CN117591337B - Computer information data interactive transmission management system and method - Google Patents
Computer information data interactive transmission management system and method Download PDFInfo
- Publication number
- CN117591337B CN117591337B CN202410064688.3A CN202410064688A CN117591337B CN 117591337 B CN117591337 B CN 117591337B CN 202410064688 A CN202410064688 A CN 202410064688A CN 117591337 B CN117591337 B CN 117591337B
- Authority
- CN
- China
- Prior art keywords
- data
- storage medium
- importance
- medium
- analysis model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 63
- 230000002452 interceptive effect Effects 0.000 title claims abstract description 18
- 238000000034 method Methods 0.000 title claims description 31
- 238000004458 analytical method Methods 0.000 claims abstract description 95
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 66
- 238000007405 data analysis Methods 0.000 claims abstract description 34
- 238000007726 management method Methods 0.000 claims abstract description 28
- 230000002159 abnormal effect Effects 0.000 claims description 35
- 238000001514 detection method Methods 0.000 claims description 13
- 238000010276 construction Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 abstract description 16
- 238000013523 data management Methods 0.000 abstract description 2
- 230000008569 process Effects 0.000 description 10
- 230000007246 mechanism Effects 0.000 description 9
- 238000012937 correction Methods 0.000 description 6
- 238000012546 transfer Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000001010 compromised effect Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013524 data verification Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000006249 magnetic particle Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/08—Error detection or correction by redundancy in data representation, e.g. by using checking codes
- G06F11/10—Adding special bits or symbols to the coded information, e.g. parity check, casting out 9's or 11's
- G06F11/1004—Adding special bits or symbols to the coded information, e.g. parity check, casting out 9's or 11's to protect a block of data words, e.g. CRC or checksum
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3037—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a memory, e.g. virtual memory, cache
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Computer Security & Cryptography (AREA)
- Mathematical Physics (AREA)
- Signal Processing For Digital Recording And Reproducing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a computer information data interactive transmission management system and a computer information data interactive transmission management method, which relate to the technical field of computer data management, wherein the use state of a storage medium is estimated through a storage medium analysis model, when the use state of the storage medium is estimated to be poor, cyclic redundancy check is carried out on a whole data set so as to ensure the integrity of data, when the use state of the storage medium is estimated to be excellent, cyclic redundancy check is carried out on data frames in a high-importance cluster, so that the cyclic redundancy check is carried out on the data frames with high importance in the data set, the data processing capacity is reduced, the data processing efficiency is effectively improved, the use state of the storage medium is estimated, and when the use state of the storage medium is estimated, a secondary analysis model is built by fusing the storage medium analysis model and the data analysis model, and whether the cyclic redundancy check is needed for the data frames in a medium-importance cluster is judged through the secondary analysis model. The management method can automatically analyze whether the cyclic redundancy check is needed to be carried out on the data set, and effectively improves the processing efficiency of the data.
Description
Technical Field
The invention relates to the technical field of computer data management, in particular to a computer information data interactive transmission management system and a computer information data interactive transmission management method.
Background
Data interactive transmission management refers to a series of management and control processes that implement efficient and secure transfer of information and data between different components, devices or systems in a computer system, and computer storage media are physical devices or media for storing and retrieving data, and these media generally fall into two main categories: main memory (RAM) and secondary memory (magnetic disks, solid state disks, optical disks, etc.).
The prior art has the following defects:
the computer realizes the long-term storage of data by means of the auxiliary memory, and adopts a Cyclic Redundancy Check (CRC) technology to detect potential errors in the data interactive transmission process, however, in the existing data transmission scene, whether the CRC technology is applied for data verification depends on the selection of a user, in the process of mass data transmission, not all data need to be subjected to CRC verification, if the user judges the data to be verified by himself, the work load is increased, and if all data are selected for verification, the data processing amount is increased, the work efficiency is reduced and the computer load is increased.
Disclosure of Invention
The invention aims to provide a computer information data interactive transmission management system and a computer information data interactive transmission management method, so as to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: the management method for computer information data interactive transmission comprises the following steps:
the management system acquires storage medium information of data based on the information of the data set according to the information of the data set required to be transmitted by a user;
evaluating the use state of the storage medium through a storage medium analysis model, and judging that the cyclic redundancy check is required to be carried out on the whole data set in the data set transmission process when the use state of the storage medium is evaluated;
dividing the data set into a plurality of data frames according to the structural characteristics of the data set, analyzing the importance of the data frames through a data analysis model, and dividing all the data frames in the data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results;
when the use state of the storage medium is evaluated to be excellent, performing cyclic redundancy check on the data frames in the high importance clusters in the data set transmission process;
a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model;
and when the use state of the storage medium is evaluated, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters or not through a secondary analysis model.
In a preferred embodiment, the management system obtains the storage medium information of the data based on the information of the data set according to the information of the data set required to be transmitted by the user, and the method comprises the following steps:
acquiring the storage medium position of a data set to be transmitted, and then acquiring storage medium information, wherein the storage medium information comprises a bad track rate, a real-time magnetic field interference degree and sector reading assignment;
the calculation logic for sector read assignment is:
reading and writing each sector on the storage medium through a hard disk detection tool on a computer, and acquiring the reading and writing time length of each sector in the storage medium;
calculating standard deviation of read-write time length and average read-write time length of all sectors in a storage medium;
if the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is less than or equal to the standard deviation threshold value, indicating that all the sectors of the storage medium are not abnormal, and assigning SDZ=2.0 to the sector reading;
if the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is more than the standard deviation threshold value, indicating that all the sectors of the storage medium are slightly abnormal, and assigning SDZ=1.8 to the sector reading;
if the average reading and writing time length is greater than the time length threshold value and the standard deviation of the reading and writing time length is greater than the standard deviation threshold value, indicating that the middles of all the sectors of the storage medium are abnormal, the sector reading assignment SDZ=1.0;
If the average read-write time length is greater than the time length threshold and the standard deviation of the read-write time length is less than or equal to the standard deviation threshold, indicating that all the sectors of the storage medium are severely abnormal, assigning SDZ=0.0 to the sector reading.
