Kitchen ware anomaly detection method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a kitchen ware anomaly detection method and device, equipment and a storage medium, and the method comprises the steps: selecting a first cooking mode matched with the food material information from a preset cooking database according to the food material information of the recognized to-be-cooked food material; cooking the food materials in a first cooking mode, and detecting whether a first cooking abnormal risk corresponding to the first cooking mode occurs in the cooking process; and when it is detected that the first cooking abnormal risk occurs, executing a first abnormal risk response task to process the first cooking abnormal risk. According to the kitchen ware anomaly detection method, device and equipment and the storage medium, the problem that danger occurs in the cooking process due to the fact that an existing steaming oven lacks an effective anomaly detection mechanism is solved, the occurrence probability of the danger caused by anomaly in the cooking process is reduced, and the safety of the cooking process is ensured.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of household appliances, and in particular to a method, device, equipment and storage medium for detecting abnormalities in kitchen appliances. Background Art

[0002] With the popularization of smart home appliances, the intelligence and performance of kitchen utensils have received more and more attention. For example, smart steam ovens have become common equipment in modern kitchens, providing users with a convenient cooking experience.

[0003] In some technologies, the selection of cooking methods in a steam oven is usually done by a user selecting a cooking method by himself, and then the steam oven is controlled to cook the food according to the selected cooking method. However, the existing steam oven lacks an effective abnormality detection mechanism when using a cooking method that is incompatible with the food or potentially dangerous, which easily leads to dangers during the cooking process and is not conducive to ensuring the safety of the cooking process. Summary of the invention

[0004] The purpose of the present invention is to at least provide a method, device, equipment and storage medium for detecting kitchen utensil anomalies, which can at least solve the technical problem that the existing steam oven lacks an effective anomaly detection mechanism when using a cooking method with mismatched ingredients or potentially dangerous ingredients, which easily leads to dangers in the cooking process and is not conducive to ensuring the safety of the cooking process. At least, by performing anomaly detection on the cooking process of ingredients, the probability of dangers caused by anomalies occurring during the cooking process can be effectively reduced, thereby ensuring the safety of the cooking process.

[0005] In order to solve the above technical problems, at least one embodiment of the present application provides a method for detecting abnormalities in a kitchen utensil, wherein the kitchen utensil is a smart kitchen utensil for cooking food, and the method comprises:

[0006] According to the identified food information of the food to be cooked, a first cooking method matching the food information is selected from a preset cooking database, wherein the cooking database stores a plurality of cooking methods, and each cooking method is correspondingly provided with a cooking process parameter;

[0007] Cooking the food by using the first cooking method, and detecting whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process;

[0008] When the occurrence of the first abnormal cooking risk is detected, a first abnormal risk response task is executed to process the first abnormal cooking risk.

[0009] At least one embodiment of the present application further provides a kitchenware abnormality detection device, comprising:

[0010] a selection module, configured to select a first cooking method matching the identified food information of the food to be cooked from a preset cooking database, wherein the cooking database stores a plurality of cooking methods, and each cooking method is correspondingly provided with a cooking process parameter;

[0011] a detection module, configured to cook the food in the first cooking method and detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process;

[0012] An execution module is used to execute a first abnormal risk response task to process the first abnormal cooking risk when the first abnormal cooking risk is detected.

[0013] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned kitchenware abnormality detection method.

[0014] At least one embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above-mentioned kitchenware abnormality detection method when executed by a processor.

[0015] The kitchenware abnormality detection method provided by the embodiment of the present application selects a first cooking method matching the food information from a preset cooking database according to the identified food information of the food to be cooked, adopts the first cooking method to cook the food, and detects whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process. When the first cooking abnormality risk is detected, the first abnormality risk response task is executed to handle the first cooking abnormality risk. In this way, by performing abnormality detection on the cooking process of the food, the probability of danger caused by abnormalities during the cooking process is effectively reduced, thereby ensuring the safety of the cooking process.

[0016] In some optional embodiments, the abnormal cooking risk includes: abnormal cooking utensil status risk;

[0017] The detecting whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process includes:

[0018] In the process of cooking the food by using the first cooking method, detecting whether the kitchenware is in a cooking process state that is contrary to the cooking process parameters corresponding to the first cooking method;

[0019] If the contradictory cooking process state occurs, it is confirmed that the abnormal risk of the kitchenware state corresponding to the first cooking method occurs.

