Food material processing method and device, electronic equipment and storage medium

Abstract

The invention relates to a food material processing method and device, electronic equipment and a storage medium. The method comprises the steps that preset health data and real-time physiological data of a target object, first food material associated data corresponding to various preset food materials in preset storage equipment and storage environment data of the various preset food materials are obtained; determining at least one first to-be-ingested element of the target object in a first preset time period and a first to-be-ingested amount corresponding to the at least one first to-be-ingested element based on the preset health data and the real-time physiological data; based on the storage environment data, the at least one first to-be-ingested element, the first to-be-ingested amount and the first food material associated data, determining at least one kind of food material processing information, and feeding back the at least one kind of food material processing information to the target object; and in response to a selection instruction for the first target processing information, controlling preset processing equipment to run according to a preset processing program. According to the embodiment of the invention, accurate matching between nutrition intake and individual health can be ensured.

Description

Technical Field

[0001] This disclosure relates to the field of smart home appliance technology, and in particular to a food processing method, apparatus, electronic device, and storage medium. Background Technology

[0002] Existing kitchen systems suffer from structural deficiencies in intelligent decision-making: food processing has long relied on human experience, requiring users to manually determine the ingredients and processing methods based on their own health management needs and the available food in storage. Consequently, ingredient selection and processing are typically dominated by human experience, lacking a real-time linkage mechanism with nutritional needs. This leads to a disconnect between nutritional intake and individual health status, as well as a lack of standardized control over food processing. Summary of the Invention

[0003] This disclosure provides a food processing method, apparatus, electronic device, and storage medium to at least address issues in related technologies such as the disconnect between nutrient intake and individual health status, and the lack of standardized control over food processing.

[0004] According to a first aspect of the present disclosure, a method for processing food ingredients is provided, comprising: Acquire the target object's preset health data, real-time physiological data, and first ingredient association data corresponding to each of the various preset ingredients in the preset storage device, as well as the storage environment data of the various preset ingredients; Based on the preset health data and the real-time physiological data, at least one first element to be ingested and the first amount to be ingested corresponding to the at least one first element to be ingested are determined for the target object in a first preset time period. Based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food ingredient association data, at least one food ingredient processing information is determined, and the at least one food ingredient processing information is fed back to the target object. In response to a selection instruction for the first target processing information, the preset processing equipment is controlled to run according to the preset processing program corresponding to the first target processing information, wherein the at least one ingredient processing information includes the first target processing information.

[0005] According to a second aspect of the present disclosure, a food processing apparatus is provided, comprising: The first data acquisition module is used to acquire the target object's preset health data, real-time physiological data, and the first ingredient association data corresponding to each of the multiple preset ingredients in the preset storage device, as well as the storage environment data of the multiple preset ingredients; The first intake data determination module is used to determine, based on the preset health data and the real-time physiological data, at least one first element to be ingested by the target object within a first preset time period and the first amount to be ingested corresponding to the at least one first element to be ingested. The food processing information determination module is used to determine at least one food processing information based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food-related data, and to feed back the at least one food processing information to the target object. A processing module is used to control a preset processing device to run according to a preset processing program corresponding to the first target processing information in response to a selection instruction for the first target processing information, wherein the at least one ingredient processing information includes the first target processing information.

[0006] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement the method as described in any one of the first aspects above.

[0007] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided such that, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in any of the first aspects of the present disclosure. According to a fifth aspect of the present disclosure, a computer program product including instructions is provided that, when run on a computer, causes the computer to perform the method described in any of the first aspects of the present disclosure.

[0008] The technical solutions provided by the embodiments of this disclosure have at least the following beneficial effects: The system acquires preset health data and real-time physiological data of the target individual, as well as the first ingredient association data and storage environment data of various preset ingredients stored in a preset storage device. Based on the preset health data and real-time physiological data, it determines at least one first element to be ingested and its first intake amount for the target individual within a first preset time period, ensuring personalized and accurate nutritional intake. Combining the storage environment data, the first element to be ingested, the first intake amount, and the first ingredient association data, it determines the processing information for at least one ingredient and feeds it back to the target individual, improving the efficiency and targeting of the processing plan generation. Responding to the selection command for the first target processing information, it controls the preset processing equipment to run according to the preset processing program corresponding to the first target processing information, ultimately optimizing the automated execution of the ingredient processing process and ensuring the accurate supplementation of the first element to be ingested by the target individual.

[0009] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0010] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0011] Figure 1 This is a schematic flowchart illustrating a food processing method according to an exemplary embodiment; Figure 2 This is a schematic diagram illustrating a process for obtaining processing information for at least one ingredient, according to an exemplary embodiment. Figure 3 This is a schematic diagram illustrating a process for obtaining sorted second target processing information according to an exemplary embodiment; Figure 4 This is a schematic diagram illustrating a process for determining the type of security problem according to an exemplary embodiment; Figure 5 This is a schematic diagram illustrating a process for determining any preset ingredient as an ingredient to be cleaned, according to an exemplary embodiment. Figure 6 This is a schematic diagram illustrating a process for determining information about a first supplementary ingredient corresponding to at least one ingredient to be supplemented, according to an exemplary embodiment. Figure 7 This is a schematic diagram illustrating a process for determining a target provider and feeding back second supplementary ingredient information and target address to the target provider according to an exemplary embodiment; Figure 8 This is a block diagram of a food processing apparatus according to an exemplary embodiment; Figure 9 This is a block diagram illustrating an electronic device for food processing according to an exemplary embodiment. Detailed Implementation

[0012] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0013] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar different contents and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0014] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0015] Figure 1 This is a schematic flowchart illustrating a food processing method according to an exemplary embodiment, such as... Figure 1 As shown, this food processing method is used in electronic devices such as servers and includes the following steps.

