CN112579785A

CN112579785A - Unmanned supermarket data processing method, device and system and storage medium

Info

Publication number: CN112579785A
Application number: CN201910859071.XA
Authority: CN
Inventors: 李小涛; 游树娟
Original assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Current assignee: China Mobile Communications Group Co Ltd; China Mobile Communications Ltd Research Institute
Priority date: 2019-09-11
Filing date: 2019-09-11
Publication date: 2021-03-30

Abstract

The invention discloses a data processing method, device and system for an unmanned supermarket and a storage medium. Wherein, the method comprises the following steps: obtaining semantic identification of a target object; determining a corresponding instance of the target object in an unmanned supermarket ontology based on the semantic identification of the target object; parsing the instance according to the first attribute of the instance; the target object is an internet of things object with semantic identification, the semantic identification comprises a semantic prefix and a first identification, the semantic prefix is used for identifying a class of the target object in the unmanned supermarket body, the first identification is source identification information of the target object, and the first attribute is used for determining an identification analysis system corresponding to the first identification so as to analyze the instance according to the corresponding identification analysis system.

Description

Unmanned supermarket data processing method, device and system and storage medium

Technical Field

The invention relates to the field of Internet of things, in particular to a data processing method, device and system for an unmanned supermarket and a storage medium.

Background

With the development of the internet of things technology, the traditional supermarket gradually evolves towards an unmanned supermarket. The unmanned supermarket adopts a mode that a customer enters a shop to verify the identity and the money is automatically deducted after the commodity is bought, and the transaction can be finished easily without a waiter and a cashier. In the related technology, commodities in an unmanned supermarket all adopt isomorphic marks (namely, the same mark analysis system is adopted), a user scans a two-dimensional code of a mobile phone at a supermarket entrance to confirm identity, the actions of the user, the commodities and the positions of the commodities are identified through a large number of sensors, the user is tracked and positioned based on image identification, the action of the user for taking the commodities is judged according to the position and the posture, a shopping list is pushed to the mobile phone of the user, the user finishes shopping after closing accounts in an APP (application program), and if the commodities in the unmanned supermarket adopt the isomerous marks, the mark analysis system cannot be correctly analyzed, so that automatic settlement cannot be realized.

Disclosure of Invention

In view of this, embodiments of the present invention provide a data processing method, apparatus, system and storage medium for an unmanned supermarket, and aim to interact with an internet of things object compatible with an isomerization identifier in the unmanned supermarket.

The technical scheme of the embodiment of the invention is realized as follows:

the embodiment of the invention provides a data processing method for an unmanned supermarket, which comprises the following steps:

obtaining semantic identification of a target object;

determining a corresponding instance of the target object in an unmanned supermarket ontology based on the semantic identification of the target object;

parsing the instance according to the first attribute of the instance;

the target object is an internet of things object with semantic identification, the semantic identification comprises a semantic prefix and a first identification, the semantic prefix is used for identifying a class of the target object in the unmanned supermarket body, the first identification is source identification information of the target object, and the first attribute is used for determining an identification analysis system corresponding to the first identification so as to analyze the instance according to the corresponding identification analysis system.

The embodiment of the invention also provides an unmanned supermarket data processing device, which comprises:

the acquisition module is used for acquiring the semantic identifier of the target object;

the determining module is used for determining a corresponding example of the target object in the unmanned supermarket body based on the semantic identification of the target object;

the analysis module is used for analyzing the example according to the first attribute of the example;

An embodiment of the present invention further provides an unmanned supermarket data processing system, including: a processor and a memory for storing a computer program capable of running on the processor, wherein the processor, when running the computer program, is adapted to perform the steps of the method according to any of the embodiments of the present invention.

The embodiment of the invention also provides a storage medium, wherein a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the method of any embodiment of the invention are realized.

According to the technical scheme provided by the embodiment of the invention, the semantic identifier of the target object is obtained; determining a corresponding instance of the target object in an unmanned supermarket ontology based on the semantic identification of the target object; parsing the instance according to the first attribute of the instance; the semantic identifier comprises a semantic prefix and a first identifier, the semantic prefix is used for identifying a class of the target object in the unmanned supermarket body, the first identifier is source identification information of the target object, and the first attribute is used for determining an identifier resolution system corresponding to the first identifier so as to be capable of resolving the instance according to the corresponding identifier resolution system, so that unified resolution of the instance of the heterogeneous identifier is supported. The embodiment of the invention can be compatible with the internet of things objects with the isomerization marks, so that the unmanned supermarket body supports interaction among the internet of things objects with the isomerization marks, and further the unmanned management of the unmanned supermarket is realized based on the unmanned supermarket body.

