management method and middle platform - Google Patents
Artificial intelligence big data analysis processing management method and middle platform Download PDFInfo
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- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
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- G06F21/316—User authentication by observing the pattern of computer usage, e.g. typical user behaviour
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Abstract
The application relates to an artificial intelligence big data analysis processing management method and a middle platform, which relate to the technical field of network information processing and solve the problem that on one hand, the login of the existing artificial intelligence big data analysis processing management platform only needs a fixed account password, if the fixed account password is known by a non-staff, the data leakage of the middle platform of the artificial intelligence big data analysis processing management is easy to cause, and the method comprises the following steps: predicting and analyzing a reading mode of the login of the user; comprehensively analyzing and determining a login password of a user by combining a predicted reading mode of the current login of the user, user identity card information and contact information, a current login period and 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 artificial intelligent big data analysis processing management console is effectively guaranteed, and a user can conveniently and timely see the data in a current trend 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 middle platform.
Background
Middle station, internet terminology, is commonly applied to large enterprises. Generally, a framework capable of flexibly and rapidly coping with changes is built, the requirements of the front end are rapidly met, repeated construction is avoided, and the purpose of improving the working efficiency is achieved.
The existing artificial intelligent big data analysis processing management console can facilitate workers to timely and effectively check service data based on needs so as to know service conditions.
With respect to the related art in the above, the inventors consider that there are the following drawbacks: on the one hand, the login of the existing artificial intelligent big data analysis and processing management platform only needs a fixed account number password, if the fixed account number password is known by non-staff, the data leakage of the artificial intelligent big data analysis and processing management platform is easy to occur, and on the other hand, after entering the artificial intelligent big data analysis and processing management platform, a user also needs to adjust the inclined browsing mode to browse the artificial intelligent big data analysis and processing management platform.
Disclosure of Invention
In order to effectively ensure the data safety of the artificial intelligent big data analysis processing management center, and facilitate users to timely see data in a current trend mode after logging in, the application provides an artificial intelligent big data analysis processing management method and the 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;
based on the login account number of the user and the reading mode selected by the history of the user for checking the information of the center in different periods, predicting and analyzing the reading mode of the login of the user;
comprehensively analyzing and determining a login password of a user by combining a predicted reading mode of the current login of the user, user identity card information and contact information, a current login period and 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, finishing login and pre-applying a viewing mode of predictive analysis and confirmation;
otherwise, further comparing whether the analysis is completely inconsistent, and if the analysis is inconsistent, taking the reading mode selected by the user current password as the confirmed reading mode; if the login passwords are completely inconsistent, the login passwords are emptied, and the login passwords are waited for the user to input again and are compared again.
Optionally, the predictive analysis of the browsing mode of the user logging in this time includes:
inquiring and acquiring a reading mode selected by a user for looking up the middle information in the current time period by taking the user and the current time period as a common inquiry object from a preset database storing the reading mode selected by the user history for looking up the middle information in different time periods;
if the selected reading mode is only one type, the corresponding reading mode is used as the reading mode of the user logging in at the time;
if the selected reading modes are 2 or more, acquiring time length duty ratio data and frequency duty ratio data of different reading modes adopted by the user in the current period;
and taking the sum of the time length proportion data and the frequency proportion data of the same reading mode as an effective value, and selecting the reading mode with the largest effective value as the reading mode of the current login of the user.
Optionally, the predictive analysis of the browsing mode of the user logging in at this time further includes a step of searching for the browsing mode selected by the user for viewing the middle information in the current period, specifically including the following steps:
if the selected reading modes are 0, acquiring time length ratio data and frequency ratio data of different reading modes adopted by users in the front and rear time periods adjacent to the current time period;
and taking the sum of the time length proportion data and the frequency proportion data of the same reading mode as an effective value, and selecting the reading mode with the largest effective value as the reading mode of the current login of the user.
