CN107886028A - The food materials input method and food materials input device of a kind of refrigerator - Google Patents
The food materials input method and food materials input device of a kind of refrigerator Download PDFInfo
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- CN107886028A CN107886028A CN201610867357.9A CN201610867357A CN107886028A CN 107886028 A CN107886028 A CN 107886028A CN 201610867357 A CN201610867357 A CN 201610867357A CN 107886028 A CN107886028 A CN 107886028A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
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Abstract
The present invention relates to a kind of food materials input method of refrigerator and food materials input device, the food materials input method includes：Obtain the indoor food materials image of storing, the N kinds prediction food materials species and its confidence level gone out according to the food materials image recognition, wherein N is positive integer, predicts that food materials species and its confidence level determine the species of the food materials according to the N kinds, also provided is the food materials input device using this method.Using the technical program so that in food materials identification process, improve the accuracy of identification, reduce the wasting of resources, and lift Consumer's Experience.
Description
Technical field
The present invention relates to the food materials input method and food materials input device of refrigerator field, more particularly to a kind of refrigerator.
Background technology
With the intelligentized development of household electrical appliances, refrigerator also begins to intellectuality, and band display screen, image recognition in the market fills
Put, have the intelligent refrigerator of recipe management function more and more.
Many refrigerators carry out food materials typing or storage management by image recognition at present, are reduced with this and are manually eaten
Material typing.But due to the article storage in refrigerator during, food materials are overlapping, food materials feature is alike etc. inevitably be present
Situation, cause the image recognition rate of article relatively low, and need user to be manually entered setting for these food materials that can not be identified, frequency
Numerous manual operation not only influences the usage experience of user, while also result in the wasting of resources of image recognition processes.
The content of the invention
It is contemplated that at least partly solve problems of the prior art, there is provided a kind of food materials for being based primarily upon refrigerator
Recognition methods so that in food materials identification process, improve the accuracy of identification, reduce the wasting of resources, lift Consumer's Experience.
The technical solution adopted by the present invention is：
A kind of food materials input method of refrigerator, including：
Obtain the indoor food materials image of storing；
The N kinds prediction food materials species and its confidence level, wherein N gone out according to the food materials image recognition is positive integer；
Predict that food materials species and its confidence level determine the species of the food materials according to the N kinds.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
When the confidence level of the prediction food materials species is more than the first confidence level preset value, determine that the prediction food materials are the food
The species of material；
When the confidence level of the prediction food materials species is less than the first confidence level preset value and is more than the second confidence level preset value
When, the species for determining the food materials is calculated according to default algorithm.
Further, the default algorithm includes：
The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient
Weighted average；
Determine that the weighted average highest prediction food materials are the food materials species.
Further, when the confidence level of the prediction food materials species is less than the first confidence level preset value and more than second
During confidence level preset value, after the species step for determining the food materials is calculated according to default algorithm, in addition to：
When detecting the action message of the refrigerator door；
The species of the food materials is identified again according to presetting method in predetermined period；
When the result that the food materials again identify that is identical with the food materials species that determination is calculated according to default algorithm, it is determined that described
The species of food materials.
Further, after the action message step of the refrigerator door is detected, in addition to：
Detect the positional information and/or weight information of the indoor food materials of the storing；
When the positional information and/or weight information of the indoor food materials of the storing change, the root again in predetermined period
The species of the food materials is identified according to presetting method.
Further, when the result that the food materials again identify that is different from the species determined according to the default algorithm
When, determine that the prediction food materials species that the confidence level is more than the first confidence level preset value is the species of the food materials.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
N kinds prediction food materials species is arranged according to the confidence level descending；
When Xi-Xi+1 >=predetermined threshold value, then it is the food materials species to exclude i+1 prediction food materials species, wherein, i is less than N's
Positive integer；
The species of the food materials is determined according to the i kinds prediction food materials species after the exclusion and its confidence level.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or
Regional information；
When the current season and/or the regional information season corresponding from predicting food materials species and/or regional information are different
When, then exclude the prediction food materials species；
The species of the food materials is determined according to the prediction food materials species after exclusion and its confidence level.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or
Regional information；
The default weight coefficient is adjusted according to season and/or regional information；
The confidence level of the N kinds prediction food materials species and corresponding edible inertia values are calculated according to the weight coefficient after adjustment
Weighted average.
Further, it is determined that after the species step of the food materials, in addition to：
Display or the determination kind of information that the food materials are sent to user；
The species of the food materials is determined according to user's confirmation in preset time.
