CN112200249A - Autonomous updating solution of unmanned intelligent container - Google Patents

Autonomous updating solution of unmanned intelligent container Download PDF

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CN112200249A
CN112200249A CN202011092526.9A CN202011092526A CN112200249A CN 112200249 A CN112200249 A CN 112200249A CN 202011092526 A CN202011092526 A CN 202011092526A CN 112200249 A CN112200249 A CN 112200249A
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向维维
肖光意
余格
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Hunan University
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    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/243—Classification techniques relating to the number of classes
    • G06F18/24323—Tree-organised classifiers

Abstract

The invention provides an autonomous updating solution of an unmanned intelligent container, which comprises a main system, wherein the main system comprises an autonomous updating system, a commodity library and a model center, the autonomous updating system comprises a priori tree stacking algorithm and a classifier mask layer algorithm, the commodity library comprises a commodity data acquisition device and a database, and the model center comprises a commodity group template, a commodity group pre-training model and an individualized customization model. The independent new scheme can realize different types of supportable identification of each intelligent container.

Description

Autonomous updating solution of unmanned intelligent container
Technical Field
The invention relates to the field of unmanned supermarkets, in particular to an autonomous updating solution of an unmanned intelligent container.
Background
The existing technical service solutions of the intelligent container are all that the existing categories of the commodity groups can only be identified by training a general fixed category commodity group model. Aiming at the new requirements of customers, the commodity volume needs to be positioned again through the commodity data acquisition device, images are acquired, and data are uploaded to the commodity library through the commodity access API. And reading all data of the commodity group and the data of the new category, performing iterative training again, and then redeploying.
The existing autonomous updating mode in the current market has a great problem that (1) each iteration updating of a model consumes too long training time, the whole process takes 1-7 days, a new model file is generated after the autonomous updating, and hundreds of model files can be formed under different updating requirements of different customers, so that great difficulty is brought to the maintenance of a model library; moreover, the deployment of each model file can cause great computational power waste, and the deployment has great influence on the maintenance of the original existing service; (2) a commodity group model contains a plurality of categories, but only partial categories of a customer actually put on a shelf are obtained under the ideal test condition, and the 99% accuracy of the existing algorithm model is obtained under the abnormal condition, which can cause the abnormal conditions such as false identification, missing identification and the like, for example, the commodity is identified as a non-shelved commodity; and with the increase of the categories of the new commodity groups in the self, the mutual exclusion criterion of the commodity groups is broken, and the semantic relation of good similar categories is not used, so that great challenge is provided for the robustness of the model.
Disclosure of Invention
The intelligent container has high requirements on the accuracy and robustness of the algorithm model, and an automatic new technology solution based on transfer learning is provided for the requirements and problems of new commodities on shelves of users of unmanned intelligent containers. The scheme provides an efficient personalized customized solution by combining a target detection technology and a migration technology so as to solve the problems mentioned in the background technology.
In order to achieve the purpose, the invention provides an autonomous updating solution of an unmanned intelligent container, which comprises a main system, wherein the main system comprises an autonomous updating system, a commodity library and a model center, the autonomous updating system comprises a priori tree stacking algorithm and a classifier mask algorithm, the commodity library comprises a commodity data acquisition device and a database, the model center comprises a commodity group template, a commodity group pre-training model and a personalized customization model, and the design can realize the intelligent container with thousands of containers and thousands of surfaces.
Preferably, the commodity data acquisition device comprises a commodity volume acquisition device, a commodity image acquisition device and a commodity bar code acquisition device, and the design can acquire corresponding data of the commodity.
Preferably, the classifier mask layer algorithm includes:
step 1: the classifier F of the commodity group template supports K classified classes, and the cross entropy loss of the classifier for target detection is as follows:
J(x，y；θK,θr)＝-logpy (1)
where p is softmax (θ)K·f(x；θr))，θK，θrAre trainable parameters of the high-level feature representation and multi-class classifier.
