CN112732355B - Background application cleaning system and method based on big data - Google Patents

Background application cleaning system and method based on big data Download PDF

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CN112732355B
CN112732355B CN202110025689.3A CN202110025689A CN112732355B CN 112732355 B CN112732355 B CN 112732355B CN 202110025689 A CN202110025689 A CN 202110025689A CN 112732355 B CN112732355 B CN 112732355B
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CN112732355A (en
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蒋耕银
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Hunan Huapu Information Industry Co.,Ltd.
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06F11/3438—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment monitoring of user actions
    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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    • G06F9/46—Multiprogramming arrangements
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Abstract

The invention discloses a background application cleaning system and method based on big data, and belongs to the technical field. The background application cleaning system based on big data comprises a detection module, a daylight control module, an intelligent cleaning module and a database; the output end of the detection module is electrically connected with the eye light control module; the output end of the eye control module is electrically connected with a database; the output end of the database is electrically connected with the intelligent cleaning module; a background application cleaning method based on big data comprises the following steps of S1, performing pause detection on user equipment; s2, when the user equipment is stuck, displaying the running background application on the screen of the equipment, and cleaning by looking at the equipment; s3, recording the cleared free memory and the application currently used after clearing, and storing the application in a database; and S4, establishing intelligent cleaning when the storage times exceed a threshold value.

Description

Background application cleaning system and method based on big data
Technical Field
The invention relates to the technical field of background application cleaning, in particular to a background application cleaning system and method based on big data.
Background
With the increasing development of science and technology, more and more applications are installed on a terminal, each program has different functions, background applications generally refer to programs which are minimally closed or not used but still run in a background, and the programs bring convenience to life of people and have great influence on the terminal.
Background application programs are more and more, power consumption of the terminal is greatly increased, and meanwhile, the terminal occupies a memory, so that a card pause result is generated, and normal use functions are influenced. In the current technical center, most terminals adopt a manual one-key mode to clear all application programs, however, the utilization rate of some application programs is very high, and users often need to start the application programs again after clearing, so that time and labor are wasted; meanwhile, in the process of blocking, manual cleaning becomes difficult and inconvenient, so that people urgently need a very convenient background application cleaning method capable of automatically cleaning.
Disclosure of Invention
The invention aims to provide a background application cleaning system and method based on big data, 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 background application cleaning system based on big data comprises a detection module, a daylight control module, an intelligent cleaning module and a database;
the output end of the detection module is electrically connected with the eye light control module; the output end of the eye control module is electrically connected with a database; the output end of the database is electrically connected with the intelligent cleaning module;
the detection module is used for monitoring whether the user equipment is blocked or not; the sight control module is used for artificially cleaning background application; the intelligent cleaning module is used for automatically cleaning according to the behavior habit of the user; the database is used for storing user behavior habits and related data.
According to the technical scheme, the detection module comprises a picture acquisition unit, a time acquisition unit and a stuck detection unit;
the output ends of the picture acquisition unit and the time acquisition unit are electrically connected with the input end of the stuck detection unit, and the output end of the stuck detection unit is electrically connected with the input end of the sight cleaning module;
the picture acquisition unit is used for acquiring pictures when the user equipment is used; the time acquisition unit is used for acquiring the time used by the user equipment and corresponding to each use picture; the pause detection unit is used for detecting the pause of the user equipment and starting the eye light control module.
In the detection module, all collected pictures are edited into groups, collected time units are paired, division processing is carried out according to unit time, all the same pictures in the groups are marked, when the number of the marks exceeds a certain threshold value, the fact that the user equipment stays in the pictures within a period of time is indicated, no change occurs, and besides personal operation of a user, the fact that the user equipment is stuck can be judged, the number of times of the pictures is too many and the pictures are not smooth, so that the visual control module is started.
According to the technical scheme, the gaze control module comprises an eyeball tracking unit, a distance measuring unit, a screen control unit and a clearing unit;
the output ends of the tracking unit and the ranging unit are electrically connected with the input end of the clearing unit; the output end of the detection module is electrically connected with the input end of the screen control unit; the output end of the clearing unit is electrically connected with the input end of the database.
The eyeball tracking unit is used for tracking and recording the moving route and the distance of the pupil; the distance measuring unit is used for measuring the linear distance from human eyes to the screen; the screen control unit is used for dividing the screen into a fixed number of areas after the gaze control module is started, and each area is provided with a background application; the clearing unit is used for clearing the background application.