Further, determine that the species of the food materials includes according to user's confirmation in preset time：
Obtain the food materials species after user's corrigendum；
The edible inertia values and/or weight coefficient of the food materials are adjusted according to the food materials species after corrigendum.
Further, the edible inertia values include one below：Edible number, edible frequency, edible quantity, edible disappear
Consumption rate.
In addition, a kind of food materials input device of refrigerator is additionally provided, including：
Image collection module, the food materials image indoor for obtaining storing；
Recognition processing module, for food materials image described in identifying processing to obtain the prediction food materials species of the food materials and its confidence
Degree；
Species determining module, for determining the species of the food materials according to the confidence level of the prediction food materials species.
Further, the species determining module includes：
First species determination subelement, for when it is described prediction food materials species confidence level be more than the first confidence level preset value when,
Determine the species that the prediction food materials are the food materials；
Second species determination subelement, for being less than the first confidence level preset value when the confidence level of the prediction food materials species
And when being more than the second confidence level preset value, the species for determining the food materials is calculated according to default algorithm.
Further, the default algorithm includes：
The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient
Weighted average；
Determine that the weighted average highest prediction food materials are the food materials species.
Further, in addition to：
First detection module, for after the second species determination subelement determines the species of the food materials, detecting the ice
The action message of chamber door body；
The recognition processing module, it is additionally operable to identify the species of the food materials again according to presetting method in predetermined period；
The species determining module, the result for being additionally operable to again identify that in the food materials according to default algorithm with calculating what is determined
When food materials species is identical, the species of the food materials is determined.
Further, in addition to：
Second detection module, for detect the refrigerator door in first detection module action signal after, detect the storage
The positional information and/or weight information of the indoor food materials of thing；
The recognition processing module, it is additionally operable to become in the positional information and/or weight information of the indoor food materials of the storing
During change, the species of the food materials is identified again according to presetting method in predetermined period.
Further, including：
The first species determination subelement, the result for being additionally operable to again identify that in the food materials with according to the default algorithm
During the species difference of determination, determine that the prediction food materials that the confidence level is more than the first confidence level preset value are the kind of the food materials
Class.
Further, in addition to：
Screening module, for by the N kinds prediction food materials species arrange according to the confidence level descending, and Xi-Xi+1 >=in advance
If during threshold value, then it is the food materials species to exclude i+1 prediction food materials, wherein, i is the positive integer less than N；
The species determining module, it is additionally operable to according to determining the i kinds prediction food materials species after the exclusion and its confidence level
The species of food materials.
It is using the beneficial effect of above-mentioned technical proposal：
First, according to the prediction food materials species and its confidence level gone out according to the indoor interior food materials image recognition of storing, institute is determined
State the species of food materials.Specifically, when the confidence level of the prediction food materials species is more than the first confidence level preset value, it is determined that described
Predict the species that food materials are the food materials；When it is described prediction food materials species confidence level be less than the first confidence level preset value and
During more than the second confidence level preset value, the prediction confidence level of food materials species and right with it is calculated according to default weight coefficient
The weighted average for the edible inertia values answered, determine that the weighted average highest prediction food materials are the food materials species.It is logical
Cross and interval division carried out to the confidence level of identification, carry out directly determining respectively with fuzzy determination, especially for fuzzy determination, according to
Default weight coefficient and edible number, edible frequency, the edible number for combining the prediction food materials species for being accustomed to extraction based on user
The edible inertia values such as amount, edible consumption rate, it is quick so as to aid in predicting that the confidence level of food materials species is weighted
The species of food materials is identified, image recognition rate is improved with this, lifts Consumer's Experience.Further, it is also current by obtaining refrigerator
Residing season and/or regional information, and the season corresponding with the prediction food materials species to prestore and/or region are believed
Breath, the default weight coefficient is adjusted according to season and/or regional information, then calculated again according to the weight coefficient after adjustment
The confidence level of food materials species and the weighted average of corresponding edible inertia values are predicted, so as to further improve food materials identification
The success rate of typing.
Secondly as when user accesses food materials every time, may all cause to move the food materials stored before, especially
For the food materials that were originally blocked may therefore and be emerging in image acquiring device image obtain in the range of, and originally appeared compared with
Therefore few food materials may appear more, therefore, be less than first confidence level in the confidence level of the prediction food materials species
Preset value and when being more than the second confidence level preset value, after the species step for determining the food materials is calculated according to default algorithm, also
The action message detection of refrigerator door is carried out, and identifies the kind of the food materials again according to presetting method in predetermined period
Class, when the result that the food materials again identify that is identical with the food materials species that determination is calculated according to default algorithm, it is determined that described
The species of food materials, so as to be again identified that to the food materials originally by fuzzy diagnosis, the accuracy of image recognition is improved with this.
