CN116301646A - Personal computer storage management system based on machine learning - Google Patents
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Abstract
The invention discloses a personal computer storage management system based on machine learning, which relates to the technical field of data storage and comprises a password verification module, a data uploading module, a data storage module, a data monitoring module, a memory monitoring module and a data cleaning module; the password verification module is used for carrying out richness verification on the access password set by the user; if the rich value FZ is more than or equal to a preset rich threshold value, the access password is detected to be qualified; otherwise, the access password is unqualified in detection, the user is prompted to reset or modify, the complexity of the access password is guaranteed, the difficulty of password cracking is improved, and therefore the data security is enhanced; the data auditing module is used for auditing the storage file uploaded by the data uploading module; the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient KY to judge whether the data storage module needs cleaning; the storage pressure of the computer is effectively reduced, and the data storage efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data storage, in particular to a personal computer storage management system based on machine learning.
Background
With the rapid development of digital information technology, computers play different important roles in life and work of people, and people are more and more separated from computers and more separated from digital information technology. However, the things are on two sides, which brings great potential safety hazards to people while bringing convenience and quickness to life and work of people. The data stored in the database can be stolen due to system flaws or malicious attacks or too simple login passwords;
the current computer storage management system generally adopts a password verification mode for an access user, sometimes the password is too simple and is easy to crack, so that the data stored in the database is leaked; meanwhile, data management disorder exists, and the storage data cannot be backed up and cleaned in time according to the storage condition of the storage block, so that the storage pressure of the storage block is reduced; based on the defects, the invention provides a personal computer storage management system based on machine learning.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a personal computer storage management system based on machine learning.
To achieve the above objective, an embodiment of the present invention provides a personal computer storage management system based on machine learning, including a password verification module, a data uploading module, a data storage module, a data monitoring module, a memory monitoring module, and a data cleaning module;
the password verification module is used for carrying out richness verification on the access password set by the user, and calculating to obtain a richness value FZ of the access password; if the rich value FZ is more than or equal to a preset rich threshold value, the access password is detected to be qualified; otherwise, the access password is unqualified to prompt the user to reset or modify;
the data uploading module is used for providing an interface for uploading the storage file by a user and transmitting the storage file to the data auditing module; the storage file contains importance level information; the data storage module is used for storing the storage files which pass the verification of the data verification module;
the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient KY to judge whether the data storage module needs cleaning; if KY is smaller than a preset first spare threshold value, generating a cleaning signal and sending the cleaning signal to a data cleaning module;
the data cleaning module is used for cleaning the storage files in the data storage module, and specifically comprises the following steps:
when a cleaning signal is received, sorting the storage files according to the sequence of the storage priority YS from large to small; marking the storage files of the X1 before sequencing as backup files, transmitting the backup files to a local disk of a computer for backup and deleting the backup files; wherein X1 is a preset value;
continuously observing the spare coefficient KY of the data storage module, and if the KY is larger than a preset second spare threshold value, completing cleaning; if not, deleting the storage files from back to front in sequence according to the sequence until KY is larger than a preset second spare threshold; wherein the second slack threshold is greater than the first slack threshold.
Further, the data cleaning module further includes:
for any storage file, acquiring importance level information of the storage file, and marking the importance level information as Di; marking the data volume of the storage file as LZ; marking the storage time length of the storage file as LT; automatically calling the observation coefficient GC of the storage file from the cloud platform;
and calculating the storage priority value YS of the storage file by using a formula YS= (Di x k1+ GC x k 2)/(LZ x k3+ LT x k 4), wherein k1, k2, k3 and k4 are coefficient factors.
Further, the data monitoring module is used for searching, referring and monitoring the storage file in the data storage module, and calculating to obtain the observation coefficient GC of the storage file, and the specific monitoring steps are as follows:
when the stored file is searched and referred, automatically counting down, wherein the counting down time is T2 time, and T2 is a preset value; continuing to search, review and monitor the stored file in the countdown stage;
when the stored file is searched and referred again, the countdown is automatically classified as the original value, and the countdown is performed again according to T2; otherwise, the countdown returns to zero, and the timing is stopped;
counting the searching and consulting times of the storage file in the countdown stage as searching frequency JP1, and counting the duration of the countdown stage as searching duration JPT; the observation coefficient GC of the storage file is obtained through calculation by using a formula GC=JP 1 xr1+JPT x r 2; wherein r1 and r2 are coefficient factors; the data monitoring module is used for stamping a time stamp on the observation coefficient GC of the storage file and storing the time stamp to the cloud platform.
