CN108319545A - A kind of information processing method and electronic equipment - Google Patents
A kind of information processing method and electronic equipment Download PDFInfo
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- CN108319545A CN108319545A CN201810101811.9A CN201810101811A CN108319545A CN 108319545 A CN108319545 A CN 108319545A CN 201810101811 A CN201810101811 A CN 201810101811A CN 108319545 A CN108319545 A CN 108319545A
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- firmware combinations
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- combinations
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
- G06F11/3419—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment by assessing time
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
Abstract
A kind of information processing method of embodiments herein offer and electronic equipment, the method includes：Obtain the firmware combinations information of equipment；Determine that the firmware combinations may cause the probability value of the equipment fault according to the firmware combinations information；Export the probability value.Information processing method in the embodiment of the present application can predict that different firmware combinations will cause the probability value of equipment fault, the probabilistic forecasting value of equipment fault will be caused by allowing users to easily to obtain any one completely new firmware combinations, and then while selecting firmware to be combined for user provides foundation.
Description
Technical field
This application involves a kind of information processing method and electronic equipments.
Background technology
Currently, for example before manufacturer server shipment, firmware (BIOS/BMC etc.) combination that it is arranged in pairs or groups can all be finished
Kind test, so that the phenomenon that such as system in case of system halt occur by the caused equipment of the firmware combinations is addressed.But when server quilt
After client buys, client may reconfigure firmware, or the firmware in more new equipment, since client cannot be to equipment
It carries out improving test, be frequently occurred so that client does not know whether newer firmware combinations version can cause equipment at present
Failure, therefore, there are certain risks when using the equipment for having updated firmware combinations by client.
Apply for content
The application problem to be solved, which is to provide, a kind of can predict that different firmware combinations will cause equipment fault
Probability information processing method and application this kind of method electronic equipment.
To solve the above-mentioned problems, the application provides a kind of information processing method, including：
Obtain the firmware combinations information of equipment；
Determine that the firmware combinations may cause the probability value of the equipment fault according to the firmware combinations information；
Export the probability value.
Preferably, further including：
It builds for determining that the firmware combinations may cause the artificial intelligence training mould of the probability value of the equipment fault
Type；
It is described to determine that the firmware combinations cause the probability value of the equipment fault according to the firmware combinations information
Specially：
Determine that the firmware combinations may cause the probability value of the equipment fault by the artificial intelligence training pattern.
Preferably, the structure is for determining that the firmware combinations may cause the people of the probability value of the equipment fault
Work intelligent training model is specially：
Build training pattern framework；
Obtain the firmware combinations information of multiple equipment；
Obtain the fault message that multiple equipment cause by the firmware combinations；
The training pattern framework is trained based on the firmware combinations information and fault message, to determine each power
Value.
Preferably, described be trained the training pattern framework based on the firmware combinations information and fault message
Specially：
Determine the renewal time of the firmware combinations；
Determine the Fisrt fault frequency for causing the equipment fault in the first time period of the firmware combinations in the updated；
Each weights are determined according to the Fisrt fault frequency.
Preferably, described be trained the training pattern framework based on the firmware combinations information and fault message
Specially：
Determine the firmware combinations information with like combinations version in multiple firmware combinations；
Determine that the interior initiation of the second time period of the firmware combinations with like combinations version in the updated is set described in corresponding
Second failure-frequency of standby failure；
Each weights are determined according to the Fisrt fault frequency and the second failure-frequency；
Wherein, second failure-frequency is different from the Fisrt fault frequency, and the duration of the second time period is more than
The duration of the first time period.
Preferably, described be trained the training pattern framework based on the firmware combinations information and fault message
Specially：
Determining at least has the firmware combinations of same firmware in multiple firmware combinations；
Determine that at least third period of the firmware combinations with same firmware in the updated interior initiation is set described in corresponding
The third failure-frequency of standby failure；
Each weights are determined according to the Fisrt fault frequency, the second failure-frequency and third failure-frequency；
Wherein, the third failure-frequency is different from the Fisrt fault frequency and the second failure-frequency.