Optionally, the comprehensively analyzing and determining the login password of the user includes:
acquiring the times of the user logging in the middle station system when the user logs in the middle station system in the current day;
the times of current login of the user in the middle station system and the current time period number are taken as classification numbers;
if the classification number is odd, selecting the last three digits of the ID card as the first 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 even, 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, using the last three digits of the mobile phone as a second section of password, and using the digits corresponding to 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 numbers corresponding to the time periods.
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 time period number includes:
acquiring a number corresponding to the time period number;
if the number corresponding to the time period number is a multiple of 3, the password is a first section password, a second section password and a third section password in the sequence from front to back;
otherwise, the passwords are a third section of password, a second section of password and a first section of password in the sequence from front to back.
Optionally, the method further comprises the steps of following the matching of the comparison and before the completion of the login and the pre-application of the viewing mode confirmed by the predicted analysis, specifically as follows:
acquiring a user account and a corresponding user-inclined mark color;
marking the number corresponding to the reading mode in the login password with the marking color which is inclined by the user so as to remind the user;
if the user does not feed back in the preset time, the login is automatically completed and the viewing mode of predicted analysis and confirmation is applied in advance.
Optionally, clearing the login password, waiting for the user to input again and comparing again includes:
clearing the login password;
adding 1 to the last digit of the login password determined by analysis to be used as the correct login password at the present time;
and acquiring the password input by the user, and re-comparing the password input by the user with the correct login password.
In a second aspect, the present application provides an artificial intelligence big data analysis processing management console, which adopts the following technical scheme:
an artificial intelligence big data analysis processing management console comprising a memory, a processor and a program stored on the memory and executable on the processor, the program being capable of implementing an artificial intelligence big data analysis processing management method as described in the first aspect when loaded and executed by the processor.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of overall steps of an artificial intelligence big data analysis processing management method according to an embodiment of the present application.
Fig. 2 is a schematic diagram showing specific steps of step S200 in fig. 1.
Fig. 3 is a schematic diagram illustrating a specific step of step S300 in fig. 1.
Fig. 4 is a schematic diagram illustrating a specific step of step S330 in fig. 3.
Fig. 5 is a schematic diagram of steps after matching, and before completion of registration and prior to application of a viewing mode of predictive analysis confirmation.
Fig. 6 is a schematic diagram showing a specific step of step SB00 in the drawing.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that all directional indicators (such as up, down, left, right, front, and rear … …) in the embodiments of the present invention are merely used to explain the relative positional relationship, movement, etc. between the components in a particular posture (as shown in the drawings), and if the particular posture is changed, the directional indicator is changed accordingly.
Furthermore, the description of "first," "second," etc. in this disclosure is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "first", "second" may include the feature explicitly or implicitly. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
In the present invention, unless specifically stated and limited otherwise, the terms "connected," "affixed," and the like are to be construed broadly, and for example, "affixed" may be a fixed connection, a removable connection, or an integral body; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
The present application is described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, an artificial intelligence big data analysis processing management method disclosed in the present application includes:
step S100, obtaining a login account of a user.
Step S200, based on the login account number of the user and the user history, the reading mode selected by the information of the center is checked at different time periods, and the reading mode of the login of the user is predicted and analyzed.
The browsing mode in step S200 may be any of various modes, and may be an automatic browsing mode or a manual browsing mode.
Referring to fig. 2, step S200 includes:
step S210, inquiring and acquiring the reading mode selected by the user for looking up the middle information in the current time period by taking the user and the current time period as common inquiry objects from a preset database storing the reading mode selected by the user for looking up the middle information in different time periods.
In step S2A0, if only one viewing mode is selected, the corresponding viewing mode is used as the viewing mode to which the user logs in at the time.
For example, assuming that the worker is browsing only in the auto-browse mode at 11, the login will directly use the auto-browse mode to browse the data.
