CN113867621A - Block chain localization cloud computing big data application analysis method - Google Patents

Block chain localization cloud computing big data application analysis method Download PDF

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CN113867621A
CN113867621A CN202110958150.3A CN202110958150A CN113867621A CN 113867621 A CN113867621 A CN 113867621A CN 202110958150 A CN202110958150 A CN 202110958150A CN 113867621 A CN113867621 A CN 113867621A
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storage
storage space
module
application
data
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CN113867621B (en
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孙诚
林怀远
王时群
李虎雄
李琪
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Tigerman Polytron Technologies Inc
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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
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/0604—Improving or facilitating administration, e.g. storage management
    • 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
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
    • G06F3/0614—Improving the reliability of storage systems
    • 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
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
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    • 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
    • G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
    • G06F3/0601—Interfaces specially adapted for storage systems
    • G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
    • G06F3/067—Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]
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Abstract

The invention discloses a big data application analysis method of block chain localization cloud computing, which belongs to the field of cloud computing and relates to a data analysis technology.A data acquisition module is connected with a computer local application to acquire the size of temporary data appearing in a storage space when a user clicks the local application and a corresponding local storage space, and the size and the corresponding local storage space are sent to a habit calculation module; the data acquisition module acquires the number of local storage spaces, corresponding storage rate, extraction rate and size of the storage spaces and sends the number, the corresponding storage rate, the extraction rate and the size of the storage spaces to the data analysis module; then the data analysis module carries out data analysis on the data sent by the data acquisition module and divides a local storage space into a storage space for extraction and a storage space for storage; the habit calculation module calculates the application storage rate according to the received data, compares the calculated application storage rate with an application storage rate threshold value, and sends a signal to the storage selection module; and finally, the storage selection module judges the storage rationality of the local application and recommends storage.

Description

Block chain localization cloud computing big data application analysis method
Technical Field
The invention belongs to the field of cloud computing, relates to a data analysis technology, and particularly relates to a block chain localization cloud computing big data application analysis method.
Background
Along with the continuous development of scientific technology, mobile phone APP and computer application programs are continuously developed, some original mobile phone APP and computer application programs are also continuously updated, what cannot be avoided by updating is the generation of patches and additional functions, what brings with that is the storage space of the mobile phone APP and the computer application programs themselves and the generated temporary data and permanent data are continuously increased, for a computer, the storage space of the computer can be large, the updating of programs which can be borne and the generation of patches can be realized, but the storage space of the computer can not be continuously reduced, and the use of various computer application programs can enable the computer to become jammed and the case to generate heat seriously, the computer is damaged, and the use experience of users can be influenced.
In order to slow down or solve the problem that the storage space of a computer is continuously reduced, a block chain localization cloud computing big data application analysis method is provided, the self damage of the computer caused by the storage space problem can be improved, and meanwhile the use experience of a user can be enhanced.
Disclosure of Invention
The invention provides a block chain localization cloud computing big data application analysis method, which aims to slow down or solve the problem that the storage space of a computer is continuously reduced, improve the self damage of the computer caused by the storage space problem and enhance the use experience of a user.
The purpose of the invention can be realized by the following technical scheme:
a big data application analysis method of block chain localization cloud computing comprises the following steps:
the method comprises the following steps: the data acquisition module is connected with the local application of the computer to acquire the size of temporary data appearing in a storage space when a user clicks the local application and the corresponding local storage space, and the size and the corresponding local storage space are sent to the habit calculation module;
step two: the data acquisition module acquires the number of local storage spaces, corresponding storage rate, extraction rate and size of the storage spaces and sends the number, the corresponding storage rate, the extraction rate and the size of the storage spaces to the data analysis module;
step three: the data analysis module is used for carrying out data analysis on the data sent by the data acquisition module and dividing a local storage space into a storage space for extraction and a storage space for storage;
step four: the habit calculation module calculates the application storage rate according to the received data, compares the calculated application storage rate with an application storage rate threshold value, and sends a signal to the storage selection module;
step five: and the storage selection module judges the storage rationality of the local application and recommends storage.
