CN117093108A - Data regulation and control system and method applied to intelligent exhibition hall interaction - Google Patents

Data regulation and control system and method applied to intelligent exhibition hall interaction Download PDF

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CN117093108A
CN117093108A CN202311353833.1A CN202311353833A CN117093108A CN 117093108 A CN117093108 A CN 117093108A CN 202311353833 A CN202311353833 A CN 202311353833A CN 117093108 A CN117093108 A CN 117093108A
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icon
attribute
sets
icons
data
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CN117093108B (en
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刘晓华
刘来方
丁昊
刘强
尹晓辉
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Jiangsu Jiecheng Mingdao Culture Technology Co ltd
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Jiangsu Jiecheng Mingdao Culture Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
    • G06F3/04817—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention relates to the technical field of intelligent exhibition hall interaction software, in particular to a data regulation and control system and method applied to intelligent exhibition hall interaction, comprising an analysis database construction module, an icon type distinguishing module, an attribute icon set analysis module, a verification analysis module and an early warning transmission module; the analysis database construction module is used for storing the log files recorded by the intelligent exhibition hall interaction software into an analysis database as a data basis; the icon type distinguishing module is used for extracting and analyzing response data recorded in the database and dividing interactive icons corresponding to the response data into effective icons and suspicious icons; the attribute icon set analysis module is used for carrying out classification analysis on the suspicious icons according to icon attribute categories and outputting attribute icon sets; the verification analysis module is used for verifying the suspicious icons based on the attribute icon set and outputting a verification result; the early warning transmission module is used for transmitting early warning signals corresponding to the doubtful icons to the intelligent exhibition hall interaction software based on the verification result.

Description

Data regulation and control system and method applied to intelligent exhibition hall interaction
Technical Field
The invention relates to the technical field of intelligent exhibition hall interaction software, in particular to a data regulation and control system and method applied to intelligent exhibition hall interaction.
Background
In order to promote city construction and strengthen city popularization, the construction of intelligent exhibition hall interactive software has become popular, and a concentration space with city representativeness is created by means of multifunctional multimedia technology, so that users can know the development transition of cities in the visiting process; in many interactive software at present, the interactive icons bear the interactive relation between users and the software, and the users realize intelligent interaction of the exhibition hall through the interactive icons, so that exhibition hall data in different forms are fully known; however, in the running process of the interactive software of the intelligent exhibition hall, the manager often neglects the research analysis of the practicability and the effectiveness direction of the user using the interactive icon, for example, the user cannot obviously determine the icon designed by the interactive software as the interactive icon; thereby causing poor user experience and reduced usage of intelligent exhibition interactive software.
Disclosure of Invention
The invention aims to provide a data regulation and control system and method applied to intelligent exhibition hall interaction, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a data regulation and control method applied to intelligent exhibition hall interaction comprises the following analysis steps:
Step S1: the method comprises the steps that a log file recorded by intelligent exhibition hall interaction software is stored into an analysis database as a data base, wherein the log file is response data corresponding to a record triggering interaction icon; extracting response data recorded in an analysis database, and dividing interactive icons corresponding to the response data into effective icons and suspicious icons;
step S2: classifying and analyzing the suspicious icons according to icon attribute categories, and outputting attribute icon sets;
step S3: checking the suspicious icons based on the attribute icon sets and outputting checking results, wherein the checking results comprise checking normals and checking anomalies;
step S4: based on the verification result, the early warning signal corresponding to the suspicious icon is transmitted to the intelligent exhibition hall interaction software.
The application analyzes the interactive icons in the interactive software to perfect the practicability of the interactive software, and the key points of the interactive software after the completion are to realize real-time interaction with the user, so that the icons are equivalent to the ties connecting the software with the user, and if the ties have problems, the effect to be realized by the software is greatly reduced, thereby bringing bad use experience to the user.
Further, in step S1, the interactive icons corresponding to the response data are divided into valid icons and suspicious icons, which includes the following analysis steps:
Step S11: the response data comprises operation data before triggering the interactive icon; before the interactive icon is triggered, the monitoring period from when the current interactive interface to which the interactive icon belongs is started to before the moment of triggering the interactive icon; marking the interactive icon without response data in the corresponding monitoring period before triggering the interactive icon as a direct icon;
step S12: acquiring first time data t of direct icon 1 And second time data t 2 The first time data refers to the time interval from the opening of the current interactive interface to the response of the interactive icon, and the second time data refers to the time interval from the response of the interactive icon to the return of the last interactive interface after triggering the corresponding interface data of the interactive icon; calculating response difference t of direct icon 0 ，t 0 =t 2 -t 1 Setting a response difference threshold t 0 ’ If t 0 ≥t 0 ’ Outputting a direct icon as a valid icon; if t 0 <t 0 ’ Outputting a direct icon as an in-doubt icon; analyzing the first time data can reflect whether the intention of the user for the interactive icon inquiry in the current interactive interface is clear or not, and analyzing the second time data can reflect whether the content displayed by the user after triggering the interactive icon response is required by the user or not;
step S13: marking an interactive icon with response data existing in a corresponding monitoring period before triggering the interactive icon as an indirect icon, acquiring operation data of the indirect icon in first time data, and outputting the indirect icon as an in-doubt icon when the operation data record has triggering operation identical to that of triggering the indirect icon and a triggering target is not the indirect icon; when the trigger operation of the operation data record is different from the trigger operation corresponding to the indirect icon and the trigger target is different, outputting the indirect icon as the effective icon.