Further, the specific analysis steps of the memory monitoring module are as follows:
obtaining the residual memory data of the data storage module and marking the residual memory data as Nc; establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph;
marking the memory change rate as NBi; comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold value, intercepting and marking a corresponding curve segment, and recording the curve segment as a propagation curve segment;
counting the number of propagation curve segments as C1 in a preset time period; integrating all propagation curve segments with respect to time to obtain a propagation reference area M1; calculating by using a formula cs=c1×r3+m1×r4 to obtain a propagation evaluation index Cs, wherein r3 and r4 are coefficient factors;
calculating a spare coefficient KY of the data storage module by using a formula KY= (Nc×r5)/(Cs×r6), wherein r5 and r6 are coefficient factors; comparing the redundancy coefficient KY with a preset first redundancy threshold; and if KY is smaller than a preset first spare threshold value, generating a cleaning signal.
Further, the specific verification steps of the password verification module are as follows:
step one: collecting an access password set by a user, wherein the access password is a plurality of characters;
numbering a plurality of characters of the access password by Arabic numerals according to the input sequence, and marking the character number as i; i=1, 2, …, n; obtaining n characters of the access password according to the n values;
step two: classifying a plurality of characters of the access password according to character categories, wherein the character categories comprise letters, numbers and special symbols; obtaining the number of character categories of the access password as Z1; counting the number of times of the same character in the access password as the repetition frequency P1;
step three: normalizing the character length, the character class number and the repetition frequency and taking the numerical value; and calculating the rich value FZ of the access password by using a formula FZ= (n multiplied by a1+Z1 multiplied by a 2)/(P1 multiplied by a 3), wherein a1, a2 and a3 are all preset coefficient factors.
Further, the data auditing module is internally provided with a limited vocabulary for auditing the storage file; the method specifically comprises the following steps:
the data auditing module is used for matching the storage file with the restricted vocabulary, and if the matching is consistent, the auditing is not passed, otherwise, the auditing is passed;
and comparing the matched storage file with the file in the data storage module for checking, and if the repetition rate exceeds the set threshold, checking is not passed, otherwise, checking is passed.
Further, the system also comprises a password setting module and a login access module; the password setting module is used for setting an access password by a user; the login access module is used for inputting an access password by a user to login the personal computer.
Compared with the prior art, the invention has the beneficial effects that:
1. the password verification module is used for carrying out richness verification on the access password set by the user, detecting the qualification of the password through a machine learning method, and if the password is not qualified, returning to the password setting module to prompt the user to reset or modify; the complexity of the access password is guaranteed, the difficulty of password cracking is improved, and therefore the data security is enhanced; the data auditing module is used for auditing the storage file uploaded by the data uploading module; the adoption of the double auditing mode of limiting vocabulary and comparison check ensures the health of the content of the data file, avoids the occupation of the storage space by repeated files, and reduces the storage pressure of a computer;
2. the data monitoring module is used for searching, referring and monitoring the storage file in the data storage module, and calculating to obtain an observation coefficient GC of the storage file; the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient, and generating a cleaning signal if the spare coefficient KY is smaller than a preset first spare threshold value; the data cleaning module is used for cleaning the storage files in the data storage module after receiving the cleaning signal, and sequencing the storage files according to the sequence of the storage priority value YS from high to low; marking the storage files of the X1 before sequencing as backup files, transmitting the backup files to a local disk of a computer for backup and deleting the backup files; if the spare coefficient KY is larger than a preset second spare threshold value, cleaning is completed; if not, deleting the storage files from back to front in sequence according to the sequence until the redundancy coefficient KY is larger than a preset second redundancy threshold; the storage pressure of the computer is effectively reduced, and the data storage efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a personal computer storage management system based on machine learning according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, 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.