[0020] By detecting the state of the cooking process, it is determined that there is an abnormal risk of the kitchen utensil state corresponding to the first cooking method, and then the first abnormal risk response task is executed according to the abnormal risk of the kitchen utensil state, so as to accurately handle the abnormal risk of the kitchen utensil state.

[0021] In some optional embodiments, before cooking the ingredients in the first cooking method, the method further includes:

[0022] Detecting whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method;

[0023] When an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method is detected, a second abnormal response task is executed to process the abnormal risk of food compatibility and / or the abnormal risk of food carrier.

[0024] By detecting whether there is an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method before cooking, abnormal risks before cooking are checked to ensure the safety of the cooking process of the kitchenware.

[0025] In some optional embodiments, after the first abnormal cooking risk is detected, the first abnormal risk response task is executed to process the first abnormal cooking risk, the method further includes:

[0026] Identify the food cooked by the first cooking method up to now, and obtain food status information of the half-cooked food;

[0027] Based on the food state information and the cooking process parameters that have not been executed in the first cooking method up to the current time, selecting a cooking method other than the first cooking method in the cooking database as the second cooking method;

[0028] Cooking the semi-cooked food by using the second cooking method, and detecting whether a second cooking abnormality risk corresponding to the second cooking method occurs during the cooking process;

[0029] When the occurrence of the second abnormal cooking risk is detected, a second abnormal risk response task is executed to process the second abnormal cooking risk.

[0030] By using a cooking method other than the first cooking method as the second cooking method, a more suitable cooking method and cooking parameters are recommended to the user.

[0031] In some optional embodiments, the selecting a cooking method other than the first cooking method in the cooking database as the second cooking method based on the food state information and the cooking process parameters that have not been executed in the first cooking method as of the current time includes:

[0032] Determine an intermediate cooking method according to the food state information and cooking process parameters that have not been executed in the first cooking method up to the present time; the intermediate cooking method satisfies: the food obtained after the semi-cooked food is cooked by the intermediate cooking method is similar to the food obtained after the first cooking method is fully used for cooking in terms of food state information;

[0033] A cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method.

[0034] By determining the intermediate cooking method, a cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method, so as to recommend a more suitable cooking method and cooking parameters to the user.

[0035] In some optional embodiments, after detecting whether there is an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method, the method further includes:

[0036] When an abnormal food compatibility risk and / or an abnormal food carrier risk corresponding to the first cooking method is detected, the food information corresponding to the abnormal food compatibility risk and / or the food carrier information corresponding to the abnormal food carrier risk is stored in a preset abnormal database.

[0037] When an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method is detected, the incompatible food information and the food carrier information that is likely to cause danger when cooked according to the first cooking method are stored in a preset abnormality database to enrich the data volume in the abnormality database.

[0038] In some optional embodiments, identifying the ingredient information of the ingredient to be cooked includes:

[0039] After placing the food to be cooked in the kitchenware, acquiring an image of the kitchenware;

[0040] The image in the kitchenware is recognized to obtain the food information of the food to be cooked.

[0041] After acquiring the image, the image is recognized to accurately obtain the ingredient information of the ingredient to be cooked. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0043] Figure 1 is a flowchart of a kitchenware abnormality detection method provided by an embodiment of the present application;

[0044] Figure 2 is a structural schematic diagram of a kitchenware abnormality detection device provided by an embodiment of the present application;

[0045] Figure 3 is a structural schematic diagram of an electronic device provided by another embodiment of the present application;

[0046] Figure 4 It is a flowchart of a kitchenware abnormality detection method provided by another embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0048] In order to facilitate understanding of the embodiments of the present application, relevant contents about the kitchenware abnormality detection method are first introduced here.

[0049] With the popularization of smart home appliances, the intelligence and performance of kitchen utensils have received more and more attention. For example, smart steam ovens have become common equipment in modern kitchens, providing users with a convenient cooking experience.

[0050] In some technologies, the selection of cooking methods in a steam oven is usually done by a user selecting a cooking method by himself, and then the steam oven is controlled to cook the food according to the selected cooking method. However, the existing steam oven lacks an effective abnormality detection mechanism when using a cooking method that is incompatible with the food or potentially dangerous, which easily leads to dangers during the cooking process and is not conducive to ensuring the safety of the cooking process.

