CN114969464A - Intelligent visual display system based on millimeter wave communication - Google Patents
Intelligent visual display system based on millimeter wave communication Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D30/00—Reducing energy consumption in communication networks
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Abstract
The invention relates to the technical field of visual display, in particular to an intelligent visual display system based on millimeter wave communication, which comprises a visual intelligent login unit, a visual intelligent verification unit, a visual cloud service unit, a visual monitoring unit, a visual dividing unit and a visual execution unit, wherein the invention carries out data association analysis by collecting input information of a user on the system so as to calculate judgment data of the user on browsing data, then carries out association processing on the judgment data and browsing records of the user so as to obtain recorded judgment data, carries out key division on the browsing documents of the user according to the judgment data and the recorded judgment data, carries out display screen proportion calculation on the divided documents, carries out display of the size of a document screen according to the display screen proportion calculation, and is convenient for the user to find out interesting data in the display screen at a glance, saving query time and improving working efficiency.
Description
Technical Field
The invention relates to the technical field of visual display, in particular to an intelligent visual display system based on millimeter wave communication.
Background
Visualization is a theory, method and technology that data is converted into graphics or images to be displayed on a screen by using computer graphics and image processing technology, and then interactive processing is performed.
Although the existing visual display system has the priority of quick conversion and high-definition display, the existing visual system cannot perform safety protection on an account of a user to cause data loss of the user, so that browsing habits cannot be analyzed according to usage records of the user, relevance analysis cannot be performed on related data during browsing, and accordingly data which the user is interested in is analyzed in terms of screen occupation size according to habits.
For this reason, we propose an intelligent visual display system based on millimeter wave communication.
Disclosure of Invention
The invention aims to provide an intelligent visual display system based on millimeter wave communication, which is characterized in that characters of an account input by a user are marked, and the marked characters are matched with stored account data to carry out security verification on the account, so that the security of the account is improved, economic loss caused by account loss is avoided, meanwhile, the system can be added to carry out accurate analysis on the use habit of the account, and the working efficiency is improved; the method comprises the steps of carrying out data association analysis by collecting input information of a user on a system, calculating judgment data of the user on browsing data, carrying out association processing on the judgment data and browsing records of the user to obtain recorded judgment data, carrying out key division on browsing documents of the user according to the judgment data and the recorded judgment data, carrying out display screen proportion calculation on the key-divided documents, carrying out display of the size of a document screen according to the display screen proportion calculation, facilitating the user to find out interesting data in the display screen at a glance, saving query time and improving working efficiency.
The purpose of the invention can be realized by the following technical scheme: the intelligent visual display system based on millimeter wave communication comprises a visual intelligent login unit, a visual intelligent verification unit, a visual cloud service unit, a visual monitoring unit, a visual branch unit, a visual division unit and a visual execution unit;
the visual intelligent login unit is used for registering and logging in personal information related to a personal account by a user and transmitting the personal information to the visual intelligent verification unit;
the visual cloud service unit is used for acquiring account information from the visual cloud service unit, performing account security processing operation on the account information and personal information together, and automatically jumping to an operation input interface according to an acquired security signal;
the visual monitoring unit is used for monitoring real operation information and monitoring information related to operation of a user on the operation input interface and transmitting the real operation information and the monitoring information to the visual branch unit;
the visual cloud service unit stores relevant visual cloud information of user browsing operation;
the visual branch unit acquires visual cloud information from the visual cloud service unit, performs visual branch processing operation on the visual cloud information, the real operation information and the monitoring information to obtain a character ratio mean value, a monitoring and deleting difference mean value, a push point ratio mean value, a point visit ratio mean value, a lower searching mean value, cloud name data and a supplementary lower searching mean value, and transmits the character ratio mean value, the monitoring and deleting difference mean value, the push point ratio mean value, the point visit ratio mean value, the lower searching mean value, the cloud name data and the supplementary lower searching mean value to the visual branch unit;
the visual dividing unit is used for performing visual dividing calculation operation on the character ratio mean value, the monitoring and deleting difference mean value, the estimated point ratio mean value, the point-to-visit ratio mean value, the lower searching mean value, the cloud credit name data and the supplementary lower searching mean value to obtain screen ratio data and preparation processing data, and transmitting the screen ratio data and the preparation processing data to the visual execution unit;
and the visual execution unit performs screen division display of the preparation processing data according to the screen proportion data.
Further, the specific operation process of the journal security processing operation is as follows:
extracting the personal account number and the personal password number in the personal information;
extracting account mark data and account secret data in the account book data;
matching the personal account number and the personal secret number with the account mark data and the account secret data to obtain a positive account signal and a wrong account signal;
extracting a positive account signal and a wrong account signal, identifying the positive account signal and the wrong account signal, automatically jumping to a visual intelligent login unit when the wrong account signal is identified, automatically popping up an input box related to a registered account number for a user to register a new account number, and automatically extracting a personal account number and account number data corresponding to the personal account number and the account number data when the positive account signal is identified;
matching the personal account number with the personal secret number and the account secret data corresponding to the account data to obtain a secret positive signal and a secret error signal;
and extracting the just signal and the error signal, identifying the just signal and the error signal, automatically visually and intelligently logging in the unit when the error signal is identified, popping up a bullet box with a login error, and allowing the user to input the account again, and automatically jumping to an operation input interface when the just signal is identified.
