CN111666961A - Intelligent household appliance, method and device for identifying food material type of intelligent household appliance and electronic equipment - Google Patents
Intelligent household appliance, method and device for identifying food material type of intelligent household appliance and electronic equipment Download PDFInfo
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Abstract
The application provides a method for improving accuracy of recognizing food material types by intelligent household appliances, wherein the method comprises the following steps: the method comprises the steps of obtaining an image of a food material to be cooked in the intelligent household appliance, carrying out image recognition on the image, and obtaining a first probability of the food material to be cooked under a preset food material type; acquiring a history record of food materials purchased by a user, and acquiring a second probability that the purchased food materials are cooked by the user in the history record; according to the first probability and the second probability, a target probability of each food material type is obtained, the food material type with the maximum target probability is selected as the target food material type, so that the accuracy of recognizing images of the cooked food materials is controlled, and the technical problems that the recognition type of the cooked food materials is large in limitation and low in accuracy in the prior art are solved.
Description
Technical Field
The invention relates to the technical field of intelligent household appliances, in particular to an intelligent household appliance, a method and a device for identifying food material types of the intelligent household appliance, electronic equipment and a storage medium.
Background
In the related art, an intelligent household appliance (e.g., a cooking appliance) can use a camera to perform image recognition on food inside the intelligent household appliance for distinguishing the type of food to be cooked. However, the related art has the following disadvantages: if the pictures of the food materials to be cooked, which are trained in advance, do not exist, the pictures cannot be accurately identified, the appearance similarity of the food materials to be cooked of different types is very high, the types of the food materials to be cooked to be identified are many, and the problems that the identification types of the food materials to be cooked are large in limitation and low in accuracy rate exist.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a method for identifying food material types by an intelligent household appliance, so as to solve the technical problems of great limitation on identification types of cooking food materials and low accuracy rate in the prior art.
The second purpose of the present invention is to provide an apparatus for identifying food material types for an intelligent household appliance.
The third purpose of the invention is to provide an intelligent household appliance.
A fourth object of the invention is to propose an electronic device.
A fifth object of the invention is to propose a non-transitory computer-readable storage medium.
In order to achieve the above object, a first embodiment of the present invention provides a method for identifying food material types for an intelligent household appliance, including the following steps: the method comprises the steps of obtaining an image of a food material to be cooked in the intelligent household appliance, carrying out image recognition on the image, and obtaining a first probability of the food material to be cooked under a preset food material type; acquiring a history record of food materials purchased by a user, and acquiring a second probability that the purchased food materials are cooked by the user in the history record; obtaining a target probability of each food material type according to the first probability and the second probability, and selecting the food material type with the maximum target probability as the target food material type.
According to an embodiment of the present invention, the obtaining a target probability of each food material type according to the first probability and the second probability includes: for each food material type, comparing the first probability and the second probability of the food material type, and selecting the maximum probability from the first probability and the second probability as a target probability of the food material type; or, for each food material type, correcting the first probability by using the second probability of the food material type to obtain a target probability of the food material type.
According to an embodiment of the invention, the correcting the first probability by using the second probability of the food material type to obtain the target probability of the food material type includes: acquiring a first weight corresponding to the first probability and a second weight corresponding to the second probability; and multiplying the first probability by the first weight, multiplying the second probability by the second weight, and adding the two multiplied results to obtain the target probability of the food material type.
According to an embodiment of the present invention, the obtaining a second probability that the purchased food material is cooked by the user in the history record includes: extracting the purchase times and the total purchase times of each food material type from the history record; and for each food material type, making a ratio of the purchase times to the total purchase times to obtain the second probability.
According to an embodiment of the invention, the history further includes a purchase time, and the method further comprises: sequencing all food material types according to the purchase time; and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
According to an embodiment of the present invention, after the selecting the food material type with the maximum target probability as the target food material type, the method further includes: and acquiring a cooking strategy matched with the type of the target food material, and controlling the cooking appliance to cook the food material to be cooked according to the cooking strategy.
