WO2024222017A1 - 一种菜谱推荐方法、装置、电子设备及可读存储介质 - Google Patents

一种菜谱推荐方法、装置、电子设备及可读存储介质 Download PDF

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Publication number
WO2024222017A1
WO2024222017A1 PCT/CN2023/142608 CN2023142608W WO2024222017A1 WO 2024222017 A1 WO2024222017 A1 WO 2024222017A1 CN 2023142608 W CN2023142608 W CN 2023142608W WO 2024222017 A1 WO2024222017 A1 WO 2024222017A1
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WO
WIPO (PCT)
Prior art keywords
cooking
information
recipe
target object
dish
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2023/142608
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English (en)
French (fr)
Inventor
林永吉
龚贞庆
黎清顾
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhuhai Lyentech Co Ltd
Gree Electric Appliances Inc of Zhuhai
Original Assignee
Zhuhai Lyentech Co Ltd
Gree Electric Appliances Inc of Zhuhai
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhuhai Lyentech Co Ltd, Gree Electric Appliances Inc of Zhuhai filed Critical Zhuhai Lyentech Co Ltd
Publication of WO2024222017A1 publication Critical patent/WO2024222017A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2457—Query processing with adaptation to user needs

Definitions

  • the present disclosure relates to the field of data processing technology, and in particular to a recipe recommendation method, device, electronic device and readable storage medium.
  • smart home devices are becoming more and more popular.
  • the emergence of smart home devices has provided people with many conveniences and changed people's lifestyles.
  • current smart home devices can provide users with recipe recommendations.
  • the recipe recommendation function currently provided by smart home devices can generally only recommend relevant recipes by analyzing user preferences and ingredients, without taking into account the differences in cooking levels among different users, and cannot recommend recipes suitable for users with different cooking levels.
  • the present disclosure provides a recipe recommendation method, device, electronic device and readable storage medium.
  • an embodiment of the present disclosure provides a recipe recommendation method, comprising:
  • first basic information corresponding to a target object Acquire first basic information corresponding to a target object, and acquire first cooking ability information corresponding to the target object, wherein the first basic information is set to represent information that affects the dietary preferences of the target object;
  • the recipe to be recommended is sent to an object device corresponding to the target object.
  • the acquiring the first cooking capability information corresponding to the target object includes:
  • the first ability level corresponding to the target cooking experience value range is determined as the first cooking ability information corresponding to the target object.
  • the obtaining the dish cooking data corresponding to the target object includes:
  • obtaining cooking process data corresponding to the dish cooking process wherein the cooking process data includes at least one dish cooking operation, and an operation score and a weight corresponding to each dish cooking operation;
  • a weighted sum operation is performed on the operation scores of all the dish cooking operations to obtain a cooking score corresponding to the dish cooking process
  • the dish cooking data is generated based on all the dish cooking processes and the cooking score corresponding to each of the dish cooking processes.
  • the acquiring the first cooking capability information corresponding to the target object includes:
  • a second ability level input by the target object is received, and the second ability level is determined as the first cooking ability information corresponding to the target object.
  • the method further includes:
  • the historical usage data includes at least one usage object, and second basic information and second cooking capability information corresponding to each usage object;
  • the recipe set is constructed based on all the recipes and the associated information corresponding to each of the recipes.
  • determining the associated information corresponding to the recipe based on the second basic information and the second cooking ability information corresponding to each user object includes:
  • the combined information whose support is greater than a preset support threshold and whose confidence is greater than a preset confidence threshold is determined as the associated information.
  • determining a recipe to be recommended from a preset recipe set based on the first basic information and the first cooking ability information includes:
  • the recipes in the recipe set whose corresponding association information is consistent with the target association information are determined as the recipes to be recommended.
  • an embodiment of the present disclosure provides a recipe recommendation device, comprising:
  • an acquisition module configured to acquire first basic information corresponding to a target object, and to acquire first cooking ability information corresponding to the target object, wherein the first basic information is configured to represent information that affects the dietary preferences of the target object;
  • a determination module configured to determine a recipe to be recommended in a preset recipe set based on the first basic information and the first cooking ability information
  • the sending module is configured to send the recipe to be recommended to the object device corresponding to the target object.
  • the acquisition module is specifically configured to:
  • the first ability level corresponding to the target cooking experience value range is determined as the first cooking ability information corresponding to the target object.
  • the acquisition module is further configured to:
  • obtaining cooking process data corresponding to the dish cooking process wherein the cooking process data includes at least one dish cooking operation, and an operation score and a weight corresponding to each dish cooking operation;
  • a weighted sum operation is performed on the operation scores of all the dish cooking operations to obtain a cooking score corresponding to the dish cooking process
  • the dish cooking data is generated based on all the dish cooking processes and the cooking score corresponding to each of the dish cooking processes.
  • the acquisition module is further configured to:
  • a second ability level input by the target object is received, and the second ability level is determined as the first cooking ability information corresponding to the target object.
  • the device further includes a building module configured to:
  • the historical usage data includes at least one usage object, and second basic information and second cooking capability information corresponding to each usage object;
  • the recipe set is constructed based on all the recipes and the associated information corresponding to each of the recipes.
  • the building module is further configured to:
  • the combined information whose support is greater than a preset support threshold and whose confidence is greater than a preset confidence threshold is determined as the associated information.
  • the determining module is specifically configured to:
  • the recipes in the recipe set whose corresponding association information is consistent with the target association information are determined as the recipes to be recommended.
  • an electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;
  • a memory arranged to store a computer program
  • the processor when configured to execute a program stored in the memory, implements any method step described in the first aspect.
  • a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.
  • a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute any of the above-described recipe recommendation methods.
  • the embodiment of the present disclosure provides a recipe recommendation method, device, electronic device and readable storage medium.
  • the present disclosure first obtains the first basic information corresponding to the target object, and obtains the first cooking ability information corresponding to the target object, wherein the first basic information is set to represent the information that affects the dietary preferences of the target object, then determines the recipe to be recommended in a preset recipe set based on the first basic information and the first cooking ability information, and finally sends the recipe to be recommended to the object device corresponding to the target object.
  • the corresponding recipe can be recommended to the user in combination with the user's basic information and cooking ability, thereby recommending recipes suitable for users with different cooking levels, and improving the user's satisfaction with the recommended recipes.