Further, the positional information of the indoor food materials of the storing is also detected after the action message of the refrigerator door is detected
And/or weight information, when the positional information and/or weight information of the indoor food materials of the storing change, in default week
The species of the food materials is identified in phase again according to presetting method, avoids user from only opening refrigerator doors with this and checks storing compartment
Interior food materials and do not carry out starting image recognition processing in the case of food materials access.Further, again identified that when the food materials
As a result with determined according to the default algorithm species difference when, determine that the confidence level is more than the first confidence level preset value
The species that food materials species is the food materials is predicted, the food materials species of previously passed fuzzy determination is corrected automatically with this, entered
And improve the accuracy of food materials identification typing.
Again, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：By the N
Kind prediction food materials species arranges according to the confidence level descending, when Xi-Xi+1 >=predetermined threshold value, then excludes i+1 prediction food
Assortment class is the food materials species, wherein, i is positive integer less than N, according to the i kinds after the exclusion predict food materials species and
Its confidence level determines the species of the food materials.Further, also by obtaining current season and/or regional information, and in advance
The season corresponding with the prediction food materials species deposited and/or regional information, when the current season and/or regional information
When the season corresponding with prediction food materials species and/or regional information difference, then the prediction food materials species is excluded, and according to row
Prediction food materials species and its confidence level after removing determine the species of the food materials, so as to reject the noise of prediction food materials species kind,
Improve the accuracy for calculating the species for determining the food materials in subsequent processes according to default algorithm.
Finally, it is determined that the species of the food materials, the determination species that the food materials are also sent by display or to user are believed
Breath, the species of the food materials is then determined according to user's confirmation in preset time.Further, when described in user's corrigendum
After the species of food materials, the food materials species after being corrected according to user adjusts the edible inertia values and/or weight coefficient of the food materials, with
This improves the accuracy of next food materials identification Input Process.
Brief description of the drawings
The above-mentioned and/or additional aspect and advantage of the present invention will become in the description from combination accompanying drawings below to embodiment
Substantially and it is readily appreciated that, wherein：
Fig. 1 is the food materials input method flow chart in first aspect present invention embodiment；
Fig. 2 is the food materials input method flow chart in a specific embodiment of the invention；
Fig. 3 is the food materials typing workflow diagram in another specific embodiment of the invention；
Fig. 4 is the food materials typing workflow diagram in another specific embodiment of the invention；
Fig. 5 is the food materials input device structural representation in second aspect of the present invention embodiment；
Fig. 6 is the food materials input device structural representation in another specific embodiment of the invention.
Embodiment
To make the goal of the invention of the present invention, technical scheme and beneficial effect of greater clarity, below in conjunction with the accompanying drawings to this
The embodiment of invention is illustrated, it is necessary to illustrate, in the case where not conflicting, in the embodiment and embodiment in the application
Feature can mutually be combined.
In the embodiment of first aspect present invention, there is provided a kind of food materials input method of refrigerator, idiographic flow is as schemed
1st, shown in Fig. 2, including：
S100：Obtain the indoor food materials image of storing；
S200：The N kinds prediction food materials species and its confidence level, wherein N gone out according to the food materials image recognition is positive integer；
S300：Predict that food materials species and its confidence level determine the species of the food materials according to the N kinds.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
S310：When the confidence level of the prediction food materials species is more than the first confidence level preset value, determine that the prediction food materials are
The species of the food materials；
S320：When the confidence level of the prediction food materials species is less than the first confidence level preset value and pre- more than the second confidence level
If during value, the species for determining the food materials is calculated according to default algorithm.