Drawings

FIG. 1 is a schematic flow chart of a data processing method for an unmanned supermarket according to an embodiment of the invention;

FIG. 2 is a diagram illustrating a semantic tag structure in an exemplary application of the present invention;

FIG. 3 is a schematic structural diagram of an unmanned supermarket body in an application example of the invention;

FIG. 4 is a schematic diagram illustrating an example of an Internet of things object mapped as an ontology in an application example of the present invention;

FIG. 5 is a schematic flow chart of a data processing method for an unmanned supermarket according to an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an unmanned supermarket data processing device according to an embodiment of the invention;

fig. 7 is a schematic structural diagram of an unmanned supermarket data processing system according to an embodiment of the invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples.

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. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

Before further detailed description of the embodiments of the present invention, terms and expressions mentioned in the embodiments of the present invention are explained, and the terms and expressions mentioned in the embodiments of the present invention are applicable to the following explanations:

a body: ontology (ontology) is a set of terms used to describe a domain, whose organization structure is hierarchical, and can be used as the skeleton and foundation of a knowledge base to acquire, describe and represent knowledge of the related domain. Ontologies can provide a common understanding of knowledge in the field, determine commonly recognized words (classes, also called concepts) within the field, and domain-specific concept definitions and relationships (attributes) between concepts, sharing between people and machines. The ontology is used as an expression mode of knowledge, and plays an important role in realizing an intelligent scene of the Internet of things and improving the Internet data retrieval quality. Ontology gives the basic terms and relationships that make up the related-art vocabulary, and uses these terms and relationships to specify the rules for the extension of these vocabularies. Ontology may be compiled by using owl (web Ontology language), and includes 5 basic modeling primitive languages: class (class), relationships (relationships), properties (properties), instances (instances), rules (rules).

Class (c): classes are also called concepts, and are used to describe the actual concepts in the field, either the actual existence of things or abstract concepts such as universities, movies, people, etc.; semantically, it represents a collection of objects.

The relationship is as follows: for describing the relationship between classes (concepts), such as part-of, kind-of, subclass-of, etc. Formally defined as a subset of the n-dimensional cartesian product.

The attributes are as follows: attributes, features, characteristics, and parameters that an object and/or class may have. The Object Properties (Object Properties) and the Data Properties (Data Properties) are divided into two types. The object attribute represents the connection between the concept or the instance; a data attribute represents a link between an instance and specific data, which may be understood as a certain attribute value of the instance. Properties may specify a domain and a range at creation time.

Example (c): is a specific example of a concept, e.g. TS _0130_ C is an example of this concept of a temperature sensor.

Rule: statements in the form of if-then statements that describe logical inferences that can be drawn from a particular form of assertion.

In the related art, the internet of things object is generally marked by an internet of things identifier. For example, commodity codes, device serial numbers, device network addresses, page URIs (Uniform Resource identifiers), and the like can be used as the identifiers of the internet of things. With the rapid development of the internet of things, the situation that various heterogeneous internet of things identifiers coexist is brought. Because the coding modes are different, the coding modes are required to be recognized firstly under the condition that different coding modes are used in one process, and then the corresponding identification analysis system completes analysis, so that the analysis and intercommunication of different codes cannot be completed by using a uniform identification system. Here, the analysis means decoding the identification information according to the corresponding encoded information and specifying the object to which the identification information actually corresponds.

In the field of unmanned supermarkets, in the related technology, isomorphic identifications are generally adopted for all objects of the internet of things in a supermarket system. For example, Amazon Go (unmanned retail store by Amazon) authentication uses a two-dimensional code generated by Amazon Go APP, settles Amazon accounts used, and adopts Amazon-specific identification for goods. The Ali unmanned supermarket uses a Taobao or Paobao account number for identity verification, and adopts a Paobao account for settlement. Such unmanned supermarkets do not support other types of authentication and payment means. In real life, however, an object usually has a plurality of heterogeneous identifications, for example, identity information of a person can be represented by an identity card number, a passport number, a WeChat account, a Paibao account, and the like; the payment account of a person comprises modes such as a bank card, a payment treasure, a WeChat, a bag and the like, each mode has own unique source identification, and the objects of the internet of things with heterogeneous identifications cannot interact with each other, so that unmanned management is difficult to achieve really.