In the eye control module, an eyeball tracking module is used for identifying and tracking the change of the pupil of the human eye, the address and the direction of the eye drop point of the human eye are judged according to the moving distance and the moving direction of the pupil of the human eye, wherein the moving direction of the pupil can be equal to the moving direction of the central point of a screen, and can be determined in a certain area of the screen, a distance measuring unit calculates the size of an angle formed by the eye drop point and the horizontal line of the screen according to the measured linear distance between the screen and the pupil, the maximum value and the minimum value are calculated, when the angle exceeds the range, the eye drop is judged to be on the outer side of the screen and is not cleared, and when the angle is in the range, a clearing unit is started to clear the application in the corresponding area.
According to the technical scheme, the intelligent cleaning module comprises a data acquisition unit, an idle memory detection unit and a cleaning unit;
the output end of the data acquisition unit is electrically connected with the input end of the database; the output end of the idle memory detection unit is electrically connected with the input end of the cleaning unit;
the data acquisition unit is used for acquiring cleaning behaviors performed by the cleaning module under the condition that the user equipment is stuck; the free memory detection unit is used for detecting a free memory of the user equipment; the cleaning unit is used for intelligently cleaning the application background and releasing the memory;
when a user starts an application, detecting a current idle memory to obtain an idle memory value, and acquiring whether the idle memory can meet the user requirement by using big data, namely whether background application cleaning can be performed under the condition of the idle memory in the behavior habit of the user or not, and if so, cleaning by using a cleaning unit;
the cleaning unit comprises a first cleaning unit and a second cleaning unit;
the first cleaning unit is used for cleaning background application according to the cleaning habit of historical data; the second cleaning unit is used for cleaning under the condition that the first cleaning unit does not reach the idle memory threshold value, and cleaning is carried out according to the common degree of the application background.
In the first cleaning unit, the behavior habit of the user is obtained according to the big data, namely, background applications frequently cleaned by the user are cleaned firstly when the applications are started, if the background applications are cleaned, the second cleaning unit is started if the memories are still insufficient, and the second cleaning unit cleans the background applications according to the common degree of the application programs, namely cleans the unused memories until a certain amount of free memories are reached.
According to the technical scheme, the database comprises a classification unit and a storage unit;
the classification unit is used for performing common grade classification on all applications in the user equipment; the storage unit is used for storing the user equipment cleaning process and various related data;
the classification method of the classification unit is according to the formula:
Figure BDA0002890230050000041
wherein, V is a classification level index value, n is the number of downloading days of the application, x is the usage duration of the application each day, y is the background duration of the application each day, k1、k2To evaluate the coefficients; the larger V indicates that the application is more common.
In the formula calculation, the download days of the application are limited, so that the calculation results are prevented from being wrong to different degrees due to different download days of some programs when the common programs are calculated, the average value of the use time of the application programs every day and the background time is solved, a result which can enable all the programs to have low influence due to different download days is obtained, and the common degree of each application program in the user equipment is judged according to the result.
A background application cleaning method based on big data comprises the following steps:
s1, collecting the picture condition of the user equipment, and detecting the blockage of the user equipment according to the change in unit time;
s2, when the user equipment is stuck, displaying the running background application on the screen of the equipment, starting gaze control, and cleaning by gaze;
s3, after cleaning, the operation is still stuck, and the step S2 is repeated; the method comprises the steps of cleaning the memory, recording the cleaned idle memory and the currently used application, and storing the idle memory and the currently used application in a database;
s4, in step S3, when the number of times of storage of an application exceeds the threshold N, an intelligent cleaning is established, that is, when the application is started, the free memory is automatically adjusted to exceed the minimum value recorded by the application.
According to the above technical solution, in step S1, the picture collected by the user equipment per unit time is recorded as a set a ═ S1，S2，S3，……，SnIn set A, if there are identical consecutive SiAnd are the same as SiWhen the number of the second detection signal exceeds the threshold value, it indicates that the second detection signal is stuck.
At SiIn the calculation, the same and continuous images must be ensured, so that the error starting caused by repeated images is avoided, and in the judgment process, a threshold value is set to prevent the erroneous judgment caused by the fact that the images are not very different at one time and the system cannot identify the images.