The embodiment of the present invention provides a kind of electronic equipment simultaneously, including
Acquiring unit, the firmware combinations information for obtaining equipment；
Processing unit, for determining that the firmware combinations may cause the equipment fault according to the firmware combinations information
Probability value；
Display unit, for showing the probability value.
Preferably, further including：Structure is for determining that the firmware combinations may cause the probability value of the equipment fault
Artificial intelligence training pattern；
The processing unit is additionally operable to determine that the firmware combinations may cause institute by the artificial intelligence training pattern
State the probability value of equipment fault.
Preferably, the structure is for determining that the firmware combinations may cause the people of the probability value of the equipment fault
Work intelligent training model is specially：
Build training pattern framework；
The processing unit be additionally operable to based on the firmware combinations information and fault message to the training pattern framework into
Row training, to determine each weights.
Preferably, the processing unit is additionally operable to：
Determine the renewal time of the firmware combinations；
Determine the Fisrt fault frequency for causing the equipment in the first time period of the firmware combinations in the updated；
Each weights are determined according to the Fisrt fault frequency.
The advantageous effect of the application is, using artificial intelligence training pattern to causing equipment because of the combination of each firmware therefore
The probability of malfunction of barrier, such as crash etc. is summarized so that user can obtain any one completely new firmware by the model
Combination will cause the probabilistic forecasting value of equipment fault, and then while being combined for user's selection firmware provides foundation.
Description of the drawings
Fig. 1 is the method flow diagram of an embodiment of the information processing method of the application.
Fig. 2 is artificial intelligence training pattern training method flow chart in the information processing method of the application.
Fig. 3 is the side of another embodiment of the artificial intelligence training pattern training method in the information processing method of the application
Method flow chart.
Fig. 4 is the structure diagram of the electronic equipment of the application.
Specific implementation mode
The application is described in detail below in conjunction with attached drawing.
It should be understood that various modifications can be made to disclosed embodiments.Therefore, following description should not regard
To limit, and only as the example of embodiment.Those skilled in the art will expect within the scope and spirit of this
Other modifications.
The attached drawing being included in the description and forms part of the description shows embodiment of the disclosure, and with it is upper
What face provided is used to explain the disclosure together to the substantially description of the disclosure and the detailed description given below to embodiment
Principle.
By the description of the preferred form of the embodiment with reference to the accompanying drawings to being given as non-limiting examples, the application's
These and other characteristic will become apparent.
It is also understood that although the application is described with reference to some specific examples, people in the art
Member realizes many other equivalents of the application in which can determine, they have feature as claimed in claim and therefore all
In the protection domain defined by whereby.
When read in conjunction with the accompanying drawings, in view of following detailed description, above and other aspect, the feature and advantage of the disclosure will become
It is more readily apparent.
The specific embodiment of the disclosure is described hereinafter with reference to attached drawing；It will be appreciated, however, that the disclosed embodiments are only
Various ways implementation can be used in the example of the disclosure.It is known and/or repeat function and structure be not described in detail to avoid
Unnecessary or extra details so that the disclosure is smudgy.Therefore, specific structural and functionality disclosed herein is thin
Section is not intended to restrictions, but as just the basis of claim and representative basis be used to instruct those skilled in the art with
Substantially any appropriate detailed construction diversely uses the disclosure.
This specification can be used phrase " in one embodiment ", " in another embodiment ", " in another embodiment
In " or " in other embodiments ", it can be referred to one or more of the identical or different embodiment according to the disclosure.
Optionally, in the embodiment of the present application, electronic equipment can be that PC, tablet computer, laptop etc. are different
Electronic equipment, the embodiment of the present application are not construed as limiting this.