Based on this, in various embodiments of the present invention, the internet of things object with the heterogeneous identifier is compatible based on a semantic identifier, and the identifier information corresponding to the internet of things object can be accurately analyzed, so that the unmanned supermarket body supports interaction between the internet of things objects with the heterogeneous identifier, and further, unmanned management of the unmanned supermarket is realized based on the unmanned supermarket body.

In the embodiment of the invention, the unmanned supermarket ontology is a term set for expressing the field of unmanned supermarkets. The categories of the unmanned supermarket body can include: supermarket, goods, customer, payment account; the supermarket is a set of supermarket objects of the unmanned supermarket, the goods are a set of goods objects of various goods of the internet of things, the customer is a set of customer objects with the identification identity of the internet of things, and the payment account is a set of various account objects.

The embodiment of the invention provides a data processing method for an unmanned supermarket, which comprises the following steps of:

step 101, obtaining semantic identification of a target object.

Here, the target object is an internet of things object with semantic identification, and the target object may be any one of the supermarket object, the goods object, the customer object, and the account object.

In practical application, before a target object is identified, the semantic identifier needs to be created for the target object, and the semantic identifier is determined based on source identification information of the target object (i.e., the internet of things identifier) and a class of the target object in the unmanned supermarket body. The semantic identifier may be solidified physical identifier information, such as a two-dimensional code label disposed on the surface of the goods; the semantic identification may also be digitized identification information, such as electronic bar code information displayed on an electronic terminal (e.g., a user's mobile phone), a web page URI, and the like.

And 102, determining a corresponding instance of the target object in the unmanned supermarket ontology based on the semantic identification of the target object.

In the embodiment of the invention, the semantic identifier includes a semantic prefix and a first identifier, the semantic prefix is used for identifying the class of the target object in the unmanned supermarket body, and the first identifier is source identification information of the target object. FIG. 2 illustrates a structure corresponding to semantic identification in an application example. As shown in fig. 2, the Semantic identifier (S-Code) is mainly composed of two parts, a Semantic prefix (Semantic prefix) and a source identifier (legacy identifier), which are connected by a symbol "/". The prefix part in the S-Code corresponds to a class in the self-service supermarket body. In practical application, a label (label) is added to each class in the self-service supermarket body, the value of the label is a code formed by a segment of numbers, and each class has a unique label code. As shown in fig. 3, the label code corresponding to Person class is 1001, and the label code corresponding to Coffee class is 3002. Therefore, the type of the target object can be determined according to the semantic prefix in the S-Code, so that the corresponding instance of the S-Code in the self-service supermarket body is determined, and the name of the instance can be the identification of the S-Code.

Step 103, parsing the instance according to the first attribute of the instance.

In the embodiment of the invention, in order to be compatible with the existing isomerization identifier, a data attribute Legacy _ ID _ Category (namely a first attribute) is defined in an unmanned supermarket body, and the type of the isomerization identifier compatible with the S-Code is judged according to the attribute value, so that a corresponding identifier analysis system can be selected according to the type of the source identifier information to carry out uniform analysis, and a corresponding target object of the example during encoding is determined. Through semantic identification, all objects with isomerization identifications in the unmanned supermarket system can be mapped to one instance in the unmanned supermarket body, and unified analysis of the isomerization identifications is supported.

In an application example, as shown in fig. 4, four types of objects (people, supermarket, goods and payment) in an unmanned supermarket body (i.e. IoT Ontology in fig. 4) respectively have different source identification information, goods (coffee) are identified by EAN code, people are identified by passport number, supermarket is identified by participating position code (GLN), and payment mode is identified by credit card number. The S-Code adds different semantic prefixes on the basis of the source identification information, and represents different categories in the self-service supermarket body, for example 1001 represents that the object is a person entering the supermarket for consumption. In addition, the system type of the source identifier corresponds to the Legacy _ ID _ Category attribute value of the mapped ontology instance, for example, the Legacy _ ID _ Category attribute value of the supermarket instance is GLN, which indicates that the source identifier information is a GLN code. In this way, the corresponding instance of the target object in the unmanned supermarket ontology can be determined by acquiring the S-Code of the target object.

In the unmanned supermarket body, besides the collection of different internet of things objects is realized through Class (Class), attribute information is also needed to describe the relationship between the instances and the inherent characteristics of the instances. The attributes of the unmanned supermarket body comprise an object attribute and a data attribute. In an embodiment, the unmanned supermarket data processing method further includes: determining the object attribute of the unmanned supermarket body based on the incidence relation among the examples of different classes in the unmanned supermarket body; and/or determining the data attribute of the unmanned supermarket body based on the characteristic parameters corresponding to the instances in the unmanned supermarket body.