In embodiments of the present invention, according to the result of image recognition, confidence interval division is carried out, according to prediction food materials
Confidence value carries out the determination of food materials species respectively, and the accuracy of image recognition food materials is improved with this.For example, described first
Confidence level preset value is 70%, and the second confidence level preset value is 10%, for A food materials, by identifying processing, obtains predicting food materials kind
Class is A and B, and the confidence level that prediction food materials species is A is 80%, and the confidence level that prediction food materials species is B is 20%, then, due to
Predict that the confidence level that food materials species is A is more than 70%, it is A food materials to determine its species；When the confidence that prediction food materials species is A food materials
Spend for 55%, prediction food materials species is 40% for the confidence level of B food materials, then, then need ability after being calculated according to default algorithm
Enough determine the species of the food materials；When the prediction food materials species of A food materials is tri- kinds of A, B, C, and predict that the confidence level that food materials are A is
50%, the confidence level that prediction food materials are B is 45%, and the confidence level that prediction food materials are C is 5%, because prediction food materials are C confidence level
Less than the second confidence level preset value 10%, therefore, prediction food materials C is rejected, only to prediction food materials A and prediction food materials B according to default
Algorithm calculates the species for determining the food materials.
In addition, illustrate for the ease of the subsequent content of technical scheme, when the confidence level of the prediction food materials species
During more than the first confidence level preset value, determine that the prediction food materials are defined as directly determining for the method for the species of the food materials
Method；When the confidence level of the prediction food materials species is less than the first confidence level preset value and is more than the second confidence level preset value
When, the method that the species for determining the food materials is calculated according to default algorithm is defined as fuzzy determination method.
Further, the default algorithm includes：
The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient
Weighted average；
Determine that the weighted average highest prediction food materials are the food materials species.
Further, the edible inertia values include one below：Edible number, edible frequency, edible quantity, edible disappear
Consumption rate.
For fuzzy determination method, according to default weight coefficient and the prediction food materials species based on user's custom extraction is combined
Edible number, edible frequency, edible quantity, the edible inertia values such as edible consumption rate, to predicting that the confidence level of food materials species is entered
Row weighted calculation, so as to aid in quickly recognizing the species of food materials, image recognition rate is improved with this, lifts Consumer's Experience.
Still by taking first, second confidence level preset value described above as an example, it is assumed that the weight of confidence level is 60%, and the power of edible inertia values
Weight is 40%, then, for A food materials, prediction food materials species is that B confidence level is 55%, and the confidence level that prediction food materials species is A is
40%, prediction food materials species is that C confidence level is 5%, and predicts that food materials B edible inertia values are 30%, and prediction food materials A's is edible
Inertia values are 60%, and food materials C edible inertia values are 10%, then prediction food materials B weighted average is 55%*60%+30%*40%
=0.45, prediction food materials A weighted average is 40%*60%+60%*40%=0.48, and prediction food materials C weighted average is 0.07
（5%*60%+10%*40%）, then, confirm that prediction food materials A is final food materials species.
It should be noted that the edible inertia values are determined based on the use habit of reaction user, frequency is eaten
Calculation can be in the statistical history time（Such as in 60 days）Certain food materials use number of days, if in the past of statistics
In 60 days, edible food materials A number of days has 15 days, then the food materials A edible frequency is 25%.However, it is possible to simple edible frequency is also
It can not reflect that user is accustomed to well, although edible frequency is higher for some food materials, but amount is less, and hence it is also possible to
With reference to the use weight or quantity of food materials) data, further to lift accuracy；During calculating according to the amount of food materials with
The ratio of amount of purchase, 10 jin of food materials B are have purchased as an amount parameter, such as user, have eaten 8 jin, then food materials B
Edible consumption is：（8÷10）×100% = 80%.
In one embodiment of the invention, as shown in figure 3, when the confidence level of the prediction food materials species is less than described the
One confidence level preset value and when being more than the second confidence level preset value, calculates according to default algorithm and determines that the species of the food materials walks
After rapid, in addition to：
When detecting the action message of the refrigerator door；
The species of the food materials is identified again according to presetting method in predetermined period；
When the result that the food materials again identify that is identical with the food materials species that determination is calculated according to default algorithm, it is determined that described
The species of food materials.
During user stores food materials to refrigerator storing compartment, it may make to be layered on top of each other and by part between food materials
Or block completely, cause pattern recognition device not accurately identify.But user may be to it in access food materials every time
He moves food materials so that and the food materials being originally blocked more are emerging in the range of the image acquisition of image acquiring device,
Moreover, image recognition work is generally all carried out after refrigerator door closing, therefore, completing a switch gate action with refrigerator is
It is accurate.It is less than the first confidence level preset value in the confidence level of the prediction food materials species and is more than the second confidence level preset value
When, after the species step for determining the food materials is calculated according to default algorithm, the action message detection of refrigerator door is also carried out, and
The species of the food materials is identified again according to presetting method in predetermined period, i.e., for food materials species is determined by fuzzy
The food materials that method determines are identified in predetermined period again according to presetting method, the result and root again identified that when the food materials
When the food materials species determined according to the calculating of default algorithm is identical, that is, the species of the food materials is determined, image recognition is improved with this
Accuracy.