Currently, for example before manufacturer server shipment, the firmware combinations (BIOS/BMC etc.) that it is arranged in pairs or groups can all be finished
Kind test, so that the phenomenon that such as system in case of system halt occur by the caused equipment of the firmware combinations is addressed.But when server quilt
After client buys, client may reconfigure firmware, or the firmware in more new equipment, since client cannot be to equipment
It carries out improving test, be frequently occurred so that client does not know whether newer firmware combinations version can cause equipment at present
Failure, therefore, there are certain risks when using the equipment for having updated firmware combinations by client.
As shown in Figure 1, in order to solve the above technical problems, the application provides a kind of information processing method comprising：
S101:Obtain the firmware combinations information of equipment；
Firmware combinations refer to carrying out matched combined use, such as BIOS, BMC by multiple firmwares, all have multiple firmwares,
It is to be formed by multiple firmware combinations.Therefore the firmware combinations information of the acquisition can be each solid in the BMC in current electronic device
Part information.
S201:Determine that firmware combinations may cause the probability value of equipment fault according to firmware combinations information；
For example, server can be made to obtain the reason of each electronic equipment is because of certain firmware combinations in advance, such as mismatch, it cannot be normal
Operation, crash etc., it, specifically can be by the firmware group with the firmware combinations information and fault message for causing electronic equipment to break down
It closes information and fault message is sent in the central keyholed back plate software of server, central keyholed back plate software can be based on previously a large amount of
The above-mentioned data information that electronic equipment is sent is come after assessing, calculating being updated transmitted by the electronic equipment produced after
Firmware combinations (that is, combining the firmware combinations formed by the combination for the firmware not occurred before) may cause equipment fault
Probability.
S301:Output probability value；
Probability value is exported, such as probability value is displayed on the screen, makes the intuitive acquisition probability value of user, or to remind,
The mode of alarm is exported, for example, setting threshold value then sends out alarm when probability value exceeds preset range, such as on the screen
Flicker is displayed in red warning lamp etc. or voice prompt etc., informs that the combination of user's current firmware easily causes equipment fault etc..
By the method in the embodiment of the present application, user can be only by by the relevant information of firmware combinations, such as firmware name
Whether information, version information etc. are sent to server or by cloud databases etc., can easily obtain the firmware combinations and can
Enough by normal use, or the risk of equipment failure whether can be reduced or be improved, and then select firmware to carry out group for user
Reliable and effective foundation is provided when conjunction.
Further, in order to improve the probability whether various updated firmware combinations will be influenced with electronic failure
The levels of precision of predictive information makes the probability value of prediction have more reference significance, is implementing according to firmware in the embodiment of the present application
When combined information determines that firmware combinations may cause the step of the probability value of equipment fault, realize in the following ways：
Structure is for determining that firmware combinations may cause the artificial intelligence training pattern of the probability value of equipment fault；
Determine that firmware combinations may cause the probability value of equipment fault by artificial intelligence training pattern.
Wherein, as shown in Fig. 2, above-mentioned structure is for determining that firmware combinations may cause the artificial of the probability value of equipment fault
Intelligent training model is specially：
Build training pattern framework；
The firmware combinations information of multiple equipment is obtained, the firmware version inventory of the BMC of multiple equipment is such as obtained, it includes
The essential information of each firmware in BMC, meanwhile, it can also obtain the renewal time of each firmware；
The fault message that multiple equipment causes by firmware combinations is obtained, such as causes equipment great mistake occur because of BMC
The information of mistake or system in case of system halt；
Training pattern framework is trained based on firmware combinations information and fault message, to determine training pattern framework
In each weights, and then obtain can high-precision forecast will cause the probability value of equipment fault in rear newer firmware combinations.
Such as using the version information of firmware combinations, name information, renewal time as input information, setting for initiation is corresponded to by each firmware combinations
Standby fault message calculates the weights in training pattern in each equation as output information based on this.
