CN117349071B - Error correction mechanism online evaluation method, system and storage medium based on big data - Google Patents
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Abstract
The invention discloses an online evaluation method, a system and a storage medium of error correction mechanisms based on big data, and relates to the technical field of error correction mechanism processing.
Description
Technical Field
The invention relates to the technical field of error correction mechanism processing, in particular to an error correction mechanism on-line evaluation method, an error correction mechanism on-line evaluation system and a storage medium based on big data.
Background
In computer systems and communication networks, error correction mechanisms are an important means of ensuring system stability and reliability. By detecting and correcting possible errors, the fault tolerance of the system is improved, and the integrity and reliability of data are ensured.
The current error correction mechanism can take corresponding measures and repair corresponding problems according to abnormal conditions in the system, but the current state type of the error correction mechanism cannot be intuitively known by the current system, and the error correction mechanism cannot be correspondingly optimized in a targeted manner, so that system resources and time can be continuously occupied in the subsequent error correction process, and the performance of the system is reduced.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention aims to provide an error correction mechanism on-line evaluation method, an error correction mechanism on-line evaluation system and a storage medium based on big data.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an error correction mechanism on-line evaluation method based on big data comprises the following steps:
step one: collecting error correction records, and sending the collected error correction records to a server for storage;
step two: obtaining a mechanism evaluation value Hf of an error correction type, setting a mechanism evaluation threshold value as Kb, and marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is more than or equal to the mechanism evaluation threshold value Kb;
step three: and marking the evaluation records with the same mark type as the same-mark evaluation records, acquiring the same-mark evaluation value Mc of the evaluation records with the same mark type, and marking the error correction mechanism as the mark type with the maximum value of the same-mark evaluation value Mc.
Further, the error correction mechanism on-line evaluation system based on big data comprises a record acquisition module, a mechanism marking module and a mechanism evaluation module;
the record acquisition module is used for acquiring error correction records and sending the acquired error correction records to the server for storage;
the mechanism marking module is used for marking mechanisms of different error correction types according to error correction records, and specifically comprises the following steps:
obtaining all error correction records before the current time of the system, calculating a time difference between the error correction ending time and the error correction starting time of each error correction record, marking the difference result as error correction time length, obtaining the error correction type of each error correction record, marking the error correction record of the same error correction type as the same kind of error correction record, obtaining the error correction time length of all the same kind of error correction records, setting each error correction time length to correspond to one standard error correction time length, comparing the error correction time length with the standard error correction time length, marking the error correction time length as reasonable error correction time length when the error correction time length is less than the standard error correction time length, obtaining a reasonable error correction value Tn, marking the error correction time length as a missing error correction time length when the error correction time length is more than or equal to the standard error correction time length, and obtaining a missing error correction value Fg;
sequencing all the similar error correction records according to the time sequence of the abnormal starting time to obtain an average identical interval Rd;
obtaining a mechanism evaluation value Hf of an error correction type, setting a mechanism evaluation threshold value as Kb, marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is more than or equal to the mechanism evaluation threshold value Kb, and not performing processing when the mechanism evaluation value Hf is less than the mechanism evaluation threshold value Kb;
the mechanism evaluation module is used for evaluating an error correction mechanism on line, and specifically comprises the following steps:
on the basis of the same time interval, acquiring an evaluation record of an error correction mechanism in a system, setting a high value of the number of to-be-optimized as Gu, setting a low value of the number of to-be-optimized as Sd, marking the evaluation record as an ideal evaluation record when the number of to-be-optimized mechanisms of the evaluation record is more than or equal to the high value of the number of to-be-optimized as Gu, marking the evaluation record as a common evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than the high value of the number of to-be-optimized as Gu, and marking the evaluation record as a disappointing evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than the low value of the number of to-be-optimized as Sd;
and marking the evaluation records with the same mark type as the same mark evaluation records, acquiring a same mark evaluation value Mc of the evaluation records with the same mark type, and evaluating an error correction mechanism as the mark type with the maximum value of the same mark evaluation value Mc.
Further, the error correction record includes an error correction type, an abnormal start time, an error correction start time, and an error correction end time.