  • FIG1 is a flow chart of a recipe recommendation method provided by an embodiment of the present disclosure
  • FIG2 is a flow chart of another recipe recommendation method provided by an embodiment of the present disclosure.
  • FIG3 is a flow chart of another recipe recommendation method provided by an embodiment of the present disclosure.
  • FIG4 is a schematic diagram of the structure of a recipe recommendation device provided by an embodiment of the present disclosure.
  • FIG5 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure.
  • FIG1 there is shown a flowchart of an embodiment of a recipe recommendation method provided by an embodiment of the present disclosure. As shown in FIG1 , the process may include the following steps:
  • a recipe recommendation method provided by an embodiment of the present disclosure can be applied to a server or to smart home devices, such as smart phones, smart refrigerators, etc.
  • Target object refers to the user to whom the recipe is to be recommended.
  • the first basic information is set to represent the information that affects the dietary preferences of the target object, such as the user's region, the user's preferred taste, etc.
  • the user when a user uses the recipe recommendation function for the first time, the user may be prompted to enter his or her basic information, and the basic information may be stored in a preset storage location (such as a local computer or a cloud).
  • a preset storage location such as a local computer or a cloud.
  • the first cooking ability information is set to represent the target object's proficiency in cooking operations, that is, the cooking level. For example, elementary is set to represent that the user's cooking level is low, intermediate is set to represent that the user's cooking level is high, advanced is set to represent that the user's cooking level is very high, and so on.
  • the user's cooking ability changes over time.
  • the user uses the recipe recommendation function each time the user uses the recipe recommendation function, The user's current operation proficiency is obtained as the first cooking ability information.
  • S102 Determine a recipe to be recommended in a preset recipe set based on the first basic information and the first cooking ability information.
  • the above recipe set includes multiple recipes and associated information corresponding to each recipe, wherein the associated information is set to mark which cooking abilities and basic information users the corresponding recipe is suitable for.
  • recipe 1 is suitable for users with strong cooking abilities and who like sweet flavors.
  • the specific implementation of determining the recipe to be recommended in the preset recipe set may include: combining the first basic information and the first cooking ability information to obtain the target association information corresponding to the target object, and determining the recipe in the recipe set whose corresponding association information is consistent with the target association information as the recipe to be recommended.
  • the dimension information and the first cooking ability information are directly combined to obtain the target association information.
  • the target association information is: intermediate + like sweet taste.
  • the first basic information includes multiple dimensional information on one dimension, or multiple dimensional information on multiple dimensions, then one dimensional information is selected on each dimension to obtain multi-dimensional information, and the multi-dimensional information is combined with the first cooking capability information to obtain multiple target-related information.
  • the user's taste dimension includes sweet taste and salty taste
  • the user's region dimension includes Guangdong region
  • the first cooking ability information is intermediate.
  • the target association information includes two situations, one is: sweet taste + Guangdong region + intermediate, and the other is: salty taste + Guangdong region + intermediate.
  • the recipe corresponding to the associated information is determined as the recipe to be recommended. In this way, the recipe to be recommended that is consistent with the basic information and cooking ability of the target object can be matched.
  • the target device refers to the device used by the target object to view the recipe, such as a mobile phone, a refrigerator with a display screen, a projector, etc.
  • the recipe to be recommended is sent to the object device corresponding to the target object, and the object device displays the recipe to be recommended, so as to facilitate the target object to view it.
  • the first basic information corresponding to the target object is obtained, and the first cooking ability information corresponding to the target object is obtained, wherein the first basic information is set to represent the information that affects the dietary preferences of the target object, and then, based on the first basic information and the first cooking ability information, the recipe to be recommended is determined in the preset recipe set, and finally, the recipe to be recommended is sent to the object device corresponding to the target object.
  • the corresponding recipe can be recommended to the user in combination with the basic information and cooking ability of the user, so as to recommend recipes suitable for the cooking of users with different cooking levels, thereby improving the user's satisfaction with the recommended recipes.
  • FIG2 is a flowchart of another recipe recommendation method provided by an embodiment of the present disclosure.
  • the process shown in FIG2 is based on the process shown in FIG1 above, and describes how to obtain the first cooking ability information corresponding to the target object. As shown in FIG2, the process may include the following steps:
  • the basic cooking experience value is a quantitative value of the cooking level that the user enters based on his or her cooking proficiency when using the recipe recommendation function for the first time. For example, if the user has no experience at all, a value of 0 can be entered.
  • the dish cooking data includes at least one dish cooking process and the cooking score corresponding to each dish cooking process, that is, the score that the target object gives himself in each recipe cooking process after using the recipe recommendation function.
  • the specific implementation of obtaining the dish cooking data corresponding to the target object may include: for each dish cooking process, obtaining the cooking process data corresponding to the dish cooking process, wherein the cooking process data includes at least one dish cooking operation, and the operation score and weight corresponding to each dish cooking operation, based on the operation score and weight corresponding to each dish cooking operation.
  • the weight is used to perform a weighted sum operation on the operation scores of all the cooking operations of the dish to obtain the cooking score corresponding to the cooking process of the dish, and the cooking data of the dish is generated based on the cooking scores corresponding to all the cooking processes of the dish and each of the cooking processes of the dish.
  • the cooking operations include but are not limited to: cutting vegetables, seasoning, controlling the heat, etc.
  • the weight corresponding to the cooking operation is set to represent the impact of the cooking operation on the overall cooking level. For example, for a dish, cutting vegetables affects the shape and taste of the dish, while seasoning has a greater impact on the final taste of the dish. Therefore, the weight of cutting vegetables can be set to be smaller than the weight of seasoning.
  • the object device can display a scoring interface, allowing the user to score each cooking operation according to the cooking situation of the dish.
  • the weights of cutting vegetables, seasoning, and heat are 1, 2, and 2, respectively.
  • the dish cooking data is generated, that is, the dish cooking data records multiple data, each data is a corresponding dish cooking process and a corresponding cooking score.
  • S203 performing a sum operation on the basic cooking experience value and the cooking experience adjustment value to obtain a target cooking experience value.
  • the cooking scores corresponding to all the cooking processes of the dishes are first summed up to obtain a cooking experience adjustment value, that is, the cooking experience adjustment value is set to represent the improvement of the user's cooking level in the process of using the recipe recommendation function.