Wherein, it is described default in the species step for identifying the food materials again according to presetting method in predetermined period
Cycle can be that user is set or refrigerator program of dispatching from the factory is default, can be specifically one week, one month, a season
Degree or the number acted according to refrigerator door, such as 10 times, 20 times, it can specifically be set according to being actually needed；It is described pre-
Equipment, method can be that RF-wise identification is determined or determined by other method identification.Preferably, it is described default
Method is determined by image recognition, particularly the Weigh Direct Determination by foregoing image recognition and fuzzy determination method.
Further, when the result that the food materials again identify that is different from the species determined according to the default algorithm
When, determine that the prediction food materials species that the confidence level is more than the first confidence level preset value is the species of the food materials.
Situation about being again identified that for above-mentioned food materials, if the confidence level of the prediction food materials species after again identifying that is more than first
Confidence level preset value, that is, the result again identified that are determined by foregoing Weigh Direct Determination, and obscure the food determined with previous
During assortment class difference, then it is defined by the result again identified that by Weigh Direct Determination.Therefore, it is necessary to by previously passed fuzzy determination
The result that mode identifies is corrected as the result of direct determination mode, so as to improve the accuracy of image recognition.
Further, after the action message step of the refrigerator door is detected, in addition to：
Detect the positional information and/or weight information of the indoor food materials of the storing；
When the positional information and/or weight information of the indoor food materials of the storing change, the root again in predetermined period
The species of the food materials is identified according to presetting method.
During refrigerator use, user may be only to open refrigerator door to check food materials situation, actually not right
The indoor food materials of storing are operated, now nonsensical if image recognition is carried out again.Therefore, it is also desirable to judge user
Whether food materials are operated, therefore, by detecting the positional information and/or weight information of the indoor food materials of storing, only when
When the positional information and/or weight information of the indoor food materials of storing change, again according to default in predetermined period
Method identifies the species of the food materials, and it is that switch gate checks error starting progress image recognition after food materials to avoid user, is ensured
The degree of accuracy of image recognition can be improved by starting image recognition each time.
In one embodiment of the invention, predict that food materials species and its confidence level determine the food materials according to the N kinds
Species include：
N kinds prediction food materials species is arranged according to the confidence level descending；
When Xi-Xi+1 >=predetermined threshold value, then it is the food materials species to exclude i+1 prediction food materials species, wherein, i is less than N's
Positive integer；
The species of the food materials is determined according to the i kinds prediction food materials species after the exclusion and its confidence level.
For example, the predetermined threshold value is set to 20%, when the prediction food materials species of A food materials is tri- kinds of A, B, C, and predicts
The confidence level that food materials are A is 50%, and the confidence level that prediction food materials are B is 45%, and the confidence level that prediction food materials are C is 5%, due to pre-
The difference for surveying the confidence level that food materials are A and the confidence level that prediction food materials are B is 5%, and the confidence level that prediction food materials are B is eaten with predicting
The difference for the confidence level that material is C is 40%, thus be excluded that prediction food materials C, by prediction food materials A and prediction food materials B according to default calculation
Method calculates the species for determining the food materials.
Further, predict that food materials species and its confidence level determine that the species of the food materials includes according to the N kinds：
Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or
Regional information；
When the current season and/or the regional information season corresponding from predicting food materials species and/or regional information are different
When, then exclude the prediction food materials species；
The species of the food materials is determined according to the prediction food materials species after exclusion and its confidence level.
For example, in Xinjiang, the probability of buying to red bayberry is relatively low, then when there is red bayberry in recognition result, rejects this prediction
Food materials species.Specifically, the season and/or regional information can be user preset or refrigerator by networking, and
Obtained from the webserver.In addition, the webserver is also possible to communicate with the every other refrigerator in a certain region
Connection, by the food materials storehouse data statistics stored to other refrigerators, when not occurring red bayberry recently in other refrigerators of the region,
Then reject red bayberry this result in recognition result.
It is determined that before food materials species, default data processing is carried out according to the confidence level distribution of prediction food materials species, and
Season/and/or regional information according to residing for refrigerator, the interference of the relatively low prediction food materials species of some confidence levels is rejected, is carried
The accuracy of high food materials identification.