Further, as shown in figure 3, due to each firmware combinations cause in use equipment failure time,
Frequency etc. is not fixed, and in order to exclude some because accidental sexual factor etc. leads to equipment fault, and is excluded the synteny of data, is kept away
Exempt to influence weight computing precision, therefore, believe using above-mentioned each firmware combinations information and corresponding failure in the embodiment of the present application
When breath is trained model framework, it is trained in combination with following manner：
First way：
Determine the renewal time of firmware combinations；
Determine the Fisrt fault frequency for causing equipment fault in the first time period of firmware combinations in the updated；
Each weights are determined according to Fisrt fault frequency.
For example, the renewal time of certain firmware combinations in BMC in multiple electronic equipments be followed successively by 1 day 2 months, 3 days 2 months, 3
Month 1 day, April 1, on the basis of each update day, obtain each equipment each one week updated in the future (that is, first time period, certainly
Be alternatively two weeks, three weeks etc.) in the number of stoppages, respectively set based on the time of the number of stoppages and first time period to calculate
The standby frequency to break down in first time period namely Fisrt fault frequency, Fisrt fault frequency is higher, then accordingly adjusts mould
The weights of the firmware combinations are corresponded in type so that the probability of malfunction value of the correspondence firmware combinations gone out is higher.
Certainly, may be based on each firmware combinations in one week after renewal time corresponding equipment break down at first when
Between carry out in auxiliary adjustment, computation model to correspond to the weights of the firmware combinations so that the probability of malfunction of the correspondence firmware combinations gone out
Value is higher.
The second way：
Determine the firmware combinations information with like combinations version in multiple firmware combinations；
Determine the renewal time of multiple firmware combinations；
It determines in the second time period of the firmware combinations with like combinations version in the updated and causes corresponding equipment
Second failure-frequency；
Each weights are determined according to Fisrt fault frequency and the second failure-frequency；
Wherein, the second failure-frequency is different from Fisrt fault frequency, and the duration of second time period is more than first time period
Duration.
For example, it is possible that certain equipment have used the firmware combinations of identical version in multiple equipment, but there is phase
The time broken down with equipment where the firmware combinations of version but and differs, and such as the first equipment and the second equipment use identical
The BMC of version, but time for breaking down of the first equipment be that second week in the updated or second month just break down, and
Second equipment be then in the updated just there is failure within first week, therefore, the power of the firmware combinations is corresponded in computation model
It needs to consider the service condition of two equipment when value, and cannot be counted according only to the service condition of an equipment
It calculates.When it is implemented, can be drafted according to the time that the equipment respectively with identical version firmware combinations first appears in the updated
One second time period, the ratio first time period which can be arranged is longer, more fully to cover above-mentioned all set
For the time broken down, said for being second time period by each firmware combinations updated two week in the present embodiment
It is bright.Then, second failure-frequency of each firmware combinations in second time period is calculated, and is based on Fisrt fault frequency and numerical value
The second minimum failure-frequency carrys out the corresponding weights for adjusting the corresponding firmware combinations, makes the caused equipment event of the reduction firmware combinations
The probability value of barrier.That is, in the equipment with identical version firmware combinations, the normal use time if any certain equipment is longer, such as
Remaining equipment is used and is broken down once week, and the equipment normal use two weeks, then it is general suitably to reduce corresponding failure
Rate value.The specific reduction amplitude of probability value can be depending on the time of equipment normal use.It certainly, can also be according to the of each equipment
One failure-frequency and the chronological variation tendency of the second failure-frequency in second time period determine that corresponding this is solid
The weights of part combination.
The third mode：
Determining at least has the firmware combinations of same firmware in multiple firmware combinations, it is, the firmware group of multiple equipment
There may be some firmware of some firmware combinations to use the firmware of same version in conjunction, be equivalent to the firmware group of multiple equipment
Closing has similitude；
It determines and causes the of corresponding equipment at least firmware combinations third period in the updated with same firmware
Three failure-frequencies；
Each weights are determined according to Fisrt fault frequency, the second failure-frequency and third failure-frequency；
Wherein, third failure-frequency is different from Fisrt fault frequency and the second failure-frequency.