  • the basic cooking experience value and the cooking experience adjustment value are summed to obtain a target cooking experience value, that is, the target cooking experience value is set to represent the user's current cooking level.
  • S204 Determine a target cooking experience value range in which the target cooking experience value is located among a plurality of preset experience value ranges.
  • the correspondence between different experience value ranges and ability levels is pre-set. For example, 0-100 experience values are elementary, 100-200 experience values are intermediate, and 200-300 experience values are advanced.
  • the corresponding ability level is determined by determining the experience value range in which the target cooking experience value is located, and the ability level is determined as the first cooking ability information corresponding to the target object.
  • the target cooking experience value is 160, which falls within the range of 100-200, and the intermediate level corresponding to the experience value of 100-200 is determined as the first cooking ability information.
  • the current cooking level (i.e., the first ability level) of the target object can be determined based on the basic cooking experience value and the dish cooking data corresponding to the target object, and used as the first cooking ability information.
  • the determined first cooking ability information can be more in line with the current situation of the target object.
  • a specific implementation of obtaining the first cooking capability information corresponding to the target object may include: receiving a second capability level input by the target object, and determining the second capability level as the first cooking capability information corresponding to the target object.
  • the second ability level is information input by the user to characterize the cooking ability.
  • the second ability level input by the target object in real time can be used as the corresponding first cooking ability information.
  • the first cooking ability information can be flexibly set according to user needs.
  • FIG3 there is shown a flowchart of another recipe recommendation method provided by an embodiment of the present disclosure. As shown in FIG3 , the process may include the following steps:
  • the historical usage data includes at least one user object, and the second basic information and second cooking ability information corresponding to each user object.
  • the user object is a user who has used the recipe recommendation function in the past.
  • the second basic information refers to the user object's information related to diet.
  • the second cooking ability information is set to represent the user's proficiency in cooking operations, that is, the cooking level.
  • the second basic information and the second cooking ability information of each user who uses the recipe recommendation function, as well as the usage of each recipe by each user can be recorded in real time, and this information can be stored in a preset storage location.
  • the historical usage data of each recipe can be obtained in the preset storage location.
  • S302 Determine association information corresponding to the recipe based on the second basic information and the second cooking ability information corresponding to each of the user objects.
  • the specific implementation of determining the associated information corresponding to the recipe based on the second basic information and the second cooking ability information corresponding to each of the user objects may include: combining the second basic information corresponding to each of the user objects and the second cooking ability information corresponding to the user objects to obtain at least one combination information, calculating the support and confidence corresponding to each of the combination information, and determining the combination information whose support is greater than a preset support threshold and whose confidence is greater than a preset confidence threshold as the associated information.
  • the second basic information includes dimension information on at least one dimension.
  • one dimension information of each user object on one dimension is taken as a separate dimension transaction
  • the second cooking capability information of each user object is taken as a separate capability transaction. Then, these dimension transactions and capability transactions are combined to obtain combined information, wherein each combined information includes one dimension information on each dimension and one second cooking capability information.
  • each combination of information its corresponding support and confidence can be calculated according to the association rule algorithm.
  • the support of the combination of information is greater than the preset support threshold, and the confidence is greater than the preset confidence threshold, it means that the combination of information is strongly related to the corresponding recipe, so the combination of information is used to determine the association information corresponding to the recipe.
  • the corresponding dimension information includes: region dimension, the user's location is Zhuhai; taste dimension: the user likes sweet taste, and the user likes salty taste; the corresponding second cooking ability information includes: intermediate and advanced.
  • the combination information includes: first, the user's location is Zhuhai + the user likes sweet taste + intermediate; second, the user's location is Zhuhai + the user likes salty taste taste + intermediate; third, the user’s location is Zhuhai + the user likes sweet taste + high-end; fourth, the user’s location is Zhuhai + the user likes salty taste + high-end.
  • S303 construct the recipe set based on all the recipes and the associated information corresponding to each of the recipes.
  • a recipe set can be constructed based on all recipes and the associated information corresponding to each recipe.
  • the recipe set not only contains multiple recipes, but also contains the associated information of each recipe.
  • the user can be matched with a suitable recipe to be recommended based on the associated information.
  • the historical usage data of each recipe can be analyzed to obtain the associated information (including basic information and cooking ability information) of users who like to use the corresponding recipe, and then a recipe set can be constructed based on all recipes and the associated information corresponding to each recipe. Through this recipe set, recipes suitable for users with different cooking levels can be matched.
  • the embodiment of the present disclosure also provides a recipe recommendation device, as shown in FIG4 , the device includes:
  • the acquisition module 401 is configured to acquire first basic information corresponding to a target object, and acquire first cooking ability information corresponding to the target object, wherein the first basic information is configured to represent information that affects the dietary preferences of the target object;
  • a determination module 402 is configured to determine a recipe to be recommended in a preset recipe set based on the first basic information and the first cooking ability information;
  • the sending module 403 is configured to send the recipe to be recommended to the object device corresponding to the target object.
  • the acquisition module is specifically configured to:
  • the first ability level corresponding to the target cooking experience value range is determined as the first cooking ability information corresponding to the target object.
  • the acquisition module is further configured to:
  • obtaining cooking process data corresponding to the dish cooking process wherein the cooking process data includes at least one dish cooking operation, and an operation score and a weight corresponding to each dish cooking operation;
  • a weighted sum operation is performed on the operation scores of all the dish cooking operations to obtain a cooking score corresponding to the dish cooking process
  • the dish cooking data is generated based on all the dish cooking processes and the cooking score corresponding to each of the dish cooking processes.
  • the acquisition module is further configured to:
  • a second ability level input by the target object is received, and the second ability level is determined as the first cooking ability information corresponding to the target object.
  • the device further includes a building module configured to:
  • the historical usage data includes at least one usage object, and second basic information and second cooking capability information corresponding to each usage object;
  • the recipe set is constructed based on all the recipes and the associated information corresponding to each of the recipes.
  • the building module is further configured to:
  • the combined information whose support is greater than a preset support threshold and whose confidence is greater than a preset confidence threshold is determined as the associated information.
  • the determining module is specifically configured to:
  • the recipes in the recipe set whose corresponding association information is consistent with the target association information are determined as the recipes to be recommended.