Further, the reasonable error correction value Tn is obtained by the following steps: calculating the difference between the standard correction time length and the reasonable correction time length, obtaining the correction time difference according to the difference result, summing all the correction time differences and taking the average value, obtaining the average correction time difference according to the average value result and marking the average correction time difference as Ew, sequencing the reasonable correction time length according to the time sequence of the correction starting time of the corresponding similar correction record, calculating the time difference between the two adjacent correction starting time after sequencing, obtaining the correction interval according to the difference result, summing all the correction intervals and taking the average value according to the average value resultObtaining an average correction interval and marking the average correction interval as Lp; using the formulaAnd obtaining a reasonable error correction value Tn, wherein a1 is an average correction time difference coefficient, and a2 is an average correction interval coefficient.
Further, the drop error correction value Fg is obtained by: calculating the difference value of the time length of the error correction of the drop and the standard time length of the error correction, obtaining the time difference of the error correction according to the difference value, summing all the time differences of the error correction, taking the average value, obtaining the average time difference of the error correction according to the average value, marking as Jd, sequencing the time length of the error correction of the drop according to the time sequence of the error correction starting time of the corresponding similar error correction record, calculating time difference values of two adjacent error correction starting moments after sequencing, obtaining error correction intervals according to difference results, summing all error correction intervals and taking an average value, obtaining an average error correction interval according to the average value results, and marking the average error correction interval as Kt; using the formulaAnd obtaining a loss error correction value Fg, wherein b1 is an average loss correction time difference coefficient, and b2 is an average loss correction interval coefficient.
Further, the average identical interval Rd is obtained by: and calculating the time difference value of the abnormal starting time of the two adjacent similar error correction records after sequencing, obtaining similar abnormal intervals according to the difference value result, carrying out summation processing on all the similar abnormal intervals, taking an average value, obtaining the average same abnormal interval according to the average value result, and marking the average same abnormal interval as Rd.
Further, the mechanism evaluation value Hf is obtained by: using the formulaThe mechanism evaluation value Hf is obtained, wherein c1 is a reasonable error correction value coefficient, c2 is a missing error correction value coefficient, and c3 is an average same-interval coefficient.
Further, the evaluation record comprises evaluation time and the number of mechanisms to be optimized.
Further, the co-standard evaluation value Mc is obtained by:obtaining the total number of evaluation records of the same mark type, marking the total number as Yn, sorting the evaluation records of the same mark type according to the sequence of the evaluation time, calculating the difference value of the evaluation time of two adjacent evaluation records after sorting, obtaining the same-standard evaluation interval according to the difference value result, carrying out summation processing on all the same-standard evaluation intervals and taking an average value, obtaining the same-standard evaluation interval according to the average value result, marking the same-standard evaluation interval as Tp, and utilizing a formulaAnd obtaining a co-standard evaluation value Mc of the same mark type, wherein d1 is a co-standard evaluation quantity coefficient, and d2 is a co-standard evaluation uniformity coefficient.
Compared with the prior art, the invention has the following beneficial effects:
1. the mechanism marking module is arranged, so that mechanisms of different error correction types can be marked according to error correction records, each mechanism to be optimized in the error correction mechanism can be visually marked, the mechanism to be optimized can be optimized in a targeted manner, and meanwhile, the subsequent online evaluation of the error correction mechanism is facilitated;
2. the mechanism evaluation module is arranged, so that the error correction mechanism can be evaluated on line according to the evaluation record, and the current state type of the error correction mechanism can be intuitively known through the on-line evaluation.
Drawings
FIG. 1 is a schematic block diagram of a mechanism marking module of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
Example 1
Referring to fig. 1, the system comprises a record acquisition module and a mechanism marking module;
the record acquisition module is used for acquiring error correction records and sending the acquired error correction records to the server for storage. The error correction record includes an error correction type (without limitation of any error type existing in the system, hereinafter denoted by A, B … …), an abnormal start time, an error correction start time, and an error correction end time. If the error correction type of the error correction record 1 is A, the abnormal starting time is 2021, 9, 5, 12:15:50, the error correction starting time is 2021, 9, 5, 12:15:58, and the error correction ending time is 2021, 9, 5, 12:16:30. The error correction type of the error correction record 1 is B, the abnormal starting time is 2021, 9, 5, 17:31:20, the error correction starting time is 2021, 9, 5, 17:31:51, and the error correction ending time is 2021, 9, 5, 12:33:11.