For example, the BMC (being equivalent to firmware combinations) in multiple equipment includes the first firmware, the BMC of multiple equipment exists
In the updated third period, such as (it is alternatively one week or two weeks certainly in three weeks namely the time of third period is not solid
Fixed, can be identical or different with first time period and second time period), in the third failure-frequency of failure, if there is certain
Trend that a or certain several equipment third failure-frequencies are relatively low or the third failure-frequency of multiple equipment changes with time
Belong to increasing trend, then it can be based on Fisrt fault frequency and under the premise of constant the second failure-frequency and according to third failure frequency
Rate adjusts the weights of the firmware combinations of corresponding the type so that the firmware combinations of the type cause the probability value drop of equipment fault
It is low.The amplitude of reduction equally can be depending on the normal use time of the equipment of the firmware combinations with the above-mentioned type.Such as, have
The equipment of the type firmware combinations in the updated can normal use one month, then corresponding probability value is reduced by 10% etc..And work as
In the case that Fisrt fault frequency and the second failure-frequency change, then need specifically to determine and correspond in conjunction with three failure-frequencies
Weights.
As shown in figure 4, embodiments herein provides a kind of electronic equipment simultaneously, including
Acquiring unit, the firmware combinations information for obtaining equipment；Wherein firmware combinations refer to being taken by multiple firmwares
With being applied in combination, such as BIOS, BMC, multiple firmwares are all had, are formed by multiple firmware combinations.Therefore the firmware of the acquisition
Combined information can be each firmware information in the BMC in current electronic device.
Processing unit, for determining that firmware combinations may cause the probability value of equipment fault according to firmware combinations information；Example
Such as, processing unit can be made to obtain the reason of each equipment is because of certain firmware combinations in advance, such as mismatch, be not normally functioning, crash etc.,
It, specifically can be by the firmware combinations information and event with the firmware combinations information and fault message for causing electronic equipment to break down
Barrier information is sent in the central keyholed back plate software of processing unit, and central keyholed back plate software can be based on previously a large amount of electronic equipment hairs
The firmware combinations after being updated transmitted by the electronic equipment produced after are assessed, calculated to the above-mentioned data information sent
(that is, combining the firmware combinations formed by the combination for the firmware not occurred before) may cause the probability of equipment fault.
Display unit is used for indicating probability value.Or exported to remind, in a manner of alarm, for example, setting threshold value, when
When probability value exceeds preset range, then alarm is sent out, such as flicker is displayed in red warning lamp or voice prompt etc. on the screen,
Inform that the combination of user's current firmware easily causes equipment fault etc..
By the electronic equipment in the embodiment of the present application, user can be only by by the relevant information of firmware combinations, such as firmware
Name information, version information etc. are sent to processing unit, can easily obtain the firmware combinations whether can by normal use, or
Whether person can reduce or improve the risk of equipment failure, so while selecting firmware to be combined for user provide it is reliable effective
Foundation.
Further, in order to improve the probability whether various updated firmware combinations will be influenced with electronic failure
The levels of precision of predictive information makes the probability value of prediction have more reference significance, is implementing according to firmware in the embodiment of the present application
When combined information determines that firmware combinations may cause the step of the probability value of equipment fault, realize in the following ways：
Structure is for determining that firmware combinations may cause the artificial intelligence training pattern of the probability value of equipment fault；
Processing unit is additionally operable to determine that firmware combinations may cause the probability of equipment fault by artificial intelligence training pattern
Value.