In one embodiment of the invention, predict that food materials species and its confidence level determine the food materials according to the N kinds
Species also include：
Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or
Regional information；
The default weight coefficient is adjusted according to season and/or regional information；
The confidence level of the N kinds prediction food materials species and corresponding edible inertia values are calculated according to the weight coefficient after adjustment
Weighted average.
For example, in some regions, the food materials difference in winter and spring is relatively large, and inertia values are eaten used in spring
It may be calculated still using the food materials in winter as foundation if now still eating inertia values using this according to default weight coefficient
If, discrimination can be caused to reduce, it is necessary to the corresponding weight coefficient for reducing the edible inertia values in preset algorithm.
In one embodiment of the invention, as shown in figure 4, it is determined that after the species step of the food materials, in addition to：
Display or the determination kind of information that the food materials are sent to user；
The species of the food materials is determined according to user's confirmation in preset time.
Further, determine that the species of the food materials includes according to user's confirmation in preset time：
Obtain the food materials species after user's corrigendum；
The edible inertia values and/or weight coefficient of the food materials are adjusted according to the food materials species after corrigendum.
Specifically, the determination kind of information of the food materials can prompt the user with confirmation by voice mode, can also
It is by sending confirmation to the customer mobile terminal associated with refrigerator, it is preferred that pass through the operation display interface on refrigerator
The determination kind of information for sending the food materials is shown to user, treats that user is confirmed.When recognition result is correct, user is carried out
Operation determines, to reduce the operation of user, can also set passage time, i.e., what user was not operated in preset time then writes from memory
Think that identification is correct；After user has found identification mistake and modifies, then the food materials information of mistake is rejected, and receive typing use
Correct food materials information after the corrigendum of family, the food materials species that can be also determined according to user adjust the food materials edible inertia values and/
Or weight coefficient, so as to continue to optimize the result of fuzzy determination, improve the accuracy of identification.
In embodiment according to a second aspect of the present invention, a kind of food materials input device of refrigerator is additionally provided, such as Fig. 5 institutes
Show, including：
Image collection module 10, the food materials image indoor for obtaining storing；
Recognition processing module 20, for food materials image described in identifying processing to obtain the prediction food materials species of the food materials and its put
Reliability；
Species determining module 30, for determining the species of the food materials according to the confidence level of the prediction food materials species.
Specifically, can by refrigerator storing is indoor or refrigerator door on set camera to obtain food materials image.
Further, the species determining module 30 includes：
First species determination subelement 31, for being more than the first confidence level preset value when the confidence level of the prediction food materials species
When, determine the species that the prediction food materials are the food materials；
Second species determination subelement 32, for being preset when the confidence level of the prediction food materials species less than first confidence level
When being worth and being more than the second confidence level preset value, the species for determining the food materials is calculated according to default algorithm.
Further, the default algorithm includes：
The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient
Weighted average；
Determine that the weighted average highest prediction food materials are the food materials species.
In one embodiment of the invention, as shown in fig. 6, also including：
First detection module 40, for after the second species determination subelement determines the species of the food materials, described in detection
The action message of refrigerator door；
The recognition processing module 20, it is additionally operable to identify the kind of the food materials again according to presetting method in predetermined period
Class；
The species determining module 30, the result for being additionally operable to again identify that in the food materials determine with being calculated according to default algorithm
Food materials species it is identical when, determine the species of the food materials.
Further, in addition to：
Second detection module 50, for detect the refrigerator door in first detection module action signal after, described in detection
The positional information and/or weight information of the indoor food materials of storing；
The recognition processing module 20, it is additionally operable to positional information and/or the weight information generation in the indoor food materials of the storing
During change, the species of the food materials is identified again according to presetting method in predetermined period.
Specifically, food can be perceived with this in the corresponding position sensor of the indoor setting of storing and/or weight sensor
Whether material occurs the change of position and/or weight.
Further, including：
The first species determination subelement 31, the result for being additionally operable to again identify that in the food materials with according to the default calculation
During the species difference that method determines, determine that the prediction food materials that the confidence level is more than the first confidence level preset value are the kind of the food materials
Class.
Determined or by its other party it should be noted that the presetting method can be RF-wise identification
Method identification determines.Preferably, the presetting method is determined by image recognition, and Fig. 6 is to be based on the presetting method
The structural representation determined by image recognition.