The mechanism marking module is used for marking mechanisms of different error correction types according to the error correction records, and specifically comprises the following steps:
obtaining all error correction records before the current time of the system, calculating a time difference value between the error correction ending time and the error correction starting time of each error correction record, marking a difference value result as an error correction time length, obtaining the error correction type of each error correction record, marking the error correction record of the same error correction type as the similar error correction record, obtaining the error correction time length of all the similar error correction records, setting each error correction time length to correspond to one standard error correction time length, comparing the error correction time length with the standard error correction time length, marking the error correction time length as a reasonable error correction time length when the error correction time length is less than the standard error correction time length, and obtaining a reasonable error correction value Tn, wherein the reasonable error correction value Tn is obtained through the following steps: calculating the difference between the standard correction time length and the reasonable correction time length, obtaining the correction time difference according to the difference result, summing all the correction time differences and taking an average value, obtaining the average correction time difference according to the average value result, marking the average correction time difference as Ew, sequencing the reasonable correction time length according to the time sequence of the correction starting time of the corresponding similar correction record, calculating the time difference between the two adjacent correction starting time after sequencing, obtaining the correction interval according to the difference result, summing all the correction intervals and taking the average value, obtaining the average correction interval according to the average value result, and marking the average correction interval as Lp; using the formulaAnd obtaining a reasonable error correction value Tn, wherein a1 is an average correction time difference coefficient, a2 is an average correction interval coefficient, the value of a1 is 0.57, and the value of a2 is 0.34. When the error correction time length is more than or equal to the standard error correction time length, marking the error correction time length as a missing error correction time length, and obtaining a missing error correction value Fg; the drop error correction value Fg is obtained by: performing the time length of the error correction of the drop and the standard time length of the error correctionCalculating a difference value, namely acquiring the error correction time difference according to a difference value result, carrying out summation treatment on all the error correction time differences and taking an average value, acquiring the average error correction time difference according to an average value result, marking as Jd, sequencing the error correction time length of the error correction according to the time sequence of error correction starting moments of corresponding similar error correction records, carrying out time difference calculation on two adjacent error correction starting moments after sequencing, acquiring error correction intervals according to the difference value result, carrying out summation treatment on all the error correction intervals and taking the average value, and acquiring the average error correction interval according to the average value result, and marking as Kt; using the formula->And obtaining a loss error correction value Fg, wherein b1 is an average error correction time difference coefficient, b2 is an average error correction interval coefficient, the value of a1 is 0.56, and the value of a2 is 0.33.
Sequencing all the similar error correction records according to the time sequence of the abnormal starting time to obtain an average identical interval Rd; the average identical interval Rd is obtained by the following steps: and calculating the time difference value of the abnormal starting time of the two adjacent similar error correction records after sequencing, obtaining similar abnormal intervals according to the difference value result, carrying out summation processing on all the similar abnormal intervals, taking an average value, obtaining the average same abnormal interval according to the average value result, and marking the average same abnormal interval as Rd.
The mechanism evaluation value Hf of the error correction type is obtained by: using the formulaThe mechanism evaluation value Hf is obtained, wherein c1 is a reasonable error correction value coefficient, c2 is a missing error correction value coefficient, c3 is an average same-interval coefficient, c1 is 0.87, c2 is 0.86, and c3 is 0.69. And setting a mechanism evaluation threshold value as Kb, marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is larger than or equal to the mechanism evaluation threshold value Kb, and not processing when the mechanism evaluation value Hf is smaller than the mechanism evaluation threshold value Kb. And when the mechanism evaluation value Hf of the A error correction type is larger than or equal to the mechanism evaluation threshold Kb, marking the mechanism of the A error correction type as a mechanism to be optimized. The mechanism marking module is arranged and can be used for correcting the error according to the errorThe error record marks the mechanisms with different error correction types, each mechanism to be optimized in the error correction mechanisms can be intuitively marked, the mechanism to be optimized is convenient to perform optimization processing in a targeted mode, and meanwhile the subsequent online evaluation of the error correction mechanisms is convenient.
