CN115221336A

CN115221336A - Method and device for determining food storage information, storage medium and electronic device

Info

Publication number: CN115221336A
Application number: CN202210745012.1A
Authority: CN
Inventors: 邓邱伟; 司福东; 张旭; 翟建光
Original assignee: Qingdao Haier Technology Co Ltd; Qingdao Haier Intelligent Home Appliance Technology Co Ltd; Haier Smart Home Co Ltd
Current assignee: Qingdao Haier Technology Co Ltd; Qingdao Haier Intelligent Home Appliance Technology Co Ltd; Haier Smart Home Co Ltd
Priority date: 2022-06-28
Filing date: 2022-06-28
Publication date: 2022-10-21
Also published as: WO2024001189A1

Abstract

The application discloses a method and a device for determining food storage information, a storage medium and an electronic device, and relates to the technical field of smart families, wherein the method for determining the food storage information comprises the following steps: acquiring a food storage query request; responding to the food storage inquiry request, acquiring a group of food characteristics of the target food and a group of refrigerator state characteristics of the target refrigerator; querying a first set of knowledge nodes associated with a set of food characteristics in a preset target knowledge graph, and querying a second set of knowledge nodes associated with a set of refrigerator state characteristics in the target knowledge graph; searching at least one pair of knowledge nodes in the first group of knowledge nodes and the second group of knowledge nodes; and under the condition that the at least one pair of knowledge nodes is found, generating food storage information according to the knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, wherein the food storage information comprises storage parameters in one or more dimensions for storing the target food on the target refrigerator.

Description

Method and device for determining food storage information, storage medium and electronic device

Technical Field

The application relates to the technical field of smart families, in particular to a method and a device for determining food storage information, a storage medium and an electronic device.

Background

The refrigerator is a preferred mode for storing food materials in more and more modern families, and how to reasonably store the food materials is an important part for keeping the original nutrition of the food materials. At present, refrigerator users store food materials according to experience, and do not scientifically select a refrigerator storage cabin and stored related information, so that food material nutrition loss is caused or the food materials cannot be eaten.

Aiming at the problem that the storage information of food cannot be provided by combining the related information of the refrigerator in the related art, an effective solution is not provided at present.

Therefore, there is a need for improvement of the related art to overcome the drawbacks of the related art.

Disclosure of Invention

The embodiment of the invention provides a method and a device for determining food storage information, a storage medium and an electronic device, which at least solve the problem that the storage information of food cannot be given by combining related information of a refrigerator.

According to an aspect of an embodiment of the present invention, there is provided a method of determining food storage information, including: acquiring a food storage query request, wherein the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator; responding to the food storage inquiry request, and acquiring a set of food characteristics of the target food and a set of refrigerator state characteristics of the target refrigerator; querying a first set of knowledge nodes associated with the set of food characteristics in a preset target knowledge graph, and querying a second set of knowledge nodes associated with the set of refrigerator state characteristics in the target knowledge graph; searching at least one pair of knowledge nodes in the first set of knowledge nodes and the second set of knowledge nodes, wherein two knowledge nodes in each pair of knowledge nodes have an association relationship and belong to the first set of knowledge nodes and the second set of knowledge nodes respectively; and under the condition that the at least one pair of knowledge nodes is found, generating food storage information according to knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, wherein the food storage information comprises storage parameters in one or more dimensions for storing the target food on the target refrigerator.

In an exemplary embodiment, finding at least one pair of knowledge nodes in the first set of knowledge nodes and the second set of knowledge nodes comprises: searching the at least one pair of knowledge nodes corresponding to a request intention characteristic in the first and second groups of knowledge nodes, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

In an exemplary embodiment, the searching for the at least one pair of knowledge nodes corresponding to the request intention characteristic in the first set of knowledge nodes and the second set of knowledge nodes comprises: in the case that the request intent feature is used to indicate a storage temperature included in the storage parameters of the request query, finding at least one pair of knowledge nodes corresponding to the storage temperature in the first and second sets of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating the storage humidity included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage humidity in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating a storage area included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage area in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating the number of storage days included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the number of storage days in the first group of knowledge nodes and the second group of knowledge nodes.

In an exemplary embodiment, said searching for at least one pair of knowledge nodes corresponding to said storage temperature in said first set of knowledge nodes and said second set of knowledge nodes comprises: searching a first knowledge node corresponding to the storage temperature in the first knowledge node group, and searching a second knowledge node group corresponding to the storage temperature in the second knowledge node group, wherein the first knowledge node is used for representing the recommended storage temperature of the target food, and each knowledge node in the knowledge node groups is used for representing the working temperature or the working temperature range in the storage area in the target refrigerator; searching a second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes; or searching the first knowledge node corresponding to the storage temperature in the first knowledge node group and searching the knowledge node group corresponding to the storage temperature in the second knowledge node group, wherein the first knowledge node is used for representing the recommended storage temperature range of the target food, and each knowledge node in the knowledge node group is used for representing the working temperature or the working temperature range in the storage area in the target refrigerator; and searching the second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature range, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes.

