CN103309844B - The learning method of a kind of remote controller, device - Google Patents
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Abstract
The present invention is applicable to remote controller technical field, it is provided that the learning method of a kind of remote controller, and described method includes: obtain two groups of learning datas that twice study of remote controller is arrived；Corresponding position in two groups of learning datas is contrasted in order, obtains position and the logarithm of variance data in two groups of learning datas；If the logarithm of variance data is more than 0, then each variance data is contrasted the most by turn with each in its place learning data respectively, if difference is less than the similar threshold value preset, then confirm the set of metadata of similar data that data are variance data of current contrast, finally count the set of metadata of similar data number of each variance data；According to the set of metadata of similar data number of the variance data that statistics obtains, variance data carries out learning the judgement of correctness.The present invention, it is possible to the two groups of data learning remote controller are analyzed, finds out the learning data occurring mistake in two groups of learning datas, it is achieved that feed back remote controller study correctness exactly.
Description
Technical field
The invention belongs to remote controller technical field, particularly relate to the learning method of a kind of remote controller, device.
Background technology
The centralized management of household electrical appliance, the particularly centralized management of home entertaining audio-visual equipment, be digital family system
Core place.At present, the home entertaining audio-visual equipment of thumping majority all uses IR remote controller to carry out the control of equipment, wants reality
The intelligent concentrated controling management of these equipment existing, it is generally required to learn the remote controller of these equipment.
Owing to remote controller can be caused indivedual by impacts such as distance, angle and surroundings during study unavoidably
There is the phenomenon that interference makes mistakes in remotely controlled data, and meanwhile, the maloperation of user is likely to cause remotely controlled corrupt data etc.
Problem, for these problems, current remote controller in learning process, have do not carry out learn success or failure judgement, directly will
All learning datas process as learning success, and have then abandons the learning data made mistakes, again carry out repetitive learning, this
Habit process greatly reduces the efficiency of remote controller learning success.
Summary of the invention
Embodiments provide the learning method of a kind of remote controller, device, it is intended to solve the distant of prior art offer
The problem that the learning method of control device can reduce the efficiency of remote controller learning success.
On the one hand, it is provided that the learning method of a kind of remote controller, described method includes:
Obtain two groups of learning datas that twice study of remote controller is arrived；
Corresponding position in two groups of learning datas is contrasted in order, obtain in two groups of learning datas the position of variance data and
Logarithm；
If the logarithm of variance data is more than 0, then each with its place learning data respectively by each variance data
Position contrasts the most by turn, if difference is less than the similar threshold value preset, then confirms that the data of current contrast are variance data
Set of metadata of similar data, finally count the set of metadata of similar data number of each variance data；
According to the set of metadata of similar data number of the variance data that statistics obtains, variance data is carried out learns correctness and sentences
Disconnected.
On the other hand, it is provided that the learning device of a kind of remote controller, described device includes:
Learning data acquiring unit, for obtaining two groups of learning datas that twice study of remote controller is arrived；
Variance data information acquisition unit, for being contrasted in order the corresponding position in two groups of learning datas, obtains two groups
The position of variance data and logarithm in learning data；
Set of metadata of similar data number statistic unit, if the logarithm for variance data is more than 0, then by each variance data difference
Contrast the most by turn with each in its place learning data, if difference is less than the similar threshold value preset, then confirm
The set of metadata of similar data that data are variance data of current contrast, finally counts the set of metadata of similar data number of each variance data；
Judgment of learning unit, the set of metadata of similar data number of the variance data for obtaining according to statistics, variance data is carried out
The judgement of study correctness.
In the embodiment of the present invention, it is possible to the two groups of data learning remote controller are analyzed, find out two groups of study
Data occur the learning data of mistake, it is achieved that remote controller study correctness is fed back exactly.
Accompanying drawing explanation
Fig. 1 is the flowchart of the learning method of the remote controller that the embodiment of the present invention one provides；
Fig. 2 is position and the realization of logarithm of variance data in two groups of learning datas of acquisition that the embodiment of the present invention one provides
Flow chart；
Fig. 3 is the flowchart of the set of metadata of similar data number obtaining each variance data that the embodiment of the present invention one provides；
Fig. 4 is the structured flowchart of the learning device of the remote controller that the embodiment of the present invention two provides.
Detailed description of the invention
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, right
The present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, and
It is not used in the restriction present invention.