  • the first basic information corresponding to the target object is obtained, and the first cooking ability information corresponding to the target object is obtained, wherein the first basic information is set to represent the information that affects the dietary preferences of the target object, and then, based on the first basic information and the first cooking ability information, the recipe to be recommended is determined in the preset recipe set, and finally, the recipe to be recommended is sent to the object device corresponding to the target object.
  • the corresponding recipe can be recommended to the user in combination with the basic information and cooking ability of the user, so as to recommend recipes suitable for the cooking of users with different cooking levels, thereby improving the user's satisfaction with the recommended recipes.
  • the embodiment of the present disclosure also provides an electronic device, as shown in FIG5 , including a processor 111, a communication interface 112, a memory 113 and a communication bus 114, wherein: The processor 111, the communication interface 112, and the memory 113 communicate with each other via the communication bus 114.
  • a memory 113 configured to store computer programs
  • the processor 111 when configured to execute a program stored in the memory 113, implements the following steps:
  • first basic information corresponding to a target object Acquire first basic information corresponding to a target object, and acquire first cooking ability information corresponding to the target object, wherein the first basic information is set to represent information that affects the dietary preferences of the target object;
  • the recipe to be recommended is sent to an object device corresponding to the target object.
  • the communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus.
  • PCI Peripheral Component Interconnect
  • EISA Extended Industry Standard Architecture
  • the communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
  • the communication interface is used for communication between the above electronic device and other devices.
  • the memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk storage.
  • RAM random access memory
  • NVM non-volatile memory
  • the memory may also be at least one storage device located away from the aforementioned processor.
  • the above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • CPU central processing unit
  • NP network processor
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a computer-readable storage medium in which a computer program is stored.
  • the computer program is executed by a processor, the steps of any of the above-mentioned recipe recommendation methods are implemented.
  • a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any recipe recommendation method in the above embodiments.
  • the computer program product includes one or more computer instructions.
  • the computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.
  • the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center.
  • the computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations.
  • the available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state drive (SSD)), etc.

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Abstract

一种菜谱推荐方法、装置、电子设备及可读存储介质。通过获取目标对象对应的第一基础信息，以及获取目标对象对应的第一烹饪能力信息；其中，第一基础信息被设置为表征影响目标对象饮食喜好的信息；基于第一基础信息和第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；向目标对象对应的对象设备发送所推荐菜谱。由此，可以结合用户的基础信息和烹饪能力，向用户推荐对应的菜谱，从而为烹饪水平不同的用户推荐适合其烹饪的菜谱，提高用户对推荐菜谱的满意度。