According to an embodiment of the present invention, before the obtaining of the cooking strategy matching the target food material type, the method further includes: detecting the weight of the food material to be cooked, and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the type of the target food material; controlling the cooking utensil to add water to the target water amount.
The embodiment of the first aspect of the invention provides a method for identifying food material types by an intelligent household appliance, which not only identifies the types of food materials to be cooked through images and comprehensively considers the purchase condition of a user on the food materials in order to improve the identification accuracy, but also corrects the image identification result according to the purchase condition, so that the final identification result is more accurate.
In order to achieve the above object, a second embodiment of the present invention provides an apparatus for identifying food material types for an intelligent appliance, including: the intelligent household appliance comprises an image identification module, a cooking management module and a control module, wherein the image identification module is used for acquiring an image of a food material to be cooked in the intelligent household appliance, carrying out image identification on the image and acquiring a first probability of the food material to be cooked under each food material type; the record acquisition module is used for acquiring a history record of food purchased by a user and acquiring a second probability that the purchased food is cooked by the user in the history record; and the food material type determining module is used for obtaining the target probability of each food material type according to the first probability and the second probability, and selecting the food material type with the maximum target probability as the target food material type.
According to an embodiment of the present invention, the food material type determining module is further configured to: for each food material type, comparing the first probability and the second probability of the food material type, and selecting the maximum probability from the first probability and the second probability as a target probability of the food material type; or, for each food material type, correcting the first probability by using the second probability of the food material type to obtain a target probability of the food material type.
According to an embodiment of the present invention, the food material type determining module is further configured to: acquiring a first weight corresponding to the first probability and a second weight corresponding to the second probability; and multiplying the first probability by the first weight, multiplying the second probability by the second weight, and adding the two multiplied results to obtain the target probability of the food material type.
According to an embodiment of the present invention, the record obtaining module is further configured to: extracting the purchase times and the total purchase times of each food material type from the history record; and for each food material type, making a ratio of the purchase times to the total purchase times to obtain the second probability.
According to an embodiment of the present invention, the history record further includes a purchase time, and the food material type determining module is further configured to: sequencing all food material types according to the purchase time; and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
According to an embodiment of the present invention, the smart appliance is a cooking appliance, and the food material type determining module is further configured to: and after the food material type with the maximum target probability is selected as the target food material type, acquiring a cooking strategy matched with the target food material type, and controlling the cooking appliance to cook the food material to be cooked according to the cooking strategy.
According to an embodiment of the present invention, the food material type determining module is further configured to: before a cooking strategy matched with the target food material type is obtained, detecting the weight of the food material to be cooked, and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the target food material type; controlling the cooking utensil to add water to the target water amount.
The embodiment of the second aspect of the invention provides a device for identifying food material types by an intelligent household appliance, which not only identifies the types of food materials to be cooked by images, but also comprehensively considers the purchase condition of the food materials by a user in order to improve the identification accuracy, and corrects the image identification result according to the purchase condition, so that the final identification result is more accurate.
In order to achieve the above object, a third aspect of the present invention provides an intelligent appliance, including: the embodiment of the second aspect of the invention provides a device for identifying food material types by an intelligent household appliance.
To achieve the above object, a fourth aspect of the present invention provides an electronic device, including a memory, a processor; the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for identifying the food material type by the intelligent household appliance according to any one of claims 1 to 6.
In order to achieve the above object, a fifth embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for identifying food material types for an intelligent household appliance.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a flowchart illustrating a method for identifying food material types by an intelligent household appliance according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating another method for identifying food material types by an intelligent household appliance according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating another method for identifying food material types by an intelligent household appliance according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for identifying food material types by an intelligent household appliance according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for recognizing food material types by an intelligent household appliance according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a method and a device for identifying food material types by an intelligent household appliance according to an embodiment of the present invention with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for identifying food material types by an intelligent household appliance according to an embodiment of the present invention. As shown in fig. 1, a method for identifying food material types by an intelligent household appliance provided in an embodiment of the present invention includes the following steps:
s101: the method comprises the steps of obtaining an image of the food to be cooked in the intelligent household appliance, carrying out image recognition on the image, and obtaining a first probability of the food to be cooked under each food type.