In embodiments of the present invention, two groups of learning datas that twice study of remote controller is arrived first are obtained；Again by two groups of study numbers
Corresponding position according to contrasts in order, obtains position and the logarithm of variance data in two groups of learning datas；If variance data
Logarithm is more than 0, then each variance data contrasted the most by turn with each in its place learning data respectively, if
Difference less than the similar threshold value preset, then confirms the set of metadata of similar data that data are variance data of current contrast, finally counts every
The set of metadata of similar data number of individual variance data；Finally according to the set of metadata of similar data number of the variance data that statistics obtains, to variance data
Carry out learning the judgement of correctness, it is possible to find out the learning data that mistake occurs in two groups of learning datas, it is achieved that to remote control
Device study correctness feeds back exactly.
Below in conjunction with specific embodiment, the realization of the present invention is described in detail:
Embodiment one
What Fig. 1 showed the learning method of the remote controller that the embodiment of the present invention one provides realizes flow process, and details are as follows:
In step S101, obtain two groups of learning datas that twice study of remote controller is arrived.
According to the Data Transport Protocol of current remote controller, host-host protocol can be divided into two classes: a class is to possess " to repeat constant
Property ", during the repeatedly remote control of the most same button, its data sent are the same (such as NEC agreements)；Another kind of is not possess
" repetition invariance ", during the repeatedly remote control of the most same button, its data sent are the different (upsets such as RC5 agreement
Position, this flip bit can produce upset when continuously repeating remote control).Flip bit is the feature of area protocol, because the present invention implements
Example needs to carry out the remotely controlled data made mistakes error correction, and error correction algorithm itself is to being consistent data in two groups of meanings
The process carried out, so after getting two groups of learning datas that twice study of remote controller is arrived, it is necessary to first reject two groups of study numbers
The error correction of learning data it is remotely controlled again after flip bit according to.
In the present embodiment, the two groups of learning datas twice study obtained are respectively stored in two arrays, the two
Array is assumed to be A and B.
In step s 102, being contrasted in order the corresponding position in two groups of learning datas, it is poor to obtain in two groups of learning datas
The position of heteromerism evidence and logarithm.
In this example, it is assumed that array A={a1, a2..., an}；Array B={b1, b2..., bn}.Wherein, a1,
a2..., anIn expression A group learning data first, second ..., n-th；b1, b2..., bnRepresent the study of B group
In data first, second ..., n-th.
D_Count represents the logarithm of variance data, total how many pairs of variance data, cyclic variable in i.e. two groups learning datas
I represents the position at learning data place, less than or equal to the figure place of learning data.Time initial, set D_Count=0, i=1.
In the present embodiment, when the difference of the corresponding position of two groups of learning datas is more than the variance data threshold value preset, then will
The learning data of this correspondence position is as variance data, and records the position that this variance data occurs, finally counts all differences
The logarithm of data.
Concrete implementation flow process as in figure 2 it is shown, wherein, whenTime, then record the value of i, and will
This i value is as the position of variance data, aiAnd biIt is variance data respectively, aiAnd biIt is referred to as a pair variance data, simultaneously by D_
Count does add one operation, and the D_Count obtained after loop ends is the logarithm of the variance data in two groups of learning datas.Its
In, " 15% " is the evaluating of the variance data preset, and this parameter can be adjusted as required, does not limits at this.
In step s 103, if the logarithm of variance data is more than 0, then each variance data is learnt with its place respectively
Each in data contrasts the most by turn, to calculate the absolute value of they differences, if the absolute value of difference is less than
The similar threshold value preset, then confirm the set of metadata of similar data that data are variance data of current contrast, finally count each difference number
According to set of metadata of similar data number.
In this example, it is assumed that array D={d of variance data position1, d2..., dm, when implementing, difference
The position dx of data represents, wherein, x is less than or equal to n less than or equal to m, m.
Sa_Count represents variance data in A group learning dataThe number of set of metadata of similar data, Sb_Count represents B group
Variance data in learning dataThe number of set of metadata of similar data, cyclic variable i represents the position at learning data place, is less than
Figure place in learning data.Time initial, set Sa_Count=0, Sb_Count=0, i=1.
Concrete, as it is shown on figure 3, variance dataWith a in A group learning dataiDifference absolute value if less than
The similar threshold value preset, then confirm thisiForSet of metadata of similar data, and make Sa_Count do to add a process；Variance dataWith B
B in group learning dataiDifference if less than default similar threshold value, then confirm this biForSet of metadata of similar data, and make Sb_
Count does and adds a process.Sa_Count and Sb_Count finally obtained is respectivelyWithRespective learning data is looked for
The number of the set of metadata of similar data arrived.
Wherein,OrIn " 5% " be the evaluating of set of metadata of similar data, this ginseng
Number can be adjusted as required.
In step S104, according to the set of metadata of similar data number of the variance data that statistics obtains, variance data is learnt
The judgement of correctness.