According to the above technical solution, in step S2, after the user equipment is stuck, the user equipment automatically enters gaze control, all application backgrounds are arranged according to a fixed area, only one background application is ensured in each area, the linear distance from the pupil to the screen is measured by the infrared sensor and recorded as D, the moving distance of the pupil is measured by the eyeball tracking sensor and recorded as D, the moving direction is recorded as w, the falling point of the gaze in the fixed area forms an included angle with the horizontal line of the screen and recorded as θ, the linear distance between the center point and the gaze falling point is recorded as L, according to the formula:
Figure BDA0002890230050000051
0°＜θ＜90°
calculating to obtain an included angle theta between the farthest falling point and the nearest falling point which are away from the central point in any fixed area1、θ2When the included angle theta is in (theta)1，θ2) And when the mobile terminal is in the middle of the time, judging that the mobile terminal is on the screen, moving the central point according to the moving direction w, judging that the mobile terminal is in a certain area, and cleaning the application in the area.
When the gaze control is started, each divided application area appears on the screen, angle judgment is firstly carried out, and the angle theta is calculated according to the linear distance from the pupil to the screen and the linear distance between the central point and the gaze drop point1、θ2Since between 0 and 90 degrees tan θ is an increasing function, only the angle is (θ)1，θ2) In the meantime, the eyes can be judged to fall on the screen; then, because the human face and the equipment can be regarded as two parallel planes, the moving angle and the moving direction of the pupil can be judged as the moving angle and the moving direction of the central point of the screen, and the screen area where the eye drop point is located can be judged according to the moving direction, so that background application in the area is cleared.
According to the above technical solution, in step S3, the storing step is as follows:
s3-100, collecting the application M currently used, and recording the cleaned application as a set B ═ P1，P2，P3，……，PnR, current free memory R;
s3-200, using application M as name, forming data set { M, R, PxT, E }, wherein PxFor all applications cleaned, T represents time and E represents count, and each new M in the database, E + 1.
In the storage process, the data sets of the same application are stored together according to the naming of the data sets, and the free memory of the application, the application cleaned when the application is started, the count and the time are recorded for carrying out big data analysis, so that the automatic and intelligent cleaning of background application is realized.
According to the technical scheme, in the step S4, database information records are called, when the storage frequency of any application exceeds N, intelligent cleaning is established on the application, and the steps are as follows:
s4-100, detecting that the application is started by a user, detecting the memory, and if the idle memory is larger than the minimum value of the idle memory in the use record, not cleaning; if the value is less than the minimum value of the free memory in the use record, the step S4-200 is carried out;
s4-200, calling information in the database, identifying an application list actively closed by the user in all times of starting the application, calculating the weight of the closed application, sequentially searching in background application according to the weight sequence, automatically cleaning, and ending if the cleaned free memory is larger than the minimum value of the free memory in the use record; otherwise, go to step S4-300;
and S4-300, calling background application common grades in the database, sequentially cleaning the rest background applications from small to large according to the classification grade index values until the free memory is larger than the minimum value of the free memory in the use record, and ending.
Compared with the prior art, the invention has the following beneficial effects: in the invention, the detection of whether the user equipment is stuck or not is realized by using the detection module, so that the possibility of misjudgment can be greatly reduced, and the working efficiency of the system is improved; the vision control module is used for liberating two hands for operation, so that the operation of a user is facilitated, the inconvenience caused by one-key cleaning can be avoided, and a good mode can be provided for the disabled for operation; the intelligent cleaning mode is established by the intelligent cleaning module according to the big data, a special background cleaning mode can be formed according to the unique user habit of each user, better use experience is provided for the user, and meanwhile, the dual cleaning units are arranged for cleaning according to different conditions, so that the bad condition that user equipment is blocked due to excessive background application is avoided; the database is used for storing data, and meanwhile, the common and uncommon classification standards of the application program are provided, so that background cleaning is facilitated; meanwhile, the invention provides a corresponding method to solve the system problem, so that the invention has higher practicability.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic structural diagram of a big data-based background application cleaning system according to the present invention;
FIG. 2 is a schematic diagram illustrating steps of a background application cleaning method based on big data according to the present invention;
FIG. 3 is a flow chart of a background application cleaning method based on big data according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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. 1-3, the present invention provides the following technical solutions: in fig. 1, a background application cleaning system based on big data includes a detection module, a daylight control module, an intelligent cleaning module, and a database;
the output end of the detection module is electrically connected with the eye light control module; the output end of the eye control module is electrically connected with a database; the output end of the database is electrically connected with the intelligent cleaning module;
the detection module is used for monitoring whether the user equipment is blocked or not; the sight control module is used for artificially cleaning background application; the intelligent cleaning module is used for automatically cleaning according to the behavior habit of the user; the database is used for storing user behavior habits and related data.