In the embodiment of the invention, the intelligent household appliance can be an intelligent refrigerator or an intelligent cooking appliance, such as an electric cooker, an electric pressure cooker and the like. The method for identifying the food material type by the intelligent household appliance provided by the embodiment of the invention is explained below by taking the intelligent household appliance as an example. Specifically, an image acquisition unit is arranged in the cooking appliance, and the image acquisition in the cooking cavity can be carried out through the image acquisition unit. When a user tries to cook, the cooking appliance can be opened, food to be cooked is put into the cooking appliance, and at the moment, the image acquisition unit can be controlled to shoot an image carrying the food to be cooked. For example, the user may control the image pickup unit to start image capturing by a button or a voice instruction.
Further, the collected images carrying the food materials to be cooked are input into a pre-trained image recognition model for recognition, and the recognition probability, namely the first probability, of the food materials to be cooked under each food material type is obtained.
The image recognition model can recognize various food material types, and is formed or constructed by machine learning based on historical images.
S102: and acquiring a history record of the food purchased by the user, and acquiring a second probability that the purchased food is cooked by the user in the history record.
It should be noted that, in order to make the final recognition result more accurate, as shown in fig. 1, in an embodiment of the present invention, after the step of recognizing the type of the food material to be cooked through the image, a first probability of the food material to be cooked under each food material type is obtained. In view of the fact that the current image recognition technology has high probability of identifying food materials to be cooked by mistake, in order to improve the accuracy of identification, the purchase condition of the user to the food materials is comprehensively considered, and the second probability of each food material type being cooked by the user is obtained by obtaining the history of the food materials purchased by the user.
Specifically, the server may collect information that the user purchases food materials in various applications such as a shopping website, social media, and the like, and store the data in the server. After the history of the food material purchased by the user is acquired from the server, the history may include the purchase frequency and the total purchase frequency of each food material type. And extracting the purchase times and the total purchase times of each food material type from the history record, and taking the ratio of the purchase times to the total purchase times of each food material type to obtain a second probability that each food material type is cooked by the user.
Optionally, the obtaining of the second probability that each food material type is cooked by the user according to the history of the user purchasing food materials may be performed by the server. Specifically, the cooking appliance may send a first request to the server, where the first request carries an identifier of the user, and after receiving the first request, the server obtains a corresponding history record according to the identifier of the user, obtains a second probability that each food material type is cooked by the user according to the history record, and feeds back the second probability that each food material type is cooked by the user to the cooking appliance.
Optionally, obtaining the second probability that each food item type is cooked by the user from the history of the user purchasing food items may be performed by the cooking appliance. Specifically, the cooking appliance may send a second request to the server, where the second request carries an identifier of the user, and after receiving the second request, the server obtains a corresponding history according to the identifier of the user, and feeds the history back to the cooking appliance, where a second probability that each food material type of the cooking appliance is cooked by the user is obtained.
It should be noted that, in the obtained history of the user over a period of time, the order of the purchase time may have an influence on the probability of each food material type being cooked. For example, the purchase time is earlier, i.e. the food materials with the purchase order earlier are more likely to be cooked; the time of purchase is late, i.e. the probability that the purchase of sequential food material is cooked is smaller. Therefore, in order to improve the identification accuracy, an embodiment of the present invention may adjust the second probability of each food material type according to the purchase history after obtaining the second probability. Specifically, all food material types are sorted according to the purchase time; and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
S103: and obtaining the target probability of each food material type according to the first probability and the second probability, and selecting the food material type with the maximum target probability as the target food material type.
The two probabilities can be combined to obtain the final probability, namely the target probability, of each food material after the first probability and the second probability of each food material type are obtained.
As a possible implementation manner, for each food material type, the first probability and the second probability of the food material type are compared, and the maximum probability of the first probability and the second probability is selected as the target probability of the food material type.