Further, the data analysis module is used for carrying out data analysis on the data sent by the data acquisition module, and the analysis process comprises the following steps:
step A1: the data acquisition module acquires the number of local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces and sends the number, the extraction rate and the size of the storage spaces to the data analysis module;
step A2: when the data analysis module receives the number of the local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces, the data analysis module is respectively marked as n, Vci, Vti and Kci; note that, where i denotes the number of the storage space; 1,2 … … n;
step A3: the data analysis module calculates and obtains a storage index Zci of the storage space, and the calculation formula is as follows:
Figure BDA0003221054370000021
the data analysis module calculates an extraction index Zti of the acquired storage space, and the calculation formula is as follows:
Figure BDA0003221054370000022
Figure BDA0003221054370000023
wherein alpha and beta are correction coefficients;
step A4: the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient CQi of a storage space with the number i, wherein the calculation formula is CQi-a 1 × ZCi + a2 × Zti;
the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient QCi of a storage space with the number of i, wherein the calculation formula is QCi-a 3 × ZCi + a4 × Zti;
step A5: the data processing module compares the sizes of the access coefficient CQi and the access coefficient QCi;
when the access coefficient QCi is more than or equal to the access coefficient CQi, marking the storage space as a storage space for extraction;
when the access coefficient CQi > accesses the coefficient QCi, the storage space is marked as storage space for storage;
and the data analysis module sends the classified and marked storage space for extraction and storage to the storage selection module.
Further, the habit calculating module is used for sending a signal to the storage selecting module according to the working habit of the user, and the working process comprises the following steps:
step A: the data acquisition module acquires the times of clicking the application by the user within the period time T, the single click browsing time, the size of temporary data appearing in a storage space when the user clicks the application and a corresponding original local storage space and sends the temporary data to the habit calculation module;
and B: the habit calculating module receives the times of clicking application by a user in a period time T, the single click browsing time length and the size of temporary data appearing in a storage space when the user clicks the application, which are sent by the data acquisition module, and then marks the times as Ctj, Sj and Dsj respectively; wherein j is the number of the application, j is 1,2 … … m;
and C: the habit calculation module calculates the application storage rate CCl according to a calculation formula
Figure BDA0003221054370000031
Wherein c1 is a predetermined coefficient;
step D: the habit calculating module sets an application storage rate threshold, and when the application storage rate CCl is greater than or equal to the application storage rate threshold, the habit calculating module sends a normal storage signal to the storage selecting module;
and when the application storage rate CCl is smaller than the application storage rate threshold value, the habit calculating module sends a frequently-taking signal to the storage selecting module.
Further, the storage selection module is used for receiving signals sent by the data analysis module and the habit calculation module to perform selection storage judgment, and comprises the following steps:
the storage selection module acquires the original capacity of the storage space for extraction and storage and the occupation percentage of the storage space after receiving the storage space for extraction and the storage space for storage;
the storage selection module respectively calculates available spaces, sorts the storage spaces for extraction and the storage spaces for storage according to the size of the available spaces, and marks the spaces as a first storage space for extraction, a second storage space for extraction … …, a first storage space for storage and a second storage space for storage … …;
after the storage selection module receives the frequently stored signal, the storage selection module acquires a storage space corresponding to the application, and if the corresponding storage space is a storage space for storage, the storage selection module does not send the signal;
if the corresponding storage space is the storage space for extraction, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space and the second storage space to the user for replacement;
when the storage selection module receives the frequently-taken signal, the storage selection module acquires the storage space corresponding to the application, and if the corresponding storage space is the storage space for extraction, the storage selection module does not send the signal;
if the corresponding storage space is used for storage, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space for extraction and the second storage space for extraction to be replaced by the user.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the capacity, the storage speed and the extraction speed of the storage space are obtained by acquiring the information of the plurality of storage spaces of the computer, the plurality of storage spaces are classified, the user habit of the application installed in the computer is acquired, the temporary data generated by the application is simultaneously acquired, the application is selectively stored according to the characteristics of the application, the safety of application data storage is ensured, and the problem of low utilization rate of the storage space due to the fact that the plurality of data are simultaneously stored in one storage space is solved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a block chain localization cloud computing big data application analysis method according to the present invention.