Example 2
Referring to fig. 2, on the basis of embodiment 1, the system further includes a mechanism evaluation module, where the mechanism evaluation module is configured to perform online evaluation on an error correction mechanism, specifically:
on the basis of the same time interval, the evaluation record of the error correction mechanism in the system is obtained, for example, every 1 day, and the evaluation record of the error correction mechanism in the system is obtained. The evaluation record includes an evaluation time (i.e., a time for acquiring an evaluation record of an error correction mechanism in the system), and a number of mechanisms to be optimized (the number of mechanisms to be optimized is marked in the error correction mechanism). Setting the high value of the to-be-optimized number as Gu, setting the low value of the to-be-optimized number as Sd, marking the evaluation record as ideal evaluation record when the number of to-be-optimized mechanisms of the evaluation record is more than or equal to the high value of the to-be-optimized number Gu, marking the evaluation record as common evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than or equal to the high value of the to-be-optimized number Gu, and marking the evaluation record as disappointing evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than the low value of the to-be-optimized number Sd.
Marking the evaluation records with the same mark type as same-standard evaluation records, and acquiring a same-standard evaluation value Mc of the evaluation records with the same mark type, wherein the same-standard evaluation value Mc is obtained through the following steps: obtaining the total number of evaluation records of the same mark type, marking the total number as Yn, sorting the evaluation records of the same mark type according to the sequence of the evaluation time, calculating the difference value of the evaluation time of two adjacent evaluation records after sorting, obtaining the same-standard evaluation interval according to the difference value result, carrying out summation processing on all the same-standard evaluation intervals and taking an average value, obtaining the same-standard evaluation interval according to the average value result, marking the same-standard evaluation interval as Tp, and utilizing a formulaObtaining a co-label evaluation value Mc of the same label type, wherein d1 is a co-label evaluation numberThe coefficient of the quantity, d2, is the same-scale evaluation uniformity coefficient, d1 takes on the value of 0.74, and d2 takes on the value of 0.81. The error correction mechanism is evaluated as the type of marker with the largest value of the co-label evaluation value Mc. There are three mark types of ideal evaluation record, normal evaluation record and disappointing evaluation record together, and when the same mark type with the largest value of the identical mark evaluation value Mc is the ideal evaluation type, the error correction mechanism is marked as the ideal evaluation type. The mechanism evaluation module is arranged, so that the error correction mechanism can be evaluated on line according to the evaluation record, and the current state type of the error correction mechanism can be intuitively known through the on-line evaluation.
Working principle:
an error correction mechanism on-line evaluation method based on big data comprises the following steps:
step one: collecting error correction records, and sending the collected error correction records to a server for storage;
step two: obtaining a mechanism evaluation value Hf of an error correction type, setting a mechanism evaluation threshold value as Kb, and marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is more than or equal to the mechanism evaluation threshold value Kb;
step three: and marking the evaluation records with the same mark type as the same-mark evaluation records, acquiring the same-mark evaluation value Mc of the evaluation records with the same mark type, and marking the error correction mechanism as the mark type with the maximum value of the same-mark evaluation value Mc.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to those skilled in the art without departing from the principles of the present invention are intended to be considered as protecting the scope of the present template.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (3)
1. An online evaluation method of an error correction mechanism based on big data is characterized by comprising the following steps:
step one: collecting error correction records, and sending the collected error correction records to a server for storage;
step two: obtaining a mechanism evaluation value Hf of an error correction type according to a reasonable error correction value Tn, a drop error correction value Fg and an average identical-different interval Rd, setting a mechanism evaluation threshold value as Kb, and marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is more than or equal to the mechanism evaluation threshold value Kb, wherein the mechanism evaluation value Hf is obtained through the following steps: using the formulaObtaining a mechanism evaluation value Hf, wherein c1 is a reasonable error correction value coefficient, c2 is a missing error correction value coefficient, c3 is an average identical interval coefficient, obtaining all error correction records before the current time of the system, wherein the error correction records comprise error correction types, abnormal starting time, error correction starting time and error correction ending time, calculating time difference between the error correction ending time and the error correction starting time of each error correction record, marking a difference result as error correction duration, obtaining the error correction type of each error correction record, marking the error correction record of the same error correction type as similar error correction record, obtaining the error correction duration of all similar error correction records, setting