In an exemplary embodiment, generating the food storage information from the knowledge nodes of each of the at least one pair of knowledge nodes belonging to the second set of knowledge nodes comprises: determining storage parameters represented by part or all knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes as storage parameters included in the food storage information under the condition that the at least one pair of knowledge nodes corresponding to each intention feature in request intention features is found in the first group of knowledge nodes and the second group of knowledge nodes, wherein the request intention features are intention features obtained by performing intention identification on the food storage query request; searching a target knowledge node having an association relation with a candidate knowledge node in the target knowledge graph under the condition that the at least one pair of knowledge nodes corresponding to partial intention characteristics in the request intention characteristics are found in the first set of knowledge nodes and the second set of knowledge nodes, wherein the candidate knowledge node is a part of or all knowledge nodes belonging to the second set of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes; and determining the storage parameters represented by the candidate knowledge nodes and the target knowledge node as the storage parameters included in the food storage information, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

In an exemplary embodiment, the searching for a target knowledge node in the target knowledge-graph, the target knowledge node having an association relationship with a candidate knowledge node, includes: in the case that the request intention characteristic is used for indicating the storage temperature, the storage area and the storage days included in the storage parameters of the request query, and the partial intention characteristic is used for indicating the storage temperature and the storage days, the target knowledge node having an association relationship with the candidate knowledge node is searched in the target knowledge graph, wherein the candidate knowledge node is a knowledge node in the second group of knowledge nodes and a knowledge node in the second group of knowledge nodes, wherein the knowledge node represents a target working temperature or a target working temperature range, and the knowledge node represents a target storage days or a target storage days range, and the target knowledge node is used for representing a target storage area in the target refrigerator.

In an exemplary embodiment, the searching for a target knowledge node having an association relationship with a candidate knowledge node in the target knowledge-graph includes: in the case that the request intention characteristic is used for indicating a storage temperature, a storage humidity and a storage area included in the storage parameters of the request query, and the partial intention characteristic is used for indicating the storage temperature and the storage humidity, the target knowledge node having an association relationship with the candidate knowledge node is searched in the target knowledge graph, wherein the candidate knowledge node is a knowledge node representing a target working temperature or a target working temperature range in the second group of knowledge nodes and a knowledge node representing a target storage humidity or a target storage humidity range in the second group of knowledge nodes, and the target knowledge node is used for representing a target storage area in the target refrigerator.

According to another aspect of the embodiments of the present invention, there is also provided a device for determining food storage information, including: the device comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a food storage query request, and the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator; a second obtaining module, configured to obtain a set of food characteristics of the target food and a set of refrigerator status characteristics of the target refrigerator in response to the food storage query request; a query module configured to query a first set of knowledge nodes associated with the set of food characteristics in a preset target knowledge graph, and query a second set of knowledge nodes associated with the set of refrigerator status characteristics in the target knowledge graph; a searching module, configured to search at least one pair of knowledge nodes in the first and second sets of knowledge nodes, where two knowledge nodes in each pair of knowledge nodes have an association relationship and belong to the first and second sets of knowledge nodes, respectively; a generating module, configured to generate food storage information according to a knowledge node belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes when the at least one pair of knowledge nodes is found, where the food storage information includes storage parameters in one or more dimensions for storing the target food on the target refrigerator.

According to still another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium having a computer program stored therein, wherein the computer program is configured to execute the above-mentioned method for determining food storage information when running.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method for determining the food storage information through the computer program.

The invention responds to a food storage query request, acquires a group of food characteristics of target food and a group of refrigerator state characteristics of a target refrigerator, queries a first group of knowledge nodes associated with the group of food characteristics and a second group of knowledge nodes associated with the group of refrigerator state characteristics in a preset target knowledge graph, further searches at least one pair of knowledge nodes in the first group of knowledge nodes and the second group of knowledge nodes, and generates food storage information according to the knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes under the condition of searching the at least one pair of knowledge nodes.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

FIG. 1 is a schematic diagram of a hardware environment of a method for determining food storage information according to an embodiment of the present application;

fig. 2 is a flowchart of a method of determining food storage information according to an embodiment of the present invention;

FIG. 3 is a schematic view of a knowledge-graph according to an embodiment of the invention;

fig. 4 is an application scenario diagram of a determination method of food storage information according to an embodiment of the present invention;

fig. 5 is a block diagram of a structure of a determination apparatus of food storage information according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

According to an aspect of an embodiment of the present application, there is provided a method of determining food storage information. The method for determining the food storage information is widely applied to full-house intelligent digital control application scenes such as Smart Home (Smart Home), smart Home equipment ecology, smart Home (Intelligent House) ecology and the like. Alternatively, in the present embodiment, the above-described method for determining food storage information may be applied to a hardware environment formed by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be configured to provide a service (e.g., an application service) for the terminal or a client installed on the terminal, set a database on the server or independent of the server, and provide a data storage service for the server 104, and configure a cloud computing and/or edge computing service on the server or independent of the server, and provide a data operation service for the server 104.

The network may include, but is not limited to, at least one of: wired networks, wireless networks. The wired network may include, but is not limited to, at least one of: wide area networks, metropolitan area networks, local area networks, which may include, but are not limited to, at least one of the following: WIFI (Wireless Fidelity), bluetooth. Terminal equipment 102 can be and not be limited to PC, the cell-phone, the panel computer, intelligent air conditioner, intelligent cigarette machine, intelligent refrigerator, intelligent oven, intelligent kitchen range, intelligent washing machine, intelligent water heater, intelligent washing equipment, intelligent dish washer, intelligent projection equipment, the intelligent TV, intelligent clothes hanger, intelligent (window) curtain, intelligence audio-visual, smart jack, intelligent stereo set, intelligent audio amplifier, intelligent new trend equipment, intelligent kitchen guarding's equipment, intelligent bathroom equipment, the intelligence robot of sweeping the floor, the intelligence robot of wiping the window, intelligence robot of mopping the floor, intelligent air purification equipment, intelligent steam ager, intelligent microwave oven, intelligent kitchen guarding, intelligent clarifier, intelligent water dispenser, intelligent lock etc..