The detection module comprises a picture acquisition unit, a time acquisition unit and a stuck detection unit;
the output ends of the picture acquisition unit and the time acquisition unit are electrically connected with the input end of the stuck detection unit, and the output end of the stuck detection unit is electrically connected with the input end of the sight cleaning module;
the picture acquisition unit is used for acquiring pictures when the user equipment is used; the time acquisition unit is used for acquiring the time used by the user equipment and corresponding to each use picture; the pause detection unit is used for detecting the pause of the user equipment and starting the eye light control module.
The sight control module comprises an eyeball tracking unit, a distance measuring unit, a screen control unit and a clearing unit;
the output ends of the tracking unit and the ranging unit are electrically connected with the input end of the clearing unit; the output end of the detection module is electrically connected with the input end of the screen control unit; the output end of the clearing unit is electrically connected with the input end of the database.
The eyeball tracking unit is used for tracking and recording the moving route and the distance of the pupil; the distance measuring unit is used for measuring the linear distance from human eyes to the screen; the screen control unit is used for dividing the screen into a fixed number of areas after the gaze control module is started, and each area is provided with a background application; the clearing unit is used for clearing the background application.
The intelligent cleaning module comprises a data acquisition unit, an idle memory detection unit and a cleaning unit;
the output end of the data acquisition unit is electrically connected with the input end of the database; the output end of the idle memory detection unit is electrically connected with the input end of the cleaning unit;
the data acquisition unit is used for acquiring cleaning behaviors performed by the cleaning module under the condition that the user equipment is stuck; the free memory detection unit is used for detecting a free memory of the user equipment; the cleaning unit is used for intelligently cleaning the application background and releasing the memory;
the cleaning unit comprises a first cleaning unit and a second cleaning unit;
the first cleaning unit is used for cleaning background application according to the cleaning habit of historical data; the second cleaning unit is used for cleaning under the condition that the first cleaning unit does not reach the idle memory threshold value, and cleaning is carried out according to the common degree of the application background.
The database comprises a classification unit and a storage unit;
the classification unit is used for performing common grade classification on all applications in the user equipment; the storage unit is used for storing the user equipment cleaning process and various related data;
the classification method of the classification unit is according to the formula:
Figure BDA0002890230050000091
wherein, V is a classification level index value, n is the number of downloading days of the application, x is the usage duration of the application each day, y is the background duration of the application each day, k1、k2To evaluate the coefficients; the larger V indicates that the application is more common.
In fig. 2-3, a background application cleaning method based on big data includes the following steps:
s1, collecting the picture condition of the user equipment, and detecting the blockage of the user equipment according to the change in unit time;
s2, when the user equipment is stuck, displaying the running background application on the screen of the equipment, starting gaze control, and cleaning by gaze;
s3, after cleaning, the operation is still stuck, and the step S2 is repeated; the method comprises the steps of cleaning the memory, recording the cleaned idle memory and the currently used application, and storing the idle memory and the currently used application in a database;
s4, in step S3, when the number of times of storage of an application exceeds the threshold N, an intelligent cleaning is established, that is, when the application is started, the free memory is automatically adjusted to exceed the minimum value recorded by the application.
In step S1, the picture collected by the user equipment per unit time is recorded as set a ═ S1，S2，S3，……，SnIn set A, if there are identical consecutive SiAnd are the same as SiWhen the number of the second detection signal exceeds the threshold value, it indicates that the second detection signal is stuck.
In step S2, after the user equipment is stuck, the user equipment automatically enters gaze control, arranges all application backgrounds according to a fixed area, ensures that only one background application exists in each area, measures the linear distance from the pupil to the screen by using an infrared sensor as D, measures the moving distance of the pupil by using an eye tracking sensor as D, measures the moving direction as w, measures the angle formed by the falling point of the gaze in the fixed area and the horizontal line of the screen as θ, measures the linear distance between the center point and the gaze falling point as L, and according to the formula:
Figure BDA0002890230050000101
0°＜θ＜90°
calculating to obtain an included angle theta between the farthest falling point and the nearest falling point which are away from the central point in any fixed area1、θ2When the included angle theta is in (theta)1，θ2) And when the mobile terminal is in the middle of the time, judging that the mobile terminal is on the screen, moving the central point according to the moving direction w, judging that the mobile terminal is in a certain area, and cleaning the application in the area.