Description

一种菜谱推荐方法、装置、电子设备及可读存储介质
本公开要求于2023年04月23日提交中国专利局、申请号为202310447578.0、发明名称为“一种菜谱推荐方法、装置、电子设备及可读存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本公开中。
技术领域
本公开涉及数据处理技术领域，尤其涉及一种菜谱推荐方法、装置、电子设备及可读存储介质。
背景技术
随着科技的进步，智能家居设备正逐步普及。智能家居设备的出现为人们提供了诸多便利，改变了人们的生活方式。在饮食方面，目前的智能家居设备可以为用户提供菜谱推荐的功能。
然而，目前智能家居设备提供的菜谱推荐功能，一般只能通过分析用户的喜好和食材来推荐相关的菜谱，未考虑到不同用户烹饪水平的不同，不能为烹饪水平不同的用户推荐适合其烹饪的菜谱。
发明内容
为了解决上述技术问题，本公开提供了一种菜谱推荐方法、装置、电子设备及可读存储介质。
第一方面，本公开实施例提供一种菜谱推荐方法，包括：
获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
向所述目标对象对应的对象设备发送所述待推荐菜谱。
在一个可能的实施方式中，所述获取所述目标对象对应的第一烹饪能力信息，包括：
获取所述目标对象对应的基础烹饪经验值，以及，获取所述目标对象对应的菜品烹饪数据，其中，所述菜品烹饪数据包括至少一次菜品烹饪过程，以及，每次所述菜品烹饪过程对应的烹饪分数；
对所有所述菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值；
对所述基础烹饪经验值和所述烹饪经验调整值进行求和运算，得到目标烹饪经验值；
在预设的多个经验值范围中，确定所述目标烹饪经验值所处的目标烹饪经验值范围；
将所述目标烹饪经验值范围对应的第一能力等级，确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述获取所述目标对象对应的菜品烹饪数据，包括：
针对每次菜品烹饪过程，获取所述菜品烹饪过程对应的烹饪过程数据，其中，所述烹饪过程数据包括至少一个菜品烹饪操作，以及，每个菜品烹饪操作对应的操作分数及权重；
基于每个所述菜品烹饪操作对应的权重，对所有所述菜品烹饪操作的操作分数进行加权求和运算，得到所述菜品烹饪过程对应的烹饪分数；
基于所有所述菜品烹饪过程以及每个所述菜品烹饪过程对应的烹饪分数，生成所述菜品烹饪数据。
在一个可能的实施方式中，所述获取所述目标对象对应的第一烹饪能力信息，包括：
接收所述目标对象输入的第二能力等级，将所述第二能力等级确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述方法还包括：
针对每个菜谱，获取所述菜谱对应的历史使用数据，其中，所述历史使用数据包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息；
基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息；
基于所有所述菜谱和每个所述菜谱对应的关联信息，构建所述菜谱集合。
在一个可能的实施方式中，所述基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息，包括：
对每个所述使用对象对应的第二基础信息，以及所述使用对象对应的第二烹饪能力信息进行组合，得到至少一个组合信息；
计算每个所述组合信息对应的支持度和置信度；
将所述支持度大于预设支持度阈值，且，所述置信度大于预设置信度阈值的组合信息，确定为所述关联信息。
在一个可能的实施方式中，所述基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱，包括：
组合所述第一基础信息和所述第一烹饪能力信息，得到所述目标对象对应的目标关联信息；
将所述菜谱集合中对应关联信息与所述目标关联信息一致的菜谱，确定为所述待推荐菜谱。
第二方面，本公开实施例提供一种菜谱推荐装置，包括：
获取模块，被设置为获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
确定模块，被设置为基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
发送模块，被设置为向所述目标对象对应的对象设备发送所述待推荐菜谱。
在一个可能的实施方式中，所述获取模块，具体被设置为：
获取所述目标对象对应的基础烹饪经验值，以及，获取所述目标对象对应的菜品烹饪数据，其中，所述菜品烹饪数据包括至少一次菜品烹饪过程，以及，每次所述菜品烹饪过程对应的烹饪分数；
对所有所述菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值；
对所述基础烹饪经验值和所述烹饪经验调整值进行求和运算，得到目标烹饪经验值；
在预设的多个经验值范围中，确定所述目标烹饪经验值所处的目标烹饪经验值范围；
将所述目标烹饪经验值范围对应的第一能力等级，确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述获取模块，还被设置为：
针对每次菜品烹饪过程，获取所述菜品烹饪过程对应的烹饪过程数据，其中，所述烹饪过程数据包括至少一个菜品烹饪操作，以及，每个菜品烹饪操作对应的操作分数及权重；
基于每个所述菜品烹饪操作对应的权重，对所有所述菜品烹饪操作的操作分数进行加权求和运算，得到所述菜品烹饪过程对应的烹饪分数；
基于所有所述菜品烹饪过程以及每个所述菜品烹饪过程对应的烹饪分数，生成所述菜品烹饪数据。
在一个可能的实施方式中，所述获取模块，还被设置为：
接收所述目标对象输入的第二能力等级，将所述第二能力等级确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述装置还包括构建模块，被设置为：
针对每个菜谱，获取所述菜谱对应的历史使用数据，其中，所述历史使用数据包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息；
基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息；
基于所有所述菜谱和每个所述菜谱对应的关联信息，构建所述菜谱集合。
在一个可能的实施方式中，所述构建模块，还被设置为：
对每个所述使用对象对应的第二基础信息，以及所述使用对象对应的第二烹饪能力信息进行组合，得到至少一个组合信息；
计算每个所述组合信息对应的支持度和置信度；
将所述支持度大于预设支持度阈值，且，所述置信度大于预设置信度阈值的组合信息，确定为所述关联信息。
在一个可能的实施方式中，所述确定模块，具体被设置为：
组合所述第一基础信息和所述第一烹饪能力信息，得到所述目标对象对应的目标关联信息；
将所述菜谱集合中对应关联信息与所述目标关联信息一致的菜谱，确定为所述待推荐菜谱。
第三方面，提供了一种电子设备，包括处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；
存储器，被设置为存放计算机程序；
处理器，被设置为执行存储器上所存放的程序时，实现第一方面任一所述的方法步骤。
第四方面，提供了一种计算机可读存储介质，其特征在于，所述计算 机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现第一方面任一所述的方法步骤。
第五方面，提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述任一所述的菜谱推荐方法。
本公开实施例有益效果：