each error correction duration to correspond to one standard error correction duration, comparing the error correction duration with the standard error correction duration, marking the error correction duration as reasonable error correction duration when the error correction duration is less than the standard error correction duration, obtaining a reasonable error correction value Tn according to the average error correction time difference and the average error correction interval, and obtaining the reasonable error correction value Tn through the following steps: calculating the difference between the standard correction time length and the reasonable correction time length, obtaining the sum correction time difference according to the difference result, summing all the sum correction time differences, taking the average value, obtaining the average sum correction time difference according to the average value result, marking the average sum correction time difference as Ew, sequencing the reasonable correction time length according to the time sequence of the correction start time of the corresponding similar correction record, and sequencing two adjacent correction start times after sequencingCalculating a time difference value, obtaining a coincidence correction interval according to a difference value result, carrying out summation treatment on all the coincidence correction intervals, taking a mean value, obtaining a mean coincidence correction interval according to a mean value result, and marking the mean coincidence correction interval as Lp; using the formula->Obtaining a reasonable error correction value Tn, wherein a1 is an average correction time difference coefficient, a2 is an average correction interval coefficient, and when the error correction time is more than or equal to the standard correction time, marking the error correction time as a missing error correction time, obtaining a missing error correction value Fg, and obtaining the missing error correction value Fg through the following steps: calculating the difference value of the time length of the error correction of the drop and the standard time length of the error correction, obtaining the time difference of the error correction according to the difference value, summing all the time differences of the error correction, taking the average value, obtaining the average time difference of the error correction according to the average value, marking as Jd, sequencing the time length of the error correction of the drop according to the time sequence of the error correction starting time of the corresponding similar error correction record, calculating time difference values of two adjacent error correction starting moments after sequencing, obtaining error correction intervals according to difference results, summing all error correction intervals and taking an average value, obtaining an average error correction interval according to the average value results, and marking the average error correction interval as Kt; using the formula->Obtaining a loss error correction value Fg, wherein b1 is an average error correction time difference coefficient, b2 is an average error correction interval coefficient, and sequencing all the similar error correction records according to the time sequence of abnormal starting time to obtain an average identical interval Rd, wherein the average identical interval Rd is obtained through the following steps: calculating time difference values of abnormal starting moments of two adjacent similar error correction records after sequencing, obtaining similar abnormal intervals according to difference results, summing all similar abnormal intervals and taking an average value, obtaining average similar abnormal intervals according to the average value results, and marking the average similar abnormal intervals as Rd;
step three: marking the evaluation records with the same mark type as the same mark evaluation records, acquiring the same mark evaluation value Mc of the evaluation records with the same mark type, and evaluating an error correction mechanism as the mark type with the maximum value of the same mark evaluation value McOn the basis of the same time interval, acquiring an evaluation record of an error correction mechanism in a system, setting a high value of the to-be-optimized number as Gu, setting a low value of the to-be-optimized number as Sd, marking the evaluation record as an ideal evaluation record when the number of the to-be-optimized mechanisms of the evaluation record is more than or equal to the high value of the to-be-optimized number as Gu, marking the evaluation record as a common evaluation record when the number of the to-be-optimized mechanisms of the evaluation record is less than the high value of the to-be-optimized number as Gu, marking the evaluation record as a disappointing evaluation record when the number of the to-be-optimized mechanisms of the evaluation record is less than the low value of the to-be-optimized number as Sd, wherein the evaluation record comprises evaluation time and the number of the to-be-optimized mechanisms, and the same-standard evaluation value Mc is obtained by the following steps: obtaining the total number of evaluation records of the same mark type, marking the total number as Yn, sorting the evaluation records of the same mark type according to the sequence of the evaluation time, calculating the difference value of the evaluation time of two adjacent evaluation records after sorting, obtaining the same-standard evaluation interval according to the difference value result, carrying out summation processing on all the same-standard evaluation intervals and taking an average value, obtaining the same-standard evaluation interval according to the average value result, marking the same-standard evaluation interval as Tp, and utilizing a formulaAnd obtaining a co-standard evaluation value Mc of the same mark type, wherein d1 is a co-standard evaluation quantity coefficient, and d2 is a co-standard evaluation uniformity coefficient.