As another possible implementation manner, for each food material type, the first probability is corrected by using the second probability of the food material type, so as to obtain a target probability of the food material type. Specifically, the two probabilities may be subjected to weight calculation, and according to a result of the weight calculation, a target probability of the food material type of the food material to be cooked is determined. Specifically, after the first probability of the food material to be cooked under each food material type is obtained, a preset first weight corresponding to the first probability is obtained. And after a second probability that each food material type is cooked by the user is obtained, a preset second weight corresponding to the second probability is obtained. Further, the first probability and the first weight are multiplied, the second probability and the second weight are multiplied, and the results of the two multiplications are added to obtain the target probability of each food material type. Further, the obtained target probability of each food material type is sequenced, and the food material type with the maximum target probability is selected as the target food material type.
In summary, according to the method for identifying the food material types of the cooking appliance provided by the embodiment of the invention, the image of the food material to be cooked in the cooking appliance is acquired, the image is identified, and the first probability of the food material in each food material type is acquired; acquiring a history record of food materials purchased by a user, and acquiring a second probability of cooking each food material type by the user according to the history record; according to the first probability and the second probability, the target probability of each food material type is obtained, the food material type with the maximum target probability is selected as the target food material type, so that the accuracy of recognizing the image of the cooked food material is controlled, and the technical problems that the recognition type of the cooked food material is large in limitation and low in accuracy in the prior art are solved.
In order to implement the foregoing embodiment, the embodiment of the present invention further provides a flowchart of another method for identifying a food material type by a cooking appliance, as shown in fig. 2.
In practical applications, the food material in the cooking appliance is identified in order to better cook the food material. Therefore, after identifying the target food material type of the food material to be cooked in the cooking cavity, the method can cook the target food material type, and specifically comprises the following steps:
s201: the weight of the food material to be cooked is detected.
Specifically, a weight sensor is arranged in the cooking appliance, and the weight of the food to be cooked in the cooking cavity can be acquired through the weight sensor. When a user tries to cook, the cooking appliance may be opened and the food to be cooked may be put in the cooking appliance, and at this time, the weight sensor may be controlled to measure the weight of the food to be cooked and store the data. For example, a user may control a weight sensor to initiate a weight measurement via a button or voice command.
S202: and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the type of the target food material.
Specifically, a result of the type of the target food material selected by the user data model and a result of the weight of the food material to be cooked, which is obtained by the weight sensor, are obtained and returned to the cooking appliance, and the cooking appliance selects the target water amount required for cooking according to the pre-stored mapping relationship between the weight, the type of the food material and the water amount.
S203: controlling the cooking utensil to add water to the target water amount.
Specifically, the cooking appliance is provided with a water filling port, can be connected with a water pipe through the water filling port, and is provided with a water filling valve. When water needs to be filled, the water filling valve is controlled to be opened, after the water filling time reaches a certain time, water is added to the target water amount, then the water filling valve is closed, and the cooking utensil is closed.
S204: and acquiring a cooking strategy matched with the type of the target food material.
Specifically, after the user data model selects the target food material type, the result of the target food material type is returned to the cooking appliance, and the cooking appliance selects a matched cooking strategy according to the target food material type. The cooking strategy mainly comprises cooking stages, cooking time of each cooking stage, a heating power adjusting point and the like.
S205: and controlling the cooking appliance to cook the food to be cooked according to the cooking strategy.
Specifically, after the cooking appliance selects a cooking strategy matched with the target food material type, a result of the selected cooking strategy is returned to the cooking appliance, and the cooking appliance cooks the food material to be cooked according to the cooking time length of each cooking stage, the adjusting point of the heating power and the like.
In order to implement the above embodiment, the embodiment of the present invention further provides a flowchart of another method for identifying a food material type by a cooking appliance.
The method for identifying the type of food material by the cooking appliance provided by the embodiment of the invention is explained below by taking the food material to be cooked as rice as an example, as shown in fig. 3. Because the identification technology of the image identification technology for the rice type is technically mistaken, the embodiment of the invention introduces the rice data purchased by the user as an effective method for improving the rice image identification accuracy, and the method specifically comprises the following steps:
s301: and acquiring a first probability x of the rice to be cooked under each type of rice through image recognition.