And step S2B0, if the selected reading modes are 2 or more, acquiring the time duration duty ratio data and the frequency duty ratio data of different reading modes adopted by the user in the current period.
For example, assume that the number of times the worker browses in the automatic browsing mode at 11 points is 30%, the time period is 60%, the number of times in the manual browsing mode is 70%, and the time period is 40%.
And step S2C0, if the selected reading modes are 0, acquiring time length ratio data and frequency ratio data of different reading modes adopted by the user in the front and rear time periods adjacent to the current time period.
And step S220, taking the sum of the time length and the time number of the same reading mode as an effective value, and selecting the reading mode with the largest effective value as the reading mode of the current login of the user.
Assuming the case illustrated in step S2B0, the manual browsing mode is selected as the browsing mode for the user to log in this time.
Step S300, comprehensively analyzing and determining the login password of the user by combining the predicted reading mode of the current login of the user, the user identity card information and the contact information, the current login period and the total number information.
Referring to fig. 3, step S300 includes:
step S310, the number of times that the user logs in the middle station system when the current day is cut off is obtained.
Step S320, the number of times of current login of the user to the middle station system and the number of current time periods are taken as the classification number.
And step S3A0, if the classification number is an odd number, selecting the last three digits of the identity card as a first section of password, using the first three digits of the mobile phone as a second section of password, and using the number corresponding to the reading mode as a third section of password.
And S3B0, if the classification tree is even, 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, using the last three digits of the mobile phone as a second section of password, and using the digits corresponding to the reading mode as a third section of password.
Step S330, based on the number corresponding to the time period number, the arrangement sequence of the first section of password, the second section of password and the third section of password is analyzed and confirmed.
For example, assuming that the number of times that the user logs in to the central station system when the user logs down is 3 times on the same day, when the current time period is 7, the classification number is 10, that is, even, then the first three digits of the identification card are selected as the first section of password, the last three digits of the mobile phone are selected as the second section of password, and the number corresponding to the browsing mode is selected as the third section of password.
Referring to fig. 4, step S330 includes:
step S331, obtaining numbers corresponding to the time period number.
In step S33A, if the number corresponding to the time period number is a multiple of 3, the passwords are the first section of password, the second section of password, and the third section of password in the order from front to back.
Step S33B, otherwise, the passwords are the third password, the second password and the first password from front to back.
Assuming that the number of periods is 7, the passwords are the third password, the second password, and the first password in this order from front to back.
Step S400, obtaining a login password of a user and comparing the login password with the login password determined by analysis.
If the comparison is consistent, the step SA00 completes the login and applies the viewing mode of the predicted analysis and confirmation in advance.
Referring to fig. 5, an artificial intelligence big data analysis processing management method further includes the steps of, after the matching, and before the completion of the login and the pre-application of the viewing mode of the predicted analysis confirmation, specifically as follows:
step Sa00, obtaining the user account and the corresponding user-inclined mark color.
And step Sb00, marking the number corresponding to the reading mode in the login password with the marking color which is prone to the user so as to remind the user.
And step Sc00, if the user does not feed back in the preset time, automatically finishing login and pre-applying a viewing mode confirmed by predictive analysis.
Step SB00, otherwise, further comparing whether the analysis is completely inconsistent, if only the reading mode is inconsistent, taking the reading mode selected by the user current password as the confirmed reading mode; if the login passwords are completely inconsistent, the login passwords are emptied, and the login passwords are waited for the user to input again and are compared again.
Referring to fig. 6, wherein the step SB00 of clearing the login password, waiting for the user to input again and comparing again includes:
step SB10, the login password is cleared.
Step SB20, add 1 to the last digit of the login password determined by analysis as the correct login password at this time.
Step SB30, obtain the password input by the user, and compare the password input by the user with the correct login password again.
Based on the same inventive concept, the embodiment of the invention provides an artificial intelligent big data analysis processing management console, which comprises a memory and a processor, wherein a program which can run on the processor to realize any one of the methods shown in fig. 1 to 6 is stored in the memory.