In a preferred embodiment, the standard deviation of the read-write time lengths and the average read-write time length of all the sectors are expressed as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the standard deviation of the read-write time length, +.>For the average read-write duration,n represents the number of sectors in the storage medium, < >>Indicating the read-write duration of the i-th sector.
In a preferred embodiment, evaluating the usage status of the storage medium by the storage medium analytical model comprises the steps of:
before data transmission, obtaining the bad track rate, the real-time magnetic field interference degree and sector reading assignment of a storage medium;
substituting the bad track rate, the real-time magnetic field interference degree and the sector reading assignment into a storage medium analysis model for analysis, and outputting a medium coefficient by the storage medium analysis model;
if the medium coefficient is more than or equal to the second abnormal threshold value, evaluating the use state of the storage medium;
if the medium coefficient is smaller than the second abnormal threshold value and the medium coefficient is larger than or equal to the first abnormal threshold value, evaluating the use state of the storage medium, and the like;
and if the medium coefficient is smaller than the first abnormal threshold value, evaluating the use state difference of the storage medium.
In a preferred embodiment, grouping all data frames within the data set into low importance clusters, medium importance clusters, and high importance clusters, respectively, according to the analysis result comprises the steps of:
acquiring the data quantity SJL and the association degree GLD of each data frame, substituting the data quantity SJL and the association degree GLD of the data frame into a data analysis model for analysis, and then outputting a data frame coefficient;
if the data frame coefficient of the data frame is more than or equal to the second importance threshold value, the data frame is divided into high importance clusters;
if the data frame coefficient of the data frame is smaller than the first importance threshold value, dividing the data frame into low importance clusters;
if the data frame coefficient of the data frame is more than or equal to the first importance threshold value and the data frame coefficient is less than the second importance threshold value, the data frame is divided into medium importance clusters.
In a preferred embodiment, fusing the storage medium analysis model with the data analysis model to construct a secondary analysis model comprises the steps of:
obtaining a medium coefficient in a storage medium analysis model, and obtaining a data frame coefficient in a data analysis model;
weighting and comprehensive calculating medium coefficient and data frame coefficient to obtain judgment indexThe expression is:wherein->Is a medium coefficient- >For data frame coefficients>、/>Weights of medium coefficients and data frame coefficients, respectively, and +.>；
Obtaining a judgment indexAfter that, judge index->And comparing the model with a preset judgment threshold value to complete the construction of a secondary analysis model.
In a preferred embodiment, evaluating the use state of the storage medium, etc., determining whether the cyclic redundancy check is required for the data frames in the medium importance clusters by the secondary analysis model includes the steps of:
when the use state of the storage medium is evaluated, the cyclic redundancy check is required to be performed on the data frames in the high-importance clusters, and the cyclic redundancy check is not required to be performed on the data frames in the low-importance clusters;
acquiring a data frame coefficient of a data frame in the medium importance cluster and a medium coefficient of a medium storage medium in a use state;
substituting the data frame coefficient and the medium coefficient into a secondary analysis model for analysis, and outputting a judgment index；
If judge the indexJudging the data frames in the medium importance clusters need to be subjected to cyclic redundancy check when the use state of the storage medium is evaluated and the like;
if judge the indexThe judgment threshold value is less than the judgment threshold value, and the cyclic redundancy check is not required for the data frames in the medium importance clusters when the use state of the storage medium is evaluated.
In a preferred embodiment, dividing the data set into a number of data frames according to the structural features of the data set comprises the steps of:
defining a structure of each data frame, wherein the structure comprises the size of the data frame, the type and the sequence of the fields;
dividing the whole data set into a plurality of data frames according to the defined data frame structure;
adding frame header and frame tail information for each data frame, wherein the frame header and the frame tail comprise identifiers, length information and check codes;
a unique sequence number is marked for the data frame to which the frame header and the frame trailer are added.
The invention also provides a computer information data interaction transmission management system which comprises a data acquisition module, a storage medium analysis module, a primary judgment module, a data analysis module, a data classification module, a secondary judgment module, a model construction module and a tertiary judgment module:
and a data acquisition module: acquiring storage medium information of data based on information of the data set;
storage medium analysis module: evaluating the use state of the storage medium through a storage medium analysis model;
and a primary judgment module: when the use state difference of the storage medium is evaluated, judging that the cyclic redundancy check is required to be carried out on the whole data set in the data set transmission process;
And a data analysis module: dividing the data set into a plurality of data frames according to the structural characteristics of the data set, and analyzing the importance of the data frames through a data analysis model;
and a data classification module: dividing all data frames in a data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results;
and a secondary judgment module: when the use state of the storage medium is evaluated to be excellent, performing cyclic redundancy check on the data frames in the high importance clusters in the data set transmission process;
model construction module: a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model;
and a three-time judging module: and when the use state of the storage medium is evaluated, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters or not through a secondary analysis model.