Further, step S2 includes:
step S21: classifying the suspicious icons according to the execution attribute of the interactive icons recorded by the intelligent exhibition hall interactive software, wherein the execution attribute refers to the execution level of interface display through the interactive icons; each execution attribute corresponds to one type of suspicious icon and is output as an attribute icon set, and each attribute icon set comprises a plurality of independent suspicious icons; the attribute icon sets comprise a homogeneous attribute icon set and a heterogeneous attribute icon set;
step S22: when the suspicious types contained in m kinds of attribute icon sets are the same and unique among the sets, outputting the m kinds of attribute icon sets as the similar attribute icon sets; m represents the total number of the attribute icon sets; the doubtful type represents the analysis mode of dividing the doubtful icons in the step S1, and each analysis mode represents one doubtful type;
when the suspicious types contained in the m-type attribute icon sets are different or the same but not unique among the sets, outputting the m-type attribute icon sets as heterogeneous attribute icon sets.
The analysis of classifying the attribute icon sets is to quickly locate the icon-belonged set when checking the suspicious icon, and extract the corresponding checking mode to effectively check, so that the checking flow is quick and efficient.
Further, step S3 includes the following analysis steps:
step S31: if the attribute icon set is the similar attribute icon set, obtaining the suspicious type corresponding to the similar attribute icon set;
when the doubt type is that the doubt icon is determined through the response difference value, acquiring an average response difference value t of the ith type attribute icon set 0i Traversing and calculating difference indexes Q, Q= |t of average response differences of m-type attribute icon sets corresponding to any two types of attribute icon sets 0i -t 0i ’ ｜，t 0i ’ Representing a difference from t 0i Average response difference values of any type attribute icon sets corresponding to the i type attribute icon sets; m is more than or equal to 2;
setting a difference threshold Q 0 Acquiring Q<Q 0 The number of difference indexes P corresponding to the time, and calculating a discrete index W, W=P/[ C (m, 2)]Wherein C (m, 2) =m |/2 | (m-2) | C (m, 2) represents the number of combined results of any two types of attribute icon sets;
when w=1, outputting a verification result as a verification normal; when W is not equal to 1, outputting a verification result as verification abnormality;
when the type of the in-doubt is that the in-doubt icon is determined through the operation data in the first time data, the operation data in the first time data corresponding to each type of attribute icon set is obtained, when the operation data in the first time data are the same in type, the operation data corresponding to the in-doubt icon uniquely meet the mode of judging the in-doubt icon in the step S13, a verification result is output as a verification normal, and the unique meeting means that the in-doubt icon has only one mode and meets the judging mode of the step S13; when the operation data in the first time data are different in type, the operation data corresponding to the suspicious icon are not single, and the mode of judging the suspicious icon in the step S13 is met; outputting a verification result as verification abnormality;
Step S32: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types when the sets are different from each other, the method comprises the following steps:
when the number of heterogeneous attribute icon sets is greater than the number of doubtful types, checking that the same doubtful type comprises attribute icon sets with different numbers of heterogeneous attribute icon sets, calculating discrete indexes and judging whether the existence types of operation data are the same, wherein m corresponding to the discrete indexes is updated to be m at the moment 1 ，m 1 The method comprises the steps of representing the types of the doubtful in the heterogeneous attribute icon set as the total number of attribute icons corresponding to the doubtful icons determined by the response difference value; and the corresponding verification result is the same as the above mode;
when the number of the heterogeneous attribute icon sets is equal to the number of the suspicious types, checking is not needed; because the number of the doubtful types is 2, when the number of the heterogeneous attribute icon sets is two, the execution attribute environment where the doubtful icons corresponding to each doubtful type are located is unique, and verification is not needed;
step S33: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types which are the same but not the same among the sets:
updating the average response difference value of the same doubtful type in each heterogeneous attribute icon set to represent the average response difference value of the heterogeneous attribute icon set to which the m heterogeneous attribute icon set belongs, calculating a discrete index corresponding to the m heterogeneous attribute icon set, and comparing the doubtful type in each heterogeneous attribute icon set to be the doubtful icon determined by the operation data in the first time data; when the W=1 and the operation data types in the first time data are the same, outputting a check result to be normal; and in other cases, outputting a verification result as a verification exception.
The verification abnormality indicates that different discrimination conditions exist in different types of in-doubt data, and the initial discrimination of the in-doubt icon may be influenced by the difference of icon attributes, so that verification and update are required.
Further, step S4 includes the following:
acquiring all the suspicious icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interactive software; the early warning signal indicates whether to change the icon form or appearance for a software manager; the transmission early warning signal indicates that the system analyzes that the suspicious icon can not meet the interactive operation or use of the user based on the interactive icon in the normal operation and maintenance process, thereby bringing inconvenience to the user and negatively affecting the operation and maintenance of the interactive software; checking that the normal state indicates that the doubtful judgment is correct, and when the execution attribute corresponding to the icon is changed, the judgment result of the doubtful icon is not influenced;
when the discrete index is analyzed, the verification result is extracted to be that the verification abnormality corresponds to Q not less than Q 0 Selecting an attribute icon set corresponding to the maximum average response difference value as a special icon set, and extracting an execution attribute recorded by the special icon set as a special attribute; if the execution attribute corresponding to the in-doubt icon is the same as the special attribute, updating the in-doubt icon to be a valid icon; if the execution attribute and the special attribute corresponding to the suspicious icon do not exist Meanwhile, the suspicious state of the suspicious icon is reserved, and an early warning signal is transmitted to intelligent exhibition hall interaction software;
the updating link indicates that the influence of the analysis doubtful mode caused by the received execution attribute difference exists, namely that the icon which is judged to be doubtful in the icon set corresponding to the specific execution attribute is reasonable and effective in the set;
when analyzing the operation data, extracting the operation data corresponding to the check exception as a check result, and if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon; if the operation data corresponding to the icon set where the verification abnormality is located is different from the operation data corresponding to the doubtful icon, the doubtful state of the doubtful icon is reserved, and an early warning signal is transmitted to intelligent exhibition hall interaction software.