Wherein, above-mentioned structure is used to determine that firmware combinations may to cause the artificial intelligence training mould of the probability value of equipment fault
Type is specially：
Build training pattern framework；
Processing unit is additionally operable to be trained training pattern framework based on firmware combinations information and fault message, with determination
Go out each weights, so obtain can high-precision forecast will cause the probability value of equipment fault in rear newer firmware combinations.Such as
Using the version information of firmware combinations, name information, renewal time as input information, the equipment that corresponds to initiation by each firmware combinations
Fault message as output information, the weights in training pattern in each equation are calculated based on this.
Further, since each firmware combinations cause time, frequency of equipment failure etc. not in use
It is fixed, in order to exclude some because accidental sexual factor etc. leads to equipment fault, and the synteny of data is excluded, avoids influencing weights
Computational accuracy, therefore, the processing unit in the embodiment of the present application are believed using above-mentioned each firmware combinations information and corresponding failure
When breath is trained model framework, it is trained in combination with following manner：
First way：
Determine the renewal time of firmware combinations；
Determine the Fisrt fault frequency for causing equipment in the first time period of firmware combinations in the updated；
Each weights are determined according to Fisrt fault frequency.
For example, the renewal time of certain firmware combinations in BMC in multiple electronic equipments be followed successively by 1 day 2 months, 3 days 2 months, 3
Month 1 day, April 1, on the basis of each update day, obtain each equipment each one week updated in the future (that is, first time period, certainly
Be alternatively two weeks, three weeks etc.) in the number of stoppages, respectively set based on the time of the number of stoppages and first time period to calculate
The standby frequency to break down in first time period namely Fisrt fault frequency, Fisrt fault frequency is higher, then accordingly adjusts mould
The weights of the firmware combinations are corresponded in type so that the probability of malfunction value of the correspondence firmware combinations gone out is higher.
Certainly, may be based on each firmware combinations in one week after renewal time corresponding equipment break down at first when
Between carry out in auxiliary adjustment, computation model to correspond to the weights of the firmware combinations so that the probability of malfunction of the correspondence firmware combinations gone out
Value is higher.
The second way：
Determine the firmware combinations information with like combinations version in multiple firmware combinations；
It determines and causes corresponding equipment event in the second time period of the firmware combinations with like combinations version in the updated
Second failure-frequency of barrier；
Each weights are determined according to Fisrt fault frequency and the second failure-frequency；
Wherein, the second failure-frequency is different from Fisrt fault frequency, and the duration of second time period is more than first time period
Duration.
For example, it is possible that certain equipment have used the firmware combinations of identical version in multiple equipment, but there is phase
The time broken down with equipment where the firmware combinations of version but and differs, and such as the first equipment and the second equipment use identical
The BMC of version, but time for breaking down of the first equipment be that second week in the updated or second month just break down, and
Second equipment be then in the updated just there is failure within first week, therefore, the power of the firmware combinations is corresponded in computation model
It needs to consider the service condition of two equipment when value, and cannot be counted according only to the service condition of an equipment
It calculates.When it is implemented, can be drafted according to the time that the equipment respectively with identical version firmware combinations first appears in the updated
One second time period, the ratio first time period which can be arranged is longer, more fully to cover above-mentioned all set
For the time broken down, said for being second time period by each firmware combinations updated two week in the present embodiment
It is bright.Then, second failure-frequency of each firmware combinations in second time period is calculated, and is based on Fisrt fault frequency and numerical value
The second minimum failure-frequency carrys out the corresponding weights for adjusting the corresponding firmware combinations, makes the caused equipment event of the reduction firmware combinations
The probability value of barrier.That is, in the equipment with identical version firmware combinations, the normal use time if any certain equipment is longer, such as
Remaining equipment is used and is broken down once week, and the equipment normal use two weeks, then it is general suitably to reduce corresponding failure
Rate value.The specific reduction amplitude of probability value can be depending on the time of equipment normal use.It certainly, can also be according to the of each equipment
One failure-frequency and the chronological variation tendency of the second failure-frequency in second time period determine that corresponding this is solid
The weights of part combination.