In order to solve the above problem, in this embodiment, a method for determining food storage information is provided, including but not limited to being applied to a cloud server or a refrigerator, and fig. 2 is a flowchart of a method for determining food storage information according to an embodiment of the present invention, where the flowchart includes the following steps:

step S202: acquiring a food storage query request, wherein the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator;

in an exemplary embodiment, the food storage query request may be presented in the form of speech, and the speech is recognized by natural speech processing techniques to determine the specific intent represented by the food storage query request.

In an exemplary embodiment, the food storage query request may be "how apples are stored in refrigerator a".

Step S204: responding to the food storage inquiry request, and acquiring a set of food characteristics of the target food and a set of refrigerator state characteristics of the target refrigerator;

in an exemplary embodiment, the target food may be image-captured to obtain a set of food characteristics of the target food, wherein the set of food characteristics of the target food may include, but is not limited to: food type, food color, food current status, food size.

In one exemplary embodiment, a set of refrigerator status characteristics of the target refrigerator, including but not limited to the model of the refrigerator, the current storage status of the refrigerator. It should be noted that after the model of the refrigerator is determined, the functions of the refrigerator, the number of storage areas of the refrigerator, and the working parameters corresponding to the storage areas of the refrigerator can be determined.

Step S206: querying a first set of knowledge nodes associated with the set of food characteristics in a preset target knowledge graph, and querying a second set of knowledge nodes associated with the set of refrigerator state characteristics in the target knowledge graph;

in an exemplary embodiment, fig. 3 is a schematic diagram of a knowledge graph according to an embodiment of the present invention, and a target knowledge graph is specifically shown in fig. 3, where the target knowledge graph in the embodiment includes, but is not limited to, a refrigerator knowledge graph and a food material knowledge graph. FIG. 3 depicts a back end data storage and data support architecture supported with respect to a refrigerator knowledge graph representing attributes of the refrigerator itself, including temperature and humidity regulation ranges, and the function of each compartment, and food material knowledge graph; the food material knowledge graph represents the nutritional ingredients and other attributes of the food material, wherein the food material is stored under the scene of no temperature and humidity for different storage times.

Step S208: searching at least one pair of knowledge nodes in the first set of knowledge nodes and the second set of knowledge nodes, wherein two knowledge nodes in each pair of knowledge nodes have an association relationship and belong to the first set of knowledge nodes and the second set of knowledge nodes respectively;

in an exemplary embodiment, the step S208 may be implemented as follows: searching the at least one pair of knowledge nodes corresponding to a request intention characteristic in the first and second groups of knowledge nodes, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

In an exemplary embodiment, finding the at least one pair of knowledge nodes corresponding to the request intention characteristic in the first and second sets of knowledge nodes may be implemented by: in the case that the request intent feature is used to indicate a storage temperature included in the storage parameters of the request query, finding at least one pair of knowledge nodes corresponding to the storage temperature in the first and second sets of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating the storage humidity included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage humidity in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating a storage area included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage area in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating the number of storage days included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the number of storage days in the first group of knowledge nodes and the second group of knowledge nodes.

In an exemplary embodiment, finding at least one pair of knowledge nodes corresponding to the storage temperature in the first and second sets of knowledge nodes may be implemented by: searching a first knowledge node corresponding to the storage temperature in the first set of knowledge nodes, and searching a set of knowledge nodes corresponding to the storage temperature in the second set of knowledge nodes, wherein the first knowledge node is used for representing the recommended storage temperature of the target food, and each knowledge node in the set of knowledge nodes is used for representing the working temperature or the working temperature range in the storage area in the target refrigerator; searching a second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range for representing the second knowledge node corresponds to the recommended storage temperature, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes; or looking up the first knowledge node corresponding to the storage temperature in the first set of knowledge nodes, and looking up the set of knowledge nodes corresponding to the storage temperature in the second set of knowledge nodes, wherein the first knowledge node is used for representing a recommended storage temperature range of the target food, and each knowledge node in the set of knowledge nodes is used for representing an operating temperature or an operating temperature range in a storage area in the target refrigerator; and searching the second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature range, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes.

It should be noted that, the operating temperature or the operating temperature range represented by the second knowledge node corresponds to the recommended storage temperature, which includes but is not limited to: and the working temperature is equal to the recommended storage temperature, and the recommended storage temperature is within the working temperature range.

It should be noted that, in the process of determining the second knowledge node from the group of knowledge nodes corresponding to the storage temperature, if there is an operating temperature or an operating temperature range that the second knowledge node is used to represent, which corresponds to the recommended temperature range of the first knowledge node, but the storage area corresponding to the second knowledge node does not have a space for storing food, then a second knowledge node may be determined again from the group of knowledge nodes corresponding to the storage temperature in the above manner, where the second knowledge node determined again has a space for storing food.