The accuracy of the whole icon analysis of the suspicious icon can be rapidly determined by comparing whether the operation data types are the same or not for verification when the operation data are analyzed.
The data regulation and control system comprises an analysis database construction module, an icon type distinguishing module, an attribute icon set analysis module, a verification analysis module and an early warning transmission module;
the analysis database construction module is used for storing the log files recorded by the intelligent exhibition hall interaction software into an analysis database as a data basis;
The icon type distinguishing module is used for extracting and analyzing response data recorded in the database and dividing interactive icons corresponding to the response data into effective icons and suspicious icons;
the attribute icon set analysis module is used for carrying out classification analysis on the suspicious icons according to icon attribute categories and outputting attribute icon sets, wherein the attribute icon sets comprise similar attribute icon sets and heterogeneous attribute icon sets;
the verification analysis module is used for verifying the suspicious icons based on the attribute icon set and outputting a verification result;
the early warning transmission module is used for transmitting early warning signals corresponding to the doubtful icons to the intelligent exhibition hall interaction software based on the verification result.
Further, the icon type distinguishing module comprises an operation data acquisition unit, a first scene icon analysis unit, a second scene icon analysis unit and an icon type output unit;
the operation data acquisition unit is used for acquiring operation data before triggering the interactive icon;
the first scene icon analysis unit is used for analyzing the interactive icon which does not have response data in the corresponding monitoring period before triggering the interactive icon;
the second scene icon analysis unit is used for analyzing the interactive icons with response data in the corresponding monitoring time period before triggering the interactive icons;
The icon type output unit outputs icons classified into a valid icon and a suspicious icon based on analysis results of the first scene icon analysis unit and the second scene icon analysis unit.
Further, the verification analysis module comprises a similar verification unit and a heterogeneous verification unit;
the similar checking unit is used for acquiring the doubtful types corresponding to the similar attribute icon sets and analyzing the checking results under the data corresponding to the different doubtful types;
the heterogeneous verification unit is used for analyzing the verification result under the data corresponding to different doubt types when the attribute icon sets are heterogeneous attribute icon sets corresponding to the doubt types which are different among the sets and the attribute icon sets are heterogeneous attribute icon sets corresponding to the doubt types which are the same among the sets but not the same.
Further, the early warning transmission module comprises an in-doubt icon early warning unit and an icon updating unit;
the suspicious icon early warning unit is used for acquiring all suspicious icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interactive software; when the discrete index is analyzed, if the execution attribute corresponding to the suspicious icon is different from the special attribute, the suspicious state of the suspicious icon is reserved; when analyzing the operation data, if the operation data corresponding to the icon set where the check exception is located is different from the operation data corresponding to the doubtful icon, the doubtful state of the doubtful icon is reserved;
The icon updating unit is used for updating the suspicious icon into the effective icon if the execution attribute corresponding to the suspicious icon is the same as the special attribute when the discrete index is analyzed; when analyzing the operation data, if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon.
Compared with the prior art, the invention has the following beneficial effects: according to the method, the log files recorded by the interactive software of the intelligent exhibition hall are analyzed, the interactive icons are initially classified into the effective icons and the in-doubt icons, then the in-doubt icons are classified according to the execution attribute, whether the analysis mode of the in-doubt icons is affected by the execution attribute difference is distinguished, the judgment result of the initial in-doubt icons is kept when the in-doubt icons are not affected by the execution attribute difference, the classification analysis is carried out when the in-doubt attribute influence is received, and early warning signals are transmitted to the interactive software to prompt a manager whether the icons need to be updated or modified, so that the operation of the interactive icons by a user is more practical, convenient and efficient, the problem that the icons are not clearly identified or are wrongly identified when the interactive software is used by the user is avoided, and the user experience and the use rate of the interactive software of the intelligent exhibition hall are improved.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a data control system for intelligent exhibition hall interaction according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Referring to fig. 1, the present invention provides the following technical solutions: a data regulation and control method applied to intelligent exhibition hall interaction comprises the following analysis steps:
step S1: the method comprises the steps that a log file recorded by intelligent exhibition hall interaction software is stored into an analysis database as a data base, wherein the log file is response data corresponding to a record triggering interaction icon; extracting response data recorded in an analysis database, and dividing interactive icons corresponding to the response data into effective icons and suspicious icons; the application background of the intelligent exhibition hall interaction software can be an intelligent city scene exhibition hall, so that city planning introduction and modern intelligent interaction are realized;
Step S2: classifying and analyzing the suspicious icons according to icon attribute categories, and outputting attribute icon sets;
step S3: checking the suspicious icons based on the attribute icon sets and outputting checking results, wherein the checking results comprise checking normals and checking anomalies;
step S4: based on the verification result, the early warning signal corresponding to the suspicious icon is transmitted to the intelligent exhibition hall interaction software.
The application analyzes the interactive icons in the interactive software to perfect the practicability of the interactive software, and the key points of the interactive software after the completion are to realize real-time interaction with the user, so that the icons are equivalent to the ties connecting the software with the user, and if the ties have problems, the effect to be realized by the software is greatly reduced, thereby bringing bad use experience to the user.