In the technical scheme, the invention has the technical effects and advantages that:
according to the invention, the use state of the storage medium is estimated through the storage medium analysis model, when the use state of the storage medium is estimated to be poor, the cyclic redundancy check is carried out on the whole data set so as to ensure the integrity of data, and when the use state of the storage medium is estimated to be good, the cyclic redundancy check is carried out on the data frames in the high-importance clusters, so that the cyclic redundancy check is carried out on the data frames with high importance in the data sets, the data processing capacity is reduced, the processing efficiency of the data is effectively improved, the use state of the storage medium is estimated, and the like, the storage medium analysis model and the data analysis model are fused to construct a secondary analysis model, and whether the cyclic redundancy check is needed on the data frames in the medium-importance clusters is judged through the secondary analysis model. The management method can automatically analyze whether the cyclic redundancy check is needed to be carried out on the data set, and effectively improves the processing efficiency of the data.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. 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.
Example 1: referring to fig. 1, the method for managing interactive transmission of computer information data according to the present embodiment includes the following steps:
the management system obtains storage medium information of data based on information of a data set according to information of the data set required to be transmitted by a user, and the method comprises the following steps:
Acquiring the storage medium position of the data set to be transmitted, and then acquiring storage medium information, wherein the storage medium information comprises the bad track rateReal-time magnetic field interference CGR and sector reading assignment SDZ;
the calculation logic of the bad track rate is as follows: and (3) performing read-write test on the storage medium through an operating system on the computer, marking out bad tracks which cannot be operated normally, and then calculating the bad track rate of the storage medium after the total block number of the storage medium is acquired through the operating system, wherein the expression is as follows:wherein->For bad track rate->For the number of bad tracks detected in the storage medium, is->The greater the defect rate of the storage medium, the more likely the data is lost or damaged during transmission, specifically:
read-write error increases: the bad track is an area on the storage medium which cannot be read and written normally, when the bad track rate is increased, the bad track rate means that more damaged areas exist on the storage medium, so that more errors can occur during read-write operation, the data on the bad track cannot be read correctly during reading, and the data can not be stored normally during writing, thereby causing data damage;
the error detection mechanism fails: storage media typically use error detection and correction mechanisms to handle bad tracks and other errors, however, when the bad track rate is too high, the error detection mechanism may not be able to effectively correct all errors, resulting in data loss or corruption in the transmission;
Data integrity is compromised: the increase of the bad track rate can reduce the data integrity of the storage medium, even if the data on the storage medium is not directly affected, the existence of the bad track can cause the related data transmission operation to become unreliable, and the risk of error occurrence in the data transmission process is increased;
affecting file system stability: the existence of a large number of bad tracks on the storage medium can influence the stability of a file system, the file system needs to manage and maintain the structure of files, when the bad track rate is too high, the file system can be abnormal, and the possibility of data loss or file system damage is increased;
the transmission speed decreases: the storage medium with the bad track needs extra time to process errors when performing read-write operation, the overall performance of the storage device is reduced due to the increase of the bad track rate, the transmission speed is slowed down, and the time window for errors in the transmission process is increased;
the service life of the hard disk is shortened: the existence of the bad track may accelerate the loss of the storage medium, shorten the life of the hard disk, and when the life of the hard disk is weakened, the stability and reliability of data storage are weakened.
The acquisition logic of the real-time magnetic field interference degree is as follows: the method comprises the steps of installing a selected magnetic field sensor near a computer storage medium or at a position possibly influenced by a magnetic field, ensuring that the position and the direction of the sensor are sensitive to magnetic field interference of a monitoring target, connecting the magnetic field sensor to an operating system of the computer, wherein the operating system is responsible for reading output signals of the sensor, the output signals are realized through an analog-to-digital converter (ADC) or other data acquisition equipment, a management system acquires real-time magnetic field interference CGR after acquiring measurement data of the magnetic field sensor from the operating system of the computer through an API (application program interface), and the greater the real-time magnetic field interference of the storage medium is, the more easily the data is lost or damaged in the transmission process, namely:
Magnetic field disturbances cause bit flipping: the high magnetic field interference may cause bit flipping of magnetic particles in the storage medium, changing the originally stored data, which may cause errors in data reading, even complete incorrect reading;
magnetic field disturbances lead to write errors: when writing data, magnetic field disturbances may cause the written data to be inaccurate, thereby introducing errors in the storage medium, which may cause the data to be corrupted during transmission;
magnetic field disturbances affect head positioning: in storage devices such as hard disk drives, the positioning of the read-write head is critical for accurately reading and writing data, and the interference of a strong magnetic field may cause the position of the read-write head to deviate, so as to influence the accurate access to the sector;
magnetic field disturbances cause data misalignments: the magnetic field disturbance may cause data on the storage medium to be misplaced, i.e. the data is wrongly written to the wrong location, and when the data is read, the wrong content may be read, resulting in data corruption;
error detection correction failure: storage media typically use error detection and correction mechanisms to handle errors in data transmissions, however, strong magnetic field disturbances may make these mechanisms ineffective in detecting and correcting errors, resulting in data loss or corruption in transmission;
Magnetic field disturbances cause equipment failure: the strong magnetic field interference can negatively affect the normal operation of the storage device, even cause device failure, and the device failure can cause the storage medium to be unable to read and write data normally, thereby causing data damage;
loss of synchronization during data transmission: the magnetic field interference may cause signals on the storage medium to be out of synchronization, so that data cannot be correctly parsed in the transmission process, which may cause the receiving end to be unable to correctly restore the original data, thereby causing data loss or damage.