That is to say, the intelligent electric cooker obtains the rice image of waiting to cook in the culinary art intracavity through image acquisition device to upload this image to image recognition model, control the image recognition model and calculate and output the discernment rate of accuracy x of rice image, and save data.
For example, through the image recognition model, the recognition accuracy of each kind of rice is obtained as follows: the accuracy rate of Jingshan Qiaomi is 80%, the accuracy rate of Wuchang rice is 50%, and the accuracy rate of masked rice is 25%. The identification accuracy of each kind of rice is recorded and stored.
S302: and acquiring a history record of the rice purchased by the user, and acquiring a second probability z of the type of the purchased rice being cooked by the user according to the history record.
Acquiring information of purchasing rice by a user, comprising: the type of rice purchased and the corresponding number of times.
Further, the probability z of each kind of rice that the user may use is obtained by dividing the number of times each kind of rice appears by the total number of times the user purchases rice.
For example, the information about the purchase of rice by the user for a period of time includes: the five-normal rice is used for 3 times, the Jingshan Qiaomi is used for 1 time, and the rice is covered for 1 time. It is determined that the user purchases rice 5 times in total within the time period. Therefore, the probability of cooking wuchang rice is 3/5, the probability of cooking beijing shan qiao rice is 1/5, and the probability of cooking covering rice is 1/5.
S303: and performing weight calculation on the first probability x and the second probability z to obtain a target probability y of the rice to be cooked under each type of rice, and acquiring a final identification type of the rice to be cooked according to the target probability y.
That is to say, the recognition accuracy of the rice image is corrected by obtaining the probability z of cooking rice of the user, so that the target probability y of each type of rice is obtained, and the rice type with the maximum target probability y is selected as the final recognition result of the rice type.
Before weight calculation, in order to improve the accuracy of rice image recognition, the weight coefficient corresponding to x is set as b1Z is a weight coefficient of b2。
Further, the image identification accuracy rate x and each cooking place of the user are determinedThe probability z of purchasing rice is weighted and calculated according to the formula y ═ x b1+z*b2And obtaining the target probability y of each kind of rice.
That is, the recognition accuracy x of the rice image and the corresponding weight coefficient b are respectively set1Multiplication and probability z of cooking rice of user and corresponding weight coefficient b2And (4) multiplying, namely adding the two multiplied results to obtain the target probability y of each type of rice. And further, analyzing and comparing the obtained target probability of each rice type, and selecting the rice type with the maximum target probability as a final identification result of the rice type.
For example, the probability weight of rice that the user may use is set to 0.4, and the weight of the output result of the image recognition system is set to 0.6. Therefore, the Wuchang rice target probability y1＝3/5*0.4+50％*0.6＝0.54；
Jingshan Qiaomi target probability y2＝1/5*0.4+80％*0.6＝0.56；
Probability y of magnifying the rice target3＝1/5*0.4+25％*0.6＝0.23。
From the above, the Jingshan Qiaomi is selected as the final recognition result. And further, selecting a matched cooking strategy according to the rice type of the final identification result.
It should be noted that the food material to be cooked may also be meat, cereal, etc., for example, when meat, the types of meat may include: beef, mutton, pork, etc.; for example, when it is a cereal, the types of the cereal include: rice, millet, wheat, and the like.
In conclusion, the embodiment of the invention introduces the rice data purchased by the user as an effective method for improving the rice image recognition accuracy, corrects the rice image recognition accuracy obtained by the image recognition model, and reduces the technical limitation and the false recognition probability of the rice recognition by the image recognition technology, thereby improving the rice image recognition accuracy of the intelligent electric rice cooker.
In order to implement the above embodiment, the present invention further provides a device for identifying a type of food material for a cooking appliance.
Fig. 4 is a block diagram illustrating an apparatus for identifying a food material type of a cooking appliance according to an embodiment of the invention. As shown in fig. 4, a cooking apparatus 100 according to an embodiment of the present invention includes: the food material type determining device comprises an image recognition module 10, a record obtaining module 20 and a food material type determining module 30.