Further, in addition to：
Screening module 60, for by the N kinds prediction food materials species arranged according to the confidence level descending, and Xi-Xi+1 >=
During predetermined threshold value, then it is the food materials species to exclude i+1 prediction food materials, wherein, i is the positive integer less than N；
The species determining module 30, it is additionally operable to determine institute according to the i kinds prediction food materials species after the exclusion and its confidence level
State the species of food materials.
In the present invention, term " first ", " second " are only used for the purpose described, and it is not intended that instruction or hint
Relative importance；Term " multiple " then refers to two or more, is limited unless otherwise clear and definite.For the common of this area
For technical staff, the concrete meaning of above-mentioned term in the present invention can be understood as the case may be.
In description of the invention, it is to be understood that the orientation or position relationship of the instruction such as term " on ", " under " are base
In orientation shown in the drawings or position relationship, description description of the invention and simplified, rather than instruction or hint are for only for ease of
Signified device or unit must have specific direction, with specific azimuth configuration and operation, it is thus impossible to be interpreted as pair
The limitation of the present invention.
In the description of this specification, term " one embodiment ", " some embodiments ", " specific embodiment " etc. are retouched
State and mean that combining specific features, structure, material or feature that the embodiment or example describe is contained at least one of the present invention
In embodiment or example.In this manual, the schematic representation of above-mentioned term is not necessarily referring to identical embodiment or
Example.Moreover, specific features, structure, material or the feature of description can be in any one or more embodiments or example
Combine in an appropriate manner.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing module, can also
That unit is individually physically present, can also two or more units be integrated in a module.Above-mentioned integrated mould
Block can both be realized in the form of hardware, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized in the form of software function module and as independent production marketing or in use, can also be stored in a computer
In read/write memory medium.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the invention, for the skill of this area
For art personnel, the present invention can have various modifications and variations.It is all within the spirit and principles of the utility model, made
Any modification, equivalent substitution and improvements etc., should be included in the scope of the protection.
Claims (19)
- A kind of 1. food materials input method of refrigerator, it is characterised in that including：Obtain the indoor food materials image of storing；The N kinds prediction food materials species and its confidence level, wherein N gone out according to the food materials image recognition is positive integer；Predict that food materials species and its confidence level determine the species of the food materials according to the N kinds.
- 2. the food materials input method of refrigerator as claimed in claim 1, it is characterised in that according to the N kinds predict food materials species and Its confidence level determines that the species of the food materials includes：When the confidence level of the prediction food materials species is more than the first confidence level preset value, determine that the prediction food materials are the food The species of material；When the confidence level of the prediction food materials species is less than the first confidence level preset value and is more than the second confidence level preset value When, the species for determining the food materials is calculated according to default algorithm.
- 3. the food materials input method of refrigerator as claimed in claim 2, it is characterised in that the default algorithm includes：The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient Weighted average；Determine that the weighted average highest prediction food materials are the food materials species.
- 4. the food materials input method of refrigerator as described in Claims 2 or 3, it is characterised in that when putting for the prediction food materials species When reliability is less than the first confidence level preset value and is more than the second confidence level preset value, calculated according to default algorithm and determine institute After the species step for stating food materials, in addition to：When detecting the action message of the refrigerator door；The species of the food materials is identified again according to presetting method in predetermined period；When the result that the food materials again identify that is identical with the food materials species that determination is calculated according to default algorithm, it is determined that described The species of food materials.
- 5. the food materials input method of refrigerator as claimed in claim 4, it is characterised in that detecting the action of the refrigerator door After information Step, in addition to：Detect the positional information and/or weight information of the indoor food materials of the storing；When the positional information and/or weight information of the indoor food materials of the storing change, the root again in predetermined period The species of the food materials is identified according to presetting method.
- 6. the food materials input method of refrigerator as claimed in claim 4, it is characterised in that when result that the food materials again identify that with During according to the species difference of the default algorithm determination, determine that the confidence level is more than the prediction food of the first confidence level preset value Assortment class is the species of the food materials.
- 7. the food materials input method of refrigerator as described in claim 1 or 2 or 3, it is characterised in that food materials are predicted according to the N kinds Species and its confidence level determine that the species of the food materials includes：N kinds prediction food materials species is arranged according to the confidence level descending；Work as Xi-Xi+1>=predetermined threshold value, then it is the food materials species to exclude i+1 prediction food materials species, wherein, i is less than N just Integer；The species of the food materials is determined according to the i kinds prediction food materials species after the exclusion and its confidence level.