In the present embodiment, set variance data and possess the similar number of fundamental type feature as similar upper limit MAX, do not have
The similar number of standby fundamental type feature is similar lower limit MIN, and the difference number that set of metadata of similar data number is between MIN and MAX
According to then thinking the inconspicuous data of feature.
According to " principle of remote controller learning data error correction algorithm ", can be by the set of metadata of similar data number of variance data to difference
The study correctness of heteromerism evidence judges.
In certain a pair variance data, if the set of metadata of similar data number of two variance data is both greater than MAX, then the two
Variance data all possesses and has fundamental type feature, and i.e. two variance data are all that study is correct, general only when twice study
To data be different key time, just there will be this phenomenon, so learning unsuccessfully.
In certain a pair variance data, if the set of metadata of similar data number of two variance data both less than or is equal to MIN, then
The two variance data does not the most possess and has fundamental type feature, and i.e. two variance data are all study mistakes, so study is lost
Lose.
In certain a pair variance data, if the set of metadata of similar data number of two variance data has one or two to be in MIN
And between MAX, then the two variance data at least one to be belonging to fundamental type feature unconspicuous, so Rule of judgment
The most abundant, it is impossible to judge the study correctness of two variance data, as study failure handling.
In certain a pair variance data, if the set of metadata of similar data number of two variance data has one less than or equal to MIN
And have one more than MAX, then illustrating at this in variance data, a variance data is study mistake, and another is poor
Heteromerism evidence is that study is correct.
As a preferred embodiment of the present invention, this meeting in a pair variance data, a variance data is to learn
Practising mistake, another variance data is the situation that study is correct, is a kind of situation meeting error correction condition, then can be with study
The variance data of correct variance data substitutive learning mistake, just achieves the error correction of learning data.
Such as, in this embodiment, Sa_Count more than MAX, Sa_Count more than Sb_Count, Sb_Count less than or
During equal to MIN, illustrating that the variance data in A group learning data is that study is correct, the variance data in B group learning data is to learn
Practise mistake, therefore, it can substitute the variance data in B group learning data by the variance data in A group learning data, it is achieved B
The error correction of the variance data in group learning data.Certainly, Sb_Count is more than Sa_Coun, Sa_Coun more than MAX, Sb_Count
During less than or equal to MIN, illustrate that the variance data in B group learning data is that study is correct, the difference number in A group learning data
According to being study mistake, therefore, it can substitute the variance data in A group learning data by the variance data in B group learning data,
Realize the error correction of variance data in A group learning data.
It addition, after a pair variance data error correction completes, the logarithm D_Count of variance data should be made to do the process that subtracts.
Explanation of giving an example is as follows:
Assuming that two groups of learning datas that remote controller obtains are respectively as follows:
1. A group learning data starts to be followed successively by from the 1st: 10,20,30,22,21,19 ...
2. B group learning data starts to be followed successively by from the 1st: 11,26,32,20,19,14 ...
First 1. A group learning data is contrasted the most by turn with B group learning data, namely " 10 and the 11 of the 1st ",
" 20 and the 26 of the 2nd ", " 30 and the 32 of the 3rd ", " 22 and the 20 of the 4th ", " 21 and the 19 of the 5th ", " 19 Hes of the 6th
14 " ... contrast respectively；Here the meaning contrasted calculates the absolute value of they differences exactly, calculates the most respectively |
10-11|、|20-26|、|30-32|、|22-20|、|21-19|、|19-14|……；If who (two learning data) is poor
The absolute value of value is more than certain scope (such as 4), then two learning datas being considered as this are variance data；Finish-unification
Count out position (i.e. who) and the number (the most how many pairs) of variance data of variance data.If the difference of two learning datas
Think more than 4 for variance data, then 1. example learning data are " the 2nd " with learning data variance data position 2.
" the 6th ", the logarithm of variance data is: 2.
Then first " 20 " of " the 2nd " respectively with the learning data at its place 1. in every learning data contrast,
Here " contrast " is the absolute value calculating they differences too, if the absolute value of difference is less than certain scope (example
Such as 3), then think similar to " 20 " to the learning data that " 20 " contrast；Equally, to " 26 " of " the 2nd " at learning data 2.
In be also carried out the statistics of set of metadata of similar data number.If the absolute value of difference thinks set of metadata of similar data less than 3, then " the 2nd " is poor
Heteromerism according in, the set of metadata of similar data of " 20 " is " 20 (self), 22,21,19 " totally 4, and the set of metadata of similar data of " 26 " is " 26 (self) "
Totally 1.Certainly, variance data " 19 " and " 14 " of " the 6th " also must be added up.