本公开实施例提供了一种菜谱推荐方法、装置、电子设备及可读存储介质，本公开通过，首先，获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息，然后，基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱，最后，向所述目标对象对应的对象设备发送所述待推荐菜谱。由此，可以结合用户的基础信息和烹饪能力，向用户推荐对应的菜谱，从而为烹饪水平不同的用户推荐适合其烹饪的菜谱，提高用户对推荐菜谱的满意度。
当然，实施本公开的任一产品或方法并不一定需要同时达到以上所述的所有优点。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本公开的实施例，并与说明书一起用于解释本公开的原理。
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本公开实施例提供的一种菜谱推荐方法的流程图；
图2为本公开实施例提供的另一种菜谱推荐方法的流程图；
图3为本公开实施例提供的另一种菜谱推荐方法的流程图；
图4为本公开实施例提供的一种菜谱推荐装置的结构示意图；
图5为本公开实施例提供的一种电子设备的结构示意图。
具体实施方式
为使本公开实施例的目的、技术方案和优点更加清楚，下面将结合本公开实施例中的附图，对本公开实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本公开的一部分实施例，而不是全部的实施例。基于本公开中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本公开保护的范围。
下面结合附图以具体实施例对本公开提供的菜谱推荐方法做出解释说明，实施例并不构成对本公开实施例的限定。
参见图1，为本公开实施例提供的一种菜谱推荐方法的实施例流程图。如图1所示，该流程可包括以下步骤：
S101，获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息。
本公开实施例提供的一种菜谱推荐方法，可以应用于服务器，也可以应用于智能家居设备，例如，智能手机、智能冰箱等。
目标对象，指待推荐菜谱的用户。
第一基础信息，被设置为表征影响目标对象饮食喜好的信息，例如，用户所在地区、用户偏好口味等。
本公开实施例中，在用户首次使用菜谱推荐功能时，可以提示用户输入自己的基础信息，并将该基础信息存储至预设的存储位置(如本地或云端)。后续使用时，则可以在该存储位置直接获取该用户对应的基础信息。
第一烹饪能力信息，被设置为表征目标对象对于烹饪的操作熟练情况，也即，烹饪水平。例如，初级，被设置为表征用户的烹饪水平较低，中级，被设置为表征用户的烹饪水平较高，高级，被设置为表征用户的烹饪水平很高，等等。
应用中，用户的烹饪能力是随着时间的变化而变化的，为了准确了解用户当前的烹饪能力，本公开实施例中，用户每次使用菜谱推荐功能时， 获取用户当前的操作熟练情况，作为第一烹饪能力信息。
至于具体如何获取目标对象对应的第一烹饪能力信息，将通过后文不同实施例进行详细的解释说明，这里先不详述。
S102，基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱。
上述菜谱集合中包含多个菜谱及每个菜谱对应的关联信息，其中，关联信息，被设置为标记对应菜谱适合哪些烹饪能力以及哪些基础信息的用户，例如，菜谱1适合烹饪能力较强的、喜欢甜口味用户。
本公开实施例中，基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱的具体实现可包括：组合所述第一基础信息和所述第一烹饪能力信息，得到所述目标对象对应的目标关联信息，将所述菜谱集合中对应关联信息与所述目标关联信息一致的菜谱，确定为所述待推荐菜谱。
其中，若第一基础信息仅包含一个维度上的一个维度信息，则直接组合该维度信息和第一烹饪能力信息，得到目标关联信息。例如，维度信息为用户喜欢甜口味，第一烹饪能力信息为中级，则目标关联信息为：中级+喜欢甜口味。
若第一基础信息包含一个维度上的多个维度信息，或者，多个维度上的多个维度信息，则，在每个维度上选择一个维度信息得到多维度信息，将多维度信息与第一烹饪能力信息组合，得到多个目标关联信息。
例如，用户口味维度上包含甜口味和咸口味，用户所在地区维度上包括广东地区，第一烹饪能力信息为中级，则，目标关联信息包括两种情况，一种是：甜口味+广东地区+中级，另一种是：咸口味+广东地区+中级。
匹配过程中，关联信息与任一目标关联信息一致，则将该关联信息对应的菜谱确定为待推荐菜谱。如此，可以匹配到与目标对象基础信息和烹饪能力相符的待推荐菜谱。
S103，向所述目标对象对应的对象设备发送所述待推荐菜谱。
对象设备，指目标对象用于查看菜谱的设备，例如，手机、带显示屏的冰箱、投影仪等。
本公开实施例中，在确定待推荐菜谱之后，向目标对象对应的对象设备发送待推荐菜谱，由对象设备对该待推荐菜谱进行展示，从而方便目标对象查看。
本公开实施例中，首先，获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息，然后，基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱，最后，向所述目标对象对应的对象设备发送所述待推荐菜谱。由此，可以结合用户的基础信息和烹饪能力，向用户推荐对应的菜谱，从而为烹饪水平不同的用户推荐适合其烹饪的菜谱，提高用户对推荐菜谱的满意度。
参见图2，为本公开实施例提供的另一种菜谱推荐方法的实施例流程图。该图2所示流程在上述图1所示流程的基础上，描述如何获取所述目标对象对应的第一烹饪能力信息。如图2所示，该流程可包括以下步骤：
S201，获取所述目标对象对应的基础烹饪经验值，以及，获取所述目标对象对应的菜品烹饪数据，其中，所述菜品烹饪数据包括至少一次菜品烹饪过程，以及，每次所述菜品烹饪过程对应的烹饪分数。
基础烹饪经验值，为用户首次使用菜谱推荐功能时，根据自身对烹饪的熟练程度输入的烹饪水平情况的量化数值，例如，完全无经验可以输入数值0。
菜品烹饪数据，包括至少一次菜品烹饪过程，以及，每次菜品烹饪过程对应的烹饪分数。即，目标对象在使用菜谱推荐功能后，历次进行菜谱烹饪过程中为自己打的分数。
本公开实施例中，获取所述目标对象对应的菜品烹饪数据的具体实现可包括：针对每次菜品烹饪过程，获取所述菜品烹饪过程对应的烹饪过程数据，其中，所述烹饪过程数据包括至少一个菜品烹饪操作，以及，每个菜品烹饪操作对应的操作分数及权重，基于每个所述菜品烹饪操作对应的 权重，对所有所述菜品烹饪操作的操作分数进行加权求和运算，得到所述菜品烹饪过程对应的烹饪分数，基于所有所述菜品烹饪过程以及每个所述菜品烹饪过程对应的烹饪分数，生成所述菜品烹饪数据。
其中，菜品烹饪操作包括但不限于：切菜、调味、火候掌握等。菜品烹饪操作对应的权重，被设置为表征该菜品烹饪操作对于整体烹饪水平提升的影响。例如：对于菜品来说切菜影响的是菜型、口感，而调味对于菜品最后的味道影响更大，因此，可以设置切菜的权重小于调味的权重。
该实施例中，用户在点击某个菜谱进行烹饪后，可以通过对象设备显示打分界面，让用户根据自己烹饪菜品的情况为每个菜品烹饪操作打分。例如，切菜、调味、火候的权重依次为1、2、2，用户打分情况为：切菜5分、调味6分、火候8分，则最终分数为：5*1+6*2+8*2＝33。
进而，基于所有菜品烹饪过程及每次菜品烹饪过程对应的烹饪分数，生成菜品烹饪数据，即，菜品烹饪数据中记录有多条数据，每条数据为对应的一次菜品烹饪过程及相应的烹饪分数。
S202，对所有所述菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值。
S203，对所述基础烹饪经验值和所述烹饪经验调整值进行求和运算，得到目标烹饪经验值。
以下对S202-S203进行统一说明：
本公开实施例中，首先对所有菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值，即，烹饪经验调整值，被设置为表征用户使用菜谱推荐功能过程中烹饪水平提升的情况。
然后，对基础烹饪经验值和烹饪经验调整值进行求和运算，得到目标烹饪经验值，即，目标烹饪经验值被设置为表征用户当前的烹饪水平情况。