The third mode：
Determining at least has the firmware combinations of same firmware in multiple firmware combinations, it is, the firmware group of multiple equipment
There may be some firmware of some firmware combinations to use the firmware of same version in conjunction, be equivalent to the firmware group of multiple equipment
Closing has similitude；
It determines and causes corresponding equipment event in at least third period of the firmware combinations with same firmware in the updated
The third failure-frequency of barrier；
Each weights are determined according to Fisrt fault frequency, the second failure-frequency and third failure-frequency；
Wherein, third failure-frequency is different from the Fisrt fault frequency and the second failure-frequency.
For example, the BMC (being equivalent to firmware combinations) in multiple equipment includes the first firmware, the BMC of multiple equipment exists
In the updated third period, such as (it is alternatively one week or two weeks certainly in three weeks namely the time of third period is not solid
Fixed, can be identical or different with first time period and second time period), in the third failure-frequency of failure, if there is certain
Trend that a or certain several equipment third failure-frequencies are relatively low or the third failure-frequency of multiple equipment changes with time
Belong to increasing trend, then it can be based on Fisrt fault frequency and under the premise of constant the second failure-frequency and according to third failure frequency
Rate adjusts the weights of the firmware combinations of corresponding the type so that the firmware combinations of the type cause the probability value drop of equipment fault
It is low.The amplitude of reduction equally can be depending on the normal use time of the equipment of the firmware combinations with the above-mentioned type.Such as, have
The equipment of the type firmware combinations in the updated can normal use one month, then corresponding probability value is reduced by 10% etc..And work as
In the case that Fisrt fault frequency and the second failure-frequency change, then need specifically to determine and correspond in conjunction with three failure-frequencies
Weights.
Above example is only the exemplary embodiment of the application, is not used in limitation the application, the protection domain of the application
It is defined by the claims.Those skilled in the art can make respectively the application in the essence and protection domain of the application
Kind modification or equivalent replacement, this modification or equivalent replacement also should be regarded as falling within the scope of protection of this application.
Claims (10)
1. a kind of information processing method, which is characterized in that including：
Obtain the firmware combinations information of equipment；
Determine that the firmware combinations may cause the probability value of the equipment fault according to the firmware combinations information；
Export the probability value.
2. according to the method described in claim 1, it is characterized in that, further including：
Structure is for determining that the firmware combinations may cause the artificial intelligence training pattern of the probability value of the equipment fault；
It is described according to the firmware combinations information determine the firmware combinations may cause the equipment fault probability value it is specific
For：
Determine that the firmware combinations may cause the probability value of the equipment fault by the artificial intelligence training pattern.
3. according to the method described in claim 1, it is characterized in that, the structure is for determining that the firmware combinations may cause
The artificial intelligence training pattern of the probability value of the equipment fault is specially：
Build training pattern framework；
Obtain the firmware combinations information of multiple equipment；
Obtain the fault message that multiple equipment cause by the firmware combinations；
The training pattern framework is trained based on the firmware combinations information and fault message, to determine each weights.
4. according to the method described in claim 3, it is characterized in that, described be based on the firmware combinations information and fault message pair
The training pattern framework is trained specially：
Determine the renewal time of the firmware combinations；
Determine the Fisrt fault frequency for causing the equipment fault in the first time period of the firmware combinations in the updated；
Each weights are determined according to the Fisrt fault frequency.
5. according to the method described in claim 4, it is characterized in that, described be based on the firmware combinations information and fault message pair
The training pattern framework is trained specially：
Determine the firmware combinations information with like combinations version in multiple firmware combinations；
It determines and causes the corresponding equipment event in the second time period of the firmware combinations with like combinations version in the updated
Second failure-frequency of barrier；
Each weights are determined according to the Fisrt fault frequency and the second failure-frequency；
Wherein, second failure-frequency is different from the Fisrt fault frequency, and the duration of the second time period is more than described
The duration of first time period.