In one embodiment, the object attributes include at least one of a second attribute for associating the supermarket with the customer, a third attribute for associating the supermarket with the good, a fourth attribute for associating the customer with the good, a fifth attribute for associating the customer with the payment account, and a sixth attribute for associating the good with the payment account.

In an embodiment, the data attribute further includes at least one of the following in addition to the first attribute: a seventh attribute for determining a price of the good, an eighth attribute for determining a time of sale of the good, a ninth attribute for determining a redeemable deadline for the good, a tenth attribute for determining whether the good is redeemable, an eleventh attribute for determining an amount of money to be paid by the payment account, a twelfth attribute for determining a brand of the good. Accordingly, the characteristic parameters of the goods object may include price, time of sale, refundable deadline, whether or not goods can be returned, brand, etc., and the characteristic parameters of the account object may include the amount of money to be paid.

In an application example, the object attributes of the supermarket ontology are shown in table 1.

Attribute name	Definition domain	Value range
			hasPerson	Store	Person
hasCommodity	Store	Commodity
			Buy	Person	Commodity
hasPayment	Person	Payment
			payedBy	Commodity	Payment

TABLE 1

Wherein, the definition domain of hassperson (i.e. the second attribute) is the "Store" class, and the value domain is the "Person" class, which is used to indicate which customers are in a supermarket; the definition domain of hasCommodity (i.e. the third attribute) is the class "Store", and the value domain is "Commodity", which indicates which commodities are in the supermarket; the definition field of Buy (i.e. the fourth attribute) is "Person", and the value field is "Commodity", which indicates that a Person bought goods; the definition domain of hasPayment (i.e. the fifth attribute) is "Person", and the value domain is "Payment", which represents a Payment account of one Person; the paydby (i.e., the sixth attribute) defines a field of "Commodity" and a value field of "payelement" indicating through which account a piece of merchandise is settled.

In an application example, the data attributes of the unmanned supermarket ontology are shown in table 2.

Attribute name	Definition domain	Value range
			Legacy_ID_Category	Thing	xs:string
hasPrice	Commodity	xs:float
			saleTime	Commodity	xs:dateTime
returnableDays	Commodity	xs:int
			isReturnable	Commodity	xs:boolean
paymentAmount	Payment	xs:float
			hasBrand	Commodity	xs:string

TABLE 2

Wherein, the Legacy _ ID _ Category (i.e. the first attribute) represents an identifier resolution system corresponding to the source identifier information of the object of the internet of things; hasrice (i.e., a seventh attribute) represents price information of a piece of merchandise; saleTime (i.e., the eighth attribute) represents the time of sale of a piece of merchandise, returnable days (i.e., the ninth attribute) represents the number of days that can be redeemed; isReturnablee (i.e., tenth attribute) indicates whether a piece of merchandise may be redeemed; paymentaccount (i.e., eleventh attribute) represents the amount the customer account needs to pay for the purchased goods; hasBrand (i.e., the twelfth attribute) represents brand information of the merchandise.

According to the embodiment of the invention, through the object attribute and the data attribute of the self-service supermarket body, the contact between the objects of the Internet of things in the self-service supermarket and the description information of the objects can be determined, so that the situation that a person enters the supermarket, purchases what things, needs to pay money and pays by using which account can be judged through semantic query, and a complete automatic shopping settlement process can be automatically realized.

In order to complete the information of the object attribute and the data attribute of the unmanned supermarket body, in an embodiment, the method further includes: updating the object attribute and/or the data attribute of the unmanned supermarket body according to a rule; wherein the rules include at least one of: the first rule is used for determining the corresponding relation between a goods object and an account object in the supermarket object; a second rule for determining whether the cargo object exceeds a return deadline.

In an application example, the first rule includes two inference rules, rule1 and rule 2. Where rule1 indicates that if customer b enters supermarket a, he purchases product c, where c is the product of supermarket a and the payment account of customer b is d, then the product c should be settled by account d. Rule2 shows that the price of commodity a is b, customer c purchases commodity a, a settles through account d, and the amount to be paid by account d is b. By joint reasoning of rule1 and rule2, it can be inferred how much money should be paid by which account for supermarket merchandise.

In one example of application, the second rule includes rule3, rule3 indicates that if article b belongs to supermarket a, b is sold at time c, the returnable days of b are d, and the number of days f obtained by subtracting c from the return occurrence time e is less than or equal to the returnable days d, then article b can be returned. Whether a commodity can be accepted by the supermarket or not can be inferred through the rule.