In step S3, the storing step is as follows:
s3-100, collecting the application M currently used, and recording the cleaned application as a set B ═ P1，P2，P3，……，PnR, current free memory R;
s3-200, using application M as name, forming data set { M, R, PxT, E }, wherein PxFor all applications cleaned, T represents time and E represents count, and each new M in the database, E + 1.
In step S4, a database information record is called, and when the number of times of storage of any application exceeds N, intelligent cleaning is established for it, the steps are as follows:
s4-100, detecting that the application is started by a user, detecting the memory, and if the idle memory is larger than the minimum value of the idle memory in the use record, not cleaning; if the value is less than the minimum value of the free memory in the use record, the step S4-200 is carried out;
s4-200, calling information in the database, identifying an application list actively closed by the user in all times of starting the application, calculating the weight of the closed application, sequentially searching in background application according to the weight sequence, automatically cleaning, and ending if the cleaned free memory is larger than the minimum value of the free memory in the use record; otherwise, go to step S4-300;
and S4-300, calling background application common grades in the database, sequentially cleaning the rest background applications from small to large according to the classification grade index values until the free memory is larger than the minimum value of the free memory in the use record, and ending.
In this embodiment, a total of 10 applications are in the background, and the classification level index value of each application is obtained according to the classification formula, and is denoted as the set J ═ V according to the degree of common use1，V2，V3，……，V10At this point the user has started a new application V11If the application is judged to be not intelligently cleared, the application carries out picture detection for a period of time, and records the set of detected pictures as A ═ S1，S2，S3，……，SnSet a threshold of 15, where S2To S20The pictures are completely the same, therefore, the user equipment is judged to be blocked, the eye control module is started, the linear distance from the pupil to the screen is set to be 40cm, the direction is leftward movement, the closest and farthest linear distances between the central point and the left area are respectively 20cm and 45cm, the corresponding pupil movement distances are respectively 0.7cm and 1.3cm, and the user equipment is judged to be blocked according to the results
Figure BDA0002890230050000111
The range of the calculated angle is 43 degrees to 64 degreesThe angle obtained by the eye passing measurement is 52 degrees, and the judgment is that the angle falls on the screen, so that the background application V in the left area is applied3Clearing, wherein the current free memory is 58%, and recording the result;
form a data set { V11，58％，V329 days 12 and month, 49}, the storage count threshold is set to 49, so V is applied11Intelligent cleaning is formed until the free memory reaches 58%.
The working principle of the invention is as follows: the detection module is used for detecting whether the user equipment is blocked or not, and if so, the eye control module is executed so as to improve the working efficiency of the system; the eye control module is used for cleaning the eye through the area of the eye drop point, so that multi-mode operation of a user is realized, inconvenience caused by one-key cleaning is avoided, and a good mode can be provided for the disabled to operate; the intelligent cleaning mode is established by the intelligent cleaning module according to the big data, and a special background cleaning mode is formed according to the unique user habit of each user, so that the times of user operation can be reduced as much as possible, and better use experience is provided for the user; the database is used for storing data, and common and uncommon classification standards of the application program are provided, so that good data reference is provided during background cleaning.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Also, 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: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (1)

1. A background application cleaning system based on big data is characterized in that: the system comprises a detection module, a daylight control module, an intelligent cleaning module and a database;
the output end of the detection module is electrically connected with the eye light control module; the output end of the eye control module is electrically connected with a database; the output end of the database is electrically connected with the intelligent cleaning module;
the detection module is used for monitoring whether the user equipment is blocked or not; the sight control module is used for artificially cleaning background application; the intelligent cleaning module is used for automatically cleaning according to the behavior habit of the user; the database is used for storing user behavior habits and related data;
the detection module comprises a picture acquisition unit, a time acquisition unit and a stuck detection unit;
the output ends of the picture acquisition unit and the time acquisition unit are electrically connected with the input end of the stuck detection unit, and the output end of the stuck detection unit is electrically connected with the input end of the sight cleaning module;
the picture acquisition unit is used for acquiring pictures when the user equipment is used; the time acquisition unit is used for acquiring the time used by the user equipment and corresponding to each use picture; the pause detection unit is used for detecting the pause of the user equipment and starting the eye light control module;
the sight control module comprises an eyeball tracking unit, a distance measuring unit, a screen control unit and a clearing unit;
the output ends of the tracking unit and the ranging unit are electrically connected with the input end of the clearing unit; the output end of the detection module is electrically connected with the input end of the screen control unit; the output end of the clearing unit is electrically connected with the input end of the database;