6. according to the method described in claim 5, it is characterized in that, described be based on the firmware combinations information and fault message pair
The training pattern framework is trained specially：
Determining at least has the firmware combinations of same firmware in multiple firmware combinations；
It determines and causes the corresponding equipment event in at least third period of the firmware combinations with same firmware in the updated
The third failure-frequency of barrier；
Each weights are determined according to the Fisrt fault frequency, the second failure-frequency and third failure-frequency；
Wherein, the third failure-frequency is different from the Fisrt fault frequency and the second failure-frequency.
7. a kind of electronic equipment, which is characterized in that including
Acquiring unit, the firmware combinations information for obtaining equipment；
Processing unit, for determining that the firmware combinations may cause the general of the equipment fault according to the firmware combinations information
Rate value；
Display unit, for showing the probability value.
8. electronic equipment according to claim 7, which is characterized in that further include：Structure is for determining the firmware combinations
The artificial intelligence training pattern of the probability value of the equipment fault may be caused；
The processing unit is additionally operable to determine that the firmware combinations may cause described set by the artificial intelligence training pattern
The probability value of standby failure.
9. electronic equipment according to claim 7, which is characterized in that the structure is for determining that the firmware combinations may
The artificial intelligence training pattern for causing the probability value of the equipment fault is specially：
Build training pattern framework；
The processing unit is additionally operable to instruct the training pattern framework based on the firmware combinations information and fault message
Practice, to determine each weights.
10. electronic equipment according to claim 9, which is characterized in that the processing unit is additionally operable to：
Determine the renewal time of the firmware combinations；
Determine the Fisrt fault frequency for causing the equipment in the first time period of the firmware combinations in the updated；
Each weights are determined according to the Fisrt fault frequency.
Priority Applications (1)
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101061464A (en) * | 2004-11-17 | 2007-10-24 | 日本电气株式会社 | Information processing device, program thereof, modular type system operation management system, and component selection method |
| CN103246771A (en) * | 2013-05-10 | 2013-08-14 | 北京航空航天大学 | Fault-tolerant circuit fault effect analytical method based on simulation |
| CN103617110A (en) * | 2013-11-11 | 2014-03-05 | 国家电网公司 | Server device condition maintenance system |
| CN103957247A (en) * | 2014-04-22 | 2014-07-30 | 安一恒通（北京）科技有限公司 | Equipment quality assessment score processing method and device |
| JP6070337B2 (en) * | 2013-03-25 | 2017-02-01 | 富士通株式会社 | Physical failure analysis program, physical failure analysis method, and physical failure analysis apparatus |
| US20170249228A1 (en) * | 2016-02-29 | 2017-08-31 | International Business Machines Corporation | Persistent device fault indicators |
-
2018
- 2018-02-01 CN CN201810101811.9A patent/CN108319545B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101061464A (en) * | 2004-11-17 | 2007-10-24 | 日本电气株式会社 | Information processing device, program thereof, modular type system operation management system, and component selection method |
| JP6070337B2 (en) * | 2013-03-25 | 2017-02-01 | 富士通株式会社 | Physical failure analysis program, physical failure analysis method, and physical failure analysis apparatus |
| CN103246771A (en) * | 2013-05-10 | 2013-08-14 | 北京航空航天大学 | Fault-tolerant circuit fault effect analytical method based on simulation |
| CN103617110A (en) * | 2013-11-11 | 2014-03-05 | 国家电网公司 | Server device condition maintenance system |
| CN103957247A (en) * | 2014-04-22 | 2014-07-30 | 安一恒通（北京）科技有限公司 | Equipment quality assessment score processing method and device |
| US20170249228A1 (en) * | 2016-02-29 | 2017-08-31 | International Business Machines Corporation | Persistent device fault indicators |
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