In an exemplary embodiment, the searching for at least one pair of knowledge nodes corresponding to the storage humidity in the first and second sets of knowledge nodes may be implemented by: searching a third knowledge node corresponding to the storage humidity in the first knowledge node group, and searching a knowledge node group corresponding to the storage humidity in the second knowledge node group, wherein the first knowledge node is used for representing the recommended storage humidity of the target food, and each knowledge node in the knowledge node group corresponding to the storage humidity is used for representing the working humidity or the working humidity range in the storage area in the target refrigerator; searching a fourth knowledge node in the group of knowledge nodes corresponding to the storage humidity, wherein the working humidity or working humidity range represented by the fourth knowledge node corresponds to the recommended storage humidity, and the third knowledge node and the fourth knowledge node are a pair of searched knowledge nodes; or searching the third knowledge node corresponding to the storage humidity in the first group of knowledge nodes, and searching the group of knowledge nodes corresponding to the storage humidity in the second group of knowledge nodes, wherein the third knowledge node is used for representing a recommended storage humidity range of the target food, and each knowledge node in the group of knowledge nodes corresponding to the storage humidity is used for representing an operating humidity or an operating humidity range in a storage area in the target refrigerator; and searching the fourth knowledge node in the group of knowledge nodes corresponding to the storage humidity, wherein the working humidity or working humidity range which is used for representing by the fourth knowledge node corresponds to the recommended storage humidity range, and the third knowledge node and the fourth knowledge node are a pair of searched knowledge nodes.

It should be noted that the working humidity or working humidity range represented by the fourth knowledge node corresponds to the recommended storage humidity, which includes but is not limited to: and the working humidity is equal to the recommended storage humidity, and the recommended storage humidity is in the working humidity range.

In an exemplary embodiment, finding at least one pair of knowledge nodes corresponding to the storage region in the first and second sets of knowledge nodes may be implemented by: searching a fifth knowledge node corresponding to the storage area in the first knowledge node group, and searching a group of knowledge nodes corresponding to the storage area in the second knowledge node group, wherein the fifth knowledge node is used for representing the recommended storage area of the target food, and each knowledge node in the group of knowledge nodes corresponding to the storage area is used for representing the storage area in the target refrigerator; and searching a sixth knowledge node in the group of knowledge nodes corresponding to the storage area, wherein the storage area which is used for representing the sixth knowledge node corresponds to the recommended storage area, and the fifth knowledge node and the sixth knowledge node are a pair of searched knowledge nodes.

In an exemplary embodiment, finding at least one pair of knowledge nodes corresponding to the storage days in the first and second sets of knowledge nodes may be implemented by: searching a seventh knowledge node corresponding to the storage area in the first group of knowledge nodes, and searching a group of knowledge nodes corresponding to the storage days in the second group of knowledge nodes, wherein the seventh knowledge node is used for representing the recommended storage days of the target food, and each knowledge node in the group of knowledge nodes corresponding to the storage days is used for representing a target storage days or a target storage days range; searching an eighth knowledge node in the group of knowledge nodes corresponding to the storage days, wherein the target storage days or the range of the target storage days used for representing by the eighth knowledge node corresponds to the recommended storage days, and the seventh knowledge node and the eighth knowledge node are a pair of searched knowledge nodes.

Step S210: and under the condition that the at least one pair of knowledge nodes is found, generating food storage information according to knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, wherein the food storage information comprises storage parameters in one or more dimensions for storing the target food on the target refrigerator.

In an exemplary embodiment, the above step S210 can be implemented by the following steps S11-S12:

step S11: determining storage parameters represented by part or all knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes as storage parameters included in the food storage information under the condition that the at least one pair of knowledge nodes corresponding to each intention feature in request intention features is found in the first group of knowledge nodes and the second group of knowledge nodes, wherein the request intention features are intention features obtained by performing intention identification on the food storage query request;

that is, if the food storage query request is used to query the storage humidity and the storage temperature of the food, and two pairs of knowledge nodes related to the storage humidity and the storage temperature are found in the first group of knowledge nodes and the second group of knowledge nodes, the storage parameters represented by some or all of the two pairs of knowledge nodes belonging to the second group of knowledge nodes may be determined as the storage parameters included in the food storage information. For example: the nodes belonging to the second group of knowledge nodes in the two pairs of knowledge nodes are a first node and a second node, wherein the temperature represented by the first node is 10 ℃, the humidity represented by the second node is 45% of relative humidity, and further the generated storage parameters are 10 ℃ and 45% of relative humidity.

Step S12: searching a target knowledge node having an association relation with a candidate knowledge node in the target knowledge graph under the condition that the at least one pair of knowledge nodes corresponding to partial intention characteristics in the request intention characteristics are found in the first set of knowledge nodes and the second set of knowledge nodes, wherein the candidate knowledge node is a part of or all knowledge nodes belonging to the second set of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes; and determining the storage parameters represented by the candidate knowledge nodes and the target knowledge node as the storage parameters included in the food storage information, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

It should be noted that the candidate knowledge nodes in the target knowledge graph have an association relationship with the target knowledge nodes, and are represented in the target knowledge graph, and the candidate knowledge nodes and the target knowledge nodes have direct or indirect edges to be connected.