S204，在预设的多个经验值范围中，确定所述目标烹饪经验值所处的目标烹饪经验值范围。
S205，将所述目标烹饪经验值范围对应的第一能力等级，确定为所述 目标对象对应的第一烹饪能力信息。
以下对S204-S205进行统一说明：
应用中，预先设置了不同经验值范围与能力等级的对应关系，例如，0-100经验值为初级，100-200经验值为中级，200-300经验值为高级。
本公开实施例中，在得到目标烹饪经验值之后，通过确定目标烹饪经验值所处的经验值范围，来确定相应的能力等级，并将该能力等级确定为目标对象对应的第一烹饪能力信息。
例如，目标烹饪经验值为160，落在100-200的范围中，则将100-200经验值对应的中级，确定为第一烹饪能力信息。
通过图2所示流程，可以基于目标对象对应的基础烹饪经验值及菜品烹饪数据，确定目标对象当前的烹饪水平(即，第一能力等级)，并将其作为第一烹饪能力信息。由此，可以使确定出的第一烹饪能力信息，更加符合目标对象的当前情况。
此外，在另一实施例中，获取所述目标对象对应的第一烹饪能力信息的具体实现可包括：接收所述目标对象输入的第二能力等级，将所述第二能力等级确定为所述目标对象对应的第一烹饪能力信息。
第二能力等级，为用户输入的用于表征烹饪能力的信息。通过该方案，可以将目标对象实时输入的第二能力等级，作为对应的第一烹饪能力信息。如此，可以根据用户需求灵活设置第一烹饪能力信息。
参见图3，为本公开实施例提供的另一种菜谱推荐方法的实施例流程图。如图3所示，该流程可包括以下步骤：
S301，针对每个菜谱，获取所述菜谱对应的历史使用数据，其中，所述历史使用数据包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息。
历史使用数据，包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息。其中，使用对象，为历史使用过该菜谱推荐功能的用户。第二基础信息，指使用对象在饮食方面的相关信 息。第二烹饪能力信息，被设置为表征使用对象对于烹饪的操作熟练情况，也即，烹饪水平。
应用中，可以实时记录每个使用菜谱推荐功能的用户的第二基础信息和第二烹饪能力信息，以及，每个用户对于每个菜谱的使用情况，并将这些信息存储至预设存储位置。
基于此，本公开实施例中，可以在该预设存储位置中获取每个菜谱的历史使用数据。
S302，基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息。
本公开实施中，基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息的具体实现可包括：对每个所述使用对象对应的第二基础信息，以及所述使用对象对应的第二烹饪能力信息进行组合，得到至少一个组合信息，计算每个所述组合信息对应的支持度和置信度，将所述支持度大于预设支持度阈值，且，所述置信度大于预设置信度阈值的组合信息，确定为所述关联信息。
其中，第二基础信息包括至少一个维度上的维度信息。这里，将每个使用对象在一个维度上的一个维度信息作为一个单独的维度事务，以及，将每个使用对象的第二烹饪能力信息作为一个单独的能力事务。进而，将这些维度事务和能力事务进行组合，得到组合信息，其中，每个组合信息中包括每个维度上的一个维度信息以及一个第二烹饪能力信息。
对于每个组合信息，可以根据关联规则算法计算其对应的支持度和置信度。在组合信息的支持度大于预设支持度阈值，且，置信度大于预设置信度阈值的情况下，意味着该组合信息与对应菜谱强相关，因此，将该组合信息确定该菜谱对应的关联信息。
例如，针对鱼香肉丝，其对应的维度信息包括：地区维度，用户所在地为珠海；口味维度：用户喜欢甜口味，以及用户喜欢咸口味；其对应的第二烹饪能力信息包括：中级和高级。则，组合信息包括：其一，用户所在地为珠海+用户喜欢甜口味+中级；其二，用户所在地为珠海+用户喜欢咸 口味+中级；其三，用户所在地为珠海+用户喜欢甜口味+高级；其四，用户所在地为珠海+用户喜欢咸口味+高级。
以第一种组合信息为例，其对应的支持度和置信度的计算过程为：支持度+用户所在地为珠海(事务D)包含{中级(事务A)∪用户喜欢甜口味(事务B)}的百分比；置信度＝D包含{A∩B}的百分比。
若第一种组合信息的支持度和置信度均大于相应的阈值，则可以得到一条结论，地区珠海、烹饪能力为中级的、喜欢甜口味的用户，对鱼香肉丝很感兴趣。因此，将用户所在地为珠海+用户喜欢甜口味+中级，作为菜谱鱼香肉丝的关联信息。
S303，基于所有所述菜谱和每个所述菜谱对应的关联信息，构建所述菜谱集合。
本公开实施例中，在确定每个菜谱对应的关联信息后，即可，基于所有菜谱和每个菜谱对应的关联信息构建菜谱集合。如此，菜谱集合中不仅包含多个菜谱，还包含每个菜谱的关联信息，在后续使用时，即可根据关联信息，为用户匹配合适的待推荐菜谱。
通过图3所示流程，可以对每个菜谱的历史使用数据进行分析，从而得到喜欢使用对应菜谱的用户的关联信息(包括基础信息和烹饪能力信息)，进而，根据所有菜谱和每个菜谱对应的关联信息构建菜谱集合。通过该菜谱集合可以为烹饪水平不同的用户匹配适合其烹饪的菜谱。
基于相同的技术构思，本公开实施例还提供了一种菜谱推荐装置，如图4所示，该装置包括：
获取模块401，被设置为获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
确定模块402，被设置为基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
发送模块403，被设置为向所述目标对象对应的对象设备发送所述待推荐菜谱。
在一个可能的实施方式中，所述获取模块，具体被设置为：
获取所述目标对象对应的基础烹饪经验值，以及，获取所述目标对象对应的菜品烹饪数据，其中，所述菜品烹饪数据包括至少一次菜品烹饪过程，以及，每次所述菜品烹饪过程对应的烹饪分数；
对所有所述菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值；
对所述基础烹饪经验值和所述烹饪经验调整值进行求和运算，得到目标烹饪经验值；
在预设的多个经验值范围中，确定所述目标烹饪经验值所处的目标烹饪经验值范围；
将所述目标烹饪经验值范围对应的第一能力等级，确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述获取模块，还被设置为：
针对每次菜品烹饪过程，获取所述菜品烹饪过程对应的烹饪过程数据，其中，所述烹饪过程数据包括至少一个菜品烹饪操作，以及，每个菜品烹饪操作对应的操作分数及权重；
基于每个所述菜品烹饪操作对应的权重，对所有所述菜品烹饪操作的操作分数进行加权求和运算，得到所述菜品烹饪过程对应的烹饪分数；
基于所有所述菜品烹饪过程以及每个所述菜品烹饪过程对应的烹饪分数，生成所述菜品烹饪数据。
在一个可能的实施方式中，所述获取模块，还被设置为：
接收所述目标对象输入的第二能力等级，将所述第二能力等级确定为所述目标对象对应的第一烹饪能力信息。
在一个可能的实施方式中，所述装置还包括构建模块，被设置为：
针对每个菜谱，获取所述菜谱对应的历史使用数据，其中，所述历史使用数据包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息；
基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息；
基于所有所述菜谱和每个所述菜谱对应的关联信息，构建所述菜谱集合。
在一个可能的实施方式中，所述构建模块，还被设置为：
对每个所述使用对象对应的第二基础信息，以及所述使用对象对应的第二烹饪能力信息进行组合，得到至少一个组合信息；
计算每个所述组合信息对应的支持度和置信度；
将所述支持度大于预设支持度阈值，且，所述置信度大于预设置信度阈值的组合信息，确定为所述关联信息。
在一个可能的实施方式中，所述确定模块，具体被设置为：
组合所述第一基础信息和所述第一烹饪能力信息，得到所述目标对象对应的目标关联信息；
将所述菜谱集合中对应关联信息与所述目标关联信息一致的菜谱，确定为所述待推荐菜谱。