The embodiments of the present invention are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in this way, therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (6)
1. The artificial intelligence big data analysis processing management method is characterized by comprising the following steps:
acquiring a login account of a user;
based on the login account number of the user and the reading mode selected by the history of the user for checking the information of the center in different periods, predicting and analyzing the reading mode of the login of the user;
comprehensively analyzing and determining a login password of a user by combining a predicted reading mode of the current login of the user, user identity card information and contact information, a current login period and 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, finishing login and pre-applying a viewing mode of predictive analysis and confirmation;
otherwise, further comparing whether the analysis is completely inconsistent, and if the analysis is inconsistent, taking the reading mode selected by the user current password as the confirmed reading mode; if the login password is completely inconsistent, the login password is emptied, and the user is waited to input again and compare again;
the predictive analysis of the browsing mode of the user logging in this time comprises the following steps:
inquiring and acquiring a reading mode selected by a user for looking up the middle information in the current time period by taking the user and the current time period as a common inquiry object from a preset database storing the reading mode selected by the user history for looking up the middle information in different time periods;
if the selected reading mode is only one type, the corresponding reading mode is used as the reading mode of the user logging in at the time;
if the selected reading modes are 2 or more, acquiring time length duty ratio data and frequency duty ratio data of different reading modes adopted by the user in the current period;
taking the sum of the time length proportion data and the frequency proportion data of the same reading mode as an effective value, and selecting the reading mode with the largest effective value as the reading mode of the current login of the user;
the predictive analysis of the browsing mode of the user logging in at this time also comprises the step of inquiring and acquiring the browsing mode selected by the user for checking the middle information at the current time, and the method specifically comprises the following steps:
if the selected reading modes are 0, acquiring time length ratio data and frequency ratio data of different reading modes adopted by users in the front and rear time periods adjacent to the current time period;
and taking the sum of the time length proportion data and the frequency proportion data of the same reading mode as an effective value, and selecting the reading mode with the largest effective value as the reading mode of the current login of the user.
2. 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 when the user logs in the middle station system in the current day;
the times of current login of the user in the middle station system and the current time period number are taken as classification numbers;
if the classification number is odd, selecting the last three digits of the ID card as the first 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 even, 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, using the last three digits of the mobile phone as a second section of password, and using the digits corresponding to 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 numbers corresponding to the time periods.
3. The artificial intelligence big data analysis processing management method according to claim 1, wherein analyzing and confirming the arrangement order 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 period number comprises:
acquiring a number corresponding to the time period number;
if the number corresponding to the time period number is a multiple of 3, the password is a first section password, a second section password and a third section password in the sequence from front to back;
otherwise, the passwords are a third section of password, a second section of password and a first section of password in the sequence from front to back.
4. A method of managing analysis and processing of big data of artificial intelligence according to any one of claims 1 to 3, further comprising the steps of, after matching, and before completion of logging in and prior to application of a viewing mode of predictive analysis confirmation, specifically:
acquiring a user account and a corresponding user-inclined mark color;
marking the number corresponding to the reading mode in the login password with the marking color which is inclined by the user so as to remind the user;
if the user does not feed back in the preset time, the login is automatically completed and the viewing mode of predicted analysis and confirmation is applied in advance.
5. The artificial intelligence big data analysis processing management method according to claim 1, wherein the step of clearing the login password, waiting for the user to input again and compare again comprises:
clearing the login password;
adding 1 to the last digit of the login password determined by analysis to be used as the correct login password at the present time;
and acquiring the password input by the user, and re-comparing the password input by the user with the correct login password.
6. An artificial intelligence big data analysis processing management platform which characterized in that: comprising a memory, a processor and a program stored on said memory and executable on said processor, which program is capable of realizing an artificial intelligence big data analysis processing management method according to any of claims 1 to 5 when loaded and executed by the processor.
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