In step S1, the interactive icons corresponding to the response data are divided into effective icons and suspicious icons, which includes the following analysis steps:
step S11: the response data comprises operation data before triggering the interactive icon; before the interactive icon is triggered, the monitoring period from when the current interactive interface to which the interactive icon belongs is started to before the moment of triggering the interactive icon; marking the interactive icon without response data in the corresponding monitoring period before triggering the interactive icon as a direct icon;
Step S12: acquiring first time data t of direct icon 1 And second time data t 2 The first time data refers to the time interval from the opening of the current interactive interface to the response of the interactive icon, and the second time dataThe time interval is used for responding to the interactive icon and triggering the interface data corresponding to the interactive icon and returning to the previous interactive interface; calculating response difference t of direct icon 0 ，t 0 =t 2 -t 1 Setting a response difference threshold t 0 ’ If t 0 ≥t 0 ’ Outputting a direct icon as a valid icon; if t 0 <t 0 ’ Outputting a direct icon as an in-doubt icon; analyzing the first time data can reflect whether the intention of the user for the interactive icon inquiry in the current interactive interface is clear or not, and analyzing the second time data can reflect whether the content displayed by the user after triggering the interactive icon response is required by the user or not; the first time data refers to the monitoring period in step S11;
step S13: marking an interactive icon with response data existing in a corresponding monitoring period before triggering the interactive icon as an indirect icon, acquiring operation data of the indirect icon in first time data, and outputting the indirect icon as an in-doubt icon when the operation data record has triggering operation identical to that of triggering the indirect icon and a triggering target is not the indirect icon; when the trigger operation of the operation data record is different from the trigger operation corresponding to the indirect icon and the trigger target is different, outputting the indirect icon as the effective icon.
The step S2 comprises the following steps:
step S21: classifying the suspicious icons according to the execution attribute of the interactive icons recorded by the intelligent exhibition hall interactive software, wherein the execution attribute refers to the execution level of interface display through the interactive icons; the method generally comprises a first stage and a second stage, wherein the first stage represents the situation that a new interface is generated after the interactive icon is triggered, and the second stage represents the situation that a popup window or an interface is newly added on the original interface after the interactive icon is triggered; each execution attribute corresponds to one type of suspicious icon and is output as an attribute icon set, and each attribute icon set comprises a plurality of independent suspicious icons; the attribute icon sets comprise a homogeneous attribute icon set and a heterogeneous attribute icon set;
step S22: when the suspicious types contained in m kinds of attribute icon sets are the same and unique among the sets, outputting the m kinds of attribute icon sets as the similar attribute icon sets; m represents the total number of the attribute icon sets; the doubtful type represents the analysis mode of dividing the doubtful icons in the step S1, and each analysis mode represents one doubtful type;
when the suspicious types contained in the m-type attribute icon sets are different or the same but not unique among the sets, outputting the m-type attribute icon sets as heterogeneous attribute icon sets.
The analysis of classifying the attribute icon sets is to quickly locate the icon-belonged set when checking the suspicious icon, and extract the corresponding checking mode to effectively check, so that the checking flow is quick and efficient.
As shown in the examples:
the execution attribute comprises a first-level popup window and a second-level popup window, and the suspicious icons are divided into two types, namely an attribute icon set A and an attribute icon set B;
the attribute icon set a contains a doubtful icon a1 (analysis mode 1) and a doubtful icon a2 (analysis mode 2),
the attribute icon set B includes an in doubt icon B1 (analysis mode 1), an in doubt icon B2 (analysis mode 2), and an in doubt icon B3 (analysis mode 1);
the corresponding type of the doubt is the analysis mode in the brackets;
the independent doubt types contained in the known attribute icon set a and the attribute icon set B are the same, and are analysis mode 1 and analysis mode 2, but are not unique, so that the two attribute icon sets are heterogeneous attribute icon sets.
Step S3 comprises the following analysis steps:
step S31: if the attribute icon set is the similar attribute icon set, obtaining the suspicious type corresponding to the similar attribute icon set;
when the doubt type is that the doubt icon is determined through the response difference value, acquiring an average response difference value t of the ith type attribute icon set 0i Traversing and calculating difference indexes Q, Q= |t of average response differences of m-type attribute icon sets corresponding to any two types of attribute icon sets 0i -t 0i ’ ｜，t 0i ’ Representing a difference from t 0i Average response difference values of any type attribute icon sets corresponding to the i type attribute icon sets; m is more than or equal to 2; calculation of response differenceThe manner is the same as the manner of calculating the response difference in step S12;
setting a difference threshold Q 0 Acquiring Q<Q 0 The number of difference indexes P corresponding to the time, and calculating a discrete index W, W=P/[ C (m, 2)]Wherein C (m, 2) =m |/2 | (m-2) | C (m, 2) represents the number of combined results of any two types of attribute icon sets;
when w=1, outputting a verification result as a verification normal; when W is not equal to 1, outputting a verification result as verification abnormality;
when the type of the in-doubt is that the in-doubt icon is determined through the operation data in the first time data, the operation data in the first time data corresponding to each type of attribute icon set is obtained, when the operation data in the first time data are the same in type, the operation data corresponding to the in-doubt icon uniquely meet the mode of judging the in-doubt icon in the step S13, a verification result is output as a verification normal, and the unique meeting means that the in-doubt icon has only one mode and meets the judging mode of the step S13; when the operation data in the first time data are different in type, the operation data corresponding to the suspicious icon are not single, and the mode of judging the suspicious icon in the step S13 is met; outputting a verification result as verification abnormality;
Step S32: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types when the sets are different from each other, the method comprises the following steps:
when the number of heterogeneous attribute icon sets is greater than the number of doubtful types, checking that the same doubtful type comprises attribute icon sets with different numbers of heterogeneous attribute icon sets, calculating discrete indexes and judging whether the existence types of operation data are the same, wherein m corresponding to the discrete indexes is updated to be m at the moment 1 ，m 1 The method comprises the steps of representing the types of the doubtful in the heterogeneous attribute icon set as the total number of attribute icons corresponding to the doubtful icons determined by the response difference value; and the corresponding verification result is the same as the above mode;
if the attribute icon set A, the attribute icon set B and the attribute icon set C exist;
the analysis modes of the suspicious icons contained in the attribute icon set A are all analysis mode 1, the analysis modes of the suspicious icons contained in the attribute icon set B are all analysis mode 2, and the attribute icon setThe analysis modes of the suspicious icons contained in the C are all analysis mode 1; the types of the doubtful corresponding to the three sets are different among the sets, and the number of the heterogeneous attribute icon sets is 3 and is more than the number of the doubtful types and is 2; the type to be calculated includes attribute icon sets with different types of attribute icon sets, namely a calculated attribute icon set A and an attribute icon set C; in the analysis mode 1, the difference index corresponding to the attribute icon set C and the difference index corresponding to the attribute icon set a are calculated, and the discrete index is further calculated, wherein the denominator for calculating the discrete index is not the number of combinations of all attribute icon sets, but is updated from m=3 to m 1 If the result w=1, outputting the suspicious icon as the check normal;
when the number of the heterogeneous attribute icon sets is equal to the number of the suspicious types, checking is not needed; because the number of the doubtful types is 2, when the number of the heterogeneous attribute icon sets is two, the execution attribute environment where the doubtful icons corresponding to each doubtful type are located is unique, and verification is not needed;
step S33: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types which are the same but not the same among the sets:
updating the average response difference value of the same doubtful type in each heterogeneous attribute icon set to represent the average response difference value of the heterogeneous attribute icon set to which the m heterogeneous attribute icon set belongs, calculating a discrete index corresponding to the m heterogeneous attribute icon set, and comparing the doubtful type in each heterogeneous attribute icon set to be the doubtful icon determined by the operation data in the first time data; when the W=1 and the operation data types in the first time data are the same, outputting a check result to be normal; and in other cases, outputting a verification result as a verification exception.