In an embodiment, the unmanned supermarket data processing method further includes: and acquiring data information corresponding to the shopping events of the supermarket objects, and updating the unmanned supermarket body based on the data information.

Here, the shopping events of the supermarket objects include: the behavior of people entering and exiting the unmanned supermarket, the behavior of people taking goods away, and the behavior of people returning goods. In practical application, various shopping events can be detected based on a visual identification technology, a Radio Frequency Identification (RFID) technology and the like, so that data information corresponding to the shopping events is obtained, and the object attribute and/or the data attribute of the self-service supermarket body are/is updated.

In an embodiment, the unmanned supermarket data processing method further includes: and outputting a corresponding query result based on the input information corresponding to the semantic query. The service operation related to the intelligent management of the unmanned supermarket can be realized through semantic query. The business operations may include: intelligent settlement, intelligent goods returning, intelligent shopping guide and the like.

In one embodiment, the outputting the corresponding query result based on the input information corresponding to the semantic query includes:

acquiring first semantic query input information, wherein the first voice query input information comprises a semantic identifier corresponding to a supermarket object;

determining an example corresponding to the supermarket object in the unmanned supermarket body based on the semantic identifier corresponding to the supermarket object;

determining an instance of the customer associated with an instance corresponding to the supermarket object based on the second attribute;

determining an instance of the good associated with the instance of the customer based on the instance of the customer and the fourth attribute;

determining an instance of the payment account associated with the instance of the good based on the instance of the good and the sixth attribute;

and sending payment information corresponding to the instance of the goods to a payment account corresponding to the instance of the payment account.

In this way, corresponding payment information can be generated for goods which are sold in a supermarket object and not settled, so that intelligent settlement can be facilitated.

acquiring second semantic query input information, wherein the second voice query input information comprises a semantic identifier corresponding to a cargo object;

determining an instance corresponding to the goods object in the unmanned supermarket body based on the semantic identifier corresponding to the goods object;

and determining whether the goods object supports goods return or not based on the eighth attribute, the ninth attribute and the current query time of the corresponding instance of the goods object, and if so, generating first prompt information of successful goods return.

Therefore, automatic goods returning of a certain goods can be realized, and the intervention of workers can be avoided, so that self-service goods returning of an unmanned supermarket is realized.

acquiring third semantic query input information, wherein the third voice query input information comprises a semantic identifier and a query condition corresponding to a cargo object;

determining data attributes corresponding to the goods objects which meet the query conditions in the unmanned supermarket body based on the semantic identifiers corresponding to the goods objects;

displaying the data attribute corresponding to the cargo object;

wherein the query condition comprises at least one of: price inquiry conditions and brand inquiry conditions.

Therefore, intelligent shopping guide of goods in an unmanned supermarket can be achieved, a user can inquire the data attribute corresponding to the goods according to the query condition of the goods, and the user can conveniently select and purchase the goods according to the attributes such as price and brand of the goods.

According to the data processing method for the unmanned supermarket, the Internet of things objects compatible with the heterogeneous identifiers are identified through semantics, unified analysis of source identifier information is supported, interaction among the Internet of things objects of the heterogeneous identifiers can be supported, and therefore various identity verification modes and various payment modes can be supported.

In addition, according to the data processing method of the unmanned supermarket, the object attribute and/or the data attribute of the unmanned supermarket body are/is updated according to rules; the intelligent interaction requirement among the instances can be met.

In addition, according to the data processing method of the unmanned supermarket, the data information corresponding to the shopping event of the supermarket object is obtained, the unmanned supermarket body is updated based on the data information, and automatic recording of self-service shopping, self-service goods return and other behaviors of the unmanned supermarket can be supported.

Thirdly, the unmanned supermarket data processing method provided by the embodiment of the invention can realize the relevant business operation of intelligent management of the unmanned supermarket through semantic query, enrich the self-service business of the unmanned supermarket and support the unmanned operation of the whole unmanned supermarket system.

The present invention will be described in further detail with reference to the following application examples.

Fig. 5 is a schematic flow chart of a data processing method for an unmanned supermarket according to an embodiment of the present invention. In the embodiment of the application, after the unmanned supermarket body is created, various objects in the supermarket identified by the S-Code can be subjected to unattended intelligent interactive cooperation through the following process, so that self-service shopping and settlement are realized. The process is as follows:

1. and mapping the Internet of things object identified by the S-Code into an unmanned supermarket ontology according to the semantic prefix to form different types of examples, wherein the names of the examples are the values of the S-Code identification.