In an exemplary embodiment, finding a target knowledge node in the target knowledge-graph, which has an association relationship with a candidate knowledge node, may be implemented by: in a case where the request intention feature indicates a storage temperature, a storage area, and a storage number of days included in the storage parameter of the request query, and the partial intention feature indicates the storage temperature and the storage number of days, finding the target knowledge node having an association relationship with the candidate knowledge node in the target knowledge graph, wherein the candidate knowledge node is a knowledge node representing a target operating temperature or a target operating temperature range in the second group of knowledge nodes and a knowledge node representing a target storage number of days or a target storage number of days range in the second group of knowledge nodes, the target knowledge node representing a target storage area in the target refrigerator.

That is, if the food storage query request is used to query the storage temperature, the storage area and the number of storage days of the food, but only the knowledge node pairs corresponding to the storage temperature and the number of storage days are queried in the first and second sets of knowledge nodes, then the target knowledge node corresponding to the storage area needs to be queried in the second set of knowledge nodes according to the storage temperature and the number of storage days. In an exemplary embodiment, in the second group of knowledge nodes, the target knowledge node corresponding to the storage area has a dependency relationship with the candidate knowledge nodes corresponding to the storage temperature and the storage days, that is, the storage area can be determined by the storage temperature and the storage days.

In an exemplary embodiment, finding a target knowledge node in the target knowledge-graph, which has an association relationship with a candidate knowledge node, may be implemented by: in the case that the request intention characteristic is used for indicating a storage temperature, a storage humidity and a storage area included in the storage parameters of the request query, and the partial intention characteristic is used for indicating the storage temperature and the storage humidity, the target knowledge node having an association relationship with the candidate knowledge node is searched in the target knowledge graph, wherein the candidate knowledge node is a knowledge node representing a target working temperature or a target working temperature range in the second group of knowledge nodes and a knowledge node representing a target storage humidity or a target storage humidity range in the second group of knowledge nodes, and the target knowledge node is used for representing a target storage area in the target refrigerator.

That is, if the food storage query request is used to query the storage humidity, and the storage area of the food, but only the knowledge node pair corresponding to the storage humidity and the storage temperature is queried in the first and second sets of knowledge nodes, then the target knowledge node corresponding to the storage area needs to be queried in the second set of knowledge nodes through the storage humidity and the storage temperature. In an exemplary embodiment, in the second set of knowledge nodes, the target knowledge node corresponding to the storage region has an affiliation with the candidate knowledge node corresponding to the storage humidity and the storage temperature, i.e., the storage region can be determined by the storage humidity and the storage temperature.

For a better understanding, in one exemplary embodiment, if the food storage query request issued by the user indicates: what is the temperature, humidity, location and number of days the apple was stored in refrigerator a? Further, through the above steps S202-S208, the user can be replied to "store in the area A of the refrigerator at 10 degrees Celsius, 45% relative humidity, and 3 days".

By the steps, a group of food characteristics of the target food and a group of refrigerator state characteristics of the target refrigerator are obtained in response to the food storage query request, a first group of knowledge nodes associated with the group of food characteristics and a second group of knowledge nodes associated with the group of refrigerator state characteristics are queried in a preset target knowledge graph, at least one pair of knowledge nodes is searched in the first group of knowledge nodes and the second group of knowledge nodes, and under the condition that the at least one pair of knowledge nodes is found, food storage information is generated according to the knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes.

It is to be understood that the above-described embodiments are only a few, but not all, embodiments of the present invention. In order to better understand the above method, the following describes the above process with reference to an embodiment, but the method is not limited to the technical solution of the embodiment of the present invention, and specifically:

in an alternative embodiment, the knowledge graph is an important ring in intelligent interaction, and provides services of knowledge, disambiguation, quick retrieval and the like in the conversation. The refrigerator knowledge graph and the food material knowledge graph are adopted to provide powerful scientific basis for the storage suggestion of food materials. On one hand, the refrigerator knowledge graph is from real data of different types of refrigerators, and the food materials and food material individual data in the food material knowledge graph are from professional food material research institutions. On the other hand, the retrieval of food materials and the retrieval of related data of the refrigerator cabin are quickly realized by adopting a natural language technology; and finally, combining an intelligent technology with a knowledge graph and the actual refrigerator food conditions of the user to perform combined analysis, and finally giving information such as the optimal storage cabin and the related temperature of the food materials of the user, so that the food materials are stored more reasonably and accurately.

As shown in fig. 3, the overall architecture of the refrigerator and the food material knowledge graph is described, and a rear-end data storage and data support architecture which supports the refrigerator knowledge graph and the food material knowledge graph is described, wherein the refrigerator knowledge graph represents the attributes of the refrigerator and the related attributes of each cabin, and the attributes of the cabins comprise the temperature and humidity regulation ranges and the functions of each cabin; the food material knowledge graph represents the nutritional ingredients and other attributes of the food material, wherein the food material is stored under the scene of no temperature and humidity for different storage times. Comprehensively judging the position where the current food materials are stored according to the temperature and humidity ranges of different cabins and the storage conditions required by the food materials and by combining the refrigerator model of a user and the current food material storage condition of the refrigerator. Therefore, powerful data support is reasonably provided for the storage of the food materials of the user, and the freshness of the food materials is guaranteed to the maximum extent.

That is to say, the refrigerator knowledge map and the related knowledge of the food material knowledge map provide the maximum judgment basis for guaranteeing the storage of food materials; in addition, the knowledge graph is used as the storage of the back-end knowledge and is combined with the natural language, so that the related query of the application service becomes faster.

Fig. 4 is an application scenario diagram of a method for determining food storage information according to an embodiment of the present invention, and as shown in fig. 4, a process of accessing a knowledge graph for food storage is described in a question and answer service form, and parameters related to recommendation of food storage are given.

The upper part of fig. 4 describes the service query statement and the requirement, and the processes of entity identification, entity linking, judgment according to the relationship, answer giving, and the like are performed. The lower part of fig. 4 shows the query process of the questions related to the entity, and the map provides answers for the upper-level service according to the known food storage conditions and the refrigerator map cabin combined analysis. In summary, fig. 4 describes an architecture system including a refrigerator and a food material graph, and a service flow. The association graph gives reasonable storage recommendation to the food compartments of the user in different scenes, so that the refrigerator food storage question answering is more intelligent and convenient, the use experience of the user on the refrigerator is improved, and the food utilization rate is improved. In the whole view, the technical architecture combining the relation graph and the food material storage question answering is realized, and the service experience is integrally improved.

It should be noted that, in the embodiment of the application, the service is provided based on the design and storage mode of the refrigerator compartment and the food material spectrum and the joint storage of the relationship between the food material storage scene and the refrigerator compartment, and simultaneously based on the food material and the refrigerator architecture spectrum system. Taking a conversation as an example, firstly, finding key words of food materials, combining the actual refrigerator condition of a user, carrying out logic analysis on the food material storage scene, the refrigerator cabin and the current refrigerator state, and giving answers of storage days in different cabins and different scenes.

In addition, according to the refrigerator food storage recommendation based on the knowledge graph, through entity identification and entity linkage, entity relationship query can obtain a storage recommendation result, a reliable basis is provided for answers of services due to stability and real reliability of food materials and refrigerator data related to the graph, overall question and answer efficiency is improved through combination and quick recall of the graph, relevance of the food materials and the refrigerator data is more complete, isolated data are associated according to requirements, the internal data and the relation of the isolated data can be associated through the same knowledge graph, logic calculation and data query are simplified, a recommendation mode of separating the refrigerator from the food materials is improved, integration of the refrigerator and food material recommendation services is guaranteed, food material storage time of different scenes is given, freshness of the food materials is effectively guaranteed, and food material utilization rate is improved. Furthermore, the natural language processing technology is adopted to analyze the problems, answers are recalled from the map quickly, the answers are guaranteed to be obtained timely, and the service efficiency is improved.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

In this embodiment, a device for determining food storage information is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, which have already been described and are not described again. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the devices described in the embodiments below are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

Fig. 5 is a block diagram of a structure of an apparatus for determining food storage information according to an embodiment of the present invention, the apparatus including:

a first obtaining module 50, configured to obtain a food storage query request, where the food storage query request is used to request a query about storage parameters for storing a target food on a target refrigerator;

a second obtaining module 52, configured to obtain a set of food characteristics of the target food and a set of refrigerator status characteristics of the target refrigerator in response to the food storage query request;

a query module 54 configured to query a preset target knowledge-graph for a first set of knowledge nodes associated with the set of food characteristics, and query the target knowledge-graph for a second set of knowledge nodes associated with the set of refrigerator status characteristics;

a searching module 56, configured to search at least one pair of knowledge nodes in the first and second sets of knowledge nodes, where two knowledge nodes in each pair have an association relationship and belong to the first and second sets of knowledge nodes, respectively;

a generating module 58, configured to generate food storage information according to the knowledge node belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, if the at least one pair of knowledge nodes is found, where the food storage information includes storage parameters in one or more dimensions for storing the target food on the target refrigerator.

By the aid of the device, a group of food characteristics of the target food and a group of refrigerator state characteristics of the target refrigerator are obtained in response to a food storage query request, a first group of knowledge nodes associated with the group of food characteristics and a second group of knowledge nodes associated with the group of refrigerator state characteristics are queried in a preset target knowledge graph, at least one pair of knowledge nodes is searched in the first group of knowledge nodes and the second group of knowledge nodes, and under the condition that the at least one pair of knowledge nodes is found, food storage information is generated according to the knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes.

In an exemplary embodiment, the finding module 56 is further configured to find the at least one pair of knowledge nodes corresponding to a request intention characteristic in the first set of knowledge nodes and the second set of knowledge nodes, wherein the request intention characteristic is an intention characteristic obtained by performing intention identification on the food storage query request.

In an exemplary embodiment, the search module 56 is further configured to search at least one pair of knowledge nodes corresponding to the storage temperature in the first set of knowledge nodes and the second set of knowledge nodes if the request intention characteristic is used to indicate the storage temperature included in the storage parameter of the request query; and/or in the case that the request intention characteristic is used for indicating the storage humidity included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage humidity in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating a storage area included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage area in the first group of knowledge nodes and the second group of knowledge nodes; and/or in the case that the request intention characteristic is used for indicating the number of storage days included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the number of storage days in the first group of knowledge nodes and the second group of knowledge nodes.

In an exemplary embodiment, the searching module 56 is further configured to search the first knowledge node set for a first knowledge node corresponding to the storage temperature, and search the second knowledge node set for a second knowledge node set corresponding to the storage temperature, wherein the first knowledge node is used for representing a recommended storage temperature of the target food, and each knowledge node set is used for representing an operating temperature or an operating temperature range in a storage area in the target refrigerator; searching a second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes; or searching the first knowledge node corresponding to the storage temperature in the first knowledge node group and searching the knowledge node group corresponding to the storage temperature in the second knowledge node group, wherein the first knowledge node is used for representing the recommended storage temperature range of the target food, and each knowledge node in the knowledge node group is used for representing the working temperature or the working temperature range in the storage area in the target refrigerator; and searching the second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature range, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes.