If the attribute icon set A and the attribute icon set B exist, the attribute icon set A and the attribute icon set B are displayed;
the analysis modes of the suspicious icons contained in the attribute icon set A are an analysis mode 1 and an analysis mode 2; the analysis modes of the suspicious icons contained in the attribute icon set B are an analysis mode 1 and an analysis mode 2;
For analysis mode 1, the average response difference value of the attribute icon set A is required to be calculated as the response difference value of the in-doubt icon corresponding to the analysis mode 1, and the average response difference value of the attribute icon set B is required to be calculated as the response difference value of the in-doubt icon corresponding to the analysis mode 1; calculating a discrete index;
for analysis mode 2, operation data in the first time data of the suspicious icons corresponding to the analysis mode 2 in the attribute icon set a and operation data in the first time data of the suspicious icons corresponding to the analysis mode 2 in the attribute icon set B need to be compared;
only when the W=1 is satisfied and the operation data types in the first time data are the same, outputting a check result to be normal.
The verification abnormality indicates that different judging conditions exist in different types of in-doubt data, and the initial judgment is that the in-doubt icon is possibly influenced by the attribute difference of the icon, so that verification and update are needed;
the premise of analyzing the operational data within the first time data is that the operational data is present within the first time data.
Step S4 includes the following:
acquiring all the suspicious icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interactive software; the early warning signal indicates whether to change the icon form or appearance for a software manager; the transmission early warning signal indicates that the system analyzes that the suspicious icon can not meet the interactive operation or use of the user based on the interactive icon in the normal operation and maintenance process, thereby bringing inconvenience to the user and negatively affecting the operation and maintenance of the interactive software; checking that the normal state indicates that the doubtful judgment is correct, and when the execution attribute corresponding to the icon is changed, the judgment result of the doubtful icon is not influenced;
When the discrete index is analyzed, the verification result is extracted to be that the verification abnormality corresponds to Q not less than Q 0 Selecting an attribute icon set corresponding to the maximum average response difference value as a special icon set, and extracting an execution attribute recorded by the special icon set as a special attribute; if the execution attribute corresponding to the in-doubt icon is the same as the special attribute, updating the in-doubt icon to be a valid icon; if the execution attribute corresponding to the doubtful icon is different from the special attribute, the doubtful state of the doubtful icon is reserved, and an early warning signal is transmitted to intelligent exhibition hall interaction software；
The updating link indicates that the influence of the analysis doubtful mode caused by the received execution attribute difference exists, namely that the icon which is judged to be doubtful in the icon set corresponding to the specific execution attribute is reasonable and effective in the set;
when analyzing the operation data, extracting the operation data corresponding to the check exception as a check result, and if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon; the method includes that at this time, operation data corresponding to the doubtful icons are possible to be multiple, rather than single judgment, the condition of the step S13 is met, and the multiple types of operation data are possibly influenced by execution attributes of icon sets corresponding to the icons, so that icon differentiation is caused under the environment of the corresponding execution attributes, and if the operation data corresponding to the icon sets where the abnormality is checked are different from the operation data corresponding to the doubtful icons, the doubtful state of the doubtful icons is reserved, and early warning signals are transmitted to intelligent exhibition interactive software. The operation data may be all operation flows recorded by the user side in the first time data.
In the analysis, two kinds of doubtful types are required to be simultaneously established, and the condition of correspondingly updating the doubtful types to the effective icons is required to be simultaneously met so as to update the effective icons, and the doubtful icons in the analysis can be any doubtful icon in the initial judgment of doubtful or effective, and the analysis refers to early warning analysis of one doubtful icon. In the analysis process, when the execution attribute corresponding to the verification abnormality is the same as the execution attribute of the in-doubt icon, the classification of the in-doubt icon is analyzed in the attribute icon set corresponding to the verification abnormality, then the attribute icon set is special, the in-doubt icon is possibly a special condition under the set, the in-doubt icon cannot be directly output to be reminded, and the in-doubt icon can be converted into an effective icon to be continuously monitored; the accuracy of the whole icon analysis of the suspicious icon can be rapidly determined by comparing whether the operation data types are the same or not for verification when the operation data are analyzed.