[0016] In step S101, the preset health data, real-time physiological data, and first ingredient association data and storage environment data of various preset ingredients corresponding to each of the target objects are obtained from the preset storage device.

[0017] In one specific embodiment, the aforementioned preset health data is benchmark information reflecting the long-term health status of the target object, and the aforementioned real-time physiological data is dynamically monitored data characterizing the current physiological status of the target object.

[0018] For example, preset health data may include at least one of the following: medical history, allergen records, and baseline metabolic indicators from a health check-up report. Specifically, the medical history in the health check-up report may include data such as diabetes and hypertension; the allergen records may include data such as seafood allergy and peanut allergy; and the baseline metabolic indicators may include data such as basal metabolic rate and cholesterol levels. Optionally, preset health data can be obtained by the target user through input on a terminal device.

[0019] For example, real-time physiological data may include at least one of the following: current transient vital signs, current metabolic parameters, and activity status. Specifically, current transient vital signs may include at least one of the following: current body temperature, blood pressure, and heart rate; current metabolic parameters may include at least one of the following: real-time blood glucose level and blood oxygen saturation; and activity status may include at least one of the following: heart rate variability during exercise and sleep quality monitoring data. Optionally, real-time physiological data may be acquired through a preset acquisition device, which may be a device used to collect at least one of the following physiological data: current transient vital signs, current metabolic parameters, and activity status.

[0020] In step S103, based on preset health data and real-time physiological data, at least one first element to be ingested and the first amount to be ingested corresponding to the first element to be ingested are determined for the target object within a first preset time period.

[0021] In one specific embodiment, the aforementioned first preset time period can be set by a user-defined mode or determined by an intelligent inference mode. Specifically, if the first preset time period is set by a user-defined mode, the target object can actively set a time period on the terminal device (e.g., specifying "lunch"), and the system will directly use that time period parameter. If the first preset time period is determined by an intelligent inference mode, the system can automatically match the default catering / cycle type (e.g., 06:00 → breakfast time period) based on the current time when the target object has not set a first preset time period.

[0022] In a specific embodiment, the above-mentioned determination of at least one first element to be ingested and the first intake amount corresponding to at least one first element to be ingested for the target object within a first preset time period based on preset health data and real-time physiological data may include: inputting preset health data and real-time physiological data into a preset health demand analysis network, analyzing the nutritional elements required by the target object, and outputting at least one first element to be ingested and the first intake amount corresponding to at least one first element to be ingested for the target object within a first preset time period.

[0023] In step S105, based on the storage environment data, at least one first element to be ingested, the first amount to be ingested, and the first food ingredient association data, at least one food ingredient processing information is determined, and the at least one food ingredient processing information is fed back to the target object.

[0024] In one specific embodiment, the aforementioned first ingredient associated data includes cost information, storage time, inventory data, real-time status data, multiple preset processing information, and theoretical content of the first element corresponding to each of the multiple preset nutritional elements contained in the multiple preset ingredients.

[0025] In one specific embodiment, real-time status data characterizes the current state of each preset ingredient. For example, real-time status data may include at least one of the following: temperature fluctuations, microbial activity, and degree of damage to the preset ingredients. Specifically, real-time status data can be collected in real time by various types of sensors, and correspondingly, these sensors can be installed in a preset storage device.

[0026] In one specific embodiment, the aforementioned preset processing information includes multiple preset processing methods, corresponding processing temperatures, and corresponding processing times. For example, the multiple preset processing methods may include stir-frying, steaming, baking, and frying.

[0027] In a specific embodiment, the theoretical content of the first element can be the standardized theoretical value of the preset nutrient elements contained per unit mass of each preset food ingredient in its unprocessed state.

[0028] For example, if the first ingredient associated data is eggs, the first ingredient associated data may include cost information of ¥0.98 / egg, storage time of 5 days, inventory data of 32 eggs remaining, real-time status data of eggshell crack index of 0, and theoretical content of the first element of 100g eggs containing 12.6g of protein, 9.5g of fat, 1.1μg of vitamin D and 23.3μg of selenium per unit mass (100g).

[0029] For example, if the first ingredient associated data is leafy greens, the first ingredient associated data may include cost information of ¥3.2 / 500g, storage time of 3 days, inventory data of 800g remaining, real-time status data of chlorophyll activity of fluorescence value of 0.85 (≥0.8 indicates high quality, ≤0.6 indicates spoilage), leaf water content of 92.4% (the critical value for taste is 90%, <85% will result in fibrous tissue), and yellowing area ratio of 0.7% (>5% is inedible). The theoretical content of the first element is 36mg of vitamin C, 1.2mg of β-carotene, 1.8g of dietary fiber, and 108mg of calcium per unit mass (100g) of leafy greens.

[0030] In a specific embodiment, such as Figure 2 As shown, the above-mentioned determination of at least one food ingredient processing information based on storage environment data, at least one first element to be ingested, first amount to be ingested, and first food ingredient association data includes: In step S201, the theoretical content, cost information and storage time of the first element are input into a preset priority analysis network to perform food processing priority analysis on a variety of preset ingredients, thereby obtaining the food priority information corresponding to each of the preset ingredients.

[0031] In one specific embodiment, the above-mentioned ingredient priority information is used to identify the urgency level of each preset ingredient being processed and used preferentially.

[0032] In step S203, multiple preset processing information is input into a preset nutrient analysis network to analyze the nutrient retention of multiple preset ingredients under the preset processing methods corresponding to the multiple preset processing information, and to obtain the actual content of multiple preset nutrients of multiple preset ingredients under the multiple preset processing methods.

[0033] In a specific embodiment, the actual content of the above elements represents the actual retention value of various preset nutrients contained in the preset food after it has been processed by a specific preset processing method.

[0034] For example, taking chicken breast as the preset ingredient, the preset processing method is boiling at 100℃ for 10 minutes and pan-frying at 180℃ for 6 minutes. The actual element content includes 0.12mg of vitamin B1 and 26.9g of protein per unit mass (100g) of chicken breast after boiling, and 0.09mg of vitamin B1 and 29.4g of protein per unit mass (100g) of chicken breast after pan-frying.

[0035] For example, taking spinach as the preset ingredient, the preset processing method is stir-frying at 220℃ for 90 seconds and blanching in boiling water for 60 seconds. The actual element content includes 32.3mg of vitamin C and 482mg of oxalic acid per unit mass (100g) of spinach after stir-frying, and 17.1mg of vitamin C and 207mg of oxalic acid per unit mass (100g) of spinach after blanching.

[0036] In step S205, the storage time, storage environment data, and real-time status data are input into a preset freshness analysis network to perform freshness analysis on various preset ingredients, thereby obtaining freshness data corresponding to each of the various preset ingredients.

[0037] In one specific embodiment, the above-mentioned freshness data characterizes the processing and utilization timeliness of the preset ingredients.

[0038] In step S207, the food priority information, actual element content, freshness data, at least one first element to be ingested, first amount to be ingested, cost information, inventory data, and multiple preset processing information are input into a preset processing analysis network for processing information analysis and processing to obtain at least one food processing information.

[0039] In the above embodiments, the theoretical content of the first element, cost information, and storage time are input into a preset priority analysis network for food processing priority analysis, scientifically selecting advantageous ingredients that meet both economic and timeliness requirements. A preset nutrient analysis network processes various preset processing information to accurately predict the actual content of each nutrient under different processing methods. A preset freshness analysis network evaluates storage time, environmental data, and real-time status data to objectively quantify food freshness data. Finally, food priority information, actual element content, freshness data, at least one first element to be ingested, first intake amount, cost information, inventory data, and processing information are integrated into a preset processing analysis network to generate food processing information, thereby maximizing food utilization and processing efficiency while ensuring precise nutrient intake for the target population.

[0040] In a specific embodiment, such as Figure 3 As shown, when the above-mentioned at least one ingredient processing information is multiple ingredient processing information, the above method further includes: In step S301, historical food processing information is obtained.

[0041] In a specific embodiment, the aforementioned historical food processing information is data on the food processing operations performed by the target object in the past, which may include preset processing information of actual application (such as specific processing methods and parameter combinations) and data such as the corresponding processing object (food type).

[0042] In step S303, historical food processing information is input into a preset preference analysis network to analyze the processing preferences of the target object and obtain the food processing preference information of the target object.

[0043] In one specific embodiment, the above-mentioned food processing preference information is a quantitative indicator of a user's tendency to prefer specific food processing methods and parameter combinations (processing temperature and processing time).

[0044] In a specific embodiment, the above-mentioned determination of at least one food processing information based on storage environment data, at least one first element to be ingested, a first amount to be ingested, and first food ingredient association data, and the feedback of at least one food processing information to the target object, includes: In step S305, processing information for multiple ingredients is determined based on storage environment data, at least one first element to be ingested, the first amount to be ingested, and the first ingredient association data.

[0045] In a specific embodiment, the above-mentioned determination of multiple food processing information based on storage environment data, at least one first element to be ingested, first intake amount, and first food ingredient association data can be referred to in step S105 above, which determines at least one food processing information based on storage environment data, at least one first element to be ingested, first intake amount, and first food ingredient association data, and will not be repeated here.

[0046] In step S307, based on the food processing preference information, the processing information of various food ingredients is sorted to obtain the sorted second target processing information, and the second target processing information is fed back to the target object.

[0047] In the above embodiments, by acquiring historical food processing information and inputting it into a preset preference analysis network, the food processing preference information of the target object is analyzed. This allows multiple food processing information initially generated based on storage environment data, at least one first element to be ingested, the first amount to be ingested, and the first food association data to be further combined with preference data to complete sorting optimization. Finally, the sorted second target processing information that is adapted to the target object's personalized needs is fed back to the target object, making the recommended second target processing information naturally match the individual's taste, shortening the target object's selection path, avoiding repeated manual adjustments, and improving the user experience.

[0048] In step S107, in response to the selection instruction for the first target processing information, the preset processing equipment is controlled to run according to the preset processing program corresponding to the first target processing information.

[0049] In one specific embodiment, the above-mentioned at least one food processing information includes first target processing information.

[0050] In one specific embodiment, the above-mentioned response to the selection instruction for the first target processing information, controlling the preset processing equipment to run according to the preset processing program corresponding to the first target processing information, may include: after receiving the selection instruction for the first target processing information, sending a control instruction to the preset processing equipment to drive the preset processing equipment to run according to the preset processing program corresponding to the first target processing information.

[0051] In a specific embodiment, such as Figure 4 As shown, the above method also includes: In step S401, the current time, gas detection data from the preset storage device, and processing equipment detection data from the preset processing device are obtained.

[0052] In one specific embodiment, the aforementioned gas detection data is volatile organic compound (VOC) concentration data characterizing the food spoilage process. For example, the gas detection data may include at least one of the following: ammonia gas content, olefin gas content, and sulfur-containing compound gas content. Specifically, the gas detection data can be collected in real time by various sensors, and correspondingly, these sensors can be stored in a pre-set storage device.

[0053] In one specific embodiment, the processing equipment detection data mentioned above reflects the concentration of food safety risk factors during processing. For example, the processing equipment detection data may include at least one of biochemical indicators such as allergen residue concentration, microbial contamination (e.g., total bacterial count), and toxin production (e.g., acrylamide). Specifically, the processing equipment detection data can be collected in real time by various sensors, which can be positioned near the preset processing equipment.

[0054] In step S403, the current time, gas detection data, processing equipment detection data, and real-time physiological data are input into a preset safety analysis network. Based on the preset storage device weight corresponding to the gas detection data, the preset processing equipment weight corresponding to the processing equipment detection data, and the preset real-time physiological weight corresponding to the real-time physiological data, the food safety is analyzed to determine the food safety index.

[0055] In one specific embodiment, the preset storage device weight, preset processing device weight, and preset real-time physiological weight are adjusted based on the current time. For example, during off-peak power supply periods (e.g., 2:00 AM - 5:00 AM), the preset storage device weight is increased due to the increased risk of cold chain interruption; during non-working periods (e.g., 11:00 PM - 6:00 AM), the preset processing device has no risk of active contamination due to standby; and during sleep periods (e.g., 12:00 AM - 6:00 AM), the preset real-time physiological weight is increased due to slowed metabolism and increased sensitization.

[0056] In step S405, if the food safety index is greater than a preset safety threshold, the type of safety problem is determined based on at least one of gas detection data, processing equipment detection data, and real-time physiological data, and the food safety problem corresponding to the type of safety problem is resolved.

[0057] For example, when the food safety index exceeds a preset safety threshold, determining the type of safety issue based on at least one of gas detection data, processing equipment detection data, and real-time physiological data, and addressing the food safety issue corresponding to that type, may include: when a preset storage device or preset processing equipment autonomously detects a high risk. Specifically, a preset storage device autonomously detecting a high risk could be indicated by gas detection data showing excessive levels of putrefactive gases in the preset storage device (e.g., ammonia > 10 ppm in cold storage, ethylene > 1 μL / L), with the corresponding response being automatic locking of the storage door and release of nitrogen to inhibit microbial growth. Similarly, a preset processing equipment autonomously detecting a high risk could be indicated by processing equipment detection data showing biochemical safety thresholds (e.g., aflatoxin in a frying pan > 5 ppb, peanut residue in a blender > 0.1 ppm, Salmonella load on knives > 50 CFU / g). When toxin levels exceed the limit (e.g., aflatoxin in a frying pan > 5 ppb), the corresponding response could be automatic oil removal from the frying pan and locking of the heating module; when allergen residue remains (peanut residue in a blender > 0.1 ppm), the corresponding response could be triggering a self-cleaning program in the blender and disabling it.

[0058] For example, when the food safety index exceeds a preset safety threshold, determining the type of safety issue based on at least one of gas detection data, processing equipment detection data, and real-time physiological data, and then addressing the food safety issue corresponding to that type, could include: when processing equipment detection data shows a safety risk and real-time physiological data confirms the occurrence of a hazard. For example, processing equipment detection data showing a safety risk could indicate a peanut protein residue concentration of 0.28 ppm, and real-time physiological data showing a drop in real-time blood oxygen saturation to 92%. In this case, it could be determined that the target individual has consumed peanuts and is allergic to them. The corresponding response could be linked to a smart medicine box, automatically popping out an adrenaline pen, sending emergency instructions to the user's phone to "inject immediately on the outside of the thigh," and automatically connecting the emergency call system to the nearest emergency center (using GPS to locate the kitchen address).

[0059] In the above embodiments, by acquiring the current time, gas detection data from a preset storage device, and processing equipment detection data from a preset processing device, these three types of data are input into a preset safety analysis network along with real-time physiological data. Based on the preset storage device weight, preset processing equipment weight, and preset real-time physiological weight that are automatically adjusted according to the current time, a food safety index is output. When the index exceeds a preset safety threshold, the type of safety problem is immediately locked according to the abnormal data source, and the corresponding type of resolution is automatically triggered, so as to achieve real-time interception and closed-loop handling of safety risks.

[0060] In a specific embodiment, such as Figure 5 As shown, the above method also includes: In step S501, the second ingredient association data corresponding to each of the various preset ingredients, the gas detection data of the preset storage device, and the storage device usage data are obtained.

[0061] In one specific embodiment, the aforementioned storage device usage data is an operation log characterizing the operational intensity and maintenance status of the preset storage device. For example, the storage device usage data may include at least one of the following: compressor start-stop ratio, number and duration of door opening and closing, refrigerant circulation efficiency, and defrosting frequency.

[0062] In one specific embodiment, the aforementioned second food ingredient association data includes a first storage location and a preset spoilage index corresponding to each of several preset food ingredients. The preset spoilage index is a baseline decay coefficient pre-calibrated for a specific food ingredient, quantifying its natural spoilage rate. For example, the preset spoilage index for fresh milk is 28.6 / day, and the preset spoilage index for tomatoes is 8.7 / day.

[0063] In step S503, storage environment data, gas detection data, storage device usage data, and preset spoilage index are input into a preset spoilage analysis network to analyze the degree of food spoilage and determine the first spoilage analysis data corresponding to each of the various preset food ingredients.

[0064] In one specific embodiment, the aforementioned first spoilage analysis data is used to indicate the remaining usable time for each of the various preset ingredients.

[0065] In step S505, first spoilage analysis data is sent to the target object so that the target object knows the remaining usable time. If the second spoilage analysis data corresponding to any of the multiple preset ingredients is greater than the preset spoilage threshold, the preset ingredient is identified as an ingredient to be cleaned. The target object is then fed back the ingredient identifier to be cleaned and the second storage location of the ingredient to be cleaned so that the target object can clean the ingredient to be cleaned.

[0066] In the above embodiments, by inputting the second food ingredient association data, gas detection data, and storage device usage data into a preset spoilage analysis network, the first spoilage analysis data is accurately output, allowing users to monitor the freshness status of each food ingredient in real time. When the spoilage data of any food ingredient exceeds a preset spoilage threshold, it is automatically marked as a food ingredient to be cleaned, and its food ingredient identifier and second storage location are fed back, driving users to quickly locate and clean high-risk food ingredients, achieving early interception and precise location and cleaning of spoilage risks, avoiding biological hazards caused by accidentally eating spoiled food ingredients, and avoiding the time-consuming manual inspection of cold storage, thus improving the user experience.

[0067] In one specific embodiment, the aforementioned first ingredient association data includes inventory data corresponding to each of the various preset ingredients and the theoretical content of the first element corresponding to each of the various preset nutritional elements contained in each of the various preset ingredients.

[0068] In a specific embodiment, such as Figure 6 As shown, the above method also includes: In step S601, based on inventory data and the theoretical content of the first element, the theoretical content of the second element corresponding to each of the various preset nutrient elements in the preset storage device is determined.

[0069] In one specific embodiment, the theoretical content of the second element is the total existing quantity of each of the various preset nutrient elements in the preset storage device.

[0070] For example, taking iron as the preset nutrient element, the preset storage device contains 50g of pork liver and 300g of spinach. The theoretical content of the first element in pork liver is 23.2mg / 100g, and the theoretical content of the first element in spinach is 2.9mg / 100g. Therefore, the theoretical content of the second element in iron in the preset storage device is 11.6mg (50g×(23.2 / 100) = 11.6mg) + 8.7mg (300g×(2.9 / 100) = 8.7mg) = 20.3mg ≈ 20mg.

[0071] In step S603, based on preset health data and real-time physiological data, the target object is determined to have at least one second element to be ingested and the corresponding amount of the second element to be ingested within a second preset time period.

[0072] In one specific embodiment, the aforementioned second preset time period can be set by a user-defined mode or determined by an intelligent inference mode. Specifically, if the first preset time period is set by a user-defined mode, the target object can actively set a time period on the terminal device (e.g., specifying 3 days), and the system will directly use this time period parameter. If the first preset time period is determined by an intelligent inference mode, the system can automatically match a default procurement cycle (e.g., one week) if the target object does not set a first preset time period.

[0073] In a specific embodiment, the detailed description of determining at least one second element to be ingested and the second intake amount corresponding to the at least one second element to be ingested within a second preset time period based on preset health data and real-time physiological data can be found in step S103 above, which determines at least one first element to be ingested and the first intake amount corresponding to the at least one first element to be ingested within a first preset time period based on preset health data and real-time physiological data. It will not be repeated here.

[0074] In step S605, based on multiple preset nutrient elements, the theoretical content of the second element, at least one second element to be ingested, and the second amount to be ingested, the information of the first supplementary food corresponding to at least one food to be supplemented is determined, and the information of the first supplementary food is fed back to the target object so that the target object can supplement the food.

[0075] In one specific embodiment, the above-mentioned at least one food ingredient to be supplemented contains at least one second element to be ingested.

[0076] For example, taking iron as the preset nutrient element, the theoretical content of the second element is 20mg, and the second intake amount is 25mg. Then the iron calorie deficit is 5mg. To avoid stockpiling food, the food to be supplemented is determined to be duck blood (the iron content of duck blood is 30mg / 100g), and the corresponding first supplementary food information A is to supplement 17g of duck blood (5mg of iron); the food to be supplemented is clams (the iron content of clams is 4mg / 100g), and the corresponding first supplementary food information B is to supplement 125g of clams (5mg of iron); the food to be supplemented is black fungus (the iron content of black fungus is 7mg / 100g), and the corresponding first supplementary food information C is to supplement 72g of black fungus (5mg of iron).

[0077] In the above embodiments, by parsing the first ingredient-related data (including the inventory data of each ingredient and the theoretical content of the first element of its nutritional elements), the theoretical content of the second element of various nutritional elements in the preset storage device (total inventory of nutrients) is accurately calculated; and by combining preset health data and real-time physiological data, the second element to be ingested and the second amount to be ingested that the target object needs to supplement in the second preset time period are dynamically determined. Finally, based on the nutritional element gap, at least one ingredient containing the required element is identified, and the first supplementary ingredient information is generated and fed back to the user, so as to achieve accurate positioning of nutritional gap and targeted guidance of ingredient supplementation, and ensure that nutritional supply and demand are matched in real time.

[0078] In a specific embodiment, such as Figure 7 As shown, the above method also includes: In step S701, historical food processing information is obtained.

[0079] In a specific embodiment, the detailed process of obtaining historical food processing information can be found in step S301, which will not be repeated here.

[0080] In step S703, historical food processing information is input into a preset preference analysis network to analyze the processing preferences of the target object and obtain the food processing preference information of the target object.

[0081] In step S705, based on the food processing preference information, the second supplementary food information for the target supplementary food is determined.

[0082] In one specific embodiment, the at least one ingredient to be supplemented includes the target supplement ingredient.

[0083] For example, if the food processing preference information based on the analysis of historical food processing information indicates that the target object likes seafood, then the target supplementary food is determined to be clams from the above supplementary food A (duck blood), supplementary food B (clams), and supplementary food C (black fungus).

[0084] In step S707, the target address of the target object and the supplementary ingredient association information of the target supplementary ingredient in at least one preset ingredient provider are obtained.

[0085] In one specific embodiment, the aforementioned supplementary ingredient association information may include at least one of the following: price information, inventory information, and distance between each preset ingredient provider and the target address of the target supplementary ingredient.

[0086] In step S709, based on the second supplementary ingredient information, the target address, and the supplementary ingredient association information, the target provider is determined, and the second supplementary ingredient information and the target address are fed back to the target provider so that the target provider can provide the target supplementary ingredient to the target address.

[0087] In one specific embodiment, the aforementioned at least one preset ingredient provider includes the target provider.

[0088] In a specific embodiment, the process of determining the target provider based on the second supplementary ingredient information, the target address, and the supplementary ingredient association information, and then feeding back the second supplementary ingredient information and the target address to the target provider so that the target provider delivers the target supplementary ingredient to the target address, may include: inputting the second supplementary ingredient information, the target address, and the supplementary ingredient association information into a preset provider analysis network, performing triple filtering, service range verification, inventory status matching and price sorting, and fulfillment efficiency sorting to lock in the optimal target provider; and then feeding back the second supplementary ingredient information and the target address to the target provider so that the target provider delivers the target supplementary ingredient to the target address.

[0089] In practical applications, the target group can activate the automatic procurement function in advance.

[0090] In the above embodiments, the system automatically learns the user's historical food processing information through a preset preference analysis network, intelligently generates food processing preference information that conforms to the user's operating habits, and determines the target supplementary food from multiple supplementary options and outputs executable second supplementary food information. Combined with the target address, the system is linked in real time with the preset food provider database, automatically selects the optimal target provider, and pushes the second supplementary food information and address. The user can receive direct delivery without any operation, saving the user's decision-making time, freeing up the energy of purchasing decision-making, and improving the user experience.

[0091] Figure 8 This is a block diagram illustrating a food processing apparatus according to an exemplary embodiment. (Refer to...) Figure 8 The device includes: The first data acquisition module 810 is used to acquire the target object's preset health data, real-time physiological data, and the first ingredient association data corresponding to each of the various preset ingredients in the preset storage device, as well as the storage environment data of the various preset ingredients; The first intake data determination module 820 is used to determine, based on preset health data and real-time physiological data, at least one first element to be ingested and the first intake amount corresponding to the first element to be ingested for the target object within a first preset time period. The food processing information determination module 830 is used to determine at least one food processing information based on stored environment data, at least one first element to be ingested, first amount to be ingested, and first food-related data, and to feed back at least one food processing information to the target object. The processing module 840 is used to control a preset processing device to run according to a preset processing program corresponding to the first target processing information in response to a selection instruction for the first target processing information, wherein at least one ingredient processing information includes the first target processing information.

[0092] In an optional embodiment, the aforementioned first ingredient association data includes cost information, storage time, inventory data, real-time status data, multiple preset processing information, and theoretical content of the first element corresponding to each of the multiple preset nutritional elements contained in each of the multiple preset ingredients. The aforementioned ingredient processing information determination module 830 includes: The ingredient priority information determination unit is used to input the theoretical content of the first element, cost information and storage time into the preset priority analysis network, perform ingredient processing priority analysis on multiple preset ingredients, and obtain the ingredient priority information corresponding to each of the multiple preset ingredients. The actual content determination unit is used to input multiple preset processing information into the preset nutrition analysis network, analyze the nutrient retention of multiple preset nutrients under the preset processing methods corresponding to the multiple preset processing information, and obtain the actual content of multiple preset nutrients under the multiple preset processing methods of multiple preset ingredients. The freshness data determination unit is used to input storage time, storage environment data and real-time status data into a preset freshness analysis network to perform freshness analysis on a variety of preset ingredients and obtain freshness data corresponding to each of the preset ingredients. The ingredient processing information determination unit is used to input ingredient priority information, actual element content, freshness data, at least one first element to be ingested, first amount to be ingested, cost information, inventory data, and multiple preset processing information into a preset processing analysis network to perform processing information analysis and processing to obtain at least one ingredient processing information.

[0093] In an optional embodiment, the above-described apparatus further includes: The second data acquisition module is used to acquire the current time, gas detection data from the preset storage device, and processing equipment detection data from the preset processing device; The food safety index determination module is used to input the current time, gas detection data, processing equipment detection data and real-time physiological data into a preset safety analysis network. Based on the preset storage device weights corresponding to the gas detection data, the preset processing equipment weights corresponding to the processing equipment detection data and the preset real-time physiological weights corresponding to the real-time physiological data, the food safety is analyzed and the food safety index is determined. The safety issue resolution module is used to determine the type of safety issue based on at least one of gas detection data, processing equipment detection data, and real-time physiological data when the food safety index exceeds a preset safety threshold, and to resolve the food safety issue corresponding to the safety issue type.

[0094] In an optional embodiment, the above-described apparatus further includes: The third data acquisition module is used to acquire the second ingredient association data corresponding to each of the various preset ingredients, the gas detection data of the preset storage device, and the storage device usage data. The first spoilage analysis data determination module is used to input storage environment data, gas detection data, storage device usage data and preset spoilage index into the preset spoilage analysis network to analyze the degree of food spoilage and determine the first spoilage analysis data corresponding to each of the various preset food ingredients. The food to be cleaned determination module is used to send first spoilage analysis data to the target object so that the target object knows the remaining usable time. If the second spoilage analysis data corresponding to any of the multiple preset food ingredients is greater than the preset spoilage threshold, the preset food ingredient is determined as the food to be cleaned. The module also feeds back the food to be cleaned ingredient identifier and the second storage location of the food to be cleaned to the target object so that the target object can clean the food to be cleaned.

[0095] In an optional embodiment, when at least one ingredient processing information is multiple ingredient processing information, the above-mentioned device further includes: The first historical food processing information acquisition module is used to acquire historical food processing information; The first food processing preference information determination module is used to input historical food processing information into a preset preference analysis network, analyze the processing preferences of the target object, and obtain the food processing preference information of the target object. The aforementioned food processing information determination module 830 includes: A unit for determining multiple food processing information is used to determine multiple food processing information based on storage environment data, at least one first element to be ingested, the first amount to be ingested, and the first food association data. The second target processing information determination unit is used to sort multiple food processing information based on food processing preference information, obtain the sorted second target processing information, and feed back the second target processing information to the target object.

[0096] In an optional embodiment, the aforementioned first ingredient association data includes inventory data corresponding to various preset ingredients and the theoretical content of the first element corresponding to various preset nutritional elements contained in each of the various preset ingredients. The aforementioned device further includes: The module for determining the theoretical content of the second element is used to determine the theoretical content of the second element for each of the various preset nutrients in the preset storage device based on inventory data and the theoretical content of the first element. The second intake data determination module is used to determine, based on preset health data and real-time physiological data, at least one second element to be ingested and the second intake amount corresponding to the at least one second element to be ingested within a second preset time period for the target object. The first supplementary food information determination module is used to determine the first supplementary food information corresponding to at least one food to be supplemented based on multiple preset nutritional elements, the theoretical content of the second element, at least one second element to be ingested and the second amount to be ingested, and to feed back the first supplementary food information to the target object so that the target object can supplement the food.