The calculation logic for sector read assignment is:
reading and writing each sector on the storage medium through a hard disk detection tool on a computer, and acquiring the reading and writing time length of each sector in the storage medium;
in the storage medium, the standard deviation of the read-write time length and the average read-write time length of all sectors are calculated, and the calculation expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the standard deviation of the read-write time length, +.>For average read-write duration +.>N represents the number of sectors in the storage medium, < >>Indicating the read-write time length of the ith sector;
if the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is less than or equal to the standard deviation threshold value, analyzing that the reading and writing speeds of all the sectors in the storage medium are high and stable, and indicating that all the sectors of the storage medium are not abnormal, and assigning SDZ=2.0 to the sector reading;
If the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is greater than the standard deviation threshold value, analyzing that the reading and writing speed of all the sectors in the storage medium is high, but the reading and writing speed is unstable (namely, the reading and writing time length of partial sectors is greater than the time length threshold value), indicating that all the sectors of the storage medium are slightly abnormal, and assigning SDZ=1.8 to the sector reading;
if the average reading and writing time length is greater than the time length threshold value and the standard deviation of the reading and writing time length is greater than the standard deviation threshold value, analyzing that the reading and writing speeds of all the sectors in the storage medium are slow, but unstable (namely, the reading and writing time length of partial sectors is less than or equal to the time length threshold value), indicating that the middle degree of all the sectors of the storage medium is abnormal, and assigning SDZ=1.0 to the sector reading;
if the average reading and writing time length is greater than the time length threshold value and the standard deviation of the reading and writing time length is less than or equal to the standard deviation threshold value, analyzing that the reading and writing speeds of all the sectors in the storage medium are slow, indicating that the severity of all the sectors of the storage medium is abnormal, and assigning SDZ=0.0 to the sector reading;
the smaller the sector read assignment, the more likely an entire sector in the storage medium will fail, and the more likely it will cause data to be lost or corrupted during transmission, specifically:
the data is not readable: an overall sector failure may cause data on the storage medium to become unreadable, and when a sector in the storage medium is damaged or cannot be read normally, the relevant data cannot be read correctly, thereby causing data loss;
Data write errors: sector failures may cause errors in writing data, resulting in data being written to the storage medium incorrectly, which may cause data corruption during transmission, as the original data cannot be correctly restored upon reading;
error detection and correction failure: storage media typically use error detection and correction mechanisms to handle errors at the sector level, however, when an overall sector failure occurs, error detection and correction may become ineffective, failing to properly detect and repair errors in the data transmission;
data dislocation: an overall sector failure may cause data misplacement on the storage medium, i.e., data is wrongly written to the wrong sector location, and when data is read, wrong content may be read, resulting in data corruption;
transmission interruption: an overall sector failure of the storage medium may cause an interruption in the data transfer process, and when the storage medium fails to provide normal data access, the data transfer may be interrupted, resulting in data loss;
file system errors: the failure of the whole sector can cause the damage of the file system structure, thereby affecting the files on the storage medium, and during the data transmission process, the files can not be read or written correctly due to the error of the file system;
Affecting the read-write performance of the magnetic disk: an overall sector failure may result in reduced overall performance of the storage device, and reduced efficiency of read and write operations, which may result in time-outs or other problems during data transmission, thereby affecting the normal transmission of data.
Evaluating the usage status of the storage medium by the storage medium analysis model, comprising the steps of:
obtaining the bad track rate of a storage medium before data transmissionReal-time magnetic field interference CGR and sector reading assignment SDZ;
substituting the bad track rate, the real-time magnetic field interference degree and the sector reading assignment into a storage medium analysis model for analysis, and outputting a medium coefficient by the storage medium analysis model;
if the medium coefficient is more than or equal to the second abnormal threshold value, evaluating the use state of the storage medium;
if the medium coefficient is smaller than the second abnormal threshold value and the medium coefficient is larger than or equal to the first abnormal threshold value, evaluating the use state of the storage medium, and the like;
if the medium coefficient is smaller than the first abnormal threshold value, evaluating the use state difference of the storage medium;
the establishment of the storage medium analysis model comprises the following steps:
after the normalized bad track rate and the real-time magnetic field interference degree are normalized, the medium coefficient is obtained by comprehensively calculating the bad track rate, the real-time magnetic field interference degree and the sector reading assignment The expression is: />In which, in the process,for the bad track rate, CGR is the real-time magnetic field interference degree, SDZ is the sector reading assignment, and +.>、/>Proportional coefficients of the bad track rate and the real-time magnetic field interference degree respectively, and +.>、/>Are all greater than 0;
from the medium coefficientThe calculation logic of (a) shows that the medium coefficient +.>The larger the value, the better the running state of the storage medium is, and therefore the medium coefficient to be obtained +.>Comparing the value with a preset first abnormal threshold value and a preset second abnormal threshold value to finish the establishment of a storage medium analysis model;
the first abnormal threshold value is smaller than the second abnormal threshold value, the second abnormal threshold value is used for distinguishing whether the use state of the storage medium is abnormal or not, and the first abnormal threshold value is used for distinguishing the abnormal severity degree of the storage medium;
after the normalized bad track rate and the real-time magnetic field interference degree are normalized, the medium coefficient is obtained by comprehensively calculating the bad track rate, the real-time magnetic field interference degree and the sector reading assignmentThe method not only can analyze the use state of the storage medium more comprehensively, but also can effectively improve the processing efficiency of the data.
When the use state of the storage medium is evaluated, it is judged that the cyclic redundancy check needs to be performed on the whole data set in the data set transmission process, and when the use state of the storage medium is evaluated, the problems of damage or loss and the like in the data set transmission process are easily caused, so that the cyclic redundancy check needs to be performed on the whole data set to ensure the integrity of data, and the method comprises the following steps:
CRC checking the full dataset: when the use of the storage medium is poor, considering a cyclic redundancy check for the entire data set, this may be achieved by calculating a CRC value for the data set and comparing it to an expected CRC value, which if a mismatch is found may indicate that some portion of the data set has been corrupted;
automatic checksum alarm: an automatic CRC check sum alarm system is realized, and if CRC check errors are found in the data transmission or storage process, an alarm is triggered in time so that an administrator can take measures to repair or restore the data;
backup strategy: considering to implement an effective backup strategy, ensuring that data can be restored rapidly when the data is found to be damaged, checking the integrity of the backup regularly, and testing the restoration process;
maintaining and updating the storage device: maintenance of the storage device, including firmware updates, driver updates, etc., is performed periodically to ensure that the device is able to function properly and has the latest security and integrity functions.