The data regulation and control system comprises an analysis database construction module, an icon type distinguishing module, an attribute icon set analysis module, a verification analysis module and an early warning transmission module;
the analysis database construction module is used for storing the log files recorded by the intelligent exhibition hall interaction software into an analysis database as a data basis;
The icon type distinguishing module is used for extracting and analyzing response data recorded in the database and dividing interactive icons corresponding to the response data into effective icons and suspicious icons;
the attribute icon set analysis module is used for carrying out classification analysis on the suspicious icons according to icon attribute categories and outputting attribute icon sets, wherein the attribute icon sets comprise similar attribute icon sets and heterogeneous attribute icon sets;
the verification analysis module is used for verifying the suspicious icons based on the attribute icon set and outputting a verification result;
the early warning transmission module is used for transmitting early warning signals corresponding to the doubtful icons to the intelligent exhibition hall interaction software based on the verification result.
The icon type distinguishing module comprises an operation data acquisition unit, a first scene icon analysis unit, a second scene icon analysis unit and an icon type output unit;
the operation data acquisition unit is used for acquiring operation data before triggering the interactive icon;
the first scene icon analysis unit is used for analyzing the interactive icon which does not have response data in the corresponding monitoring period before triggering the interactive icon;
the second scene icon analysis unit is used for analyzing the interactive icons with response data in the corresponding monitoring time period before triggering the interactive icons;
the icon type output unit outputs icons classified into a valid icon and a suspicious icon based on analysis results of the first scene icon analysis unit and the second scene icon analysis unit.
The verification analysis module comprises a similar verification unit and a heterogeneous verification unit;
the similar checking unit is used for acquiring the doubtful types corresponding to the similar attribute icon sets and analyzing the checking results under the data corresponding to the different doubtful types;
the heterogeneous verification unit is used for analyzing the verification result under the data corresponding to different doubt types when the attribute icon sets are heterogeneous attribute icon sets corresponding to the doubt types which are different among the sets and the attribute icon sets are heterogeneous attribute icon sets corresponding to the doubt types which are the same among the sets but not the same.
The early warning transmission module comprises an in-doubt icon early warning unit and an icon updating unit;
the suspicious icon early warning unit is used for acquiring all suspicious icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interactive software; when the discrete index is analyzed, if the execution attribute corresponding to the suspicious icon is different from the special attribute, the suspicious state of the suspicious icon is reserved; when analyzing the operation data, if the operation data corresponding to the icon set where the check exception is located is different from the operation data corresponding to the doubtful icon, the doubtful state of the doubtful icon is reserved;
the icon updating unit is used for updating the suspicious icon into the effective icon if the execution attribute corresponding to the suspicious icon is the same as the special attribute when the discrete index is analyzed; when analyzing the operation data, if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. The data regulation and control method applied to intelligent exhibition hall interaction is characterized by comprising the following analysis steps:
step S1: storing a log file recorded by intelligent exhibition hall interaction software into an analysis database as a data base, wherein the log file is response data corresponding to a record triggering interaction icon; extracting response data recorded in an analysis database, and dividing interactive icons corresponding to the response data into effective icons and suspicious icons;
step S2: classifying and analyzing the suspicious icons according to icon attribute categories, and outputting attribute icon sets;
step S3: checking the suspicious icons based on the attribute icon sets and outputting checking results, wherein the checking results comprise checking normals and checking anomalies;
step S4: based on the verification result, the early warning signal corresponding to the suspicious icon is transmitted to the intelligent exhibition hall interaction software.
2. The data conditioning method applied to intelligent exhibition hall interaction according to claim 1, wherein the data conditioning method comprises the following steps: in the step S1, the interactive icons corresponding to the response data are divided into effective icons and suspicious icons, which includes the following analysis steps:
step S11: the response data comprise operation data before triggering the interactive icon; the pre-triggering of the interactive icon refers to a monitoring period from when a current interactive interface to which the interactive icon belongs is started to before the moment of triggering the interactive icon; marking the interactive icon without response data in the corresponding monitoring period before triggering the interactive icon as a direct icon;
Step S12: acquiring first time data t of direct icon 1 And second time data t 2 The first time data refers to the time from the opening of the current interactive interface to the response of the interactive iconThe second time data refers to a time interval for responding to the interactive icon and triggering the interface data corresponding to the interactive icon and returning to the previous interactive interface; calculating response difference t of direct icon 0 ，t 0 =t 2 -t 1 Setting a response difference threshold t 0 ’ If t 0 ≥t 0 ’ Outputting a direct icon as a valid icon; if t 0 <t 0 ’ Outputting a direct icon as an in-doubt icon;
step S13: marking an interactive icon with response data existing in a corresponding monitoring period before triggering the interactive icon as an indirect icon, acquiring operation data of the indirect icon in first time data, and outputting the indirect icon as an in-doubt icon when the operation data record has triggering operation identical to that of triggering the indirect icon and a triggering target is not the indirect icon; when the trigger operation of the operation data record is different from the trigger operation corresponding to the indirect icon and the trigger target is different, outputting the indirect icon as the effective icon.
3. The data regulation method applied to intelligent exhibition hall interaction according to claim 2, wherein the method comprises the following steps: the step S2 includes:
Step S21: classifying the suspicious icons according to the execution attribute of the interactive icons recorded by the intelligent exhibition hall interactive software, wherein the execution attribute refers to the execution level of interface display through the interactive icons; each execution attribute corresponds to one type of suspicious icon and is output as an attribute icon set, and each attribute icon set comprises a plurality of independent suspicious icons; the attribute icon sets comprise a homogeneous attribute icon set and a heterogeneous attribute icon set;
step S22: when the suspicious types contained in m kinds of attribute icon sets are the same and unique among the sets, outputting the m kinds of attribute icon sets as the similar attribute icon sets; m represents the total number of the attribute icon sets; the doubtful type represents the analysis mode of dividing the doubtful icons in the step S1, and each analysis mode represents one doubtful type;
when the suspicious types contained in the m-type attribute icon sets are different or the same but not unique among the sets, outputting the m-type attribute icon sets as heterogeneous attribute icon sets.