As shown in fig. 1, a personal computer storage management system based on machine learning includes a password setting module, a login access module, a password verification module, a data uploading module, a data auditing module, a data storage module, a data monitoring module, a cloud platform, a memory monitoring module and a data cleaning module;
the password setting module is used for setting an access password by a user; the login access module is used for inputting an access password by a user to login the personal computer;
the password verification module is connected with the password setting module and is used for carrying out complexity verification on the access password set by the user, carrying out qualification detection on the password through a machine learning method, and if the password is not qualified, returning to the password setting module to prompt the user to reset or modify; the specific verification steps are as follows:
step one: collecting an access password set by a user, wherein the access password is a plurality of characters;
numbering a plurality of characters of the access password by Arabic numerals according to the input sequence, and marking the character number as i; i=1, 2, …, n; obtaining n characters of the access password according to the n values;
step two: classifying a plurality of characters of the access password according to character categories, wherein the character categories comprise letters (case and case distinguishing), numbers, special symbols and the like; obtaining the number of character categories of the access password as Z1; counting the number of times of the same character in the access password as the repetition frequency P1;
step three: normalizing the character length, the character class number and the repetition frequency and taking the numerical value; calculating to obtain a rich value FZ of the access password by using a formula FZ= (n multiplied by 1+Z1 multiplied by a 2)/(P1 multiplied by a 3), wherein a1, a2 and a3 are all preset coefficient factors;
step four: comparing the rich value FZ with a preset rich threshold value;
if the rich value FZ is more than or equal to a preset rich threshold value, the access password is detected to be qualified; if the rich value FZ is smaller than the preset rich threshold value, the access password is unqualified in detection, and the user is prompted to reset or modify;
according to the invention, the complexity of the access password set by the user is checked through the password checking module, and the qualification of the password is detected through the machine learning method, so that the complexity of the access password is ensured, the difficulty of password cracking is improved, and the data security is enhanced;
the data uploading module is used for providing an interface for uploading the storage file by the user and transmitting the storage file to the data auditing module; the storage file contains importance level information;
the data auditing module is used for auditing the storage file uploaded by the data uploading module; the data auditing module is internally provided with a limited vocabulary;
the data auditing module is used for matching the storage file with the restricted vocabulary, and if the matching is consistent, the auditing is not passed, otherwise, the auditing is passed; comparing the matched storage file with the file in the data storage module for checking, and if the repetition rate exceeds a set threshold, checking is not passed, otherwise, checking is passed; the invention adopts a double auditing mode of limiting vocabulary and comparison check through the data auditing module, thereby not only ensuring the health of the content of the data file, but also avoiding the occupation of the storage space by the repeated file and reducing the storage pressure of the computer;
the data storage module is used for storing the storage files which pass the verification of the data verification module; the data monitoring module is connected with the data storage module and is used for searching, referring and monitoring the storage file in the data storage module; the specific monitoring steps are as follows:
when the stored file is searched and referred, automatically counting down, wherein the counting down time is T2 time, and T2 is a preset value; for example, T2 is 2 hours;
the storage file is continuously searched, referred and monitored in the countdown stage, when the storage file is searched, referred and monitored again, the countdown is automatically classified as the original value, and the countdown is carried out again according to T2; otherwise, the countdown returns to zero, and the timing is stopped;
counting the searching and consulting times of the storage file in the countdown stage as searching frequency JP1, and counting the duration of the countdown stage as searching duration JPT; the observation coefficient GC of the storage file is obtained through calculation by using a formula GC=JP 1 xr1+JPT x r 2; wherein r1 and r2 are coefficient factors; the data monitoring module is used for marking a time stamp on an observation coefficient GC of the storage file and storing the observation coefficient GC to the cloud platform;
the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient to judge whether the data storage module needs cleaning; the specific analysis steps are as follows:
obtaining the residual memory data of the data storage module and marking the residual memory data as Nc;
establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph; marking the memory change rate as NBi;
comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold value, corresponding curve segments are intercepted in the corresponding curve graph and marked, and the curve segments are marked as propagation curve segments;
counting the number of propagation curve segments as C1 in a preset time period; integrating all propagation curve segments with respect to time to obtain a propagation reference area M1; calculating by using a formula cs=c1×r3+m1×r4 to obtain a propagation evaluation index Cs, wherein r3 and r4 are coefficient factors;
calculating by using a formula KY= (Nc×r5)/(Cs×r6) to obtain a spare coefficient KY of the data storage module, wherein r5 and r6 are coefficient factors; comparing the redundancy coefficient KY with a preset first redundancy threshold; if KY is smaller than a preset first vacant threshold value, generating a cleaning signal;
the memory monitoring module is used for sending a cleaning signal to the data cleaning module; the data cleaning module is used for cleaning the storage files in the data storage module, and the specific cleaning steps are as follows:
for any storage file, acquiring importance level information of the storage file, and marking the importance level information as Di; marking the data volume of the storage file as LZ; marking the storage time length of the storage file as LT; automatically retrieving an observation coefficient GC of a storage file from a cloud platform;
calculating to obtain a storage priority value YS of the storage file by using a formula YS= (Di x k1+ GC x k 2)/(LZ x k3+ LT x k 4), wherein k1, k2, k3 and k4 are coefficient factors; sorting the storage files according to the order of the storage priority values YS from large to small;
when the data cleaning module receives a cleaning signal, marking the storage files of the X1 before sequencing as backup files, transmitting the backup files to a local disk of a computer for backup, and deleting the corresponding backup files from the data storage module; wherein X1 is a preset value;
continuously observing the spare coefficient KY of the data storage module, and if the spare coefficient KY is larger than a preset second spare threshold value, completing cleaning; if not, deleting the storage files from back to front in sequence according to the sequence until the redundancy coefficient KY is larger than a preset second redundancy threshold; wherein the second slack threshold is greater than the first slack threshold; the storage pressure of the computer is effectively reduced, and the data storage efficiency is improved.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the personal computer storage management system based on machine learning is characterized in that when in work, a password verification module is used for carrying out richness verification on an access password set by a user, the password is subjected to qualification detection through a machine learning method, and if the password is not qualified, a password setting module is returned to prompt the user to reset or modify; the complexity of the access password is guaranteed, the difficulty of password cracking is improved, and therefore the data security is enhanced; the data auditing module is used for auditing the storage file uploaded by the data uploading module; the adoption of the double auditing mode of limiting vocabulary and comparison check ensures the health of the content of the data file, avoids the occupation of the storage space by repeated files, and reduces the storage pressure of a computer;
the data monitoring module is used for searching, consulting and monitoring the storage file in the data storage module, and calculating to obtain an observation coefficient GC of the storage file; the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient, and generating a cleaning signal if the spare coefficient KY is smaller than a preset first spare threshold value; the data cleaning module is used for cleaning the storage files in the data storage module after receiving the cleaning signal, and sequencing the storage files according to the sequence of the storage priority value YS from large to small; marking the storage files of the X1 before sequencing as backup files, transmitting the backup files to a local disk of a computer for backup and deleting the backup files; if the spare coefficient KY is larger than a preset second spare threshold value, cleaning is completed; if not, deleting the storage files from back to front in sequence according to the sequence until the redundancy coefficient KY is larger than a preset second redundancy threshold; the storage pressure of the computer is effectively reduced, and the data storage efficiency is improved.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The personal computer storage management system based on machine learning is characterized by comprising a password verification module, a data uploading module, a data storage module, a data monitoring module, a memory monitoring module and a data cleaning module;
the password verification module is used for carrying out richness verification on the access password set by the user, and calculating to obtain a richness value FZ of the access password; if the rich value FZ is more than or equal to a preset rich threshold value, the access password is detected to be qualified; otherwise, the access password is unqualified to prompt the user to reset or modify;
the data uploading module is used for providing an interface for uploading the storage file by a user and transmitting the storage file to the data auditing module; the storage file contains importance level information; the data storage module is used for storing the storage files which pass the verification of the data verification module;
the memory monitoring module is used for monitoring the residual memory data of the data storage module and analyzing the spare coefficient KY to judge whether the data storage module needs cleaning; if KY is smaller than a preset first spare threshold value, generating a cleaning signal and sending the cleaning signal to a data cleaning module;
the data cleaning module is used for cleaning the storage files in the data storage module, and specifically comprises the following steps:
when a cleaning signal is received, sorting the storage files according to the sequence of the storage priority YS from large to small; marking the storage files of the X1 before sequencing as backup files, transmitting the backup files to a local disk of a computer for backup and deleting the backup files; wherein X1 is a preset value;
continuously observing the spare coefficient KY of the data storage module, and if the spare coefficient KY is larger than a preset second spare threshold value, completing cleaning; if not, deleting the storage files from back to front in sequence according to the sequence until the redundancy coefficient KY is larger than a preset second redundancy threshold; wherein the second slack threshold is greater than the first slack threshold.
2. The machine learning based personal computer storage management system of claim 1, wherein the data cleansing module further comprises:
for any storage file, acquiring importance level information of the storage file, and marking the importance level information as Di; marking the data volume of the storage file as LZ; marking the storage time length of the storage file as LT; automatically calling the observation coefficient GC of the storage file from the cloud platform;
and calculating the storage priority value YS of the storage file by using a formula YS= (Di x k1+ GC x k 2)/(LZ x k3+ LT x k 4), wherein k1, k2, k3 and k4 are coefficient factors.