Dividing the data set into a plurality of data frames according to the structural characteristics of the data set, comprising the following steps:
defining a structure of a data frame: before dividing the data set, defining the structure of each data frame, which includes determining the size of the data frame, the type and sequence of the fields, etc., and designing the structure in consideration of the semantics and usage scenario of the data;
Determining the size of the frame: determining the size of each data frame, depending on the nature of the data set and the transmission requirements, the size of the frame should generally be small enough to accommodate the requirements of the transmission protocol, and large enough to transmit meaningful data blocks in each frame;
dividing the data set: dividing the whole data set into a plurality of data frames according to the defined data frame structure, and ensuring that each data frame contains enough information so as to correctly restore the original data at a receiving end;
adding a frame head and a frame tail: adding header and trailer information to each data frame so that a receiving end can identify the start and end positions of the frame, wherein the header and trailer generally comprise identifiers, length information, check codes and the like;
marking a frame number: if the data sets need to be transmitted in sequence, a unique serial number is marked for each data frame, which facilitates the receiving end to reassemble the data in the correct sequence;
checksum error detection: a checksum or other error detection mechanism is added to each data frame to help identify errors that may occur during transmission, which helps to ensure data integrity.
Analyzing the importance degree of the data frames through a data analysis model, and dividing all the data frames in the data set into a low importance degree cluster, a medium importance degree cluster and a high importance degree cluster according to analysis results, wherein the method comprises the following steps of:
Acquiring the data quantity SJL and the association degree GLD of each data frame, substituting the data quantity SJL and the association degree GLD of the data frame into a data analysis model for analysis, and then outputting a data frame coefficient;
if the data frame coefficient of the data frame is more than or equal to the second importance threshold value, the data frame is divided into high importance clusters;
if the data frame coefficient of the data frame is smaller than the first importance threshold value, dividing the data frame into low importance clusters;
if the data frame coefficient of the data frame is more than or equal to the first importance threshold value and the data frame coefficient is less than the second importance threshold value, dividing the data frame into medium importance clusters;
the establishment of the data analysis model comprises the following steps:
data frame coefficient is established after data quantity and association degree are standardizedThe expression is:wherein->、/>Data amount and association degree respectively +.>、/>Proportional coefficients of data quantity and correlation, respectively, and +.>、/>Are all greater than 0;
comparing the acquired data frame coefficient with a first importance threshold and a second importance threshold, and completing the establishment of a data analysis model, wherein the first importance threshold is smaller than the second importance threshold, and the first importance threshold and the second importance threshold are used for classifying the data frame;
the acquisition logic of the data size SJL is: the size of the divided data frame is obtained through the operating system of the computer, namely the data volume of the data frame.
The calculation logic of the association GLD is as follows: after the cosine similarity of the selected data frame and other data frames is obtained, the cosine similarity of the selected data frame and other data frames is added to obtain a correlation GLD; the expression is:in which, in the process,，/>for the number of data frames +.>Representing cosine similarity of the selected data frame and the ith data frame;
the cosine similarity of the selected data frame and any data frame is calculated as follows:
the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->Respectively the inner products of the selected data frame vector and any data frame vector,respectively selecting a data frame vector modular length and any data frame vector modular length;
the data frame vector is obtained by the following steps:
representation of data frames: first, it is necessary to determine how to represent the content of a data frame as a mathematical vector, involving mapping individual fields or features in the frame to elements of the vector;
feature extraction: extracting features from the data frame, the features being certain fields in the frame, statistical information, frequency domain information, etc., the choice of features being dependent on the requirements of the application and the nature of the data;
feature vector construction: the extracted features are combined into a vector, each feature can be seen as an element in the vector, which is the data frame vector.
When the use state of the storage medium is evaluated to be excellent, in the data set transmission process, the cyclic redundancy check is carried out on the data frames in the high importance clusters, so that the cyclic redundancy check is carried out on the data frames with high importance in the data sets, the data processing amount is reduced, and the data processing efficiency is effectively improved, and the method comprises the following steps:
making a cyclic redundancy check execution rule: formulating a rule for performing cyclic redundancy check based on the defined cyclic redundancy check policy and the marked data frame clusters, for example, performing cyclic redundancy check only on data frames in the high importance clusters;
and (3) performing cyclic redundancy check: in the process of data transmission or storage, cyclic redundancy check is carried out on the data frames in the high importance clusters according to the formulated rules, which may require an automatic check mechanism to be implemented in the system;
monitoring and alarming: in the cyclic redundancy check process, a monitoring and alarming mechanism is established, and the situation that the cyclic redundancy check fails or the state of a storage medium is poor is found in time so as to take further processing measures;
performance evaluation: system performance, including speed of data transfer, usage status of storage media, overhead of cyclic redundancy check, etc., is periodically evaluated to ensure efficient operation of the overall system.
And (3) fusing the storage medium analysis model and the data analysis model to construct a secondary analysis model, evaluating the use state of the storage medium, and judging whether cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters through the secondary analysis model.