[0051] In order to solve the technical problem that the above-mentioned existing steam oven lacks an effective abnormality detection mechanism when using a cooking method with mismatched ingredients or potentially dangerous ingredients, which easily leads to dangers in the cooking process and is not conducive to ensuring the safety of the cooking process, the present invention proposes a kitchen utensil abnormality detection method. The implementation details of the kitchen utensil abnormality detection method of this embodiment are specifically described below. The following content is only the implementation details provided for easy understanding and is not necessary for the implementation of this solution.

[0052] Embodiment 1:

[0053] The kitchenware abnormality detection method of this embodiment can be applied to electronic devices with communication, computing and data storage capabilities. The specific process can be as follows: Figure 1 As shown, including:

[0054] Step 101, according to the identified food information of the food to be cooked, a first cooking method matching the food information is selected from a preset cooking database, wherein a plurality of cooking methods are stored in the cooking database, and each cooking method is correspondingly provided with cooking process parameters.

[0055] Specifically, the kitchenware is an intelligent kitchenware for cooking ingredients, such as a steam oven, a microwave oven and the like. The cooking database includes a plurality of cooking methods, and each cooking method is correspondingly provided with cooking process parameters. For example, when the cooking method is to steam the ingredients to be cooked, the cooking process parameters include the steaming time, the steaming temperature and the like. The number of ingredients to be cooked can be one or more. After identifying the ingredient information of the ingredients to be cooked, a first cooking method matching the ingredient information is selected from the plurality of cooking methods in the cooking database, wherein the number of the selected first cooking methods can be one or more. In some examples, a matching value between the cooking method and the ingredients can be set. By comparing the matching values ​​of the plurality of cooking methods, a cooking method having a matching value greater than a preset value is selected as the first cooking method. In this way, the selected first cooking methods are all cooking methods with a high degree of matching with the ingredients to be cooked.

[0056] Step 102: Cook the ingredients using a first cooking method, and detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process.

[0057] Specifically, after the first cooking method is selected, the kitchenware is controlled to cook the cooking ingredients in the first cooking method. During the cooking process, it is detected whether the first cooking abnormality risk corresponding to the first cooking method occurs to ensure the safety of the cooking process. In some examples, the cooking abnormality risk includes the kitchenware state abnormality risk. For example, in the first cooking method, the steaming temperature of the steam oven is set to 100°C, and the steaming time is set to 10 minutes. According to the first cooking method, the steam oven should stop heating after 10 minutes. However, after 10 minutes, the steam oven still maintains a heating state of 100°C. At this time, the first cooking abnormality risk occurs during the cooking process using the first cooking method. The first cooking abnormality risk is that the temperature of the steam oven still remains at 100 degrees Celsius after 10 minutes.

[0058] Step 103: When a first abnormal cooking risk is detected, a first abnormal risk response task is executed to process the first abnormal cooking risk.

[0059] Specifically, the first abnormal risk response task includes at least one of issuing a warning message and controlling the end of cooking. When the first abnormal cooking risk corresponding to the first cooking method is detected, the kitchenware is controlled to issue a warning message, such as a warning light and / or a warning sound and / or a warning text, or the kitchenware is controlled to end cooking, or the kitchenware is controlled to end cooking and issue a warning message to handle the first abnormal cooking risk, and it is convenient for the user to understand the abnormality of the kitchenware and handle it. In some examples, after 10 minutes, the steam oven still maintains a heating state of 100°C, and the kitchenware can be controlled to end cooking to handle the current abnormal cooking risk, waiting for the user to take out the ingredients in the steam oven or perform other operations.

[0060] In this embodiment, according to the identified food information of the food to be cooked, a first cooking method matching the food information is selected from a preset cooking database, the food is cooked using the first cooking method, and during the cooking process, it is detected whether a first cooking abnormality risk corresponding to the first cooking method occurs. When the first cooking abnormality risk is detected, the first abnormality risk response task is executed to process the first cooking abnormality risk. In this way, by performing abnormality detection on the cooking process of the food, the probability of danger caused by abnormality during the cooking process is effectively reduced, thereby ensuring the safety of the cooking process.

[0061] In some embodiments, the cooking abnormality risk includes: the risk of abnormal kitchenware status;

[0062] Detecting whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process includes:

[0063] In the process of cooking the food by using the first cooking method, detecting whether the kitchenware has a cooking process state that is contrary to the cooking process parameters corresponding to the first cooking method;

[0064] If an inconsistent cooking process state occurs, it is confirmed that there is a risk of abnormal kitchenware state corresponding to the first cooking method.

[0065] Specifically, the risk of abnormal cooking includes the risk of abnormal kitchenware status. The risk of abnormal kitchenware status refers to the risk that continuing cooking after the kitchenware status becomes abnormal during the cooking process is prone to danger. In the process of cooking ingredients using the first cooking method, it is detected whether the kitchenware has a cooking process state that is inconsistent with the cooking process parameters corresponding to the first cooking method. In some examples, in the first cooking method, the cooking process parameters are: the steaming temperature of the steam oven is set to 100°C, and the steaming time is set to 10 minutes. During the cooking process, it is found that the kitchenware has a cooking process state that is inconsistent with the cooking process parameters, that is, after 10 minutes, the steam oven still maintains a heating state of 100°C. If heating is continued, it is easy to cause the food to burn and catch fire. At this time, it is confirmed that the risk of abnormal kitchenware status corresponding to the first cooking method occurs, that is, after 10 minutes, the temperature of the steam oven still remains at 100 degrees Celsius.

[0066] In some embodiments, before cooking the food in the first cooking method, the method further includes:

[0067] Detecting whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method;

[0068] When the food compatibility abnormality risk and / or food carrier abnormality risk corresponding to the first cooking method is detected, a second abnormal response task is executed to process the food compatibility abnormality risk and / or food carrier abnormality risk.

[0069] Specifically, before the first cooking method is used to cook the ingredients, the compatibility between the ingredients and whether the carrier used to hold the ingredients is suitable for the current first cooking method are also abnormal risk factors that are prone to cause danger. Therefore, before cooking, it is necessary to detect whether there is an abnormal risk of ingredient compatibility and / or an abnormal risk of ingredient carrier corresponding to the first cooking method. In some examples, in the first cooking method, the hot baking temperature is 90°C, the ingredients are eggs, and direct cooking of eggs at high temperature may cause explosions, and the whole egg is not suitable for being placed in a steam oven. For example, in the first cooking method, the steaming temperature is 90°C, and the ingredients are two kinds of ingredients that are easy to cause food poisoning when cooked at the same time. For example, in the first cooking method, the steaming temperature is 90°C, the steaming time is 5 minutes, and the ingredients are ingredients that cannot be fully cooked in the current first cooking method and are not cooked. It is easy to cause food poisoning. Then, according to the ingredient information and the first cooking method, it is detected that there is an abnormal risk of ingredient compatibility. In some examples, in the first cooking method, the carrier for holding ingredients is not suitable for the first cooking method, which is easy to cause explosions, and then according to the ingredient carrier information and the first cooking method, it is detected that there is an abnormal risk of ingredient carrier.

[0070] In this embodiment, the second abnormal risk response task includes at least one of issuing a warning message and controlling the end of cooking. When the abnormal risk of food compatibility and / or the abnormal risk of food carrier corresponding to the first cooking method is detected, the kitchenware is controlled to issue a warning message, or the kitchenware is controlled to end cooking, or the kitchenware is controlled to end cooking and issue a warning message, so as to handle the abnormal risk of food compatibility and / or the abnormal risk of food carrier, and facilitate the user to understand and handle the abnormality of the kitchenware.

[0071] In some embodiments, when the first cooking abnormality risk is detected, after executing the first abnormality risk response task to process the first cooking abnormality risk, the method further includes:

[0072] The food cooked by the first cooking method up to now is identified to obtain food status information of the half-cooked food.

[0073] Specifically, after the first cooking abnormality risk is detected, it means that an abnormality has occurred in the cooking of ingredients using the first cooking method. At this time, the ingredients are already in a semi-cooked state, that is, the ingredients have been cooked by the kitchen utensils using the first cooking method for a period of time.

[0074] Based on the food status information and the cooking process parameters that have not been executed in the current first cooking method, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method.

[0075] Specifically, after the food is cooked by the first cooking method, the cooked food is detected as the food state information by detecting the doneness information of the cooked food, and combined with the cooking process parameters that have not been executed in the first cooking method, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method. In some examples, in the first cooking method, the cooking process parameters are: the steaming oven is steamed at a cooking temperature of 100°C for 10 minutes, and then steamed at a cooking temperature of 80°C for 8 minutes. During the cooking process, it is found that the kitchenware has a cooking process state that is inconsistent with the cooking process parameters, that is, the steaming oven still maintains a heating state of 100°C after 10 minutes. At this time, the first abnormal risk response task is executed to handle the abnormal cooking risk. At this time, the food state information of the food is that the doneness information of the food is medium-rare. After the first cooking method is used for cooking, the cooking process parameters that have not been executed are that it still needs to be steamed at a cooking temperature of 80°C for 8 minutes. Combined with the food state information and the cooking process parameters that have not been executed, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method.

[0076] The semi-cooked food is cooked using a second cooking method, and during the cooking process, it is detected whether a second cooking abnormality risk corresponding to the second cooking method occurs.

[0077] Specifically, the second cooking method is used to cook the food cooked by the first cooking method (i.e., the food half-cooked), and during the cooking process, whether the second cooking abnormality risk corresponding to the second cooking method occurs is detected to ensure the safety of the cooking process. In some examples, during the cooking process at a cooking temperature of 80°C for 8 minutes, a heating interruption or other abnormal state of the kitchenware occurs. At this time, a cooking abnormality risk occurs during the cooking process using the second cooking method, and the second cooking abnormality risk is that the temperature of the kitchenware drops due to the heating interruption during heating.

[0078] When the occurrence of a second abnormal cooking risk is detected, a second abnormal risk response task is executed to process the second abnormal cooking risk.

[0079] Specifically, the second abnormal risk response task includes at least one of issuing a warning message and controlling the end of cooking. When the second abnormal cooking risk corresponding to the second cooking method is detected, the kitchenware is controlled to issue a warning message, such as a warning light and / or a warning sound and / or a warning text, or the kitchenware is controlled to end cooking, or the kitchenware is controlled to end cooking and issue a warning message, so as to handle the second abnormal cooking risk and facilitate the user to understand and handle the abnormality of the kitchenware.

[0080] In this embodiment, the food cooked by the first cooking method is identified to obtain the food status information of the half-cooked food; based on the food status information and the cooking process parameters that have not been executed in the first cooking method, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method; the half-cooked food is cooked by the second cooking method, and during the cooking process, whether a second cooking abnormality risk corresponding to the second cooking method occurs is detected; when the second cooking abnormality risk is detected, the second abnormality risk response task is executed to handle the second cooking abnormality risk. In this way, by using a cooking method other than the first cooking method as the second cooking method, a more suitable cooking method and cooking parameters can be recommended to the user.

[0081] In some embodiments, based on the food state information and the cooking process parameters that have not been executed in the current first cooking method, selecting a cooking method other than the first cooking method from the cooking database as the second cooking method includes:

[0082] Determine an intermediate cooking method according to the food state information and the cooking process parameters that have not been executed in the current first cooking method; the intermediate cooking method satisfies: the food obtained by cooking the half-cooked food in the intermediate cooking method is similar to the food obtained by cooking the food in the first cooking method in terms of food state information;

[0083] A cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method.

[0084] Specifically, after the ingredients are cooked by the first cooking method, the ingredients are in a semi-cooked state. By determining the intermediate cooking method, the ingredients obtained by cooking the semi-cooked ingredients by the intermediate cooking method are similar to the ingredients obtained by cooking the ingredients by the first cooking method throughout the whole process in terms of ingredient state information. After obtaining the intermediate cooking method, a cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method. In some examples, in the first cooking method, the cooking process parameters are: the steaming oven is steamed at a steaming temperature of 100°C for 10 minutes, and then steamed at a steaming temperature of 80°C for 8 minutes. During the cooking process, it is found that the kitchenware has a cooking process state that is inconsistent with the cooking process parameters, that is, after 10 minutes, the steaming oven still maintains a heating state of 100°C. At this time, the first abnormal risk response task is executed to handle the abnormal cooking risk. At this time, the food status information of the food is that the doneness information of the food is medium-rare. After the first cooking method is adopted at present, the unexecuted cooking process parameters are that it still needs to be steamed at a cooking temperature of 80°C for 8 minutes. The intermediate cooking method is steaming at a cooking temperature of 80°C for 8 minutes. A cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method.

[0085] In some embodiments, after detecting whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method, the method further includes:

[0086] When an abnormal food compatibility risk and / or an abnormal food carrier risk corresponding to the first cooking method is detected, the food information corresponding to the abnormal food compatibility risk and / or the food carrier information corresponding to the abnormal food carrier risk is stored in a preset abnormal database.

[0087] Specifically, before cooking, it is detected whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method. When it is detected that there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method, the incompatible food information and the food carrier information that is likely to cause danger when cooking according to the first cooking method are stored in a preset abnormal database to enrich the amount of data in the abnormal database. Specifically, the food information and the abnormal risk of food compatibility in the abnormal database correspond to the first cooking method, and the food carrier information and the abnormal risk of food carrier correspond to the first cooking method. When the first cooking method is used for cooking next time, it can be directly determined based on the same food information and / or food carrier information whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier.

[0088] In some embodiments, identifying the ingredient information of the ingredient to be cooked includes:

[0089] After placing the food to be cooked in the kitchenware, acquiring an image of the kitchenware;

[0090] The images in the kitchenware are recognized to obtain the food information of the food to be cooked.

[0091] Specifically, the ingredients to be cooked are photographed by visual inspection equipment to obtain images of the ingredients to be cooked in the kitchenware, and then the images are recognized to accurately obtain the ingredient information of the ingredients to be cooked.

[0092] In some embodiments, the method further comprises:

[0093] Obtain user preference data;

[0094] Based on the user preference data, multiple cooking methods are weighted and the weight value of each cooking method is determined, wherein the weight value is positively correlated with the user's preference degree;

[0095] Specifically, when selecting a first cooking method that matches the ingredient information in a preset cooking database, or when selecting a cooking method other than the first cooking method in the cooking database as the second cooking method, the cooking method with the largest weight value can be selected as the first cooking method or the second cooking method according to the weight values ​​of the cooking methods, so as to better suit the actual needs of the user.

[0096] Embodiment 2:

[0097] like Figure 4 As shown, this embodiment provides exemplary content of the first embodiment, that is, provides an exemplary process of a kitchenware abnormality detection method, and the specific content includes:

[0098] By integrating advanced computer vision technology, image recognition algorithms and intelligent analysis modules, the system can intelligently identify the type of food in the steam oven and automatically match the best cooking method based on the preset cooking knowledge base. At the same time, the system also has anomaly detection function to ensure the safety and efficiency of the cooking process.

[0099] The food recognition module identifies the food information. Specifically, the high-definition camera captures the image of the food in the steam oven and uses the deep learning algorithm to recognize the image. The system can accurately identify the type, size, quantity and other characteristics of the food, providing basic data for subsequent cooking method matching.

[0100] The matching analysis module retrieves the best cooking method from the cooking knowledge base based on the results of the food recognition module, and calculates the matching degree between the currently selected cooking method and the food. The cooking knowledge base contains a lot of information on cooking characteristics of ingredients, optimal cooking methods, cooking time, temperature, etc. At the same time, the knowledge base also contains the compatibility information of ingredients to ensure that different ingredients will not produce adverse reactions during the cooking process.

[0101] If the system identifies a cooking method that is not suitable for the current food, it will issue a preliminary warning. For example, when the system identifies that the user puts an egg in, it will immediately check whether the cooking method is suitable for eggs. If it identifies that the steam oven cannot be used to cook eggs, it will cause danger (such as direct cooking at high temperatures may cause explosions), then it will issue an abnormal warning. It is recommended that the user change the cooking method. The matching analysis module will share the analysis results with the anomaly detection module and the recommendation module.

[0102] After the matching analysis module passes, it will enter the anomaly detection module. The anomaly detection module identifies the ingredients put in by the current user, and combines the cooking knowledge base to determine the compatibility and safety of these ingredients. For example, whether these ingredients are safe when the temperature is too high or when cooking, whether there are requirements for cooking time, incompatibility of ingredients, or whether the current user's processing of ingredients is reasonable, whether harmful parts are cut off, etc. Once an abnormality is found, it will be immediately displayed to the user through the user interaction module, and the user will be prompted to make corresponding adjustments. For example, when the system detects that the user puts two incompatible ingredients into the steam oven for cooking, it will immediately issue a warning and suggest that the user take out one of the ingredients. Or when the user uses an unsuitable utensil, the system will also identify the material of the utensil, and then issue an abnormal risk warning, such as the utensil is not suitable for the steam oven and there is a risk of explosion. The anomaly detection module will share the detected abnormal conditions with the matching analysis module and the recommendation module.

[0103] Through the recommendation module, after receiving the information transmitted by the two modules above, the recommendation module can work according to the information transmitted above. When the matching analysis module and the anomaly detection module transmit data abnormalities and dangers, another cooking method and production method will be recommended to the user according to the current food situation. Recommend more suitable cooking methods and parameters for users. For example, when the system detects that the food cooked by the user may cause nutrition loss, it will recommend a more suitable cooking method to preserve the nutrients of the food. In addition, the system will provide personalized cooking suggestions based on the user's taste preferences and dietary needs. Of course, users can also continue to perform their own operations. The system only allows users to understand and recommend some knowledge. It will not interfere with the user's choice excessively. These suggestions will be presented in a non-mandatory manner, and users can choose whether to adopt them. However, if the previous two modules detect extremely dangerous information, they will force the stop operation. To ensure safety. The recommendation module will share the recommendation results and user feedback with the matching analysis module and the anomaly detection module.

[0104] The user interaction module provides a friendly user interface, supports users to manually adjust cooking parameters, and displays information and prompts provided by the system. Users can view food recognition results, matching degree, recommended cooking methods and other information through the interface, which is convenient for users to operate and adjust.

[0105] In this embodiment, the following can be achieved: Data sharing: Each module shares its processing results and data with other modules. For example, the results of the matching analysis module will be used by the anomaly detection module and the recommendation module; the monitoring results of the anomaly detection module will be used by the matching analysis module and the recommendation module; the suggestions and user feedback of the recommendation module will be used by the matching analysis module and the anomaly detection module. Mutual calling: Each module can call the functions of other modules when necessary. For example, the matching analysis module can call the functions of the anomaly detection module to confirm certain abnormal situations during analysis; the recommendation module can call the functions of the matching analysis module to obtain the best cooking method. Unified user interaction: The results and suggestions of all modules are presented to the user through a unified user interaction module to ensure consistency in user experience.

[0106] In this embodiment, by intelligently identifying food types, automatically matching the best cooking method, real-time monitoring of abnormal conditions and providing personalized cooking suggestions, the intelligent level, safety and user experience of cooking are greatly improved. The system is not only suitable for home use, but also for cooking needs in restaurants, hotels and other places.

[0107] In this embodiment, by introducing computer vision technology and image recognition algorithms, intelligent recognition of food types in the steam oven is achieved, and the best cooking method is automatically matched in combination with a preset cooking knowledge base. At the same time, the system also has an anomaly detection function, which can issue an early warning in time when an incompatible or potentially dangerous cooking method is found to ensure the safety of the cooking process. In addition, the system can also recommend optimized cooking parameters based on food type and cooking method to improve cooking effects and reduce nutrient loss. This invention not only improves the intelligence level of the steam oven, but also greatly improves the user's cooking experience and safety.

[0108] Embodiment three:

[0109] Another embodiment of the present application relates to a kitchen utensil abnormality detection device. The implementation details of the kitchen utensil abnormality detection device of this embodiment are described in detail below. The following content is only provided for the convenience of understanding the implementation details and is not necessary for implementing this solution. The schematic diagram of the kitchen utensil abnormality detection device of this embodiment can be as follows: Figure 2 As shown, including:

[0110] The selection module 201 is used to select a first cooking method that matches the food information from a preset cooking database according to the identified food information of the food to be cooked, wherein the cooking database stores a plurality of cooking methods, and each cooking method is correspondingly provided with a cooking process parameter;

[0111] A detection module 202 is used to cook the food in a first cooking method and detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process;

[0112] The execution module 203 is used to execute the first abnormal risk response task to process the first abnormal cooking risk when the first abnormal cooking risk is detected.

[0113] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0114] Embodiment 4:

[0115] Another embodiment of the present application relates to an electronic device, such as Figure 3 As shown, it includes: at least one processor 901; and a memory 902 that is communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions that can be executed by the at least one processor 901, and the instructions are executed by the at least one processor 901 so that the at least one processor 901 can execute the kitchenware abnormality detection method in the above-mentioned embodiments.

[0116] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0117] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0118] Embodiment five:

[0119] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0120] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0121] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A kitchenware abnormality detection method, characterized in that: The kitchen utensil is a smart kitchen utensil for cooking food, and the method comprises: According to the identified food information of the food to be cooked, a first cooking method matching the food information is selected from a preset cooking database, wherein the cooking database stores a plurality of cooking methods, and each cooking method is correspondingly provided with a cooking process parameter; Cooking the food by using the first cooking method, and detecting whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process; When the occurrence of the first abnormal cooking risk is detected, a first abnormal risk response task is executed to process the first abnormal cooking risk.

2. The kitchenware abnormality detection method according to claim 1, characterized in that: The abnormal cooking risks include: abnormal kitchenware status risks; The detecting whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process includes: In the process of cooking the food by using the first cooking method, detecting whether the kitchenware is in a cooking process state that is contrary to the cooking process parameters corresponding to the first cooking method; If the contradictory cooking process state occurs, it is confirmed that the abnormal risk of the kitchenware state corresponding to the first cooking method occurs.

3. The kitchenware abnormality detection method according to claim 1, characterized in that: Before the first cooking method is used to cook the food, the method further includes: Detecting whether there is an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method; When an abnormal risk of food compatibility and / or an abnormal risk of food carrier corresponding to the first cooking method is detected, a second abnormal response task is executed to process the abnormal risk of food compatibility and / or the abnormal risk of food carrier.

4. The kitchenware abnormality detection method according to claim 1, characterized in that: After the first abnormal risk response task is executed to process the first abnormal risk of cooking when the first abnormal risk of cooking is detected, the method further includes: Identify the food cooked by the first cooking method up to now, and obtain food status information of the half-cooked food; Based on the food state information and the cooking process parameters that have not been executed in the first cooking method up to the current time, selecting a cooking method other than the first cooking method in the cooking database as the second cooking method; Cooking the semi-cooked food by using the second cooking method, and detecting whether a second cooking abnormality risk corresponding to the second cooking method occurs during the cooking process; When the occurrence of the second abnormal cooking risk is detected, a second abnormal risk response task is executed to process the second abnormal cooking risk.

5. The kitchenware abnormality detection method according to claim 4, characterized in that: The selecting a cooking method other than the first cooking method as the second cooking method from the cooking database based on the food state information and the cooking process parameters that have not been executed in the first cooking method up to the current time includes: Determine an intermediate cooking method according to the food state information and cooking process parameters that have not been executed in the first cooking method up to the present time; the intermediate cooking method satisfies: the food obtained after the semi-cooked food is cooked by the intermediate cooking method is similar to the food obtained after the first cooking method is fully used for cooking in terms of food state information; A cooking method other than the first cooking method that is similar to the intermediate cooking method is selected from the cooking database as the second cooking method.

6. The kitchenware abnormality detection method according to claim 3, characterized in that: After detecting whether there is an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method, the method further includes: When an abnormal food compatibility risk and / or an abnormal food carrier risk corresponding to the first cooking method is detected, the food information corresponding to the abnormal food compatibility risk and / or the food carrier information corresponding to the abnormal food carrier risk is stored in a preset abnormal database.

7. The kitchenware abnormality detection method according to claim 1, characterized in that: Identify ingredient information of ingredients to be cooked, including: After placing the food to be cooked in the kitchenware, acquiring an image of the kitchenware; The image in the kitchenware is recognized to obtain the food information of the food to be cooked.

8. A kitchenware abnormality detection device, characterized in that: include: a selection module, configured to select a first cooking method matching the identified food information of the food to be cooked from a preset cooking database, wherein the cooking database stores a plurality of cooking methods, and each cooking method is correspondingly provided with a cooking process parameter; a detection module, configured to cook the food in the first cooking method and detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process; An execution module is used to execute a first abnormal risk response task to process the first abnormal cooking risk when the first abnormal cooking risk is detected.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the kitchenware abnormality detection method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the kitchenware abnormality detection method according to any one of claims 1 to 7 is implemented.

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