2. And continuously increasing or updating data (namely data information corresponding to the shopping events) generated by the supermarket objects into the unmanned supermarket body. For example, when a customer (1001/E501129487) enters a supermarket through authentication, an instance of the customer is added in the "Person" class, with the instance name being his S-Code identification; a HasPolson attribute is added between the supermarket instance (1002/8713381138577) and the 1001/E501129487 instance. If the customer removes a good (3002/8938515483013), a Buy attribute is added between the 1001/E501129487 instance and 3002/8938515483013, and if the customer replaces the good, the Buy attribute between the two is deleted from the body. The identification of the purchased goods by customers in the unmanned supermarket is directly realized by adopting a visual identification technology, an RFID technology and the like, and the detailed description is omitted here.

3. And setting an inference rule, and carrying out logic inference on the self-service supermarket to obtain more implicit knowledge. E.g., rules r: if (. According to the inference result of the rule r, it can be judged what account a commodity is paid by.

4. And on the basis of the inferred ontology, semantic retrieval is carried out through an SPARQL ontology query language to obtain the action to be triggered by each object in the supermarket. For example, paying a certain amount from an account, receiving a return from a supermarket, etc.

Based on the work flow, on the basis of semantic identification and the unmanned supermarket ontology, some typical application examples of the unmanned supermarket system are introduced through specific inference rules and SPARQL query statements.

1) Self-help shopping

For self-help shopping and settlement, two inference rules of rule1 and rule2 are designed in the embodiment of the application. rule1 indicates that if customer b enters supermarket a, he purchases item c, which is the item in supermarket a, and the payment account for customer b is d, then this item c should be settled by account d. rule2 indicates that price of commodity a is b, customer c purchases commodity a, a settles through account d, and then the amount to be paid by account d is b. By joint reasoning of rule1 and rule2, it can be inferred how much money should be paid by which account for supermarket merchandise.

rule1:if(？a hasPersonb),(？b Buyc),(？a hasCommodityc),(？b hasPaymentd)→(？c payedByd)

rule2:if(？a hasPriceb),(？c Buya),(？a payedByd)→(？d paymentAmount？b)

The body after reasoning is obtained by reasoning the body and the semantic rule, and the payment information corresponding to the commodities (i.e. goods) in the supermarket (1002/8713381138577) can be retrieved from the current body by utilizing the following SPARQl query language. The customer and payment information corresponding to the unmanned supermarket commodity are shown in table 3. Then, the account type is judged according to the Legacy _ ID _ Category information of the Payment instance, and the Payment of the appointed amount is completed through a corresponding Payment system.

SPARQL1：

SelectPersonCommodityPaymentAmount where{

“1002/8713381138577”hasPersonPerson.

？Person BuyCommodity.

？Commodity payedByPayment.

？Payment paymentAmountAmount.}

Person	Commodity	Payment	Amount
					1001/E501129487	3002/8938515483013	1003/6221260012170051	18
1001/wx3000570	2003/80050278	1003/134644030642599578	20
				1001/110111199711021111	3004/6901285991530	1003/Jeff@gmai.com	5

Watch 32), self-service return

For self-service return, the embodiment of the application designs the following inference rule. If the commodity b belongs to the supermarket a, the commodity b is sold at the time c, the returnable days of the commodity b are d, and the days f obtained by subtracting the c from the return occurrence time e are less than or equal to the returnable days d, the commodity b can be returned. Whether a commodity can be accepted by the supermarket or not can be inferred through the rule.

rule3:(？a hasCommodityb),(？b saleTimec),(？b returnableDaysd),le(difference(now(？e),？c,？f),？d)→(？b isReturnable‘true’)

After the supermarket ontology and rule3 are not found, the information about whether the goods are returned or not can be obtained through the SPARQL2 query statement. As shown in table 4, when the return event occurs in 2019, day 3 and day 22, the first item can be returned, and the second item will not be supported for return due to the exceeding of the returnable days.

SPARQL2：

SelectCommoditydaytimereturn where{

？Commodity returnableDaysday.

？Commodity saleTimetime.

？Commodity isReturnablereturn.}

Commodity	day	time	return
				3004/6901285991240	2019-3-15	7	true
3004/6921168509256	2019-3-10	7	false

TABLE 4

3) Intelligent shopping guide

Based on the unmanned supermarket body, the intelligent shopping guide task can be realized. SPARQL3 is a query statement for drinking water with price not more than 3 yuan, and the query result returns the S-Code identification, price and brand of the commodity, as shown in table 5. Corresponding to different shopping guide requirements, the shopping guide can be completed through corresponding SPARQL semantic query.