According to the method, the use state of the storage medium is estimated through the storage medium analysis model, when the use state of the storage medium is estimated to be poor, cyclic redundancy check is carried out on the whole data set to ensure the integrity of data, when the use state of the storage medium is estimated to be good, cyclic redundancy check is carried out on data frames in a high-importance cluster, so that cyclic redundancy check is carried out on the data frames with high importance in the data set, the data processing capacity is reduced, the processing efficiency of the data is effectively improved, the use state of the storage medium is estimated, and the like, the storage medium analysis model and the data analysis model are fused to construct a secondary analysis model, and whether the cyclic redundancy check is needed on the data frames in a medium-importance cluster is judged through the secondary analysis model. The management method can automatically analyze whether the cyclic redundancy check is needed to be carried out on the data set, and effectively improves the processing efficiency of the data.
Example 2: a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model, the use state of the storage medium is evaluated, and the like, and whether cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters is judged through the secondary analysis model;
The method for constructing the secondary analysis model by fusing the storage medium analysis model and the data analysis model comprises the following steps of:
acquiring media coefficients in a storage media analysis modelAcquiring data frame coefficients +.>；
Weighting and comprehensive calculating medium coefficient and data frame coefficient to obtain judgment indexThe expression is:wherein->Is a medium coefficient->For data frame coefficients>、/>Weights of medium coefficients and data frame coefficients, respectively, and +.>；
Obtaining a judgment indexAfter that, judge index->Comparing the model with a preset judgment threshold value to complete the construction of a secondary analysis model;
evaluating the use state of the storage medium and the like, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters through a secondary analysis model comprises the following steps:
when the use state of the storage medium is evaluated, the cyclic redundancy check is required to be performed on the data frames in the high-importance clusters, and the cyclic redundancy check is not required to be performed on the data frames in the low-importance clusters;
acquiring a data frame coefficient of a data frame in the medium importance cluster and a medium coefficient of a medium storage medium in a use state;
substituting the data frame coefficient and the medium coefficient into a secondary analysis model for analysis, and outputting a judgment index If judging index->If the judgment threshold is not less than, judging that when the use state of the storage medium is evaluated, the cyclic redundancy check is required to be carried out on the data frames in the medium importance cluster, and if the judgment index is +>Judging when the judgment threshold is smaller than the judgment thresholdWhen the use state of the storage medium is in middle, the cyclic redundancy check is not needed to be carried out on the data frames in the medium importance clusters;
in the application, when a data frame exists in the middle importance cluster, the data frame in the middle importance cluster is in two selection states of performing cyclic redundancy check or not, and in order to further analyze whether the cyclic redundancy check is required to be performed on the data frame in the middle importance cluster, the importance of the data is judged by combining the importance of the data with the use state of a storage medium, so that the analysis accuracy of the data is improved.
Example 3: the computer information data interaction transmission management system comprises a data acquisition module, a storage medium analysis module, a primary judgment module, a data analysis module, a data classification module, a secondary judgment module, a model construction module and a tertiary judgment module:
and a data acquisition module: acquiring storage medium information of data based on information of a data set, and sending the storage medium information to a storage medium analysis module;
Storage medium analysis module: the using state of the storage medium is estimated through the storage medium analysis model, and the using state estimation result of the storage medium is sent to the primary judging module, the secondary judging module and the tertiary judging module;
and a primary judgment module: when the use state of the storage medium is evaluated, it is judged that the cyclic redundancy check needs to be performed on the whole data set in the data set transmission process, and when the use state of the storage medium is evaluated, the problems of damage or loss and the like easily occur in the data set transmission process, so that the cyclic redundancy check needs to be performed on the whole data set to ensure the integrity of data;
and a data analysis module: dividing the data set into a plurality of data frames according to the structural characteristics of the data set, analyzing the importance of the data frames through a data analysis model, and transmitting the data importance analysis result to a data classification module;
and a data classification module: dividing all data frames in a data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results, and sending data classification results to a secondary judgment module and a tertiary judgment module;
and a secondary judgment module: when the use state of the storage medium is evaluated to be excellent, in the data set transmission process, the cyclic redundancy check is carried out on the data frames in the high importance clusters, so that the cyclic redundancy check is carried out on the data frames with high importance in the data sets, the data processing amount is reduced, and the data processing efficiency is effectively improved;
Model construction module: a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model, and the secondary analysis model is sent to a tertiary judgment module;
and a three-time judging module: and when the use state of the storage medium is evaluated, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters or not through a secondary analysis model.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
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 (6)
1. The computer information data interactive transmission management method is characterized in that: the management method comprises the following steps:
the management system acquires storage medium information of data based on the information of the data set according to the information of the data set required to be transmitted by a user;
evaluating the use state of the storage medium through a storage medium analysis model, and judging that the cyclic redundancy check is required to be carried out on the whole data set in the data set transmission process when the use state of the storage medium is evaluated;
dividing the data set into a plurality of data frames according to the structural characteristics of the data set, analyzing the importance of the data frames through a data analysis model, and dividing all the data frames in the data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results;
When the use state of the storage medium is evaluated to be excellent, performing cyclic redundancy check on the data frames in the high importance clusters in the data set transmission process;
a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model;
when the use state of the storage medium is evaluated, judging whether cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters or not through a secondary analysis model;
the establishment of the storage medium analysis model comprises the following steps:
after the normalized bad track rate and the real-time magnetic field interference degree are normalized, the medium coefficient is obtained by comprehensively calculating the bad track rate, the real-time magnetic field interference degree and the sector reading assignmentThe expression is: />Wherein->For the bad track rate, CGR is the real-time magnetic field interference degree, SDZ is the sector reading assignment, and +.>、/>Proportional coefficients of the bad track rate and the real-time magnetic field interference degree respectively, and +.>、/>Are all greater than 0;
from the medium coefficientThe calculation logic of (a) shows that the medium coefficient +.>The larger the value, the better the running state of the storage medium is, and therefore the medium coefficient to be obtained +.>Comparing the value with a preset first abnormal threshold value and a preset second abnormal threshold value to finish the establishment of a storage medium analysis model;
The establishment of the data analysis model comprises the following steps:
data frame coefficient is established after data quantity and association degree are standardizedThe expression is: />Wherein->、/>Data amount and association degree respectively +.>、/>Proportional coefficients of data quantity and correlation, respectively, and +.>、/>Are all greater than 0;
comparing the acquired data frame coefficient with a first importance threshold and a second importance threshold to complete the establishment of a data analysis model;
evaluating the usage status of the storage medium by the storage medium analysis model includes the steps of:
before data transmission, obtaining the bad track rate, the real-time magnetic field interference degree and sector reading assignment of a storage medium;
substituting the bad track rate, the real-time magnetic field interference degree and the sector reading assignment into a storage medium analysis model for analysis, and outputting a medium coefficient by the storage medium analysis model;
if the medium coefficient is more than or equal to the second abnormal threshold value, evaluating the use state of the storage medium;
if the medium coefficient is smaller than the second abnormal threshold value and the medium coefficient is larger than or equal to the first abnormal threshold value, evaluating the use state of the storage medium, and the like;
if the medium coefficient is smaller than the first abnormal threshold value, evaluating the use state difference of the storage medium;
dividing all data frames in a data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results, wherein the method comprises the following steps of:
Acquiring the data quantity SJL and the association degree GLD of each data frame, substituting the data quantity SJL and the association degree GLD of the data frame into a data analysis model for analysis, and then outputting a data frame coefficient;
if the data frame coefficient of the data frame is more than or equal to the second importance threshold value, the data frame is divided into high importance clusters;
if the data frame coefficient of the data frame is smaller than the first importance threshold value, dividing the data frame into low importance clusters;
if the data frame coefficient of the data frame is more than or equal to the first importance threshold value and the data frame coefficient is less than the second importance threshold value, dividing the data frame into medium importance clusters;
the method for constructing the secondary analysis model by fusing the storage medium analysis model and the data analysis model comprises the following steps of:
obtaining a medium coefficient in a storage medium analysis model, and obtaining a data frame coefficient in a data analysis model;
weighting and comprehensive calculating medium coefficient and data frame coefficient to obtain judgment indexThe expression is:wherein->Is a medium coefficient->For data frame coefficients>、/>Weights of medium coefficients and data frame coefficients, respectively, and +.>；
Acquisition judgment fingerNumber of digitsAfter that, judge index->And comparing the model with a preset judgment threshold value to complete the construction of a secondary analysis model.
2. The computer information data interactive transmission management method according to claim 1, wherein: the management system obtains the storage medium information of the data based on the information of the data set according to the information of the data set required to be transmitted by the obtained user, and the method comprises the following steps:
Acquiring the storage medium position of a data set to be transmitted, and then acquiring storage medium information, wherein the storage medium information comprises a bad track rate, a real-time magnetic field interference degree and sector reading assignment;
the calculation logic for sector read assignment is:
reading and writing each sector on the storage medium through a hard disk detection tool on a computer, and acquiring the reading and writing time length of each sector in the storage medium;
calculating standard deviation of read-write time length and average read-write time length of all sectors in a storage medium;
if the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is less than or equal to the standard deviation threshold value, indicating that all the sectors of the storage medium are not abnormal, and assigning SDZ=2.0 to the sector reading;
if the average reading and writing time length is less than or equal to the time length threshold value and the standard deviation of the reading and writing time length is more than the standard deviation threshold value, indicating that all the sectors of the storage medium are slightly abnormal, and assigning SDZ=1.8 to the sector reading;
if the average reading and writing time length is greater than the time length threshold value and the standard deviation of the reading and writing time length is greater than the standard deviation threshold value, indicating that the middles of all the sectors of the storage medium are abnormal, the sector reading assignment SDZ=1.0;
if the average read-write time length is greater than the time length threshold and the standard deviation of the read-write time length is less than or equal to the standard deviation threshold, indicating that all the sectors of the storage medium are severely abnormal, assigning SDZ=0.0 to the sector reading.
3. The computer information data interactive transmission management method according to claim 2, wherein: the standard deviation of the read-write time length and the average read-write time length of all sectors are calculated as follows:the method comprises the steps of carrying out a first treatment on the surface of the In (1) the->For the standard deviation of the read-write time length, +.>For average read-write duration +.>N represents the number of sectors in the storage medium, < >>Indicating the read-write duration of the i-th sector.
4. A computer information data interactive transmission management method according to claim 3, characterized in that: evaluating the use state of the storage medium and the like, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters through a secondary analysis model comprises the following steps:
when the use state of the storage medium is evaluated, the cyclic redundancy check is required to be performed on the data frames in the high-importance clusters, and the cyclic redundancy check is not required to be performed on the data frames in the low-importance clusters;
acquiring a data frame coefficient of a data frame in the medium importance cluster and a medium coefficient of a medium storage medium in a use state;
substituting the data frame coefficient and the medium coefficient into a secondary analysis model for analysis, and outputting a judgment index；
If judge the indexJudging the data frames in the medium importance clusters need to be subjected to cyclic redundancy check when the use state of the storage medium is evaluated and the like;
If judge the indexThe judgment threshold value is less than the judgment threshold value, and the cyclic redundancy check is not required for the data frames in the medium importance clusters when the use state of the storage medium is evaluated.