Detailed Description
The core of the invention is to provide a block chain localization cloud computing big data application analysis method, which comprises the steps of obtaining the capacity, the storage speed and the extraction speed of a storage space by obtaining information of a plurality of storage spaces of a computer, classifying the storage spaces, obtaining temporary data generated by an application installed in the computer while obtaining the habit of a user for the application, selectively storing the temporary data according to the characteristics of the application, ensuring the safety of application data storage, and avoiding the problem of low utilization rate of the storage space due to the fact that a plurality of data are stored in one storage space at the same time.
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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, fig. 1 is a block chain localization cloud computing big data application analysis method. In this embodiment, a block chain localization cloud computing big data application analysis method is implemented based on a block chain localization cloud computing big data application analysis system, and the block chain localization cloud computing big data application analysis system includes a data acquisition module, a data analysis module, a habit calculation module, a local storage space, an application reminding module, and a storage selection module;
the data acquisition module is used for acquiring data, and the acquired data specifically comprises the number of local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces;
the data acquisition module is also used for acquiring habit data of a user using the computer, and the specific habit data of the user comprises the times of clicking the application by the user within the period time T and the single click browsing duration;
meanwhile, the data acquisition module is also connected with an application to acquire the size of temporary data appearing in the storage space when the user clicks the application and a corresponding local storage space;
it should be noted that, when the application is installed on the computer, the application storage location and the application temporary data storage location are selected during the installation process;
the application storage position and the application temporary data storage position are generally automatically set and are stored in the C disk; the data acquisition module transmits the acquired data to the data analysis module and the habit calculation module according to the data requirements;
the data analysis module is used for carrying out data analysis on the data sent by the data acquisition module, and the specific data analysis process comprises the following steps:
the data acquisition module acquires the number of local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces and sends the number of the local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces to the data analysis module;
when the data analysis module receives the number of the local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces, the data analysis module is respectively marked as n, Vci, Vti and Kci; note that, where i denotes the number of the storage space; 1,2 … … n;
the data analysis module calculates and obtains a storage index Zci of the storage space, and the calculation formula is as follows:
Figure BDA0003221054370000061
Figure BDA0003221054370000062
the data analysis module calculates an extraction index Zti of the acquired storage space, and the calculation formula is as follows:
Figure BDA0003221054370000063
Figure BDA0003221054370000064
wherein alpha and beta are correction coefficients;
the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient CQi of a storage space with the number i, wherein the calculation formula is CQi-a 1 × ZCi + a2 × Zti;
the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient QCi of a storage space with the number of i, wherein the calculation formula is QCi-a 3 × ZCi + a4 × Zti;
it should be noted that a1, a2, a3 and a4 are preset proportionality coefficients;
the data processing module compares the sizes of the access coefficient CQi and the access coefficient QCi;
when the access coefficient QCi is more than or equal to the access coefficient CQi, marking the storage space as a storage space for extraction;
when the access coefficient CQi > accesses the coefficient QCi, the storage space is marked as storage space for storage;
and the data analysis module sends the classified and marked storage space for extraction and storage to the storage selection module.
It should be further explained that the data acquisition module sends the number of times of clicking the application by the user within the period time T, the single click browsing duration, the size of the temporary data appearing in the storage space when the user clicks the application, and the corresponding original local storage space to the habit calculation module, the habit calculation module is configured to send a signal to the storage selection module according to the working habit of the user, and the specific working process of the habit calculation module includes the following steps:
step A: the data acquisition module acquires the times of clicking the application by the user within the period time T, the single click browsing time, the size of temporary data appearing in a storage space when the user clicks the application and a corresponding original local storage space and sends the temporary data to the habit calculation module;
and B: the habit calculating module receives the times of clicking application by a user in a period time T, the single click browsing time length and the size of temporary data appearing in a storage space when the user clicks the application, which are sent by the data acquisition module, and then marks the times as Ctj, Sj and Dsj respectively; wherein j is the number of the application, j is 1,2 … … m;
and C: the habit calculation module calculates the application storage rate CCl according to a calculation formula
Figure BDA0003221054370000071
Wherein c1 is a predetermined coefficient;
step D: the habit calculating module sets an application storage rate threshold, and when the application storage rate CCl is greater than or equal to the application storage rate threshold, the habit calculating module sends a normal storage signal to the storage selecting module;
and when the application storage rate CCl is smaller than the application storage rate threshold value, the habit calculating module sends a frequently-taking signal to the storage selecting module.