Set of metadata of similar data number according to the variance data counted on, carries out learning the judgement of correctness to variance data.
Such as set of metadata of similar data number learns correctly more than thinking of 3 (MAX=3), and set of metadata of similar data number is recognized less than or equal to 1 (MIN=1's)
For study mistake, then in " the 2nd " variance data, the similar number of " 20 " is 4 (having 4 more than 3), it is believed that it is
Learning correct, the similar number of " 26 " is 1 (having 1 less than or equal to 1), it is believed that it is study mistake." the 2nd "
Variance data in, have a study correct, have a study mistake, this has just possessed error correction condition, " 26 " of mistake has been replaced
On behalf of correct " 20 ", just complete the error correction of variance data on the 2nd.Certainly, " the 6th " variance data is the most necessary
Judge according to above-mentioned condition and get corresponding results.
Learning data 1. and 2. in have how many positions (to) variance data, it is necessary to as " the 2nd " variance data in example
Equally walk one time, just can complete the error correction of all differences data.
The present embodiment, it is possible to the two groups of data learning remote controller are analyzed, and find out in two groups of learning datas
The learning data of mistake occurs, it is achieved that remote controller study correctness is fed back exactly.It addition, when a pair difference number
Having one according to is that study is correct, another is when being study mistake, can be by the correct variance data substitutive learning of study
The variance data of mistake, it is achieved that the correction process to learning data, can carry significantly learning unsuccessfully to be changed into learning success
The efficiency that high remote controller successfully learns.
One of ordinary skill in the art will appreciate that all or part of step realizing in the various embodiments described above method is can
Completing instructing relevant hardware by program, corresponding program can be stored in a computer read/write memory medium
In, described storage medium, such as ROM/RAM, disk or CD etc..
Embodiment two
Fig. 4 shows the concrete structure block diagram of the learning device of the remote controller that the embodiment of the present invention two provides, for the ease of
Illustrate, illustrate only the part relevant to the embodiment of the present invention.This device includes: learning data acquiring unit 41, variance data
Information acquisition unit 42, set of metadata of similar data number statistic unit 43 and judgment of learning unit 44.
Wherein, learning data acquiring unit 41, for obtaining two groups of learning datas that twice study of remote controller is arrived；
Variance data information acquisition unit 42, for being contrasted in order the corresponding position in two groups of learning datas, obtains two
Organize position and the logarithm of variance data in learning data；
Set of metadata of similar data number statistic unit 43, if the logarithm for variance data is more than 0, then divides each variance data
Do not contrast the most by turn with each in its place learning data, to calculate the absolute value of they differences, if poor
The absolute value of value less than the similar threshold value preset, then confirms the set of metadata of similar data that data are variance data of current contrast, finish-unification
Count out the set of metadata of similar data number of each variance data；
Judgment of learning unit 44, the set of metadata of similar data number of the variance data for obtaining according to statistics, variance data is entered
The judgement of row study correctness.
Further, described device also includes:
Flip bit culling unit, for rejecting the flip bit in two groups of learning datas.
Further, described device also includes:
Correcting data error unit, if in a pair variance data, a variance data is study mistake, and another is poor
Heteromerism is according to being that study is correct, then by the variance data learning correct variance data substitutive learning mistake.
Concrete, described judgment of learning unit 44 includes:
First judge module, in a pair variance data, if the set of metadata of similar data number of two variance data is the biggest
In similar upper limit MAX, then two variance data are all that study is correct；
Second judge module, in a pair variance data, if the set of metadata of similar data number of two variance data is the least
In or equal to similar lower limit MIN, then two variance data are all study mistakes；
3rd judge module, in a pair variance data, if the set of metadata of similar data number of two variance data has one
Individual or two be between similar lower limit MIN and similar upper limit MAX, then cannot judge the study of two variance data correct with
No；
4th judge module, in a pair variance data, if the set of metadata of similar data number of two variance data has one
Individual less than or equal to similar lower limit MIN and also have one then a variance data be study mistake more than similar upper limit MAX,
Another variance data is that study is correct.
The device that the embodiment of the present invention provides can be applied in the embodiment of the method one of aforementioned correspondence, and details see above-mentioned
The description of embodiment one, does not repeats them here.
It should be noted that in said apparatus embodiment, included unit simply carries out drawing according to function logic
Point, but it is not limited to above-mentioned division, as long as being capable of corresponding function；It addition, each functional unit is concrete
Title also only to facilitate mutually distinguish, is not limited to protection scope of the present invention.