In an exemplary embodiment, the generating module 58 is further configured to, in a case that the at least one pair of knowledge nodes corresponding to each intention feature in the request intention feature is found in the first group of knowledge nodes and the second group of knowledge nodes, determine, as the storage parameter included in the food storage information, the storage parameter represented by some or all knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, where the request intention feature is an intention feature obtained by performing intention identification on the food storage query request; searching a target knowledge node in the target knowledge graph, wherein the target knowledge node has an association relationship with a candidate knowledge node under the condition that the at least one pair of knowledge nodes corresponding to part of the request intention characteristics is found in the first and second groups of knowledge nodes, and the candidate knowledge node is part or all of knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes; and determining the storage parameters represented by the candidate knowledge nodes and the target knowledge node as the storage parameters included in the food storage information, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

In an exemplary embodiment, the generating module 58 is further configured to find the target knowledge node having an association relationship with the candidate knowledge node in the target knowledge graph, where the request intention characteristic is used to indicate a storage temperature, a storage area and a storage number of days included in the storage parameter of the request query, and the partial intention characteristic is used to indicate the storage temperature and the storage number of days, where the candidate knowledge node is a knowledge node in the second set of knowledge nodes representing a target operating temperature or a target operating temperature range and a knowledge node in the second set of knowledge nodes representing a target storage number of days or a target storage number of days, and the target knowledge node is used to represent a target storage area in the target refrigerator.

In an exemplary embodiment, the generating module 58 is further configured to find the target knowledge node having an association relationship with the candidate knowledge node in the target knowledge graph, where the request intention characteristic is used to indicate a storage temperature, a storage humidity and a storage area included in the storage parameter of the request query, and the partial intention characteristic is used to indicate the storage temperature and the storage humidity, where the candidate knowledge node is a knowledge node in the second set of knowledge nodes representing a target operating temperature or a target operating temperature range and a knowledge node in the second set of knowledge nodes representing a target storage humidity or a target storage humidity range, and the target knowledge node is used to represent a target storage area in the target refrigerator.

Embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program is arranged to perform the steps of any of the above-mentioned method embodiments when executed.

Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:

s1, obtaining a food storage query request, wherein the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator;

s2, responding to the food storage query request, and acquiring a group of food characteristics of the target food and a group of refrigerator state characteristics of the target refrigerator;

s3, inquiring a first group of knowledge nodes associated with the group of food characteristics in a preset target knowledge graph, and inquiring a second group of knowledge nodes associated with the group of refrigerator state characteristics in the target knowledge graph;

s4, at least one pair of knowledge nodes is searched in the first group of knowledge nodes and the second group of knowledge nodes, wherein the two knowledge nodes in each pair of knowledge nodes have an incidence relation and respectively belong to the first group of knowledge nodes and the second group of knowledge nodes;

and S5, under the condition that the at least one pair of knowledge nodes are found, generating food storage information according to knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, wherein the food storage information comprises storage parameters in one or more dimensions for storing the target food on the target refrigerator.

In an exemplary embodiment, the computer readable storage medium may include, but is not limited to: various media capable of storing computer programs, such as a usb disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk.

For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary embodiments, and details of this embodiment are not repeated herein.

Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.

Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:

the method comprises the steps of S1, obtaining a food storage query request, wherein the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator;

In an exemplary embodiment, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.

It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented in a general purpose computing device, they may be centralized in a single computing device or distributed across a network of multiple computing devices, and they may be implemented in program code that is executable by a computing device, such that they may be stored in a memory device and executed by a computing device, and in some cases, the steps shown or described may be executed in an order different from that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps therein may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.

Claims

1. A method for determining food storage information, comprising:

acquiring a food storage query request, wherein the food storage query request is used for requesting to query storage parameters for storing target food on a target refrigerator;

responding to the food storage inquiry request, and acquiring a set of food characteristics of the target food and a set of refrigerator state characteristics of the target refrigerator;

querying a first set of knowledge nodes associated with the set of food characteristics in a preset target knowledge graph, and querying a second set of knowledge nodes associated with the set of refrigerator state characteristics in the target knowledge graph;

searching at least one pair of knowledge nodes in the first and second groups of knowledge nodes, wherein two knowledge nodes in each pair of knowledge nodes have an association relationship and belong to the first and second groups of knowledge nodes respectively;

and under the condition that the at least one pair of knowledge nodes is found, generating food storage information according to knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes, wherein the food storage information comprises storage parameters in one or more dimensions for storing the target food on the target refrigerator.

2. The method of claim 1, wherein finding at least one pair of knowledge nodes in the first set of knowledge nodes and the second set of knowledge nodes comprises:

searching the first and second groups of knowledge nodes for the at least one pair of knowledge nodes corresponding to a request intention characteristic, wherein the request intention characteristic is an intention characteristic obtained by intention recognition of the food storage query request.