- 8. the food materials input method of refrigerator as described in claim 1 or 2 or 3, it is characterised in that food materials are predicted according to the N kinds Species and its confidence level determine that the species of the food materials includes：Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or Regional information；When the current season and/or the regional information season corresponding from predicting food materials species and/or regional information are different When, then exclude the prediction food materials species；The species of the food materials is determined according to the prediction food materials species after exclusion and its confidence level.
- 9. the food materials input method of refrigerator as claimed in claim 3, it is characterised in that according to the N kinds predict food materials species and Its confidence level determines that the species of the food materials includes：Obtain current season and/or regional information, and prestore with the prediction food materials species corresponding season and/or Regional information；The default weight coefficient is adjusted according to season and/or regional information；The confidence level of the N kinds prediction food materials species and corresponding edible inertia values are calculated according to the weight coefficient after adjustment Weighted average.
- 10. the food materials input method of refrigerator as described in claim 1 or 2 or 3, it is characterised in that it is determined that the kind of the food materials After class step, in addition to：Display or the determination kind of information that the food materials are sent to user；The species of the food materials is determined according to user's confirmation in preset time.
- 11. the food materials input method of refrigerator as claimed in claim 9, it is characterised in that confirmed according to the user in preset time Information determines that the species of the food materials includes：Obtain the food materials species after user's corrigendum；The edible inertia values and/or weight coefficient of the food materials are adjusted according to the food materials species after corrigendum.
- 12. the food materials input method of refrigerator as claimed in claim 3, it is characterised in that the edible inertia values include it is following it One：Edible number, edible frequency, edible quantity, edible consumption rate.
- A kind of 13. food materials input device of refrigerator, it is characterised in that including：Image collection module, the food materials image indoor for obtaining storing；Recognition processing module, for food materials image described in identifying processing to obtain the prediction food materials species of the food materials and its confidence Degree；Species determining module, for determining the species of the food materials according to the confidence level of the prediction food materials species.
- 14. the food materials input device of refrigerator as claimed in claim 13, it is characterised in that the species determining module includes：First species determination subelement, for when it is described prediction food materials species confidence level be more than the first confidence level preset value when, Determine the species that the prediction food materials are the food materials；Second species determination subelement, for being less than the first confidence level preset value when the confidence level of the prediction food materials species And when being more than the second confidence level preset value, the species for determining the food materials is calculated according to default algorithm.
- 15. the food materials input device of refrigerator as claimed in claim 14, it is characterised in that the default algorithm includes：The confidence level of N kinds prediction food materials species and corresponding edible inertia values are calculated according to default weight coefficient Weighted average；Determine that the weighted average highest prediction food materials are the food materials species.
- 16. the food materials input device of refrigerator as described in claims 14 or 15, it is characterised in that also include：First detection module, for after the second species determination subelement determines the species of the food materials, detecting the ice The action message of chamber door body；The recognition processing module, it is additionally operable to identify the species of the food materials again according to presetting method in predetermined period；The species determining module, the result for being additionally operable to again identify that in the food materials according to default algorithm with calculating what is determined When food materials species is identical, the species of the food materials is determined.
- 17. the food materials input device of refrigerator as claimed in claim 16, it is characterised in that also include：Second detection module, for detect the refrigerator door in first detection module action signal after, detect the storage The positional information and/or weight information of the indoor food materials of thing；The recognition processing module, it is additionally operable to become in the positional information and/or weight information of the indoor food materials of the storing During change, the species of the food materials is identified again according to presetting method in predetermined period.
- 18. the food materials input device of refrigerator as claimed in claim 16, it is characterised in that including：The first species determination subelement, the result for being additionally operable to again identify that in the food materials with according to the default algorithm During the species difference of determination, determine that the prediction food materials that the confidence level is more than the first confidence level preset value are the kind of the food materials Class.
- 19. according to the food materials input device of 13 or 14 or 15 refrigerator of claim, it is characterised in that also include：Screening module, for N kinds prediction food materials species to be arranged according to the confidence level descending, and in Xi-Xi+1>=default During threshold value, then it is the food materials species to exclude i+1 prediction food materials, wherein, i is the positive integer less than N；The species determining module, it is additionally operable to according to determining the i kinds prediction food materials species after the exclusion and its confidence level The species of food materials.
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| CN110211282A (en) * | 2019-05-23 | 2019-09-06 | 深兰科技（上海）有限公司 | A kind of automatic vending method and vending machine |
| CN111444880A (en) * | 2020-04-10 | 2020-07-24 | 海信集团有限公司 | Food material identification method and refrigerator |
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