3. The personal computer storage management system based on machine learning according to claim 2, wherein the data monitoring module is configured to search, review and monitor a storage file in the data storage module, calculate an observation coefficient GC of the storage file, and specifically monitor the following steps:
when the stored file is searched and referred, automatically counting down, wherein the counting down time is T2 time, and T2 is a preset value; continuing to search, review and monitor the stored file in the countdown stage;
when the stored file is searched and referred again, the countdown is automatically classified as the original value, and the countdown is performed again according to T2; otherwise, the countdown returns to zero, and the timing is stopped;
counting the searching and consulting times of the storage file in the countdown stage as searching frequency JP1, and counting the duration of the countdown stage as searching duration JPT; the observation coefficient GC of the storage file is obtained through calculation by using a formula GC=JP 1 xr1+JPT x r 2; wherein r1 and r2 are coefficient factors; the data monitoring module is used for stamping a time stamp on the observation coefficient GC of the storage file and storing the time stamp to the cloud platform.
4. The machine learning based personal computer storage management system of claim 1, wherein the specific analysis steps of the memory monitoring module are:
obtaining the residual memory data of the data storage module and marking the residual memory data as Nc; establishing a graph of the change of the remaining memory data Nc along with time, and deriving the graph to obtain a memory change rate graph;
marking the memory change rate as NBi; comparing NBi with a preset rate threshold; if NBi is more than or equal to a preset rate threshold value, intercepting and marking a corresponding curve segment, and recording the curve segment as a propagation curve segment;
counting the number of propagation curve segments as C1 in a preset time period; integrating all propagation curve segments with respect to time to obtain a propagation reference area M1; calculating by using a formula cs=c1×r3+m1×r4 to obtain a propagation evaluation index Cs, wherein r3 and r4 are coefficient factors;
calculating a spare coefficient KY of the data storage module by using a formula KY= (Nc×r5)/(Cs×r6), wherein r5 and r6 are coefficient factors; comparing the redundancy coefficient KY with a preset first redundancy threshold; and if KY is smaller than a preset first spare threshold value, generating a cleaning signal.
5. The personal computer storage management system based on machine learning of claim 1, wherein the specific verification steps of the password verification module are:
step one: collecting an access password set by a user, wherein the access password is a plurality of characters;
numbering a plurality of characters of the access password by Arabic numerals according to the input sequence, and marking the character number as i; i=1, 2, …, n; obtaining n characters of the access password according to the n values;
step two: classifying a plurality of characters of the access password according to character categories, wherein the character categories comprise letters, numbers and special symbols; obtaining the number of character categories of the access password as Z1; counting the number of times of the same character in the access password as the repetition frequency P1;
step three: normalizing the character length, the character class number and the repetition frequency and taking the numerical value; and calculating the rich value FZ of the access password by using a formula FZ= (n multiplied by a1+Z1 multiplied by a 2)/(P1 multiplied by a 3), wherein a1, a2 and a3 are all preset coefficient factors.
6. The personal computer storage management system based on machine learning of claim 1, wherein the data auditing module is internally provided with a limited vocabulary for auditing a storage file; the method specifically comprises the following steps:
the data auditing module is used for matching the storage file with the restricted vocabulary, and if the matching is consistent, the auditing is not passed, otherwise, the auditing is passed;
and comparing the matched storage file with the file in the data storage module for checking, and if the repetition rate exceeds the set threshold, checking is not passed, otherwise, checking is passed.
7. The machine learning based personal computer storage management system of claim 1, further comprising a password setup module and a login access module; the password setting module is used for setting an access password by a user; the login access module is used for inputting an access password by a user to login the personal computer.
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| CN111090395A (en) * | 2019-12-09 | 2020-05-01 | 张艺馨 | Intelligent electronic information storage system for accounting industry |
| CN112259170A (en) * | 2020-10-27 | 2021-01-22 | 大连理工大学 | Biological information analysis platform based on machine learning algorithm |
| CN113254978A (en) * | 2021-06-24 | 2021-08-13 | 国能大渡河大数据服务有限公司 | Data security management system based on machine learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111090395A (en) * | 2019-12-09 | 2020-05-01 | 张艺馨 | Intelligent electronic information storage system for accounting industry |
| CN112259170A (en) * | 2020-10-27 | 2021-01-22 | 大连理工大学 | Biological information analysis platform based on machine learning algorithm |
| CN113254978A (en) * | 2021-06-24 | 2021-08-13 | 国能大渡河大数据服务有限公司 | Data security management system based on machine learning |
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