4. A data conditioning method for intelligent exhibition hall interaction according to claim 3, wherein: the step S3 includes the following analysis steps:
step S31: if the attribute icon set is the similar attribute icon set, obtaining the suspicious type corresponding to the similar attribute icon set;
When the doubt type is that the doubt icon is determined through the response difference value, acquiring an average response difference value t of the ith type attribute icon set 0i Traversing and calculating difference indexes Q, Q= |t of average response differences of m-type attribute icon sets corresponding to any two types of attribute icon sets 0i -t 0i ’ ｜，t 0i ’ Representing a difference from t 0i Average response difference values of any type attribute icon sets corresponding to the i type attribute icon sets; m is more than or equal to 2;
setting a difference threshold Q 0 Acquiring Q<Q 0 The number of difference indexes P corresponding to the time, and calculating a discrete index W, W=P/[ C (m, 2)]Wherein C (m, 2) =m |/2 | (m-2) | C (m, 2) represents the number of combined results of any two types of attribute icon sets;
when w=1, outputting a verification result as a verification normal; when W is not equal to 1, outputting a verification result as verification abnormality;
when the type of the in-doubt is that the in-doubt icon is determined through the operation data in the first time data, the operation data in the first time data corresponding to each type of attribute icon set is obtained, when the operation data in the first time data are the same in type, the operation data corresponding to the in-doubt icon uniquely meet the mode of judging the in-doubt icon in the step S13, a verification result is output as a verification normal, and the unique meeting means that the in-doubt icon has only one mode and meets the judging mode of the step S13; when the operation data in the first time data are different in type, the different types refer to the mode that the operation data corresponding to the suspicious icons do not singly meet the judgment of the suspicious icons in the step S13; outputting a verification result as verification abnormality;
Step S32: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types when the sets are different from each other, the method comprises the following steps:
when the number of heterogeneous attribute icon sets is greater than the number of doubtful types, checking that the same doubtful type comprises attribute icon sets with different numbers of heterogeneous attribute icon sets, calculating discrete indexes and judging whether the existence types of operation data are the same, wherein m corresponding to the discrete indexes is updated to be m at the moment 1 ，m 1 The method comprises the steps of representing the types of the doubtful in the heterogeneous attribute icon set as the total number of attribute icons corresponding to the doubtful icons determined by the response difference value; and the corresponding verification result is the same as the above mode;
when the number of the heterogeneous attribute icon sets is equal to the number of the suspicious types, checking is not needed;
step S33: if the attribute icon sets are heterogeneous attribute icon sets corresponding to the suspicious types which are the same but not the same among the sets:
updating the average response difference value of the same doubtful type in each heterogeneous attribute icon set to represent the average response difference value of the heterogeneous attribute icon set to which the m heterogeneous attribute icon set belongs, calculating a discrete index corresponding to the m heterogeneous attribute icon set, and comparing the doubtful type in each heterogeneous attribute icon set to be the doubtful icon determined by the operation data in the first time data; when the W=1 and the operation data types in the first time data are the same, outputting a check result to be normal; and in other cases, outputting a verification result as a verification exception.
5. The data conditioning method applied to intelligent exhibition hall interaction according to claim 4, wherein the data conditioning method comprises the following steps: the step S4 includes the following steps:
acquiring all the suspicious icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interactive software; the early warning signal indicates whether to change the icon form or appearance for a software manager;
when the discrete index is analyzed, the verification result is extracted to be that the verification abnormality corresponds to Q not less than Q 0 Selecting an attribute icon set corresponding to the maximum average response difference value as a special icon set, and extracting an execution attribute recorded by the special icon set as a special attribute;if the execution attribute corresponding to the in-doubt icon is the same as the special attribute, updating the in-doubt icon to be a valid icon; if the execution attribute corresponding to the doubtful icon is different from the special attribute, reserving the doubtful state of the doubtful icon, and transmitting an early warning signal to intelligent exhibition hall interaction software;
when analyzing the operation data, extracting the operation data corresponding to the check exception as a check result, and if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon; if the operation data corresponding to the icon set where the verification abnormality is located is different from the operation data corresponding to the doubtful icon, the doubtful state of the doubtful icon is reserved, and an early warning signal is transmitted to intelligent exhibition hall interaction software.
6. A data regulation and control system applying the data regulation and control method applied to intelligent exhibition hall interaction according to any one of claims 1-5, which is characterized by comprising an analysis database construction module, an icon type distinguishing module, an attribute icon set analysis module, a verification analysis module and an early warning transmission module;
the analysis database construction module is used for storing the log files recorded by the intelligent exhibition hall interaction software into an analysis database as a data basis;
the icon type distinguishing module is used for extracting response data recorded in the analysis database and dividing interactive icons corresponding to the response data into effective icons and suspicious icons;
the attribute icon set analysis module is used for carrying out classification analysis on the suspicious icons according to icon attribute categories and outputting attribute icon sets, wherein the attribute icon sets comprise similar attribute icon sets and heterogeneous attribute icon sets;
the verification analysis module is used for verifying the suspicious icons based on the attribute icon set and outputting a verification result;
and the early warning transmission module is used for transmitting early warning signals corresponding to the doubtful icons to the intelligent exhibition hall interaction software based on the verification result.