The foregoing is only presently preferred embodiments of the present invention, not in order to limit the present invention, all essences in the present invention
Any amendment, equivalent and the improvement etc. made within god and principle, should be included within the scope of the present invention.
Claims (6)
1. the learning method of a remote controller, it is characterised in that described method includes:
Obtain two groups of learning datas that twice study of remote controller is arrived；
Corresponding position in two groups of learning datas is contrasted in order, obtains the position of variance data in two groups of learning datas and right
Number；
If the logarithm of variance data is more than 0, then each variance data is pressed with each in its place learning data respectively
Order contrasts by turn, to calculate the absolute value of they differences, if the absolute value of difference is less than the similar threshold value preset, then
Confirm the set of metadata of similar data that data are variance data of current contrast, finally count the set of metadata of similar data number of each variance data；
According to the set of metadata of similar data number of the variance data that statistics obtains, variance data carries out learning the judgement of correctness；
The set of metadata of similar data number of the described variance data obtained according to statistics, carries out learning the judgement of correctness to variance data
Including:
In a pair variance data, if the set of metadata of similar data number of two variance data is both greater than similar upper limit MAX, then two differences
Heteromerism evidence is all that study is correct；
In a pair variance data, if the set of metadata of similar data number of two variance data both less than or is equal to similar lower limit MIN, then
Two variance data are all study mistakes；
In a pair variance data, if the set of metadata of similar data number of two variance data has one or two to be in similar lower limit
Between MIN to similar upper limit MAX, then cannot judge the study correctness of two variance data；
In a pair variance data, if the set of metadata of similar data number of two variance data has one less than or equal to similar lower limit
MIN and have one then a variance data be study mistake more than similar upper limit MAX, another variance data is study
Correct.
2. the method for claim 1, it is characterised in that obtain two groups of learning datas arriving of twice study of remote controller it
After, also include:
Reject the flip bit in two groups of learning datas.
3. the method for claim 1, it is characterised in that in the set of metadata of similar data of the described variance data obtained according to statistics
Number, after variance data carries out the judgement of study correctness, also includes:
If in a pair variance data, a variance data is study mistake, and another variance data is that study is correct, then
By the variance data learning correct variance data substitutive learning mistake.
4. the learning device of a remote controller, it is characterised in that described device includes:
Learning data acquiring unit, for obtaining two groups of learning datas that twice study of remote controller is arrived；
Variance data information acquisition unit, for being contrasted in order the corresponding position in two groups of learning datas, obtains two groups of study
The position of variance data and logarithm in data；
Set of metadata of similar data number statistic unit, if for the logarithm of variance data more than 0, then by each variance data respectively with its
Each in the learning data of place contrasts the most by turn, to calculate the absolute value of they differences, if difference is exhausted
To value less than the similar threshold value preset, then confirm the set of metadata of similar data that data are variance data of current contrast, finally count every
The set of metadata of similar data number of individual variance data；
Judgment of learning unit, the set of metadata of similar data number of the variance data for obtaining according to statistics, variance data is learnt
The judgement of correctness；
Described judgment of learning unit includes:
First judge module, in a pair variance data, if the set of metadata of similar data number of two variance data is both greater than phase
Like upper limit MAX, then two variance data are all that study is correct；
Second judge module, in a pair variance data, if the set of metadata of similar data number of two variance data both less than or
Equal to similar lower limit MIN, then two variance data are all study mistakes；
3rd judge module, in a pair variance data, if the set of metadata of similar data number of two variance data have one or
Two are between similar lower limit MIN and similar upper limit MAX, then cannot judge the study correctness of two variance data；
4th judge module, in a pair variance data, if the set of metadata of similar data number of two variance data have one little
In or equal to similar lower limit MIN and also have one then a variance data be study mistake more than similar upper limit MAX, another
Individual variance data is that study is correct.
5. device as claimed in claim 4, it is characterised in that described device also includes:
Flip bit culling unit, for rejecting the flip bit in two groups of learning datas.
6. device as claimed in claim 4, it is characterised in that described device also includes:
Correcting data error unit, if in a pair variance data, a variance data is study mistake, another difference number
According to being that study is correct, then by the variance data learning correct variance data substitutive learning mistake.
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| CN104064021B (en) * | 2014-06-20 | 2018-07-27 | Tcl集团股份有限公司 | A kind of learning method of remote controler, device and amusement audio-visual equipment |
| CN106228796B (en) * | 2016-08-30 | 2019-07-05 | 深圳市友连电子科技有限公司 | The learning method and device of infrared remote control |
| CN110164117A (en) * | 2019-05-24 | 2019-08-23 | 珠海多士科技有限公司 | A kind of IR remote controller signal compression storage method |
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