In fact, we only need the classification task of the K' class according to the commodity class on the shelf of the customer. To focus on this subset tag space, we implement it by masking (set to- ∞) (K-K' off-shelf commodity) the corresponding vector. We propose a masking layer:
VK＝masked-out(VKm) (2)
wherein m is a one-hot coded representation of the K' class from a screening of the entire commodity group template class, and
Figure BDA0002722621070000031
and
Figure BDA0002722621070000032
are all K-dimensional vectors. Corresponding to vector V not being screenedKIs set to-infinity.
Thus, the actual mask classification penalty is defined as
Figure BDA0002722621070000033
Wherein
Figure BDA0002722621070000034
Preferably, the a priori tree stacking algorithm includes:
step 2:
a. by carrying out index classification on varieties and brand packages of a database, a knowledge priori tree of the database, such as a commodity group, has K leaf nodes corresponding to K leaf categories, and the leaf nodes are grouped into G super categories, wherein K & gt G;
b. then dynamically constructing a knowledge tree of the whole commodity group category from a dynamic library in the whole commodity library category;
c. we achieve this in the goal of learning the model by reconstructing the classified parameters of classifier F for each class of classifier F by borrowing sibling and parent-child relationships, for which each leaf class k is associated with a weight vector
Figure BDA0002722621070000035
Are associated, and each super class node g is associated with a vector
Figure BDA0002722621070000036
Correlation, where G ∈ 1, ·, | G |. We define the following generalized model for θ
θG～N(0,δ·ID),θK～N(θπ(k),δ·ID) ④
Where N (-) is a Gaussian distribution with diagonal covariance, θGIs a trainable parameter of the super class, θKIs a trainable parameter of the target prediction class, we set the hyper-parameter δ to 0.01 in the experiment the a priori hierarchy function π forces the relationship between classes.
We wish to minimise a priori tree losses
Figure BDA0002722621070000041
Wherein, the sub-term θk∈θKIs trainable, but parent θπ(k)∈θGIs stationary. Is provided with CgThe parameters of the super class can be estimated by:
Figure BDA0002722621070000042
d. algorithm 1:
inputting: data D ═ a priori tree table pt
Input device
Figure BDA0002722621070000043
// update only θrOf (2) a bottleeck layer
Figure BDA0002722621070000044
Figure BDA0002722621070000051
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, the classification mask layer algorithm is designed, so that the abnormal condition that the commodity is mistakenly identified as a non-shelved commodity in the new service process is avoided, the prior tree stacking algorithm is designed, so that the iterative increase of the commodity group template category in the new service process of the owner is reduced, and through the cooperation of the classification mask layer algorithm and the prior tree stacking algorithm, the accuracy and robustness of the algorithm model can be effectively improved, so that the purpose that each intelligent container can support different identification categories is realized.
Drawings
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is an autonomic update flow diagram of the present invention;
FIG. 3 is a schematic diagram of a classifier mask layer algorithm of the present invention;
FIG. 4 is a schematic diagram of the prior tree stacking algorithm of the present invention;
fig. 5 is a structural diagram of the commodity data acquisition device of the present invention.
In the figure: A. a main system; 1. a commodity library; 2. a model center; 3. autonomously installing a new system; 11. a commodity data acquisition device; 12. a database; 21. a commodity group template; 22. a commodity group pre-training model; 23. customizing the model in a personalized way; 31. a classifier mask layer algorithm; 32. a priori tree stacking algorithm; 111. a commodity volume acquisition device; 112. a commodity image acquisition device; 113. commodity bar code collection system.
Detailed Description
In order to facilitate an understanding of the invention, the invention will now be described more fully hereinafter with reference to the accompanying drawings, in which several embodiments of the invention are shown, but which may be embodied in different forms and not limited to the embodiments described herein, but which are provided so as to provide a more thorough and complete disclosure of the invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may be present, and when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present, as the terms "vertical", "horizontal", "left", "right" and the like are used herein for descriptive purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs, and the knowledge of the terms used herein in the specification of the present invention is for the purpose of describing particular embodiments and is not intended to limit the present invention, and the term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Referring to fig. 1-5, the present invention provides an autonomous updating solution for an unmanned intelligent container, which includes a main system a, wherein the main system a includes an autonomous updating system 3, a commodity library 1 and a model center 2, the autonomous updating system 3 includes a priori tree stacking algorithm 32 and a classifier mask layer algorithm 31, the commodity library 1 includes a commodity data acquisition device 11 and a database 12, the model center 2 includes a commodity group template 21, a commodity group pre-training model 22 and a personalized customization model 23, and the design can customize a personalized model according to different requirements of each intelligent container.