5. The method for managing interactive transmission of computer information data according to claim 4, wherein: dividing the data set into a plurality of data frames according to the structural characteristics of the data set comprises the following steps:
defining a structure of each data frame, wherein the structure comprises the size of the data frame, the type and the sequence of the fields;
dividing the whole data set into a plurality of data frames according to the defined data frame structure;
adding frame header and frame tail information for each data frame, wherein the frame header and the frame tail comprise identifiers, length information and check codes;
a unique sequence number is marked for the data frame to which the frame header and the frame trailer are added.
6. A computer information data interactive transmission management system for implementing the management method of any one of claims 1 to 5, characterized in that: the system comprises a data acquisition module, a storage medium analysis module, a primary judgment module, a data analysis module, a data classification module, a secondary judgment module, a model construction module and a tertiary judgment module:
and a data acquisition module: acquiring storage medium information of data based on information of the data set;
Storage medium analysis module: evaluating the use state of the storage medium through a storage medium analysis model;
and a primary judgment module: when the use state difference of the storage medium is evaluated, judging that the cyclic redundancy check is required to be carried out on the whole data set in the data set transmission process;
and a data analysis module: dividing the data set into a plurality of data frames according to the structural characteristics of the data set, and analyzing the importance of the data frames through a data analysis model;
and a data classification module: dividing all data frames in a data set into a low importance cluster, a medium importance cluster and a high importance cluster according to analysis results;
and a secondary judgment module: when the use state of the storage medium is evaluated to be excellent, performing cyclic redundancy check on the data frames in the high importance clusters in the data set transmission process;
model construction module: a storage medium analysis model and a data analysis model are fused to construct a secondary analysis model;
and a three-time judging module: and when the use state of the storage medium is evaluated, judging whether the cyclic redundancy check is needed to be carried out on the data frames in the medium importance clusters or not through a secondary analysis model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410064688.3A CN117591337B (en) | 2024-01-17 | 2024-01-17 | Computer information data interactive transmission management system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410064688.3A CN117591337B (en) | 2024-01-17 | 2024-01-17 | Computer information data interactive transmission management system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117591337A CN117591337A (en) | 2024-02-23 |
| CN117591337B true CN117591337B (en) | 2024-03-29 |
Family
ID=89910221
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410064688.3A Active CN117591337B (en) | 2024-01-17 | 2024-01-17 | Computer information data interactive transmission management system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117591337B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112948281A (en) * | 2021-02-24 | 2021-06-11 | 上海商汤智能科技有限公司 | Data processing method, device, equipment and storage medium |
| CN115527604A (en) * | 2022-09-30 | 2022-12-27 | 苏州浪潮智能科技有限公司 | Hard disk detection method and device, electronic equipment and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080253256A1 (en) * | 2007-04-13 | 2008-10-16 | Andreas Eckleder | Apparatus for writing data and redundancy data on a storage medium |
| US11169726B2 (en) * | 2018-09-13 | 2021-11-09 | Toshiba Memory Corporation | Pool-level storage management |
-
2024
- 2024-01-17 CN CN202410064688.3A patent/CN117591337B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112948281A (en) * | 2021-02-24 | 2021-06-11 | 上海商汤智能科技有限公司 | Data processing method, device, equipment and storage medium |
| CN115527604A (en) * | 2022-09-30 | 2022-12-27 | 苏州浪潮智能科技有限公司 | Hard disk detection method and device, electronic equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| SoC 芯片的 RomCode 设计与 FPGA 验证研究;张梅娟;《电子设计工程》;20231115;1-6 * |
| USING CYCLIC REDUNDANCY CHECK TO ELIMINATE KEY STORAGE FOR REVOCABLE IRIS TEMPLATES;Marwa Fouad;《IEEE》;20111215;1-4 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117591337A (en) | 2024-02-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111104293A (en) | Method, apparatus and computer program product for supporting disk failure prediction | |
| CN102135925B (en) | Method and device for detecting error check and correcting memory | |
| CN104282342A (en) | Flash memory device, memory controller and control method of flash memory | |
| CN111949459B (en) | Hard disk failure prediction method and system based on transfer learning and active learning | |
| CN111813585A (en) | Prediction and processing of slow discs | |
| CN111309502A (en) | Solid state disk service life prediction method | |
| US6842867B2 (en) | System and method for identifying memory modules having a failing or defective address | |
| CN109919335A (en) | Disk failure forecasting system based on deep learning | |
| CN116775362A (en) | Method and system for processing path blocking of redundant array of independent disks | |
| US8843781B1 (en) | Managing drive error information in data storage systems | |
| Pinciroli et al. | Lifespan and failures of SSDs and HDDs: similarities, differences, and prediction models | |
| CN107807862A (en) | Detect the method, apparatus and server of hard disk failure point | |
| CN117591337B (en) | Computer information data interactive transmission management system and method | |
| CN107301109B (en) | Hard disk quality screening method and device | |
| US7356443B2 (en) | Systems and methods for analyzing the selection of measurements of a communication network | |
| CN106648969B (en) | Method and system for inspecting damaged data in disk | |
| CN117271222A (en) | Cloud disaster recovery backup method and system for big data | |
| WO2023061209A1 (en) | Method for predicting memory fault, and electronic device and computer-readable storage medium | |
| US8089712B2 (en) | Abnormality determining apparatus | |
| CN113625957B (en) | Method, device and equipment for detecting hard disk faults | |
| CN113127274A (en) | Disk failure prediction method, device, equipment and computer storage medium | |
| CN111552582B (en) | Method and device for screening weak flash blocks and solid state disk | |
| CN112084097B (en) | Disk alarm method and device | |
| CN111858139A (en) | Method and device for detecting silent data errors | |
| CN107544860A (en) | A kind of data in magnetic disk detection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |