Kitchenware anomaly detection method, device, equipment and storage medium

Abstract

This invention discloses a method, apparatus, device, and storage medium for detecting kitchen utensil anomalies. The method includes: selecting a first cooking method matching the identified ingredient information from a preset cooking database; cooking the ingredient using the first cooking method and detecting whether a first cooking anomaly risk corresponding to the first cooking method occurs during the cooking process; and executing a first anomaly risk response task to handle the first cooking anomaly risk when it is detected. The kitchen utensil anomaly detection method, apparatus, device, and storage medium disclosed in this application solve the problem of existing steam ovens lacking an effective anomaly detection mechanism, leading to dangers during cooking. This reduces the probability of anomalies occurring during cooking and ensures the safety of the cooking process.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, and in particular to a method, apparatus, device, and storage medium for detecting abnormalities in kitchen utensils. Background Technology

[0002] With the popularization of smart home appliances, the intelligence and performance of kitchen utensils have received increasing 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 cooking mode selection in steam ovens is typically done by the user, who then controls the oven to cook the food according to the selected cooking mode. However, existing steam ovens lack effective anomaly detection mechanisms when using cooking modes that are incompatible with the food or potentially dangerous, which can easily lead to hazards during the cooking process and is detrimental to ensuring cooking safety. Summary of the Invention

[0004] The purpose of this invention is to provide at least one method, apparatus, device, and storage medium for detecting abnormalities in kitchen utensils. This invention can at least solve the technical problem that existing steam ovens lack an effective abnormality detection mechanism when using cooking methods with incompatible ingredients or potential dangers, which can easily lead to dangers during the cooking process and is not conducive to ensuring the safety of the cooking process. At least by detecting abnormalities in the cooking process of ingredients, this invention can effectively reduce the probability of abnormalities occurring during the cooking process and thus ensure the safety of the cooking process.

[0005] To address the aforementioned technical problems, at least one embodiment of this application provides a method for detecting abnormalities in kitchen utensils, wherein the kitchen utensils are intelligent kitchen utensils used for cooking food, and the method includes:

[0006] Based on the identified ingredient information of the ingredients to be cooked, a first cooking method matching the ingredient information is selected from a preset cooking database. The cooking database stores multiple cooking methods, and each cooking method is set with corresponding cooking process parameters.

[0007] The ingredients are 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;

[0008] When the first cooking abnormality risk is detected, a first abnormality risk response task is executed to handle the first cooking abnormality risk.

[0009] At least one embodiment of this application also provides a kitchen utensil anomaly detection device, comprising:

[0010] The selection module is used to select a first cooking method that matches the identified ingredient information from a preset cooking database. The cooking database stores multiple cooking methods, and each cooking method is set with corresponding cooking process parameters.

[0011] The detection module is used to detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process when the ingredients are cooked using the first cooking method.

[0012] The execution module is used to execute a first abnormal risk response task when the first cooking abnormal risk is detected, so as to handle the first cooking abnormal risk.

[0013] At least one embodiment of this application also provides an electronic device, including: 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, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described kitchenware anomaly detection method.

[0014] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for detecting abnormal kitchen utensils.

[0015] The kitchen utensil anomaly detection method provided in this application selects a first cooking method matching the identified ingredient information from a preset cooking database, cooks the ingredient using the first cooking method, and detects whether a first cooking anomaly risk corresponding to the first cooking method occurs during the cooking process. When the first cooking anomaly risk is detected, a first anomaly risk response task is executed to handle the first cooking anomaly risk. Thus, by detecting anomalies during the cooking process, the probability of danger caused by anomalies during cooking is effectively reduced, ensuring the safety of the cooking process.

[0016] In some optional embodiments, the cooking abnormality risk includes: abnormal status risk of kitchen utensils;

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

[0018] During the cooking process of the ingredients using the first cooking method, it is detected whether the cookware exhibits a cooking process state that contradicts the cooking process parameters corresponding to the first cooking method;

[0019] If the contradictory cooking process states occur, it is confirmed that there is a risk of abnormal kitchenware status corresponding to the first cooking method.

[0020] By detecting the status of the cooking process, an abnormal risk of kitchenware status corresponding to the first cooking method is identified. Then, based on the abnormal risk of kitchenware status, the first abnormal risk response task is executed to accurately handle the abnormal risk of kitchenware status.

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

[0022] Detect whether there is a risk of incompatibility with the food ingredients corresponding to the first cooking method and / or a risk of abnormality with the food carrier;

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

[0024] By detecting any potential food incompatibility risks and / or food carrier risks corresponding to the first cooking method before cooking, the safety of the cooking process can be ensured by identifying and addressing any potential risks before cooking.

[0025] In some optional embodiments, after executing a first abnormal risk response task to handle the first cooking abnormal risk when it is detected, the method further includes:

[0026] Identify the ingredients that have been cooked using the first cooking method up to the present time, and obtain the ingredient status information of the partially cooked ingredients;

[0027] Based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method.

[0028] The semi-cooked ingredients are cooked using the second cooking method, and during the cooking process, it is detected whether a second cooking abnormality risk corresponding to the second cooking method occurs;

[0029] When the second cooking anomaly risk is detected, a second anomaly risk response task is executed to handle the second cooking anomaly risk.

[0030] By using a cooking method other than the first cooking method as a second cooking method, more suitable cooking methods and parameters can be recommended to users.

[0031] In some optional embodiments, the step of selecting a cooking method other than the first cooking method as the second cooking method from the cooking database based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present time includes:

[0032] Based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present, an intermediate cooking method is determined; the intermediate cooking method satisfies the following condition: the ingredients obtained after cooking the semi-cooked ingredients using the intermediate cooking method are similar in ingredient status information to the ingredients obtained after cooking the entire process using the first cooking method.

[0033] Select a cooking method from the cooking database that is similar to the intermediate cooking method, excluding the first cooking method, as the second cooking method.

[0034] By determining an intermediate cooking method, a cooking method similar to the intermediate cooking method (excluding the first 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 a risk of food compatibility abnormalities and / or food carrier abnormalities corresponding to the first cooking method, the method further includes:

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

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

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

[0039] After placing the ingredients to be cooked into the kitchen utensil, an image of the inside of the kitchen utensil is acquired;

[0040] The image inside the kitchen utensils is identified to obtain the ingredient information of the food to be cooked.

[0041] By acquiring images and then performing image recognition, the ingredient information of the food to be cooked can be accurately obtained. Attached Figure Description

[0042] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0043] Figure 1 This is a schematic flowchart of a method for detecting abnormalities in kitchen utensils provided in one embodiment of this application;

[0044] Figure 2 This is a schematic diagram of the structure of a kitchenware anomaly detection device provided in one embodiment of this application;

[0045] Figure 3 This is a schematic diagram of the structure of an electronic device provided in another embodiment of this application;

[0046] Figure 4 This is a flowchart illustrating a kitchenware anomaly detection method provided in another embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0048] To facilitate understanding of the embodiments of this application, the relevant content regarding the method for detecting abnormalities in kitchen utensils will be introduced first.

[0049] With the popularization of smart home appliances, the intelligence and performance of kitchen utensils have received increasing 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 cooking mode selection in steam ovens is typically done by the user, who then controls the oven to cook the food according to the selected cooking mode. However, existing steam ovens lack effective anomaly detection mechanisms when using cooking modes that are incompatible with the food or potentially dangerous, which can easily lead to hazards during the cooking process and is detrimental to ensuring cooking safety.

[0051] To address the technical problem that existing steam ovens lack effective anomaly detection mechanisms when using cooking methods with incompatible ingredients or potential hazards, which can easily lead to dangers during cooking and compromise the safety of the cooking process, this invention proposes a kitchenware anomaly detection method. The implementation details of the kitchenware anomaly detection method in this embodiment are described below. The following content is only for ease of understanding and is not necessary for implementing this solution.

[0052] Example 1:

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

[0054] Step 101: Based on the identified ingredient information of the ingredients to be cooked, select the first cooking method that matches the ingredient information from the preset cooking database. The cooking database stores multiple cooking methods, and each cooking method has corresponding cooking process parameters.

[0055] Specifically, the kitchen appliances are intelligent kitchen appliances used for cooking ingredients, such as steam ovens and microwave ovens. The cooking database includes various cooking methods, each with corresponding cooking process parameters. For example, when the cooking method is steaming or boiling the ingredients, the cooking process parameters include steaming / boiling time and temperature. The number of ingredients to be cooked can be one or more. After identifying the ingredient information, the cooking method that matches the ingredient information is selected from the various cooking methods in the database. The number of selected first cooking methods can be one or more. In some examples, a matching value between the cooking method and the ingredient can be set. By comparing the matching values ​​of multiple cooking methods, the cooking method with 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 the first cooking method, and detect whether any first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process.

[0057] Specifically, after selecting a first cooking method, the cookware is controlled to cook the ingredients according to that method. During the cooking process, the system detects any first cooking abnormality risks corresponding to the first cooking method to ensure safety. In some examples, cooking abnormality risks include cookware malfunction risks. For instance, in the first cooking method, the steam oven's cooking temperature is set to 100°C and the cooking time is set to 10 minutes. According to the first cooking method, the steam oven should stop heating after 10 minutes. However, if the steam oven remains at 100°C after 10 minutes, a first cooking abnormality risk occurs during the cooking process. This first cooking abnormality risk is that the steam oven temperature remains at 100°C after 10 minutes.

[0058] Step 103: When a first cooking abnormality risk is detected, execute the first abnormality risk response task to handle the first cooking abnormality risk.

[0059] Specifically, the first abnormal risk response task includes at least one of issuing a warning message and controlling the termination of cooking. When a first cooking abnormal risk corresponding to the first cooking method is detected, the cookware is controlled to issue a warning message, such as a warning light and / or a warning sound and / or a warning text; alternatively, the cookware is controlled to terminate cooking; or the cookware is controlled to terminate cooking and issue a warning message, in order to handle the first cooking abnormal risk and facilitate the user's understanding and handling of the abnormality. In some examples, if the steam oven remains at 100°C after 10 minutes, the cookware can be controlled to terminate cooking to handle the current cooking abnormal risk, allowing the user to remove the food from the steam oven or perform other operations.

[0060] In this embodiment, based on the identified ingredient information, a first cooking method matching the ingredient information is selected from a preset cooking database. The ingredient is then cooked using the first cooking method, and the system detects whether a first cooking anomaly risk corresponding to the first cooking method occurs during the cooking process. When a first cooking anomaly risk is detected, a first anomaly risk response task is executed to handle the risk. Thus, by detecting anomalies during the cooking process, the probability of accidents caused by anomalies is effectively reduced, ensuring the safety of the cooking process.

[0061] In some embodiments, cooking abnormality risks include: abnormal status of kitchen utensils;

[0062] During the cooking process, detect whether any first cooking abnormality risks corresponding to the first cooking method occur, including:

[0063] During the cooking process of the ingredients using the first cooking method, it is detected whether the kitchen utensils exhibit a cooking process state that contradicts the cooking process parameters corresponding to the first cooking method;

[0064] If contradictory cooking process states occur, it is confirmed that there is a risk of abnormal kitchenware status corresponding to the first cooking method.

[0065] Specifically, cooking abnormality risks include the risk of abnormal cookware condition. This refers to the risk that continuing to cook may lead to danger if the cookware becomes abnormal during the cooking process. During the cooking process using the first cooking method, it is detected whether the cookware exhibits a cooking process state that contradicts the cooking process parameters corresponding to the first cooking method. In some examples, the cooking process parameters in the first cooking method are: the steam oven's cooking temperature is set to 100℃, and the cooking time is set to 10 minutes. If, during the cooking process, it is found that the cookware exhibits a cooking process state that contradicts the cooking process parameters, i.e., the steam oven remains at 100℃ after 10 minutes, and continuing to heat could easily lead to the food burning and catching fire. In this case, an abnormal cookware condition risk corresponding to the first cooking method is confirmed, i.e., the steam oven temperature remains at 100 degrees Celsius after 10 minutes.

[0066] In some embodiments, before cooking the ingredients using the first cooking method, the method further includes:

[0067] Detect whether there is a risk of food incompatibility with the first cooking method and / or a risk of food carrier abnormality;

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

[0069] Specifically, before cooking ingredients using the first cooking method, the compatibility between ingredients and whether the container used to hold the ingredients is suitable for the current first cooking method are also potential risk factors that could cause danger. Therefore, it is necessary to check for any potential ingredient compatibility risks and / or container risks corresponding to the first cooking method before cooking. In some examples, the first cooking method uses a 90°C oven with eggs as the ingredient. Directly cooking eggs at high temperatures could cause them to explode, making whole eggs unsuitable for a steam oven. Similarly, the first cooking method uses a 90°C steaming temperature with two ingredients that, when cooked together, could easily cause food poisoning. Or, the first cooking method uses a 90°C steaming temperature for 5 minutes with ingredients that cannot be fully cooked under the current first cooking method and could easily cause food poisoning if undercooked. In these cases, based on the ingredient information and the first cooking method, an ingredient compatibility risk is detected. In some cases, the container used to hold the ingredients is unsuitable for the first cooking method and could easily cause an explosion. In these cases, based on the container information and the first cooking method, an abnormal risk is detected regarding the container.

[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 an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method is detected, the cookware is controlled to issue a warning message, or the cookware is controlled to end cooking, or the cookware is controlled to end cooking and issue a warning message, so as to handle the abnormal risk of food compatibility and / or abnormal risk of food carrier, and to facilitate the user to understand and handle the abnormality of the cookware.

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

[0072] Identify the ingredients that have been cooked using the first cooking method up to the present time, and obtain the ingredient status information of the semi-cooked ingredients.

[0073] Specifically, after detecting the first cooking abnormality risk, it means that an abnormal situation has occurred when cooking the 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 for a period of time using the cooking utensils of the first cooking method.

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

[0075] Specifically, after cooking the ingredients using the first cooking method, the cooked doneness information of the ingredients is used as the ingredient status information. Combined with the cooking process parameters that have not yet been executed in the first cooking method, a second cooking method is selected from the cooking database, excluding the first cooking method. In some examples, the cooking process parameters in the first cooking method are: steaming in a steam oven at 100℃ for 10 minutes, then steaming at 80℃ for 8 minutes. However, during the cooking process, it is found that the cookware exhibits a cooking process state contrary to the parameters, i.e., the steam oven remains at 100℃ after 10 minutes. At this point, the first abnormal risk response task is executed to handle the cooking abnormality risk. The ingredient status information at this time is that the ingredient is medium-rare (50% cooked). Up to the point of using the first cooking method, the unexecuted cooking process parameter is that it still needs to be steamed at 80℃ for 8 minutes. Combining the ingredient status information and the unexecuted cooking process parameter, a second cooking method, excluding the first cooking method, is selected from the cooking database.

[0076] The ingredients that have been partially cooked are cooked using a second cooking method, and during the cooking process, the risk of any abnormality corresponding to the second cooking method is detected.

[0077] Specifically, a second cooking method is used to cook the ingredients that have already been cooked using the first cooking method (i.e., partially cooked ingredients). During the cooking process, the system detects any potential second cooking malfunctions corresponding to the second cooking method to ensure safety. In some cases, during steaming at 80°C for 8 minutes, if heating is interrupted or other kitchen appliances malfunction, a second cooking malfunction risk occurs during the second cooking method. This second cooking malfunction risk is a drop in the temperature of the kitchen appliances due to heating interruption.

[0078] When a second cooking anomaly risk is detected, a second anomaly risk response task is executed to handle the second cooking anomaly 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 a second cooking abnormal risk corresponding to the second cooking method is detected, the cookware is controlled to issue a warning message, such as a warning light and / or a warning sound and / or a warning text, or the cookware is controlled to end cooking, or the cookware is controlled to end cooking and issue a warning message, so as to handle the second cooking abnormal risk and make it easier for the user to understand and handle the abnormality of the cookware.

[0080] In this embodiment, the ingredients cooked using the first cooking method up to the current stage are identified to obtain the ingredient status information of the partially cooked ingredients. Based on the ingredient status information and the cooking process parameters not executed in the first cooking method up to the current stage, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method. The partially cooked ingredients are then cooked using the second cooking method, and during the cooking process, it is detected whether a second cooking anomaly risk corresponding to the second cooking method occurs. When a second cooking anomaly risk is detected, a second anomaly risk response task is executed to handle the second cooking anomaly 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 are recommended to the user.

[0081] In some embodiments, based on ingredient status information and cooking process parameters not yet 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, including:

[0082] Based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present, an intermediate cooking method is determined; the intermediate cooking method satisfies the following: the ingredients obtained after cooking the partially cooked ingredients using the intermediate cooking method are similar in ingredient status information to the ingredients obtained after cooking the entire first cooking method.

[0083] Select one cooking method from the cooking database that is similar to the intermediate cooking method, excluding the first cooking method, as the second cooking method.

[0084] Specifically, after cooking the ingredients using the first cooking method, the ingredients are in a semi-cooked state. An intermediate cooking method is determined to ensure that the ingredients obtained by cooking the semi-cooked ingredients using this intermediate method are similar in state information to those obtained by cooking using the first cooking method throughout the entire process. After obtaining the intermediate cooking method, a cooking method similar to the intermediate method (excluding 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 steam oven steams at 100℃ for 10 minutes, then at 80℃ for 8 minutes. However, during the cooking process, it is found that the cookware exhibits a cooking process state contrary to the parameters, i.e., the steam oven remains at 100℃ after 10 minutes. In this case, the first abnormal risk response task is executed to handle the cooking abnormal risk. At this point, the ingredient status information is that the ingredient is cooked to medium-rare. After using the first cooking method, the unexecuted cooking process parameters are that it still needs to be steamed at 80℃ for 8 minutes. The intermediate cooking method is to steam at 80℃ for 8 minutes. Select a cooking method similar to the intermediate cooking method, other than the first cooking method, from the cooking database as the second cooking method.

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

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

[0087] Specifically, before cooking, the system detects whether there are any food compatibility issues or / or food carrier issues corresponding to the first cooking method. When such issues are detected, the information on incompatible ingredients and food carriers that are likely to cause danger when cooked according to the first cooking method is stored in a preset anomaly database to enrich the database. Specifically, the food information and food compatibility issues in the anomaly database correspond to the first cooking method, and the food carrier information and food carrier issues correspond to the first cooking method. When cooking with the first cooking method again, the presence of food compatibility issues and / or food carrier issues can be directly determined based on the same food information and / or food carrier information.

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

[0089] After placing the ingredients to be cooked into the kitchen utensils, obtain an image of the inside of the kitchen utensils;

[0090] The image inside the kitchen utensils is identified to obtain the ingredient information of the food to be cooked.

[0091] Specifically, visual inspection equipment is used to photograph the ingredients to be cooked to obtain images of the ingredients inside the kitchen utensils, and then the images are identified to accurately obtain the ingredient information of the ingredients to be cooked.

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

[0093] Obtain user preference data;

[0094] Based on user preference data, multiple cooking methods are weighted to determine the weight value of each cooking method. The weight value is positively correlated with the user's preference level.

[0095] Specifically, when selecting a first cooking method that matches the ingredient information from a preset cooking database, or when selecting a cooking method other than the first cooking method from the cooking database as the second cooking method, the cooking method with the highest weight value can be selected as the first or second cooking method to better suit the user's actual needs.

[0096] Example 2:

[0097] like Figure 4 As shown, this embodiment provides an exemplary content of Embodiment 1, namely, an exemplary process for a method of detecting abnormal kitchen utensils, specifically including:

[0098] By integrating advanced computer vision technology, image recognition algorithms, and intelligent analysis modules, the system can intelligently identify the types of food inside the steam oven and automatically match the optimal cooking method based on a preset cooking knowledge base. Simultaneously, the system also features anomaly detection capabilities to ensure the safety and efficiency of the cooking process.

[0099] The system identifies food information through a food recognition module. Specifically, it captures images of food inside the steam oven using a high-definition camera and then uses deep learning algorithms to recognize the images. The system can accurately identify characteristics such as food type, size, and quantity, providing basic data for matching subsequent cooking methods.

[0100] Based on the results from the food recognition module, the matching analysis module retrieves the optimal cooking method from the cooking knowledge base and calculates the degree of match between the currently selected cooking method and the food. The cooking knowledge base contains a wealth of information on the cooking characteristics of ingredients, optimal cooking methods, cooking times, and temperatures. It also includes ingredient compatibility information to ensure that different ingredients do not produce adverse reactions during cooking.

[0101] If the system detects a cooking method unsuitable for the current food, it will issue an initial warning. For example, when the system detects that a user has placed an egg inside, it will immediately check whether the cooking method is suitable for the egg. If it detects that cooking an egg in a steam oven would be dangerous (e.g., high-temperature direct cooking could cause an explosion), it will issue an anomaly warning and suggest 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, the system proceeds to the anomaly detection module. This module identifies the ingredients the user has placed in the oven and, using a cooking knowledge base, determines their compatibility and safety. For example, it checks whether the ingredients are safe at high temperatures or during the current cooking process, whether there are cooking time requirements, whether the ingredients are incompatible, and whether the user's handling of the ingredients is appropriate, such as whether harmful parts have been removed. If an anomaly is detected, it is immediately displayed to the user through the user interaction module, prompting the user to make appropriate adjustments. For example, if the system detects that a user has placed two incompatible ingredients in a steam oven, it will immediately issue a warning and suggest removing one of the ingredients. Similarly, if a user uses unsuitable utensils, the system will identify the material of the utensil and issue an anomaly risk warning, such as the utensil being unsuitable for a steam oven and posing a risk of explosion. The anomaly detection module shares the detected anomalies with the matching analysis and recommendation modules.

[0103] The recommendation module receives information from the two modules mentioned above and then operates accordingly. When the matching analysis and anomaly detection modules report data anomalies indicating potential danger, they recommend alternative cooking methods and preparation techniques based on the current ingredients. For example, if the system detects that the user's cooking might cause nutrient loss, it recommends a more suitable cooking method to preserve the food's nutritional components. Furthermore, the system provides personalized cooking suggestions based on the user's taste preferences and dietary needs. Users can continue with their own actions; the system simply provides information and recommendations without excessively interfering with user choices. These suggestions are presented in a non-mandatory manner, allowing users to choose whether to accept them. However, if the two modules detect extremely dangerous information, they will forcibly stop the operation to ensure safety. The recommendation module shares the recommendation results and user feedback with the matching analysis and anomaly detection modules.

[0104] The user interaction module provides a user-friendly interface, allowing users to manually adjust cooking parameters and display system information and prompts. Users can view information such as food identification results, matching degree, and recommended cooking methods through the interface, facilitating operation and adjustments.

[0105] In this embodiment, the following can be achieved: Data sharing: Each module shares its processed results and data with other modules. For example, the results of the matching analysis module are used by the anomaly detection module and the recommendation module; the monitoring results of the anomaly detection module are used by the matching analysis module and the recommendation module; the suggestions and user feedback from the recommendation module are used by the matching analysis module and the anomaly detection module. Inter-module calling: Each module can call the functions of other modules when needed. For example, the matching analysis module can call the functions of the anomaly detection module to confirm certain anomalies 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, ensuring a consistent user experience.

[0106] In this embodiment, by intelligently identifying food types, automatically matching the optimal cooking method, monitoring abnormal situations in real time, and providing personalized cooking suggestions, the system significantly improves the intelligence, safety, and user experience of cooking. This system is suitable not only for home use but also for the cooking needs of restaurants, hotels, and other similar establishments.

[0107] In this embodiment, by introducing computer vision technology and image recognition algorithms, intelligent identification of food types within the steam oven is achieved. Combined with a pre-set cooking knowledge base, the system automatically matches the optimal cooking method. Simultaneously, the system also features an anomaly detection function, issuing timely warnings when incompatible or potentially dangerous cooking methods are detected, ensuring safety during the cooking process. Furthermore, the system can recommend optimized cooking parameters based on food type and cooking method, improving cooking results and reducing nutrient loss. This invention not only enhances the intelligence level of the steam oven but also significantly improves the user's cooking experience and safety.

[0108] Example 3:

[0109] Another embodiment of this application relates to a kitchen utensil anomaly detection device. The implementation details of this embodiment's kitchen utensil anomaly detection device are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution. A schematic diagram of this embodiment's kitchen utensil anomaly detection device can be seen as follows: Figure 2 As shown, it includes:

[0110] The selection module 201 is used to select a first cooking method that matches the ingredient information from a preset cooking database based on the identified ingredient information of the ingredients to be cooked. The cooking database stores multiple cooking methods, and each cooking method has corresponding cooking process parameters.

[0111] The detection module 202 is used to cook the ingredients using the 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 a first abnormal risk response task when a first cooking abnormal risk is detected, so as to handle the first cooking abnormal risk.

[0113] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0114] Example 4:

[0115] Another embodiment of this application relates to an electronic device, such as... Figure 3 As shown, it includes: at least one processor 901; and a memory 902 communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions executable by the at least one processor 901, the instructions being executed by the at least one processor 901 to enable the at least one processor 901 to perform the kitchenware anomaly detection method in the above embodiments.

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

[0117] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0118] Example 5:

[0119] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0120] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0121] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for detecting abnormalities in kitchen utensils, characterized in that, The kitchen appliance is an intelligent kitchen appliance for cooking ingredients, and the method includes: Based on the identified ingredient information of the ingredients to be cooked, a first cooking method matching the ingredient information is selected from a preset cooking database. The cooking database stores multiple cooking methods, and each cooking method is set with corresponding cooking process parameters. The ingredients are 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 handle the first cooking abnormality risk; After executing a first abnormal risk response task to handle the first cooking abnormal risk when it is detected, the method further includes: Identify the ingredients that have been cooked using the first cooking method up to the present time, and obtain the ingredient status information of the partially cooked ingredients; Based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present, a cooking method other than the first cooking method is selected from the cooking database as the second cooking method; wherein, the weight value of the second cooking method is positively correlated with the user's preference level; The semi-cooked ingredients are cooked using the second cooking method, and during the cooking process, it is detected whether a second cooking abnormality risk corresponding to the second cooking method occurs; When the second cooking abnormality risk is detected, a second abnormality risk response task is executed to handle the second cooking abnormality risk; The step of selecting a cooking method other than the first cooking method as a second cooking method from the cooking database based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present time includes: Based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the present, an intermediate cooking method is determined; the intermediate cooking method satisfies the following condition: the ingredients obtained after cooking the semi-cooked ingredients using the intermediate cooking method are similar in ingredient status information to the ingredients obtained after cooking the entire process using the first cooking method. Select a cooking method from the cooking database that is similar to the intermediate cooking method, excluding the first cooking method, as the second cooking method.

2. The method for detecting abnormalities in kitchen utensils according to claim 1, characterized in that, The aforementioned cooking abnormality risks include: risks related to abnormal condition of kitchen utensils; The detection of whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process includes: During the cooking process of the ingredients using the first cooking method, it is detected whether the cookware exhibits a cooking process state that contradicts the cooking process parameters corresponding to the first cooking method; If the contradictory cooking process states occur, it is confirmed that there is a risk of abnormal kitchenware status corresponding to the first cooking method.

3. The method for detecting abnormalities in kitchen utensils according to claim 1, characterized in that, Before cooking the ingredients using the first cooking method, the method further includes: Detect whether there is a risk of incompatibility with the food ingredients corresponding to the first cooking method and / or a risk of abnormality with the food carrier; When an abnormal risk of food compatibility and / or abnormal risk of food carrier corresponding to the first cooking method is detected, a second abnormality response task is executed to handle the abnormal risk of food compatibility and / or abnormal risk of food carrier.

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

5. The method for detecting abnormalities in kitchen utensils according to claim 1, characterized in that, Identify the ingredient information of the ingredients to be cooked, including: After placing the ingredients to be cooked into the kitchen utensil, an image of the inside of the kitchen utensil is acquired; The image inside the kitchen utensils is identified to obtain the ingredient information of the food to be cooked.

6. A kitchen utensil anomaly detection device, characterized in that, include: The selection module is used to select a first cooking method that matches the identified ingredient information from a preset cooking database. The cooking database stores multiple cooking methods, and each cooking method is set with corresponding cooking process parameters. The detection module is used to detect whether a first cooking abnormality risk corresponding to the first cooking method occurs during the cooking process when the ingredients are cooked using the first cooking method. An execution module is used to execute a first abnormal risk response task when the first cooking abnormal risk is detected, so as to handle the first cooking abnormal risk; After executing a first abnormal risk response task to handle the first cooking abnormal risk when the first cooking abnormal risk is detected, the device is further configured to: identify the ingredients cooked using the first cooking method up to the present, and obtain the ingredient status information of the partially cooked ingredients; based on the ingredient status information and the cooking process parameters not executed in the first cooking method up to the present, select a cooking method other than the first cooking method as a second cooking method from the cooking database; wherein the weight value of the second cooking method is positively correlated with the user's preference; cook the partially cooked ingredients using the second cooking method, and during the cooking process, detect whether a second cooking abnormal risk corresponding to the second cooking method occurs; when the second cooking abnormal risk is detected, execute a second abnormal risk response task to handle the second cooking abnormal risk; The device is further configured to determine an intermediate cooking method based on the ingredient status information and the cooking process parameters that have not been executed in the first cooking method up to the current time; the intermediate cooking method satisfies the following: the ingredients obtained after cooking the semi-cooked ingredients using the intermediate cooking method are similar in ingredient status information to the ingredients obtained after cooking the entire process using the first cooking method; and 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.

7. 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 to enable the at least one processor to perform the kitchenware anomaly detection method as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the kitchenware anomaly detection method according to any one of claims 1 to 5.

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