Referring to fig. 5, the commodity data collecting device 11 includes a commodity volume collecting device 111, a commodity image collecting device 112, and a commodity barcode collecting device 113, and this design can collect data information related to commodities.
Referring to fig. 3, the classifier mask layer algorithm 31 includes:
step 1: the classifier F of the commodity group template 21 supports K classes of classification, and the cross entropy loss of the classifier for target detection is as follows:
J(x，y；θK,θr)＝-logpy (1)
where p is softmax (θ)K·f(x；θr))，θK，θrAre trainable parameters of the high-level feature representation and multi-class classifier.
In fact, we only need the classification task of the K' class according to the commodity class on the shelf of the customer. To focus on this subset tag space, we implement it by masking (set to- ∞) (K-K' off-shelf commodity) the corresponding vector. We propose a masking layer:
VK＝masked-out(VKm) (2)
where m is a one-hot coded representation of the K' class from the entire commodity set template 21 class screen, and
Figure BDA0002722621070000071
and
Figure BDA0002722621070000072
are all K-dimensional vectors. Corresponding to vector V not being screenedKIs set to-infinity.
Thus, the actual mask classification penalty is defined as
Figure BDA0002722621070000073
Wherein
Figure BDA0002722621070000074
By means of the design, the abnormal situation that the commodity is mistakenly identified as the non-shelving commodity can be reduced.
Referring to fig. 4, the a priori tree stacking algorithm 32 includes:
step 2:
a. by index classifying the varieties and brand packages of the database 12, the prior knowledge tree of the database 12, such as a commodity group, has K leaf nodes corresponding to K leaf categories, which are grouped into G super categories, where K > G;
b. then dynamically constructing a knowledge tree of the whole commodity group category from a dynamic library in the whole commodity library 1 category;
c. we achieve this in the goal of learning the model by reconstructing the classified parameters of classifier F for each class of classifier F by borrowing sibling and parent-child relationships, for which each leaf class k is associated with a weight vector
Figure BDA0002722621070000081
Are associated, and each super class node g is associated with a vector
Figure BDA0002722621070000082
Correlation, where G ∈ 1, ·, | G |. We define the following generalized model for θ
θG～N(0,δ·ID),θK～N(θπ(k),δ·ID) ④
Where N (-) is a Gaussian distribution with diagonal covariance, θGIs a trainable parameter of the super class, θKIs a trainable parameter of the target prediction class, we set the hyper-parameter δ to 0.01 in the experiment the a priori hierarchy function π forces the relationship between classes.
We wish to minimise a priori tree losses
Figure BDA0002722621070000083
Wherein, the sub-term θk∈θKIs trainable, but parent θπ(k)∈θGIs stationary. Is provided with CgWhen { k | pi (k) } g }, the following procedure can be usedEstimating parameters of the super class:
Figure BDA0002722621070000084
d. algorithm 1:
inputting: data D ═ a priori tree table pt
Input device
Figure BDA0002722621070000085
// update only θrOf (2) a bottleeck layer
Figure BDA0002722621070000091
The design can reduce the iterative growth of commodity group model categories in the new service process.
The specific operation mode of the invention is as follows:
when the autonomous new solution of the unmanned intelligent container is used, a user acquires commodity data through the commodity data acquisition device 11, judges whether data of corresponding categories exist in the database 12 of the commodity library 1 through the picture acquisition uploading interface, when a new product is not in the commodity group template 21, combines the data of related commodities in the database 12 and the data of the commodity data acquisition device 11 through the prior tree stacking algorithm 32, uses the original commodity group template 21 as the commodity group pre-training model 22 for retraining, further filters the category classifier of the commodity group pre-training model 22 through the classifier mask layer algorithm 31, and generates the corresponding personalized customized model 23 through the commodity category information selected by the user to be put on the shelf;
in the commodity group template 21, the new product filters the category classifier of the commodity group pre-training model 22 by adopting the classifier mask layer algorithm 31, retraining is not needed, and the corresponding personalized customized model 23 is generated by selecting the category information of the commodities to be put on the shelf by the user.
The invention is described above with reference to the accompanying drawings, it is obvious that the invention is not limited to the above-described embodiments, and it is within the scope of the invention to adopt such insubstantial modifications of the inventive method concept and solution, or to apply the inventive concept and solution directly to other applications without modification.