本公开实施例中，首先，获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息，然后，基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱，最后，向所述目标对象对应的对象设备发送所述待推荐菜谱。由此，可以结合用户的基础信息和烹饪能力，向用户推荐对应的菜谱，从而为烹饪水平不同的用户推荐适合其烹饪的菜谱，提高用户对推荐菜谱的满意度。
基于相同的技术构思，本公开实施例还提供了一种电子设备，如图5所示，包括处理器111、通信接口112、存储器113和通信总线114，其中， 处理器111，通信接口112，存储器113通过通信总线114完成相互间的通信，
存储器113，被设置为存放计算机程序；
处理器111，被设置为执行存储器113上所存放的程序时，实现如下步骤：
获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
向所述目标对象对应的对象设备发送所述待推荐菜谱。
上述电子设备提到的通信总线可以是外设部件互连标准(Peripheral Component Interconnect，PCI)总线或扩展工业标准结构(Extended Industry Standard Architecture，EISA)总线等。该通信总线可以分为地址总线、数据总线、控制总线等。为便于表示，图中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
通信接口用于上述电子设备与其他设备之间的通信。
存储器可以包括随机存取存储器(Random Access Memory，RAM)，也可以包括非易失性存储器(Non-Volatile Memory，NVM)，例如至少一个磁盘存储器。可选的，存储器还可以是至少一个位于远离前述处理器的存储装置。
上述的处理器可以是通用处理器，包括中央处理器(Central Processing Unit，CPU)、网络处理器(Network Processor，NP)等；还可以是数字信号处理器(Digital Signal Processing，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现场可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。
在本公开提供的又一实施例中，还提供了一种计算机可读存储介质，该计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现上述任一菜谱推荐方法的步骤。
在本公开提供的又一实施例中，还提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机执行上述实施例中任一菜谱推荐方法。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本公开实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘Solid State Disk(SSD))等。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还 存在另外的相同要素。
以上所述仅是本公开的具体实施方式，使本领域技术人员能够理解或实现本公开。对这些实施例的多种修改对本领域的技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本公开的精神或范围的情况下，在其它实施例中实现。因此，本公开将不会被限制于本文所示的这些实施例，而是要符合与本文所申请的原理和新颖特点相一致的最宽的范围。

Claims (10)

  1. 一种菜谱推荐方法，所述方法包括：
    获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
    基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
    向所述目标对象对应的对象设备发送所述待推荐菜谱。
  2. 根据权利要求1所述的方法，其中，所述获取所述目标对象对应的第一烹饪能力信息，包括：
    获取所述目标对象对应的基础烹饪经验值，以及，获取所述目标对象对应的菜品烹饪数据，其中，所述菜品烹饪数据包括至少一次菜品烹饪过程，以及，每次所述菜品烹饪过程对应的烹饪分数；
    对所有所述菜品烹饪过程对应的烹饪分数进行求和运算，得到烹饪经验调整值；
    对所述基础烹饪经验值和所述烹饪经验调整值进行求和运算，得到目标烹饪经验值；
    在预设的多个经验值范围中，确定所述目标烹饪经验值所处的目标烹饪经验值范围；
    将所述目标烹饪经验值范围对应的第一能力等级，确定为所述目标对象对应的第一烹饪能力信息。
  3. 根据权利要求2所述的方法，其中，所述获取所述目标对象对应的菜品烹饪数据，包括：
    针对每次菜品烹饪过程，获取所述菜品烹饪过程对应的烹饪过程数据，其中，所述烹饪过程数据包括至少一个菜品烹饪操作，以及，每个菜品烹饪操作对应的操作分数及权重；
    基于每个所述菜品烹饪操作对应的权重，对所有所述菜品烹饪操作的操作分数进行加权求和运算，得到所述菜品烹饪过程对应的烹饪分数；
    基于所有所述菜品烹饪过程以及每个所述菜品烹饪过程对应的烹饪分数，生成所述菜品烹饪数据。
  4. 根据权利要求1所述的方法，其中，所述获取所述目标对象对应的第一烹饪能力信息，包括：
    接收所述目标对象输入的第二能力等级，将所述第二能力等级确定为所述目标对象对应的第一烹饪能力信息。
  5. 根据权利要求1所述的方法，其中，所述方法还包括：
    针对每个菜谱，获取所述菜谱对应的历史使用数据，其中，所述历史使用数据包括至少一个使用对象，以及，每个所述使用对象对应的第二基础信息和第二烹饪能力信息；
    基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息；
    基于所有所述菜谱和每个所述菜谱对应的关联信息，构建所述菜谱集合。
  6. 根据权利要求5所述的方法，其中，所述基于每个所述使用对象对应的第二基础信息和第二烹饪能力信息，确定所述菜谱对应的关联信息，包括：
    对每个所述使用对象对应的第二基础信息，以及所述使用对象对应的第二烹饪能力信息进行组合，得到至少一个组合信息；
    计算每个所述组合信息对应的支持度和置信度；
    将所述支持度大于预设支持度阈值，且，所述置信度大于预设置信度阈值的组合信息，确定为所述关联信息。
  7. 根据权利要求5所述的方法，其中，所述基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱，包括：
    组合所述第一基础信息和所述第一烹饪能力信息，得到所述目标对象 对应的目标关联信息；
    将所述菜谱集合中对应关联信息与所述目标关联信息一致的菜谱，确定为所述待推荐菜谱。
  8. 一种菜谱推荐装置，所述装置包括：
    获取模块，被设置为获取目标对象对应的第一基础信息，以及，获取所述目标对象对应的第一烹饪能力信息，其中，所述第一基础信息被设置为表征影响所述目标对象饮食喜好的信息；
    确定模块，被设置为基于所述第一基础信息和所述第一烹饪能力信息，在预设的菜谱集合中确定待推荐菜谱；
    发送模块，被设置为向所述目标对象对应的对象设备发送所述待推荐菜谱。
  9. 一种电子设备，包括处理器、通信接口、存储器和通信总线，其中，处理器，通信接口，存储器通过通信总线完成相互间的通信；
    存储器，被设置为存放计算机程序；
    处理器，被设置为执行存储器上所存放的程序时，实现权利要求1-7任一所述的方法步骤。
  10. 一种计算机可读存储介质，所述计算机可读存储介质内存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1-7任一所述的方法步骤。
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