The image identification module 10 is configured to acquire an image of a food material to be cooked in the intelligent household appliance, perform image identification on the image, and acquire a first probability of the food material to be cooked under each food material type; the record obtaining module 20 is configured to obtain a history record of food material purchased by a user, and obtain a second probability that the purchased food material is cooked by the user in the history record; and a food material type determining module 30, configured to obtain a target probability of each food material type according to the first probability and the second probability, and select the food material type with the highest target probability as the target food material type.
Further, the food material type determining module 30 is further configured to: comparing the first probability and the second probability of the food material types aiming at each food material type, and selecting the maximum one of the first probability and the second probability as a target probability of the food material type; or, for each food material type, correcting the first probability by using the second probability of the food material type to obtain a target probability of the food material type.
Further, the food material type determining module 30 obtains a first weight corresponding to the first probability and a second weight corresponding to the second probability; and multiplying the first probability by the first weight, multiplying the second probability by the second weight, and adding the two multiplied results to obtain the target probability of the food material type.
Further, the record obtaining module 20 extracts the purchase times and total purchase times of each food material type from the history record; and for each food material type, making a ratio of the purchase times to the total purchase times to obtain a second probability.
Further, the food material type determining module 30 is further configured to: sorting all food material types according to the purchase time; and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
Further, the food material type determining module 30 is further configured to: and after the food material type with the maximum target probability is selected as the target food material type, acquiring a cooking strategy matched with the target food material type, and controlling a cooking appliance to cook the food material to be cooked according to the cooking strategy.
Further, the food material type determining module 30 is further configured to: before a cooking strategy matched with the target food material type is obtained, detecting the weight of the food material to be cooked, and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the target food material type; controlling the cooking utensil to add water to the target water amount.
It should be noted that the explanation of the embodiment of the method for improving the accuracy of identifying the food material type by the intelligent household appliance is also applicable to the device for identifying the food material type by the intelligent household appliance of the embodiment, and the details are not repeated herein.
In order to implement the foregoing embodiment, the present invention further provides an electronic device 200, as shown in fig. 5, which includes a memory 40, a processor 50, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the method for identifying food material types by an intelligent appliance is implemented.
In order to implement the foregoing embodiments, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the foregoing method for identifying food material types for an intelligent household appliance.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (17)
1. A method for identifying food material types by an intelligent household appliance is characterized by comprising the following steps:
the method comprises the steps of obtaining an image of a food material to be cooked in the intelligent household appliance, carrying out image recognition on the image, and obtaining a first probability of the food material to be cooked under a preset food material type;
acquiring a history record of food materials purchased by a user, and acquiring a second probability that the purchased food materials are cooked by the user in the history record;
obtaining a target probability of each food material type according to the first probability and the second probability, and selecting the food material type with the maximum target probability as the target food material type.
2. The method for identifying food material types by an intelligent household appliance according to claim 1, wherein obtaining the target probability of each food material type according to the first probability and the second probability comprises:
for each food material type, comparing the first probability and the second probability of the food material type, and selecting the maximum probability from the first probability and the second probability as a target probability of the food material type; alternatively, the first and second electrodes may be,
and for each food material type, correcting the first probability by using the second probability of the food material type to obtain a target probability of the food material type.
3. The method for identifying food material types by an intelligent household appliance according to claim 2, wherein the modifying the first probability by using the second probability of the food material type to obtain the target probability of the food material type comprises:
acquiring a first weight corresponding to the first probability and a second weight corresponding to the second probability;
and multiplying the first probability by the first weight, multiplying the second probability by the second weight, and adding the two multiplied results to obtain the target probability of the food material type.
4. The method for identifying food material types by an intelligent household appliance according to any one of claims 1 to 3, wherein the obtaining of the second probability that the purchased food material is cooked by the user in the history record comprises:
extracting the purchase times and the total purchase times of each food material type from the history record;
and for each food material type, making a ratio of the purchase times to the total purchase times to obtain the second probability.