the eyeball tracking unit is used for tracking and recording the moving route and the distance of the pupil; the distance measuring unit is used for measuring the linear distance from human eyes to the screen; the screen control unit is used for dividing the screen into a fixed number of areas after the gaze control module is started, and each area is provided with a background application; the clearing unit is used for clearing the background application;
the intelligent cleaning module comprises a data acquisition unit, an idle memory detection unit and a cleaning unit;
the output end of the data acquisition unit is electrically connected with the input end of the database; the output end of the idle memory detection unit is electrically connected with the input end of the cleaning unit;
the data acquisition unit is used for acquiring cleaning behaviors performed by the cleaning module under the condition that the user equipment is stuck; the free memory detection unit is used for detecting a free memory of the user equipment; the cleaning unit is used for intelligently cleaning the application background and releasing the memory;
the cleaning unit comprises a first cleaning unit and a second cleaning unit;
the first cleaning unit is used for cleaning background application according to the cleaning habit of historical data; the second cleaning unit is used for cleaning under the condition that the first cleaning unit does not reach the idle memory threshold value and cleaning according to the common degree of the application background;
the database comprises a classification unit and a storage unit;
the classification unit is used for performing common grade classification on all applications in the user equipment; the storage unit is used for storing the user equipment cleaning process and various related data;
the classification method of the classification unit is according to the formula:
Figure FDA0003361125630000031
wherein, V is the index value of classification level, n is the download days of the application, xiLength of use per day for this application, yiBackground duration, k, per day for the application1、k2To evaluate the coefficients; the larger V is, the more common the application is;
the use method of the system comprises the following steps:
s1, collecting the picture condition of the user equipment, and detecting the blockage of the user equipment according to the change in unit time;
s2, when the user equipment is stuck, displaying the running background application on the screen of the equipment, starting gaze control, and cleaning by gaze;
s3, after cleaning, the operation is still stuck, and the step S2 is repeated; the method comprises the steps of cleaning the memory, recording the cleaned idle memory and the currently used application, and storing the idle memory and the currently used application in a database;
s4, in step S3, when the storage frequency of a certain application exceeds a threshold value N, intelligent cleaning is established, namely when the application is started, the idle memory is automatically adjusted to exceed the minimum value recorded by the application;
in step S1, the picture collected by the user equipment per unit time is recorded as set a ═ S1，S2，S3，……，SnIn set A, if there are identical consecutive SiAnd are the same as SiWhen the number of the first and second detection signals exceeds the threshold value, the occurrence of the jamming is indicated;
in step S2, after the user equipment is stuck, the user equipment automatically enters gaze control, arranges all application backgrounds according to a fixed area, ensures that only one background application exists in each area, measures the linear distance from the pupil to the screen by using an infrared sensor as D, measures the moving distance of the pupil by using an eye tracking sensor as D, measures the moving direction as w, measures the angle formed by the falling point of the gaze in the fixed area and the horizontal line of the screen as θ, measures the linear distance between the center point and the gaze falling point as L, and according to the formula:
Figure FDA0003361125630000041
calculating to obtain an included angle theta between the farthest falling point and the nearest falling point which are away from the central point in any fixed area1、θ2When the included angle theta is in (theta)1，θ2) When the mobile terminal is in the middle of the time interval, judging that the mobile terminal is on the screen, moving the central point according to the moving direction w, judging that the mobile terminal is in a certain area, and cleaning the application in the area;
in step S3, the storing step is as follows:
s3-100, collecting the application M currently used, and recording the cleaned application as a set B ═ P1，P2，P3，……，PnR, current free memory R;
s3-200, using application M as name, forming data set { M, R, PxT, E }, wherein PxFor all cleaned applications, T represents time, E represents count, and each new M in the database, E + 1;
in step S4, a database information record is called, and when the number of times of storage of any application exceeds N, intelligent cleaning is established for it, the steps are as follows:
s4-100, detecting that the application is started by a user, detecting the memory, and if the idle memory is larger than the minimum value of the idle memory in the use record, not cleaning; if the value is less than the minimum value of the free memory in the use record, the step S4-200 is carried out;
s4-200, calling information in the database, identifying an application list actively closed by the user in all times of starting the application, calculating the weight of the closed application, sequentially searching in background application according to the weight sequence, automatically cleaning, and ending if the cleaned free memory is larger than the minimum value of the free memory in the use record; otherwise, go to step S4-300;
and S4-300, calling background application common grades in the database, sequentially cleaning the rest background applications from small to large according to the classification grade index values until the free memory is larger than the minimum value of the free memory in the use record, and ending.
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