The storage selection module is used for receiving signals sent by the data analysis module and the habit calculation module to perform selection storage judgment, and the specific process comprises the following steps:
the storage selection module acquires the original capacity of the storage space for extraction and storage and the occupation percentage of the storage space after receiving the storage space for extraction and the storage space for storage;
the storage selection module respectively calculates available spaces, sorts the storage spaces for extraction and the storage spaces for storage according to the size of the available spaces, and marks the spaces as a first storage space for extraction, a second storage space for extraction … …, a first storage space for storage and a second storage space for storage … …;
after the storage selection module receives the frequently stored signal, the storage selection module acquires a storage space corresponding to the application, and if the corresponding storage space is a storage space for storage, the storage selection module does not send the signal;
if the corresponding storage space is the storage space for extraction, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space and the second storage space to the user for replacement;
when the storage selection module receives the frequently-taken signal, the storage selection module acquires the storage space corresponding to the application, and if the corresponding storage space is the storage space for extraction, the storage selection module does not send the signal;
if the corresponding storage space is used for storage, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space for extraction and the second storage space for extraction to the user for replacement;
it should be noted that, when all applications are reminded, the storage selection module recommends a storage space and the user selects the storage space, the storage selection module reorders the storage spaces, so as to prevent the problems that the same storage space is continuously reduced due to the fact that a plurality of applications correspond to the same storage space, a computer becomes jammed due to various computer application programs, and the case heats seriously.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: firstly, a data acquisition module is connected with a local application of a computer to acquire the size of temporary data appearing in a storage space when a user clicks the local application and the corresponding local storage space, and the size and the corresponding local storage space are sent to a habit calculation module; the data acquisition module acquires the number of local storage spaces, corresponding storage rate, extraction rate and size of the storage spaces and sends the number, the corresponding storage rate, the extraction rate and the size of the storage spaces to the data analysis module; then the data analysis module carries out data analysis on the data sent by the data acquisition module and divides a local storage space into a storage space for extraction and a storage space for storage; the habit calculation module calculates the application storage rate according to the received data, compares the calculated application storage rate with an application storage rate threshold value, and sends a signal to the storage selection module; and finally, the storage selection module judges the storage rationality of the local application and recommends storage.
In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and there may be other divisions when the actual implementation is performed; the modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the method of the embodiment.
It will also be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above examples are only intended to illustrate the technical process of the present invention and not to limit the same, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made to the technical process of the present invention without departing from the spirit and scope of the technical process of the present invention.

Claims (4)

1. A big data application analysis method of block chain localization cloud computing is characterized by comprising the following steps:
the method comprises the following steps: the data acquisition module is connected with the local application of the computer to acquire the size of temporary data appearing in a storage space when a user clicks the local application and the corresponding local storage space, and the size and the corresponding local storage space are sent to the habit calculation module;
step two: the data acquisition module acquires the number of local storage spaces, corresponding storage rate, extraction rate and size of the storage spaces and sends the number, the corresponding storage rate, the extraction rate and the size of the storage spaces to the data analysis module;
step three: the data analysis module is used for carrying out data analysis on the data sent by the data acquisition module and dividing a local storage space into a storage space for extraction and a storage space for storage;
step four: the habit calculation module calculates the application storage rate according to the received data, compares the calculated application storage rate with an application storage rate threshold value, and sends a signal to the storage selection module;
step five: and the storage selection module judges the storage rationality of the local application and recommends storage.