7. The data conditioning system of claim 6, wherein: the icon type distinguishing module comprises an operation data acquisition unit, a first scene icon analysis unit, a second scene icon analysis unit and an icon type output unit;
The operation data acquisition unit is used for acquiring operation data before triggering the interactive icon;
the first scene icon analysis unit is used for analyzing the interactive icon which does not have response data in the corresponding monitoring period before triggering the interactive icon;
the second scene icon analysis unit is used for analyzing the interactive icons with response data in the corresponding monitoring period before triggering the interactive icons;
the icon type output unit divides the icon into a valid icon and an in-doubt icon based on analysis results of the first scene icon analysis unit and the second scene icon analysis unit.
8. The data conditioning system of claim 7, wherein: the verification analysis module comprises a similar verification unit and a heterogeneous verification unit;
the similar checking unit is used for acquiring the doubtful types corresponding to the similar attribute icon sets and analyzing the checking results under the data corresponding to different doubtful types;
the heterogeneous verification unit is used for analyzing the verification result under the data corresponding to different in-doubt types when the attribute icon sets are heterogeneous attribute icon sets corresponding to the in-doubt types which are different among the sets and the attribute icon sets are heterogeneous attribute icon sets corresponding to the in-doubt types which are the same among the sets but not the same.
9. The data conditioning system of claim 8, wherein: the early warning transmission module comprises an in-doubt icon early warning unit and an icon updating unit;
the doubtful icon early warning unit is used for acquiring all doubtful icons with the verification results of being normal, and transmitting early warning signals to intelligent exhibition hall interaction software; when the discrete index is analyzed, if the execution attribute corresponding to the suspicious icon is different from the special attribute, the suspicious state of the suspicious icon is reserved; when analyzing the operation data, if the operation data corresponding to the icon set where the check exception is located is different from the operation data corresponding to the doubtful icon, the doubtful state of the doubtful icon is reserved;
the icon updating unit is used for updating the suspicious icon into the effective icon if the execution attribute corresponding to the suspicious icon is the same as the special attribute when the discrete index is analyzed; when analyzing the operation data, if the operation data type corresponding to the icon set where the check exception is located is identical to the operation data type corresponding to the doubtful icon, updating the doubtful icon to be a valid icon.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117651101A (en) * 2024-01-30 2024-03-05 南京功夫豆信息科技有限公司 User operation service management system and method

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104714718A (en) * 2013-12-16 2015-06-17 腾讯科技（深圳）有限公司 Method and device for marking operable icons in interactive applications
KR20160097072A (en) * 2015-02-06 2016-08-17 한국전자통신연구원 Aapplication icon arranging system and method adapted context of a smart device
WO2017088669A1 (en) * 2015-11-27 2017-06-01 努比亚技术有限公司 Desktop icon display device and method
CN107562305A (en) * 2017-08-01 2018-01-09 努比亚技术有限公司 A kind of method to set up of dynamic icon, terminal and computer-readable recording medium
US20190107937A1 (en) * 2017-10-10 2019-04-11 Konica Minolta, Inc. Controller, shortcut icon registration method, and non-transitory recording medium storing shortcut icon registration program
US11112945B1 (en) * 2020-09-30 2021-09-07 Snap Inc. Content detection and transmission in response to receiving user interactions
WO2021232968A1 (en) * 2020-05-18 2021-11-25 Oppo广东移动通信有限公司 Desktop icon layout method and apparatus, storage medium, and electronic device
CN114911388A (en) * 2022-05-26 2022-08-16 深圳价值在线信息科技股份有限公司 Intelligent updating method and communication system for website LOGO icon
CN114995716A (en) * 2022-08-02 2022-09-02 北京麟卓信息科技有限公司 Operation method of android application related file on desktop system
CN116149527A (en) * 2023-03-14 2023-05-23 平安壹钱包电子商务有限公司 Icon switching method and device, electronic equipment and computer readable storage medium
CN116610239A (en) * 2023-01-13 2023-08-18 深圳传音控股股份有限公司 Icon processing method, intelligent terminal and storage medium

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104714718A (en) * 2013-12-16 2015-06-17 腾讯科技（深圳）有限公司 Method and device for marking operable icons in interactive applications
KR20160097072A (en) * 2015-02-06 2016-08-17 한국전자통신연구원 Aapplication icon arranging system and method adapted context of a smart device
WO2017088669A1 (en) * 2015-11-27 2017-06-01 努比亚技术有限公司 Desktop icon display device and method
CN107562305A (en) * 2017-08-01 2018-01-09 努比亚技术有限公司 A kind of method to set up of dynamic icon, terminal and computer-readable recording medium
US20190107937A1 (en) * 2017-10-10 2019-04-11 Konica Minolta, Inc. Controller, shortcut icon registration method, and non-transitory recording medium storing shortcut icon registration program
WO2021232968A1 (en) * 2020-05-18 2021-11-25 Oppo广东移动通信有限公司 Desktop icon layout method and apparatus, storage medium, and electronic device
US11112945B1 (en) * 2020-09-30 2021-09-07 Snap Inc. Content detection and transmission in response to receiving user interactions
CN114911388A (en) * 2022-05-26 2022-08-16 深圳价值在线信息科技股份有限公司 Intelligent updating method and communication system for website LOGO icon
CN114995716A (en) * 2022-08-02 2022-09-02 北京麟卓信息科技有限公司 Operation method of android application related file on desktop system
CN116610239A (en) * 2023-01-13 2023-08-18 深圳传音控股股份有限公司 Icon processing method, intelligent terminal and storage medium
CN116149527A (en) * 2023-03-14 2023-05-23 平安壹钱包电子商务有限公司 Icon switching method and device, electronic equipment and computer readable storage medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117651101A (en) * 2024-01-30 2024-03-05 南京功夫豆信息科技有限公司 User operation service management system and method
CN117651101B (en) * 2024-01-30 2024-04-05 南京功夫豆信息科技有限公司 User operation service management system and method

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