SPARQL3：

SelectCommodityPriceBrand where{

？Commodity rdf:type:Water.

？Commodity:hasPricePrice.

？Commodity:hasBrandBrand.

FILTER(？Price<＝3)}

Commodity	Price	Brand
			3004/6901285991240	1.5	Yibao (good health)
3004/6921168509256	2.0	Farm spring

TABLE 5

In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides an unmanned supermarket data processing apparatus, as shown in fig. 6, the apparatus includes:

an obtaining module 601, configured to obtain a semantic identifier of a target object;

a determining module 602, configured to determine, based on the semantic identification of the target object, a corresponding instance of the target object in an unmanned supermarket ontology;

a parsing module 603, configured to parse the instance according to the first attribute of the instance;

the semantic identifier comprises a semantic prefix and a first identifier, the semantic prefix is used for identifying a class of the target object in the supermarket body, the first identifier is source identification information of the target object, and the first attribute is used for determining an identifier resolution system corresponding to the first identifier so as to be capable of resolving the instance according to the corresponding identifier resolution system.

In some embodiments, the apparatus further comprises: an identification module 604, configured to create the semantic identifier for the target object, where the semantic identifier is determined based on source identification information of the target object and a class of the target object in the supermarket ontology.

In some embodiments, the determining module 602 is further configured to: determining the object attribute of the unmanned supermarket body based on the incidence relation among the examples of different classes in the unmanned supermarket body; and/or determining the data attribute of the unmanned supermarket body based on the characteristic parameters corresponding to the instances in the unmanned supermarket body.

Wherein, the class in unmanned supermarket body includes: supermarket, goods, customer, payment account;

the object attributes comprise at least one of a second attribute for associating the supermarket with the customer, a third attribute for associating the supermarket with the good, a fourth attribute for associating the customer with the good, a fifth attribute for associating the customer with the payment account, and a sixth attribute for associating the good with the payment account;

the data attributes further include at least one of: a seventh attribute for determining a price of the good, an eighth attribute for determining a time of sale of the good, a ninth attribute for determining a redeemable deadline for the good, a tenth attribute for determining whether the good is redeemable, an eleventh attribute for determining an amount of money to be paid by the payment account, a twelfth attribute for determining a brand of the good.

In some embodiments, the apparatus further comprises: an inference module 605, the inference module 605 to: updating the object attribute and/or the data attribute of the unmanned supermarket body according to a rule; wherein the rules include at least one of: the first rule is used for determining the corresponding relation between a goods object and an account object in the supermarket object; a second rule for determining whether the cargo object exceeds a return deadline.

In some embodiments, the apparatus further comprises: the updating module 606 is configured to obtain data information corresponding to a shopping event of a supermarket object, and update the unmanned supermarket body based on the data information.

In some embodiments, the apparatus further comprises: and a semantic query module 607, configured to output a corresponding query result based on the input information corresponding to the semantic query.

In some embodiments, the semantic query module 607 is specifically configured to:

displaying the data attribute corresponding to the cargo object;

In actual application, the obtaining module 601, the determining module 602, the analyzing module 603, the identifying module 604, the reasoning module 605, the updating module 606, and the semantic query module 607 may be implemented by a processor in the unmanned supermarket data processing apparatus. Of course, the processor needs to run a computer program in memory to implement its functions.

It should be noted that: in the unmanned supermarket data processing device provided in the above embodiment, when the unmanned supermarket data processing is performed, only the division of the above program modules is exemplified, and in practical applications, the processing may be distributed to different program modules according to needs, that is, the internal structure of the device may be divided into different program modules to complete all or part of the above-described processing. In addition, the unmanned supermarket data processing device provided by the embodiment and the embodiment of the unmanned supermarket data processing method belong to the same concept, and the specific implementation process is described in the embodiment of the method for details, which is not described herein again.

Based on the hardware implementation of the program module, in order to implement the method of the embodiment of the present invention, an unmanned supermarket data processing system is further provided in the embodiment of the present invention. Fig. 7 shows only an exemplary configuration of the supermarket remote control system, and not the entire configuration, and a part of or the entire configuration shown in fig. 7 may be implemented as necessary.