2. The analysis method for the big data application of the block chain localization cloud computing according to claim 1, wherein the data analysis module is used for performing data analysis on the data sent by the data acquisition module, and the analysis process includes the following steps:
step A1: the data acquisition module acquires the number of local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces and sends the number, the extraction rate and the size of the storage spaces to the data analysis module;
step A2: when the data analysis module receives the number of the local storage spaces, the storage rate corresponding to the local storage spaces, the extraction rate and the size of the storage spaces, the data analysis module is respectively marked as n, Vci, Vti and Kci; note that, where i denotes the number of the storage space; 1,2 … … n;
step A3: the data analysis module calculates and obtains a storage index Zci of the storage space, and the calculation formula is as follows:
Figure FDA0003221054360000011
the data analysis module calculates an extraction index Zti of the acquired storage space, and the calculation formula is as follows:
Figure FDA0003221054360000012
Figure FDA0003221054360000021
wherein alpha and beta are correction coefficients;
step A4: the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient CQi of a storage space with the number i, wherein the calculation formula is CQi-a 1 × ZCi + a2 × Zti;
the data analysis module jointly stores the index ZCi and the extraction index Zti to calculate an access coefficient QCi of a storage space with the number of i, wherein the calculation formula is QCi-a 3 × ZCi + a4 × Zti;
step A5: the data processing module compares the sizes of the access coefficient CQi and the access coefficient QCi;
when the access coefficient QCi is more than or equal to the access coefficient CQi, marking the storage space as a storage space for extraction;
when the access coefficient CQi > the access coefficient QCi, marking the storage space as storage space;
and the data analysis module sends the classified and marked storage space for extraction and storage to the storage selection module.
3. The big data application analysis method for block chain localization cloud computing according to claim 1, wherein the habit computing module is configured to send a signal to the storage selection module according to a working habit of a user, and the working process includes the following steps:
step A: the data acquisition module acquires the times of clicking the application by the user within the period time T, the single click browsing time, the size of temporary data appearing in a storage space when the user clicks the application and a corresponding original local storage space and sends the temporary data to the habit calculation module;
and B: the habit calculating module receives the times of clicking application by a user in a period time T, the single click browsing time length and the size of temporary data appearing in a storage space when the user clicks the application, which are sent by the data acquisition module, and then marks the times as Ctj, Sj and Dsj respectively; wherein j is the number of the application, j is 1,2 … … m;
and C: the habit calculation module calculates the application storage rate CCl according to a calculation formula
Figure FDA0003221054360000031
Wherein c1 is a predetermined coefficient;
step D: the habit calculating module sets an application storage rate threshold, and when the application storage rate CCl is greater than or equal to the application storage rate threshold, the habit calculating module sends a normal storage signal to the storage selecting module;
and when the application storage rate CCl is smaller than the application storage rate threshold value, the habit calculating module sends a frequently-taking signal to the storage selecting module.
4. The analysis method for the big data application of the block chain localization cloud computing according to claim 1, wherein the storage selection module is used for receiving the signals sent by the data analysis module and the habit calculation module to perform the selection storage judgment, and comprises the following steps:
the storage selection module acquires the original capacity of the storage space for extraction and storage and the occupation percentage of the storage space after receiving the storage space for extraction and the storage space for storage;
the storage selection module respectively calculates available spaces, sorts the storage spaces for extraction and the storage spaces for storage according to the size of the available spaces, and marks the spaces as a first storage space for extraction, a second storage space for extraction … …, a first storage space for storage and a second storage space for storage … …;
after the storage selection module receives the frequently stored signal, the storage selection module acquires a storage space corresponding to the application, and if the corresponding storage space is a storage space for storage, the storage selection module does not send the signal;
if the corresponding storage space is the storage space for extraction, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space and the second storage space to the user for replacement;
when the storage selection module receives the frequently-taken signal, the storage selection module acquires the storage space corresponding to the application, and if the corresponding storage space is the storage space for extraction, the storage selection module does not send the signal;
if the corresponding storage space is used for storage, the storage selection module sends a reminding signal to the application reminding module, the application reminding module reminds a user of carrying out replacement setting on the storage space, and meanwhile, the application reminding module recommends the first storage space for extraction and the second storage space for extraction to be replaced by the user.
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