3. The method according to claim 2, wherein said finding said at least one pair of knowledge nodes corresponding to a request intent characteristic in said first and second sets of knowledge nodes comprises:

in the case that the request intent feature is used to indicate a storage temperature included in the storage parameters of the request query, finding at least one pair of knowledge nodes corresponding to the storage temperature in the first and second sets of knowledge nodes; and/or

In the case that the request intention characteristic is used for indicating storage humidity included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage humidity in the first group of knowledge nodes and the second group of knowledge nodes; and/or

In the case that the request intention characteristic is used for indicating a storage area included in the storage parameters of the request query, searching at least one pair of knowledge nodes corresponding to the storage area in the first and second groups of knowledge nodes; and/or

In the case that the request intention characteristic is used for indicating the storage days included in the storage parameters of the request query, at least one pair of knowledge nodes corresponding to the storage days is searched in the first group of knowledge nodes and the second group of knowledge nodes.

4. The method of claim 3, wherein the finding at least one pair of knowledge nodes corresponding to the storage temperature in the first set of knowledge nodes and the second set of knowledge nodes comprises:

searching a first knowledge node corresponding to the storage temperature in the first knowledge node group, and searching a second knowledge node group corresponding to the storage temperature in the second knowledge node group, wherein the first knowledge node is used for representing the recommended storage temperature of the target food, and each knowledge node in the knowledge node groups is used for representing the working temperature or the working temperature range in the storage area in the target refrigerator; searching a second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes; or

Searching the first knowledge node corresponding to the storage temperature in the first knowledge node group, and searching the knowledge node group corresponding to the storage temperature in the second knowledge node group, wherein the first knowledge node is used for representing a recommended storage temperature range of the target food, and each knowledge node in the knowledge node group is used for representing an operating temperature or an operating temperature range in a storage area in the target refrigerator; and searching the second knowledge node in the group of knowledge nodes, wherein the working temperature or the working temperature range which is used for representing by the second knowledge node corresponds to the recommended storage temperature range, and the first knowledge node and the second knowledge node are a pair of searched knowledge nodes.

5. The method of claim 1, wherein generating food storage information from knowledge nodes in each of the at least one pair of knowledge nodes belonging to the second set of knowledge nodes comprises:

determining storage parameters represented by part or all knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes as storage parameters included in the food storage information under the condition that the at least one pair of knowledge nodes corresponding to each intention feature in request intention features is found in the first group of knowledge nodes and the second group of knowledge nodes, wherein the request intention features are intention features obtained by performing intention identification on the food storage query request;

searching a target knowledge node in the target knowledge graph, wherein the target knowledge node has an association relationship with a candidate knowledge node under the condition that the at least one pair of knowledge nodes corresponding to part of the request intention characteristics is found in the first and second groups of knowledge nodes, and the candidate knowledge node is part or all of knowledge nodes belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes; and determining the storage parameters represented by the candidate knowledge nodes and the target knowledge node as the storage parameters included in the food storage information, wherein the request intention characteristic is an intention characteristic obtained by intention identification of the food storage query request.

6. The method according to claim 5, wherein searching the target knowledge-graph for target knowledge nodes having an association relationship with candidate knowledge nodes comprises:

in the case that the request intention characteristic is used for indicating the storage temperature, the storage area and the storage days included in the storage parameters of the request query, and the partial intention characteristic is used for indicating the storage temperature and the storage days, the target knowledge node having an association relationship with the candidate knowledge node is searched in the target knowledge graph, wherein the candidate knowledge node is a knowledge node in the second group of knowledge nodes and a knowledge node in the second group of knowledge nodes, wherein the knowledge node represents a target working temperature or a target working temperature range, and the knowledge node represents a target storage days or a target storage days range, and the target knowledge node is used for representing a target storage area in the target refrigerator.

7. The method of claim 5, wherein the searching for the target knowledge-graph having an association relationship with the candidate knowledge node comprises:

in the case that the request intention characteristic is used for indicating a storage temperature, a storage humidity and a storage area included in the storage parameters of the request query, and the partial intention characteristic is used for indicating the storage temperature and the storage humidity, searching the target knowledge node having an association relationship with the candidate knowledge node in the target knowledge graph, wherein the candidate knowledge node is a knowledge node in the second group of knowledge nodes representing a target working temperature or a target working temperature range and a knowledge node in the second group of knowledge nodes representing a target working humidity or a target working humidity range, and the target knowledge node is used for representing a target storage area in the target refrigerator.

8. An apparatus for determining food storage information, comprising:

the food storage inquiry device comprises a first acquisition module, a first storage module and a second acquisition module, wherein the first acquisition module is used for acquiring a food storage inquiry request, and the food storage inquiry request is used for requesting to inquire storage parameters for storing target food on a target refrigerator;

a second obtaining module, configured to obtain a set of food characteristics of the target food and a set of refrigerator status characteristics of the target refrigerator in response to the food storage query request;

a query module configured to query a first set of knowledge nodes associated with the set of food characteristics in a preset target knowledge graph, and query a second set of knowledge nodes associated with the set of refrigerator status characteristics in the target knowledge graph;

a searching module, configured to search at least one pair of knowledge nodes in the first and second sets of knowledge nodes, where two knowledge nodes in each pair of knowledge nodes have an association relationship and belong to the first and second sets of knowledge nodes, respectively;

a generating module, configured to generate food storage information according to a knowledge node belonging to the second group of knowledge nodes in each pair of knowledge nodes in the at least one pair of knowledge nodes when the at least one pair of knowledge nodes is found, where the food storage information includes storage parameters in one or more dimensions for storing the target food on the target refrigerator.

9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.