2. The online evaluation system of the error correction mechanism based on the big data is applied to the online evaluation method of the error correction mechanism based on the big data as claimed in claim 1, and is characterized by comprising a record acquisition module, a mechanism marking module and a mechanism evaluation module;
the record acquisition module is used for acquiring error correction records and sending the acquired error correction records to the server for storage;
the mechanism marking module is used for marking mechanisms of different error correction types according to error correction records, and specifically comprises the following steps:
obtaining all error correction records before the current time of the system, calculating a time difference between the error correction ending time and the error correction starting time of each error correction record, marking the difference result as error correction time length, obtaining the error correction type of each error correction record, marking the error correction record of the same error correction type as the same kind of error correction record, obtaining the error correction time length of all the same kind of error correction records, setting each error correction time length to correspond to one standard error correction time length, comparing the error correction time length with the standard error correction time length, marking the error correction time length as reasonable error correction time length when the error correction time length is less than the standard error correction time length, obtaining a reasonable error correction value Tn, marking the error correction time length as a missing error correction time length when the error correction time length is more than or equal to the standard error correction time length, and obtaining a missing error correction value Fg;
sequencing all the similar error correction records according to the time sequence of the abnormal starting time to obtain an average identical interval Rd;
obtaining a mechanism evaluation value Hf of an error correction type according to a reasonable error correction value Tn, a missing error correction value Fg and an average identical-different interval Rd, setting a mechanism evaluation threshold value as Kb, marking the mechanism of the error correction type in the error correction mechanism as a mechanism to be optimized when the mechanism evaluation value Hf is more than or equal to the mechanism evaluation threshold value Kb, and not processing when the mechanism evaluation value Hf is less than the mechanism evaluation threshold value Kb;
the mechanism evaluation module is used for evaluating an error correction mechanism on line, and specifically comprises the following steps:
on the basis of the same time interval, acquiring an evaluation record of an error correction mechanism in a system, setting a high value of the number of to-be-optimized as Gu, setting a low value of the number of to-be-optimized as Sd, marking the evaluation record as an ideal evaluation record when the number of to-be-optimized mechanisms of the evaluation record is more than or equal to the high value of the number of to-be-optimized as Gu, marking the evaluation record as a common evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than the high value of the number of to-be-optimized as Gu, and marking the evaluation record as a disappointing evaluation record when the number of to-be-optimized mechanisms of the evaluation record is less than the low value of the number of to-be-optimized as Sd;
and marking the evaluation records with the same mark type as the same mark evaluation records, acquiring a same mark evaluation value Mc of the evaluation records with the same mark type, and evaluating an error correction mechanism as the mark type with the maximum value of the same mark evaluation value Mc.
3. An error correction mechanism based on big data online evaluating storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the method according to claim 1.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026617A1 (en) * | 2000-08-25 | 2002-02-28 | Tadashi Kojima | Data processing method and apparatus, recording medium, reproducidng method and apparatus using the same method |
| US20090031196A1 (en) * | 2007-07-24 | 2009-01-29 | Princeton Technology Corporation | Error-correcting method used for decoding data transmissions |
| CN110444243A (en) * | 2019-07-31 | 2019-11-12 | 至誉科技（武汉）有限公司 | Store test method, system and the storage medium of equipment read error error correcting capability |
| CN111626048A (en) * | 2020-05-22 | 2020-09-04 | 腾讯科技（深圳）有限公司 | Text error correction method, device, equipment and storage medium |
-
2023
- 2023-10-26 CN CN202311398464.8A patent/CN117349071B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020026617A1 (en) * | 2000-08-25 | 2002-02-28 | Tadashi Kojima | Data processing method and apparatus, recording medium, reproducidng method and apparatus using the same method |
| US20090031196A1 (en) * | 2007-07-24 | 2009-01-29 | Princeton Technology Corporation | Error-correcting method used for decoding data transmissions |
| CN110444243A (en) * | 2019-07-31 | 2019-11-12 | 至誉科技（武汉）有限公司 | Store test method, system and the storage medium of equipment read error error correcting capability |
| CN111626048A (en) * | 2020-05-22 | 2020-09-04 | 腾讯科技（深圳）有限公司 | Text error correction method, device, equipment and storage medium |
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