Further, the specific process of matching the personal account number and the personal secret number with the account data and the account secret data is as follows:
extracting personal account number and account data, performing character marking on codes corresponding to the personal account data, marking each code as a character, and performing serial number marking on each character according to a sequence from front to back to obtain a character string and a serial number string;
carrying out character marking on codes corresponding to the account data, marking the codes corresponding to each account data as characters, marking the characters as character groups, and marking serial numbers of the characters in the character groups from front to back so as to obtain the character groups and the serial number groups;
matching the character string and the serial number string with the character group and the serial number group, matching the character string and the character group, selecting a character combination matched with the corresponding character string in the character group, marking the character combination matched with the corresponding character string in the character group as a character bar, and matching the serial number group corresponding to the character bar with the serial number string corresponding to the character string, specifically:
and when the matching results of the serial number group corresponding to the character bar and the serial number string corresponding to the character string are not consistent, judging that the account number is wrong, and generating an account error signal.
Further, the specific operation process of the visual processing operation is as follows:
extracting time operation data, operation push data, operation point data and operation data in the actual operation information;
extracting the monitoring data, the monitoring deletion data and the monitoring text data in the monitoring information;
extracting cloud credit name data, cloud credit storage data, cloud credit search data, cloud credit downloading data and cloud credit time data in the video cloud information;
extracting the monitoring data and the monitoring data, and performing query matching processing on the monitoring data and the monitoring data to obtain a character ratio and a character ratio mean value;
extracting the monitoring and deleting data and the operating data, selecting a plurality of time periods according to the operating data, selecting the monitoring and deleting data of the user in the time period, carrying out mean value calculation on the monitoring and deleting data corresponding to the plurality of time periods, and calculating a monitoring and deleting mean value;
respectively carrying out difference calculation on the plurality of monitoring and deleting data and a monitoring and deleting mean value, calculating a plurality of monitoring and deleting difference values, and carrying out mean value calculation on the plurality of monitoring and deleting difference values so as to calculate a monitoring and deleting difference mean value;
extracting operation time data, operation push data, operation point data and navigation data, selecting a plurality of time periods according to the operation time data, selecting the operation push data, the operation point data and the navigation data in the time periods, carrying out proportion calculation on the operation push data and the operation point data, calculating a push point proportion value, calculating push point proportion values corresponding to the time periods according to a calculation method of the push point proportion value, carrying out mean value calculation on the push point proportion values, and calculating a push point proportion mean value;
calculating the occupation ratio of the operating point data and the operating data in a plurality of time periods, calculating a plurality of point exhibition occupation ratio values, calculating the mean value of the plurality of point exhibition occupation ratio values, and calculating the average value of the point exhibition occupation ratio;
and extracting cloud credit name data, and performing supplementary matching processing on the cloud credit name data and the plurality of supervisory data to obtain an under-investigation mean value and a supplementary under-investigation mean value.
Further, the specific process of performing query matching processing on the query data and the supervisory data is as follows:
carrying out character marking on the monitoring data, marking the marked characters as monitoring characters, carrying out character marking on the monitoring data, marking the marked characters as a monitoring character group, and carrying out character matching on the monitoring characters and the monitoring character group:
when the matching result of each character in the inquiry character and the inquiry character group is consistent, generating a positive word signal, counting the occurrence frequency of the positive word signal, identifying the number of characters in the inquiry character group, carrying out proportion calculation on the occurrence frequency of the positive word signal and the number of characters in the inquiry character group, and calculating the character proportion value;
according to the character ratio value calculation method, a plurality of character ratio values corresponding to different pieces of query data are calculated, the character ratio values are subjected to mean value calculation, and a character ratio mean value is calculated.
Further, the specific process of performing supplementary matching processing on the cloud credit name data and the plurality of supervisory message data is as follows:
performing supplementary matching processing on the cloud credit name data and the plurality of supervisory message data so as to match a plurality of cloud credit name data matched with the supervisory message data, calibrating the cloud credit name data as credit selection data, and extracting cloud credit storage data, cloud credit check data, cloud credit data and cloud credit time data corresponding to the credit selection data;
dividing a plurality of credit selection name data into a plurality of time periods according to cloud credit time data, sorting the corresponding cloud credit data in the plurality of time periods of the credit selection name data from large to small, removing the maximum value and the minimum value in the sorting to obtain credit sorting data, carrying out mean value calculation on cloud credit data corresponding to the plurality of credit selection name data, calculating a cloud credit average value, carrying out mean value calculation on cloud credit data corresponding to the plurality of credit selection name data, calculating a cloud credit lower mean value, carrying out ratio calculation on the cloud credit average value and the cloud credit lower mean value, and calculating a credit lower mean value;
extracting character ratio mean value, monitoring data and cloud credit data, performing character matching on the monitoring data and the cloud credit data, matching character matching numbers of the monitoring data and the cloud credit data, performing ratio calculation on the character matching numbers and the character numbers corresponding to the cloud credit data, calculating a real credit ratio, performing difference calculation on the real credit ratio and the character ratio mean value, calculating a real credit ratio difference value, performing positive and negative value calibration on the real credit ratio difference value, when the real credit ratio difference value is larger than zero, calibrating the corresponding real credit ratio difference value as a positive credit ratio difference value, when the real credit ratio difference value is smaller than or equal to zero, calibrating the corresponding real credit ratio difference value as a negative credit ratio difference value, sequencing a plurality of positive credit ratio difference values from large to small to obtain positive credit ratio difference value data, and selecting the three-cloud credit data before the positive credit ratio difference value sequencing data, scaling the cloud credit name data of the first three of the forward proportion difference sorting data as supplementary cloud credit name data;
and calculating the under-investigation mean value corresponding to the supplementary cloud credit data according to the calculation method of the data under-investigation mean value, and calibrating the under-investigation mean value as the supplementary under-investigation mean value.
Further, the specific operation process of the view division calculation operation is as follows:
the character ratio mean value, the monitoring and deleting difference mean value, the estimated point ratio mean value, the point-to-visit ratio mean value and the lower-searching mean value corresponding to the monitoring data are brought into a division conversion calculation formula, and a division conversion value Fhi is calculated;
sorting the monitoring text data from large to small according to the division conversion values to obtain division conversion value sorting data, setting a selection value M, and selecting the monitoring text data with the division conversion value larger than M in the division conversion value sorting data as prepared display data;
and (4) searching the supplementary lower mean value according to a calculation formula:
calculating a supplementary division conversion value Fbi, wherein Fbi is expressed as a supplementary division conversion value, Bci is expressed as a supplementary lower-search mean value, e3 is expressed as a division conversion influence factor of the supplementary lower-search mean value, and r1 is expressed as a conversion deviation correction factor of the supplementary lower-search mean value;
matching the supplementary division conversion value with a division conversion value corresponding to the supervisory data in the prepared display data, adding the supplementary division conversion value into the prepared display data, and calibrating the updated prepared display data into prepared processing data;
performing ratio analysis on each numerical value in the pre-processing data, respectively calibrating each numerical value as YCl, i =1, 2, 3.. n, and performing ratio enumeration on corresponding scores of the pre-processing data as follows: YC 1: YC 2: YC 3: ... YCl, summing the corresponding scores of the preliminary processing data, calculating a total score value, dividing the corresponding score of the preliminary processing data by the total score value, calculating a screen occupation ratio, and dividing the preliminary processing data corresponding to the corresponding score of the preliminary processing data according to the screen occupation ratio to obtain screen occupation data.
Further, the partition conversion calculation formula specifically includes:
where Fhi is expressed as a division conversion value, Zzi is expressed as a character-to-character ratio mean, u1 is expressed as a weight coefficient of the character-to-character ratio mean, Jci is expressed as a monitoring-deletion mean, u2 is expressed as a weight coefficient of the monitoring-deletion mean, Tdi is expressed as a push-point-to-character ratio mean, u3 is expressed as a weight coefficient of the push-point-to-character ratio mean, Dli is expressed as a point-view ratio mean, u4 is expressed as a weight coefficient of the point-to-character ratio mean, Cji is expressed as a search-down mean, u5 is expressed as a weight coefficient of the search-down mean, e1 is expressed as a division conversion influence factor of the monitoring-deletion mean, the push-point-to-character ratio mean, and the point-to-view ratio mean, e2 is expressed as a division conversion influence factor of the character-to-character ratio mean and the search-down mean, i =1, 2, 3.
The invention has the beneficial effects that:
(1) the account number input by the user is subjected to character marking, and the marked characters are matched with the stored account number data to carry out security verification on the account number, so that the security of the account number is improved, economic loss caused by account number loss is avoided, meanwhile, the accurate analysis of the use habit of the account number by a system can be increased, and the working efficiency is improved;
(2) the method comprises the steps of carrying out data association analysis by collecting input information of a user on a system, calculating judgment data of the user on browsing data, carrying out association processing on the judgment data and browsing records of the user to obtain recorded judgment data, carrying out key division on browsing documents of the user according to the judgment data and the recorded judgment data, carrying out display screen proportion calculation on the key-divided documents, carrying out display of the size of a document screen according to the display screen proportion calculation, facilitating the user to find out interesting data in the display screen at a glance, saving query time and improving working efficiency.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of 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. 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.
Referring to fig. 1, the present invention is an intelligent visual display system based on millimeter wave communication, including a visual intelligent login unit, a visual intelligent verification unit, a visual cloud service unit, a visual monitoring unit, a visual branch unit, a visual division unit and a visual execution unit;
the visual intelligent login unit is used for registering and logging in a personal account by a user, calibrating relevant information input by registering and logging in the personal account by the user into personal information, and transmitting the personal information to the visual intelligent verification unit;
the visual cloud service unit is internally stored with account information related to an input account and a password when a user logs in the account, the visual intelligent verification unit is used for acquiring the account information from the visual cloud service unit and carrying out the account security processing operation on the account information and the personal information together, and the specific operation process of the account security processing operation is as follows:
extracting personal account numbers and personal password numbers in the personal information, wherein the personal account numbers refer to code data corresponding to the account numbers input by the user in real time, and the personal passwords refer to passwords corresponding to the account numbers input by the user in real time;
extracting account mark data and account secret data in the account book data, wherein the account mark data refers to codes corresponding to accounts input by all users in the record, and the account secret data refers to passwords corresponding to accounts input by all users in the record;
the personal account number and the personal secret number are matched with the account data and the account secret data, and the specific matching process is as follows:
extracting personal account number and account data, performing character marking on codes corresponding to the personal account data, marking each code as a character, and performing serial number marking on each character according to a sequence from front to back to obtain a character string and a serial number string;
carrying out character marking on codes corresponding to the account mark data, marking the codes corresponding to each account mark data into characters, marking the characters into character groups, and carrying out serial number marking on the characters in the character groups from front to back so as to obtain the character groups and the serial number groups;
matching the character string and the serial number string with the character group and the serial number group, matching the character string and the character group, selecting a character combination matched with the corresponding character string in the character group, marking the character combination matched with the corresponding character string in the character group as a character bar, and matching the serial number group corresponding to the character bar with the serial number string corresponding to the character string, specifically:
when the matching results of the serial number groups corresponding to the character bars and the serial number strings corresponding to the character strings are consistent, judging that the account number is correct, generating a positive account signal, and when the matching results of the serial number groups corresponding to the character bars and the serial number strings corresponding to the character strings are inconsistent, judging that the account number is wrong, and generating a wrong account signal;
extracting a positive account signal and a wrong account signal, identifying the positive account signal and the wrong account signal, automatically jumping to a visual intelligent login unit when the wrong account signal is identified, automatically popping up an input box related to a registered account number for a user to register a new account number, and automatically extracting a personal account number and account number data corresponding to the personal account number and the account number data when the positive account signal is identified;
matching personal account number and account number data corresponding to the personal account number and the account number data, comparing each character of the personal account number and the account number data, judging that the password is correct when the comparison result of each character of the personal account number and the account number data is completely consistent, generating a just signal, and judging that the password is wrong when the comparison result of each character of the personal account number and the account number data is inconsistent, and generating a wrong signal;
extracting a just signal and a wrong signal, identifying the just signal and the wrong signal, automatically visually and intelligently logging in a unit when the wrong signal is identified, popping up a bullet box with a login error, and allowing a user to input an account again, and automatically jumping to an operation input interface when the just signal is identified;
the visual monitoring unit is used for monitoring the operation of a user on the operation input interface and marking the operation of the user on the operation input interface as actual operation information, monitoring related data during the operation of the user, marking the monitored related data during the operation of the user as monitoring information and transmitting the actual operation information and the monitoring information to the visual branch unit;
the visual cloud service unit stores related records of user browsing operation, and marks the related records of the user browsing operation as visual cloud information;
the visual branch unit acquires visual cloud information from the visual cloud service unit and performs visual branch processing operation on the visual cloud information, the real operation information and the monitoring information, and the specific operation process of the visual branch processing operation is as follows:
extracting real operation information, extracting operation time data in the real operation information, wherein the operation time data refers to a time point when a user operates an operation input interface, extracting operation push data in the real operation information, the operation push data refers to the times of a document recommended by the user on the operation input interface, and the operation point data in the real operation information refers to the times of the user clicking to open the recommended document on the operation input interface, and the operation data refers to the condition of browsing on the operation input interface by the user;
extracting monitoring information, extracting monitoring data in the monitoring information, wherein the monitoring data refers to data of search content input by a user in an operation input interface, and extracting monitoring and deleting data in the monitoring information, the monitoring and deleting data refers to the times of deleting input characters when the user inputs the search content, and the monitoring text data in the monitoring information refers to the name of relevant information clicked by the user after the user inputs characters in the operation input interface;
extracting video cloud information, extracting cloud credit name data in the video cloud information, wherein the cloud credit data refers to names of documents browsed in all data, extracting cloud credit storage data in the video cloud information, the cloud credit storage data refers to the size of storage occupied space corresponding to the names of the documents browsed in all data, and extracting cloud credit check data in the video cloud information, the cloud credit check data refers to the query times of the names of the documents browsed in all data, the cloud credit data in the video cloud information is extracted, the cloud credit data refers to the download times of the names of the documents browsed in all data, the cloud credit time data in the video cloud information is extracted, and the cloud credit time data refers to time points corresponding to user browsing and login in all data;
extracting the monitoring data and the monitoring text data, performing character marking on the monitoring data, marking the marked characters as monitoring characters, performing character marking on the monitoring text data, marking the marked characters as a monitoring character group, and performing character matching on the monitoring characters and the monitoring character group:
when the matching result of each character in the inquiry character and the inquiry character group is consistent, generating a positive word signal, counting the occurrence frequency of the positive word signal, identifying the number of characters in the inquiry character group, carrying out proportion calculation on the occurrence frequency of the positive word signal and the number of characters in the inquiry character group, and calculating the character proportion value;
because the query data refers to the data of the search content input by the user on the operation input interface, a plurality of positive word signals corresponding to the query characters appear, a plurality of character ratio values corresponding to different query data are calculated according to a calculation method of the character ratio values, the average value of the character ratio values is calculated, and the average value of the character ratio values is calculated;
extracting monitoring and deleting data and operating data, selecting a plurality of time periods according to the operating data, selecting the monitoring and deleting data of a user in the time period, carrying out mean value calculation on the monitoring and deleting data corresponding to the time periods, and calculating a monitoring and deleting mean value, wherein the time periods are set as the time periods within thirty seconds from a preset time point to the time point;
respectively carrying out difference calculation on the plurality of monitoring and deleting data and a monitoring and deleting mean value, calculating a plurality of monitoring and deleting difference values, and carrying out mean value calculation on the plurality of monitoring and deleting difference values so as to calculate a monitoring and deleting difference mean value;
extracting operation time data, operation push data, operation point data and navigation data, selecting a plurality of time periods according to the operation time data, selecting the operation push data, the operation point data and the navigation data in the time periods, carrying out proportion calculation on the operation push data and the operation point data, calculating a push point proportion value, calculating push point proportion values corresponding to the time periods according to a calculation method of the push point proportion value, carrying out mean value calculation on the push point proportion values, and calculating a push point proportion mean value;
calculating the proportion of the operating point data and the operating data in a plurality of time periods, calculating a plurality of point proportion values, calculating the mean value of the point proportion values, and calculating the mean value of the point proportion values, wherein each time period in the plurality of time periods refers to a time period from the time point when the set time starts to thirty seconds after the time point;
extracting cloud credit name data, matching the cloud credit name data with a plurality of supervisory message data so as to match a plurality of cloud credit name data matched with the supervisory message data, calibrating the cloud credit name data into credit selection data, and extracting cloud credit storage data, cloud credit inquiry data, cloud credit data and cloud credit time data corresponding to the credit selection data;
dividing a plurality of credit selection name data into a plurality of time periods according to the cloud credit time data, wherein the plurality of time periods are also expressed as one to thirty seconds, sorting the corresponding cloud credit data in the plurality of time periods of the credit selection name data from large to small, removing the maximum value and the minimum value in the sorting to obtain credit sorting data, carrying out mean value calculation on the cloud credit data corresponding to the plurality of credit selection name data, calculating a cloud credit mean value, carrying out ratio calculation on the cloud credit mean value and the cloud credit mean value, and calculating a found mean value;
extracting character ratio mean value, monitoring data and cloud credit data, performing character matching on the monitoring data and the cloud credit data, matching character matching numbers of the monitoring data and the cloud credit data, performing ratio calculation on the character matching numbers and the character numbers corresponding to the cloud credit data, calculating a real credit ratio, performing difference calculation on the real credit ratio and the character ratio mean value, calculating a real credit ratio difference value, performing positive and negative value calibration on the real credit ratio difference value, when the real credit ratio difference value is larger than zero, calibrating the corresponding real credit ratio difference value as a positive credit ratio difference value, when the real credit ratio difference value is smaller than or equal to zero, calibrating the corresponding real credit ratio difference value as a negative credit ratio difference value, sequencing a plurality of positive credit ratio difference values from large to small to obtain positive credit ratio difference value data, and selecting the three-cloud credit data before the positive credit ratio difference value sequencing data, scaling the cloud credit name data of the first three of the forward proportion difference sorting data as supplementary cloud credit name data;
calculating a lower-check mean value corresponding to the supplementary cloud credit data according to a data lower-check mean value calculation method, and calibrating the lower-check mean value as a supplementary lower-check mean value;
transmitting the character ratio mean value, the monitoring and deleting difference mean value, the inferred point ratio mean value, the point-visit ratio mean value, the lower-searching mean value, the cloud credit name data and the supplementary lower-searching mean value to the visual dividing unit;
the visual dividing unit is used for carrying out visual dividing calculation operation on the character ratio mean value, the monitoring and deleting difference mean value, the inferred point ratio mean value, the point-to-visit ratio mean value, the lower-searching mean value, the cloud credit name data and the supplementary lower-searching mean value, and the specific operation process of the visual dividing calculation operation is as follows:
and (3) bringing the character ratio mean value, the monitoring and deleting difference mean value, the estimated point ratio mean value, the point-to-visit ratio mean value and the found mean value corresponding to the monitoring data into a division conversion calculation formula:
wherein Fhi is expressed as a division conversion value, Zzi is expressed as a character-to-character ratio mean, u1 is expressed as a weight coefficient of the character-to-character ratio mean, Jci is expressed as a monitoring-deletion mean, u2 is expressed as a weight coefficient of the monitoring-deletion mean, Tdi is expressed as a push-point-to-character ratio mean, u3 is expressed as a weight coefficient of the push-point-to-character ratio mean, Dli is expressed as a point-to-view ratio mean, u4 is expressed as a weight coefficient of the point-to-character ratio mean, Cji is expressed as a search-down mean, u5 is expressed as a weight coefficient of the search-down mean, e1 is expressed as a division conversion influence factor of the monitoring-deletion mean, the push-point-to-character ratio mean, and the point-to-view ratio mean, e2 is expressed as a division conversion influence factor of the character-to-character ratio mean and the search-down mean, i =1, 2, 3. 737 > u5 > u4 > 3 > and e 85 1 > 8536;
sorting the monitoring text data from large to small according to the division conversion values to obtain division conversion value sorting data, setting a selection value M, and selecting the monitoring text data with the division conversion value larger than M in the division conversion value sorting data as prepared display data;
and (4) searching the supplementary lower mean value according to a calculation formula:
calculating a supplementary division conversion value Fbi, wherein Fbi is expressed as a supplementary division conversion value, Bci is expressed as a supplementary lower-search mean value, e3 is expressed as a division conversion influence factor of the supplementary lower-search mean value, and r1 is expressed as a conversion deviation correction factor of the supplementary lower-search mean value;
matching the supplementary division conversion value with a division conversion value corresponding to the supervisory data in the prepared display data, adding the supplementary division conversion value into the prepared display data, and calibrating the updated prepared display data into prepared processing data;
performing ratio analysis on each numerical value in the pre-processing data, respectively calibrating each numerical value as YCl, i =1, 2, 3.. n, and performing ratio enumeration on corresponding scores of the pre-processing data as follows: YC 1: YC 2: YC 3: ... YCl, summing the corresponding scores of the preliminary processing data, calculating a total score value, dividing the corresponding score of the preliminary processing data by the total score value, calculating a screen occupation ratio, and dividing the preliminary processing data corresponding to the corresponding score of the preliminary processing data according to the screen occupation ratio to obtain screen occupation data;
transmitting the screen proportion data and the preparation processing data to a visual execution unit;
the visual execution unit is used for receiving the screen proportion data and the preliminary processing data, displaying the preliminary processing data corresponding to the screen proportion data according to the screen proportion data, and displaying, and is specifically a tablet computer.
The related numerical values of the calculation formula are all subjected to quantization processing, and the numerical values are selected and do not carry units.
When the intelligent login system works, the visual intelligent login unit is used for registering a user and logging in a personal account, relevant information input by the user for registering and logging in the personal account is marked as personal information, and the personal information is transmitted to the visual intelligent verification unit; the method comprises the steps that account information related to an input account and a password when a user logs in an account is stored in a visual cloud service unit, a visual smart check unit obtains the account information from the visual cloud service unit, the account information and personal information are subjected to account security processing operation together, and the operation input interface is automatically jumped to according to an obtained secret and positive signal; the visual monitoring unit monitors real operation information and monitoring information related to operation of a user on the operation input interface and transmits the real operation information and the monitoring information to the visual branch unit; the visual cloud service unit stores relevant visual cloud information of user browsing operation; the visual branch unit acquires visual cloud information from the visual cloud service unit, performs visual branch processing operation on the visual cloud information, the real operation information and the monitoring information to obtain a character ratio mean value, a monitoring and deleting difference mean value, a push point ratio mean value, a point visit ratio mean value, a lower average value, cloud credit name data and a supplementary lower average value, and transmits the character ratio mean value, the monitoring and deleting difference mean value, the push point ratio mean value, the point visit ratio mean value, the lower average value, the cloud credit name data and the supplementary lower average value to the visual branch unit; the visual dividing unit performs visual dividing calculation operation on the character ratio mean value, the monitoring and deleting difference mean value, the inferred point ratio mean value, the point-to-visit ratio mean value, the lower searching mean value, the cloud credit name data and the supplementary lower searching mean value to obtain screen ratio data and preparation processing data, and transmits the screen ratio data and the preparation processing data to the visual execution unit; the visual execution unit performs screen division display of the preliminary processing data according to the screen proportion data.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (8)
1. The intelligent visual display system based on millimeter wave communication is characterized by comprising a visual intelligent login unit, a visual intelligent verification unit, a visual cloud service unit, a visual monitoring unit, a visual branch unit, a visual division unit and a visual execution unit;
the visual intelligent login unit is used for registering and logging in personal information related to a personal account by a user and transmitting the personal information to the visual intelligent verification unit;
the visual cloud service unit is used for acquiring account information from the visual cloud service unit, performing account security processing operation on the account information and personal information together, and automatically jumping to an operation input interface according to an acquired security signal;
the visual monitoring unit is used for monitoring real operation information and monitoring information related to operation of a user on the operation input interface and transmitting the real operation information and the monitoring information to the visual branch unit;
the visual cloud service unit stores relevant visual cloud information of user browsing operation;
the visual branch unit acquires visual cloud information from the visual cloud service unit, performs visual branch processing operation on the visual cloud information, the real operation information and the monitoring information to obtain a character ratio mean value, a monitoring and deleting difference mean value, a push point ratio mean value, a point visit ratio mean value, a lower searching mean value, cloud name data and a supplementary lower searching mean value, and transmits the character ratio mean value, the monitoring and deleting difference mean value, the push point ratio mean value, the point visit ratio mean value, the lower searching mean value, the cloud name data and the supplementary lower searching mean value to the visual branch unit;
the visual dividing unit is used for performing visual dividing calculation operation on the character ratio mean value, the monitoring and deleting difference mean value, the estimated point ratio mean value, the point-to-visit ratio mean value, the lower searching mean value, the cloud credit name data and the supplementary lower searching mean value to obtain screen ratio data and preparation processing data, and transmitting the screen ratio data and the preparation processing data to the visual execution unit;
and the visual execution unit performs screen division display of the preparation processing data according to the screen proportion data.
2. The intelligent visual display system based on millimeter wave communication according to claim 1, characterized in that the specific operation process of the police processing operation is:
extracting the personal account number and the personal password number in the personal information;
extracting account mark data and account secret data in the account book data;
matching the personal account number and the personal secret number with the account mark data and the account secret data to obtain a positive account signal and a wrong account signal;
extracting a positive account signal and a wrong account signal, identifying the positive account signal and the wrong account signal, automatically jumping to a visual intelligent login unit when the wrong account signal is identified, automatically popping up an input box related to a registered account number for a user to register a new account number, and automatically extracting a personal account number and account number data corresponding to the personal account number and the account number data when the positive account signal is identified;
matching the personal account number with the personal password number and account password data corresponding to the account data to obtain a positive password signal and a wrong password signal;
and extracting the just signal and the error signal, identifying the just signal and the error signal, automatically visually and intelligently logging in the unit when the error signal is identified, popping up a bullet box with a login error, and allowing the user to input the account again, and automatically jumping to an operation input interface when the just signal is identified.
3. The intelligent visual display system based on millimeter wave communication according to claim 2, characterized in that the specific process of matching the personal account number and the personal secret number with the account data and the account secret data is as follows:
extracting personal account number and account data, performing character marking on codes corresponding to the personal account data, marking each code as a character, and performing serial number marking on each character according to a sequence from front to back to obtain a character string and a serial number string;
carrying out character marking on codes corresponding to the account mark data, marking the codes corresponding to each account mark data into characters, marking the characters into character groups, and carrying out serial number marking on the characters in the character groups from front to back so as to obtain the character groups and the serial number groups;
matching the character string and the serial number string with the character group and the serial number group, matching the character string and the character group, selecting a character combination matched with the corresponding character string in the character group, marking the character combination matched with the corresponding character string in the character group as a character bar, and matching the serial number group corresponding to the character bar with the serial number string corresponding to the character string, specifically:
and when the matching results of the serial number group corresponding to the character bar and the serial number string corresponding to the character string are not consistent, judging that the account number is wrong, and generating an account error signal.
4. The intelligent visual display system based on millimeter wave communication according to claim 3, characterized in that the specific operation process of the visual processing operation is:
extracting time operation data, operation push data, operation point data and operation data in the actual operation information;
extracting the monitoring data, the monitoring deletion data and the monitoring text data in the monitoring information;
extracting cloud credit name data, cloud credit storage data, cloud credit search data, cloud credit downloading data and cloud credit time data in the video cloud information;
extracting the monitoring data and the monitoring text data, and performing text query matching processing on the monitoring data and the monitoring text data to obtain a character ratio and a character ratio mean value;
extracting the monitoring and deleting data and the operating data, selecting a plurality of time periods according to the operating data, selecting the monitoring and deleting data of the user in the time period, carrying out mean value calculation on the monitoring and deleting data corresponding to the plurality of time periods, and calculating a monitoring and deleting mean value;
respectively carrying out difference calculation on the plurality of monitoring and deleting data and a monitoring and deleting mean value, calculating a plurality of monitoring and deleting difference values, and carrying out mean value calculation on the plurality of monitoring and deleting difference values so as to calculate a monitoring and deleting difference mean value;
extracting operation time data, operation push data, operation point data and navigation data, selecting a plurality of time periods according to the operation time data, selecting the operation push data, the operation point data and the navigation data in the time periods, carrying out proportion calculation on the operation push data and the operation point data, calculating a push point proportion value, calculating push point proportion values corresponding to the time periods according to a calculation method of the push point proportion value, carrying out mean value calculation on the push point proportion values, and calculating a push point proportion mean value;
calculating the occupation ratio of the operating point data and the operating data in a plurality of time periods, calculating a plurality of point exhibition occupation ratio values, calculating the mean value of the plurality of point exhibition occupation ratio values, and calculating the average value of the point exhibition occupation ratio;
and extracting cloud credit name data, and performing supplementary matching processing on the cloud credit name data and the plurality of supervisory data to obtain an under-investigation mean value and a supplementary under-investigation mean value.
5. The intelligent visual display system based on millimeter wave communication according to claim 4, wherein the specific process of performing query matching processing on the query data and the supervisory data is as follows:
carrying out character marking on the monitoring data, marking the marked characters as monitoring characters, carrying out character marking on the monitoring data, marking the marked characters as a monitoring character group, and carrying out character matching on the monitoring characters and the monitoring character group:
when each character matching result in the inquiry character set is consistent with that in the inquiry character set, generating a positive word signal, counting the number of times of occurrence of the positive word signal, identifying the number of characters in the inquiry character set, performing proportion calculation on the number of times of occurrence of the positive word signal and the number of characters in the inquiry character set, and calculating a character proportion value;
according to the character ratio value calculation method, a plurality of character ratio values corresponding to different pieces of query data are calculated, the character ratio values are subjected to mean value calculation, and a character ratio mean value is calculated.
6. The intelligent visual display system based on millimeter wave communication according to claim 5, wherein the specific process of performing the supplementary matching processing on the cloud credit name data and the plurality of supervisory text data is as follows:
performing supplementary matching processing on the cloud credit name data and the plurality of supervisory message data so as to match a plurality of cloud credit name data matched with the supervisory message data, calibrating the cloud credit name data as credit selection data, and extracting cloud credit storage data, cloud credit check data, cloud credit data and cloud credit time data corresponding to the credit selection data;
dividing a plurality of credit selection name data into a plurality of time periods according to cloud credit time data, sorting cloud credit data corresponding to the credit selection name data in the plurality of time periods from large to small, removing the maximum value and the minimum value in the sorting to obtain credit sorting data, performing mean value calculation on cloud credit data corresponding to the plurality of credit selection name data to calculate a credit mean value, performing mean value calculation on cloud credit data corresponding to the plurality of credit selection name data to calculate a cloud credit lower mean value, performing proportion calculation on the credit mean value and the cloud credit lower mean value, and calculating a credit lower mean value;
extracting character ratio mean value, monitoring data and cloud credit data, performing character matching on the monitoring data and the cloud credit data, matching character matching numbers of the monitoring data and the cloud credit data, performing ratio calculation on the character matching numbers and the character numbers corresponding to the cloud credit data, calculating a real credit ratio, performing difference calculation on the real credit ratio and the character ratio mean value, calculating a real credit ratio difference value, performing positive and negative value calibration on the real credit ratio difference value, when the real credit ratio difference value is larger than zero, calibrating the corresponding real credit ratio difference value as a positive credit ratio difference value, when the real credit ratio difference value is smaller than or equal to zero, calibrating the corresponding real credit ratio difference value as a negative credit ratio difference value, sequencing a plurality of positive credit ratio difference values from large to small to obtain positive credit ratio difference value data, and selecting the three-cloud credit data before the positive credit ratio difference value sequencing data, scaling the cloud credit name data of the first three of the forward proportion difference sorting data as supplementary cloud credit name data;
and calculating the under-investigation mean value corresponding to the supplementary cloud credit name data according to the data under-investigation mean value calculation method, and calibrating the under-investigation mean value as the supplementary under-investigation mean value.
7. The intelligent visual display system based on millimeter wave communication according to claim 6, characterized in that the specific operation process of the view division calculation operation is:
the character ratio mean value, the monitoring and deleting difference mean value, the estimated point ratio mean value, the point-to-visit ratio mean value and the lower-searching mean value corresponding to the monitoring data are brought into a division conversion calculation formula, and a division conversion value Fhi is calculated;
sorting the monitoring text data from large to small according to the division conversion values to obtain division conversion value sorting data, setting a selection value M, and selecting the monitoring text data with the division conversion value larger than M in the division conversion value sorting data as prepared display data;
and (4) searching the supplementary lower mean value according to a calculation formula:
calculating a supplementary division conversion value Fbi, wherein Fbi is expressed as a supplementary division conversion value, Bci is expressed as a supplementary lower-search mean value, e3 is expressed as a division conversion influence factor of the supplementary lower-search mean value, and r1 is expressed as a conversion deviation correction factor of the supplementary lower-search mean value;
matching the supplementary division conversion value with a division conversion value corresponding to the supervisory data in the prepared display data, adding the supplementary division conversion value into the prepared display data, and calibrating the updated prepared display data into prepared processing data;
performing ratio analysis on each numerical value in the pre-processing data, respectively calibrating each numerical value as YCl, i =1, 2, 3.. n, and performing ratio enumeration on corresponding scores of the pre-processing data as follows: YC 1: YC 2: YC 3: ... YCl, summing the corresponding scores of the preliminary processing data, calculating a total score value, dividing the corresponding score of the preliminary processing data by the total score value, calculating a screen occupation ratio, and dividing the preliminary processing data corresponding to the corresponding score of the preliminary processing data according to the screen occupation ratio to obtain screen occupation data.
8. The intelligent visual display system based on millimeter wave communication according to claim 7, wherein the partition conversion calculation formula is specifically:
where Fhi is expressed as a division conversion value, Zzi is expressed as a character-to-character ratio mean, u1 is expressed as a weight coefficient of the character-to-character ratio mean, Jci is expressed as a monitoring-deletion mean, u2 is expressed as a weight coefficient of the monitoring-deletion mean, Tdi is expressed as a push-point-to-character ratio mean, u3 is expressed as a weight coefficient of the push-point-to-character ratio mean, Dli is expressed as a point-view ratio mean, u4 is expressed as a weight coefficient of the point-to-character ratio mean, Cji is expressed as a search-down mean, u5 is expressed as a weight coefficient of the search-down mean, e1 is expressed as a division conversion influence factor of the monitoring-deletion mean, the push-point-to-character ratio mean, and the point-to-view ratio mean, e2 is expressed as a division conversion influence factor of the character-to-character ratio mean and the search-down mean, i =1, 2, 3.
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