Claims (4)

1. An autonomous updating solution of an unmanned intelligent container, comprising a main system (a), characterized in that: the system comprises a main system (A) and a system model (2), wherein the main system (A) comprises an autonomous updating system (3), a commodity library (1) and a model center (2), the autonomous updating system (3) comprises a prior tree stacking algorithm (32) and a classifier mask layer algorithm (31), the commodity library (1) comprises a commodity data acquisition device (11) and a database (12), and the model center (2) comprises a commodity group template (21), a commodity group pre-training model (22) and an individualized customization model (23).
2. The autonomous update solution for unmanned intelligent containers of claim 1, characterized in that: the commodity data acquisition device (11) comprises a commodity volume acquisition device (111), a commodity image acquisition device (112) and a commodity bar code acquisition device (113).
3. The autonomous update solution for unmanned intelligent containers of claim 1, characterized in that: the classifier mask layer algorithm (31) comprises:
step 1: the classifier F of the commodity group template (21) supports K classified classes, and the cross entropy loss of the classifier of the target detection is as follows:
J(x，y；θK,θr)＝-logpy ①
where p is softmax (θ)K·f(x；θr))，θK，θrAre trainable parameters of the high-level feature representation and multi-class classifier.
In fact, we only need the classification task of the K' class according to the commodity class on the shelf of the customer. To focus on this subset tag space, we implement it by masking (set to- ∞) (K-K' off-shelf commodity) the corresponding vector. We propose a masking layer:
VK＝masked_out(VKm) ②
wherein m is a one-hot coded representation of the K' class from a screening of the entire commodity group template class, and
Figure FDA0002722621060000011
and
Figure FDA0002722621060000012
are all K-dimensional vectors. Corresponding to vector V not being screenedKIs set to-infinity. Thus, the actual mask classification penalty is defined as
Figure FDA0002722621060000013
Wherein
Figure FDA0002722621060000021
4. The autonomous update solution for unmanned intelligent containers of claim 1, characterized in that: the a priori tree stacking algorithm (32) comprises:
step 2:
a. through index classification of varieties and brand packages of the database (12), when K leaf nodes corresponding to K leaf categories exist in a commodity group, the K leaf nodes are grouped into G super categories by a knowledge prior tree of the database (12), wherein K & gt G;
b. then dynamically constructing a knowledge tree of the whole commodity group category from a dynamic library in the whole commodity library (1) category;
c. we achieve this in the goal of learning the model by reconstructing the classified parameters of classifier F for each class of classifier F by borrowing sibling and parent-child relationships, for which each leaf class k is associated with a weight vector
Figure FDA0002722621060000022
Are associated, and each super class node g is associated with a vector
Figure FDA0002722621060000023
Correlation, where G e 1, …, | G |. We define the following generalized model for θ
θG～N(0,δ·ID),θK～N(θπ(k),δ·ID) ④
Where N (-) is a Gaussian distribution with diagonal covariance, θGIs a trainable parameter of the super class, θKIs a trainable parameter of the target prediction class, we set the hyper-parameter δ to 0.01 in the experiment the a priori hierarchy function π forces the relationship between classes.
We wish to minimise a priori tree losses
Figure FDA0002722621060000024
Wherein, the sub-term θk∈θKIs trainable, but parent θπ(k)∈θGIs stationary. Is provided with CgThe parameters of the super class can be estimated by:
Figure FDA0002722621060000025
d. algorithm 1:
Figure FDA0002722621060000031
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