5. The method for identifying food material types by an intelligent household appliance according to claim 4, wherein the history record further comprises a purchase time, and the method further comprises:
sequencing all food material types according to the purchase time;
and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
6. The method for identifying food material types by an intelligent household appliance according to any one of claims 1 to 3, wherein the intelligent household appliance is a cooking appliance, and after the food material type with the highest target probability is selected as the target food material type, the method further comprises:
and acquiring a cooking strategy matched with the type of the target food material, and controlling the cooking appliance to cook the food material to be cooked according to the cooking strategy.
7. The method for identifying food material types by an intelligent household appliance according to claim 6, wherein before the obtaining of the cooking strategy matched with the target food material type, the method further comprises:
detecting the weight of the food material to be cooked, and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the type of the target food material;
controlling the cooking utensil to add water to the target water amount.
8. An intelligent household appliance food material type identification device is characterized by comprising:
the intelligent household appliance comprises an image identification module, a cooking management module and a control module, wherein the image identification module is used for acquiring an image of a food material to be cooked in the intelligent household appliance, carrying out image identification on the image and acquiring a first probability of the food material to be cooked under each food material type;
the record acquisition module is used for acquiring a history record of food purchased by a user and acquiring a second probability that the purchased food is cooked by the user in the history record;
and the food material type determining module is used for obtaining the target probability of each food material type according to the first probability and the second probability, and selecting the food material type with the maximum target probability as the target food material type.
9. The apparatus for identifying food material types for an intelligent household appliance according to claim 8, wherein the food material type determining module is further configured to:
for each food material type, comparing the first probability and the second probability of the food material type, and selecting the maximum probability from the first probability and the second probability as a target probability of the food material type; alternatively, the first and second electrodes may be,
and for each food material type, correcting the first probability by using the second probability of the food material type to obtain a target probability of the food material type.
10. The apparatus for identifying food material types for an intelligent household appliance according to claim 9, wherein the food material type determining module is further configured to:
acquiring a first weight corresponding to the first probability and a second weight corresponding to the second probability;
and multiplying the first probability by the first weight, multiplying the second probability by the second weight, and adding the two multiplied results to obtain the target probability of the food material type.
11. The apparatus for identifying food material types for an intelligent appliance according to any one of claims 8 to 10, wherein the record obtaining module is further configured to:
extracting the purchase times and the total purchase times of each food material type from the history record;
and for each food material type, making a ratio of the purchase times to the total purchase times to obtain the second probability.
12. The apparatus for identifying food material types for an intelligent household appliance according to claim 11, wherein the history record further includes a purchase time, and the food material type determining module is further configured to:
sequencing all food material types according to the purchase time;
and acquiring a probability adjustment coefficient matched with the sequence of the food material types according to the sequence of the food material types, and adjusting the second probability of the food material types by using the adjustment coefficient.
13. The apparatus for identifying food material types for an intelligent household appliance according to any one of claims 8 to 10, wherein the intelligent household appliance is a cooking appliance, and the food material type determining module is further configured to:
and after the food material type with the maximum target probability is selected as the target food material type, acquiring a cooking strategy matched with the target food material type, and controlling the cooking appliance to cook the food material to be cooked according to the cooking strategy.
14. The apparatus for identifying food material types for an intelligent household appliance according to claim 13, wherein the food material type determining module is further configured to:
before a cooking strategy matched with the target food material type is obtained, detecting the weight of the food material to be cooked, and determining the target water amount needed by the food material to be cooked according to the weight of the food material to be cooked and the target food material type;
controlling the cooking utensil to add water to the target water amount.
15. An intelligent appliance, comprising: the apparatus for identifying food material type of intelligent household appliance according to any one of claims 8-14.
16. An electronic device comprising a memory, a processor;
the processor executes a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to implement the method for identifying the food material type by the intelligent household appliance according to any one of claims 1 to 7.
17. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the program, when executed by a processor, implements the method for identifying food material type of an intelligent appliance according to any one of claims 1-7.
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