As shown in fig. 7, an unmanned supermarket data processing system 700 provided in an embodiment of the present invention includes: at least one processor 701, memory 702, user interface 703, and at least one network interface 704. The various components in the supermarket unmanned data processing system 700 are coupled together by a bus system 705. It will be appreciated that the bus system 705 is used to enable communications among the components. The bus system 705 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for clarity of illustration the various busses are labeled in figure 7 as the bus system 705.

The user interface 703 may include, among other things, a display, a keyboard, a mouse, a trackball, a click wheel, a key, a button, a touch pad, or a touch screen.

The memory 702 in embodiments of the present invention is used to store various types of data to support the operation of the unmanned supermarket data processing system. Examples of such data include: any computer program for operating on an unmanned supermarket data processing system.

The data processing method for the unmanned supermarket disclosed by the embodiment of the invention can be applied to the processor 701 or realized by the processor 701. The processor 701 may be an integrated circuit chip having signal processing capabilities. In the implementation process, the steps of the data processing method for the unmanned supermarket can be completed through hardware integrated logic circuits or instructions in the form of software in the processor 701. The Processor 701 may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor 701 may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed by the embodiment of the invention can be directly implemented by a hardware decoding processor, or can be implemented by combining hardware and software modules in the decoding processor. The software module may be located in a storage medium located in the memory 702, and the processor 701 reads information in the memory 702, and completes the steps of the data processing method for the unmanned supermarket according to the embodiment of the present invention by combining hardware thereof.

In an exemplary embodiment, the unmanned supermarket data processing system can be implemented by one or more Application Specific Integrated Circuits (ASICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), FPGAs, general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the aforementioned methods.

It will be appreciated that the memory 702 can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The described memory for embodiments of the present invention is intended to comprise, without being limited to, these and any other suitable types of memory.

In an exemplary embodiment, an embodiment of the present invention further provides a storage medium, that is, a computer storage medium, which may be specifically a computer-readable storage medium, for example, including a memory 702 storing a computer program, where the computer program is executable by a processor 701 of an unmanned supermarket data processing system to perform the steps described in the method according to the embodiment of the present invention. The computer readable storage medium may be a ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface Memory, optical disk, or CD-ROM, among others.

It should be noted that: "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.

In addition, the technical solutions described in the embodiments of the present invention may be arbitrarily combined without conflict.

The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An unmanned supermarket data processing method is characterized by comprising the following steps:

obtaining semantic identification of a target object;

parsing the instance according to the first attribute of the instance;

2. The method of claim 1, further comprising:

and creating the semantic identifier for the target object, wherein the semantic identifier is determined based on the source identification information of the target object and the class of the target object in the unmanned supermarket body.

3. The method of claim 1, further comprising:

determining the object attribute of the unmanned supermarket body based on the incidence relation among the examples of different classes in the unmanned supermarket body; and/or the presence of a gas in the gas,

determining the data attribute of the unmanned supermarket body based on the characteristic parameters corresponding to the examples in the unmanned supermarket body;

the data attributes include the first attribute, the data attributes further including at least one of: a seventh attribute for determining a price of the good, an eighth attribute for determining a time of sale of the good, a ninth attribute for determining a redeemable deadline for the good, a tenth attribute for determining whether the good is redeemable, an eleventh attribute for determining an amount of money to be paid by the payment account, a twelfth attribute for determining a brand of the good.

4. The method of claim 3, further comprising:

updating the object attribute and/or the data attribute of the unmanned supermarket body according to a rule;

wherein the rules include at least one of:

the first rule is used for determining the corresponding relation between a goods object and an account object in the supermarket object;

a second rule for determining whether the cargo object exceeds a return deadline.

5. The method of claim 1, further comprising:

and acquiring data information corresponding to the shopping events of the supermarket objects, and updating the unmanned supermarket body based on the data information.

6. The method of claim 3, further comprising:

and outputting a corresponding query result based on the input information corresponding to the semantic query.

7. The method of claim 6, wherein outputting the corresponding query result based on the input information corresponding to the semantic query comprises:

8. The method of claim 6, wherein outputting the corresponding query result based on the input information corresponding to the semantic query comprises:

9. The method of claim 6, wherein outputting the corresponding query result based on the input information corresponding to the semantic query comprises:

displaying the data attribute corresponding to the cargo object;

10. An unmanned supermarket data processing device, characterized by comprising:

11. An unmanned supermarket data processing system, characterized by comprising: a processor and a memory for storing a computer program capable of running on the processor, wherein,

the processor, when executing the computer program, is adapted to perform the steps of the method of any of claims 1 to 9.

12. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method of any one of claims 1 to 9.