[0097] In an optional embodiment, the above further includes: The second historical food processing information acquisition module is used to acquire historical food processing information. The second food processing preference information determination module is used to input historical food processing information into a preset preference analysis network, analyze the processing preferences of the target object, and obtain the food processing preference information of the target object. The second supplementary ingredient information determination module is used to determine the second supplementary ingredient information of the target supplementary ingredient based on ingredient processing preference information; The fourth data acquisition module is used to acquire the target address of the target object and the supplementary ingredient association information of the target supplementary ingredient from at least one preset ingredient provider; The procurement module is used to determine the target supplier based on the second supplementary ingredient information, the target address, and the supplementary ingredient association information, and to feed back the second supplementary ingredient information and the target address to the target supplier so that the target supplier can provide the target supplementary ingredient to the target address.

[0098] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0099] Figure 9 This is a block diagram illustrating an electronic device for food processing according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a food processing method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0100] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present disclosure and does not constitute a limitation on the electronic device to which the present disclosure is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. In an exemplary embodiment, an electronic device is also provided, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is used to execute the instructions to implement the food processing method as described in the embodiments of this disclosure.

[0101] In an exemplary embodiment, a computer-readable storage medium is also provided, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform the food processing method of the present disclosure embodiments.

[0102] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the food processing method of the present disclosure embodiments.

[0103] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0104] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0105] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for processing food ingredients, characterized in that, The method includes: Acquire the target object's preset health data, real-time physiological data, and first ingredient association data corresponding to each of the various preset ingredients in the preset storage device, as well as the storage environment data of the various preset ingredients; Based on the preset health data and the real-time physiological data, at least one first element to be ingested and the first amount to be ingested corresponding to the at least one first element to be ingested are determined for the target object in a first preset time period. Based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food ingredient association data, at least one food ingredient processing information is determined, and the at least one food ingredient processing information is fed back to the target object. In response to a selection instruction for the first target processing information, the preset processing equipment is controlled to run according to the preset processing program corresponding to the first target processing information, wherein the at least one ingredient processing information includes the first target processing information.

2. The method according to claim 1, characterized in that, The first ingredient association data includes cost information, storage time, inventory data, real-time status data, multiple preset processing information, and the theoretical content of the first element corresponding to each of the multiple preset nutritional elements contained in each of the multiple preset ingredients. Determining the processing information of at least one ingredient based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first ingredient association data includes: The theoretical content of the first element, the cost information, and the storage time are input into a preset priority analysis network to perform food processing priority analysis on the multiple preset ingredients, thereby obtaining the food priority information corresponding to each of the multiple preset ingredients. The various preset processing information is input into a preset nutrition analysis network to analyze the nutrient retention of the various preset ingredients under the preset processing methods corresponding to the various preset processing information, and to obtain the actual content of the various preset nutrients of the various preset ingredients under the various preset processing methods. The storage time, the storage environment data, and the real-time status data are input into a preset freshness analysis network to perform freshness analysis on the various preset ingredients, thereby obtaining freshness data corresponding to each of the various preset ingredients. The food ingredient priority information, the actual content of the elements, the freshness data, the at least one first element to be ingested, the first amount to be ingested, the cost information, the inventory data, and the multiple preset processing information are input into a preset processing analysis network to perform processing information analysis and processing to obtain the processing information of the at least one food ingredient.

3. The method according to claim 1, characterized in that, The method further includes: Acquire the current time, the gas detection data from the preset storage device, and the processing equipment detection data from the preset processing device; The current time, the gas detection data, the processing equipment detection data, and the real-time physiological data are input into a preset safety analysis network. Based on the preset storage device weight corresponding to the gas detection data, the preset processing equipment weight corresponding to the processing equipment detection data, and the preset real-time physiological weight corresponding to the real-time physiological data, the food safety is analyzed to determine the food safety index. The preset storage device weight, the preset processing equipment weight, and the preset real-time physiological weight are adjusted based on the current time. If the food safety index is greater than a preset safety threshold, the type of safety problem is determined based on at least one of the gas detection data, the processing equipment detection data, and the real-time physiological data, and the food safety problem corresponding to the type of safety problem is resolved.

4. The method according to claim 1, characterized in that, The method further includes: Acquire the second food association data corresponding to each of the multiple preset food ingredients, the gas detection data of the preset storage device, and the storage device usage data. The second food association data includes the first storage location and preset spoilage index corresponding to each of the multiple preset food ingredients. The storage environment data, the gas detection data, the storage device usage data, and the preset spoilage index are input into a preset spoilage analysis network to analyze the degree of food spoilage and determine the first spoilage analysis data corresponding to each of the various preset food ingredients. The first spoilage analysis data is used to indicate the remaining usable time of each of the various preset food ingredients. The first spoilage analysis data is sent to the target object so that the target object knows the remaining usable time. If the second spoilage analysis data corresponding to any of the multiple preset ingredients is greater than the preset spoilage threshold, the preset ingredient is identified as an ingredient to be cleaned. The target object is then fed back the ingredient identifier to be cleaned and the second storage location of the ingredient to be cleaned so that the target object can clean the ingredient to be cleaned.

5. The method according to claim 1, characterized in that, When the at least one ingredient processing information is multiple ingredient processing information, the method further includes: Obtain historical food processing information; The historical food processing information is input into a preset preference analysis network to analyze the processing preferences of the target object and obtain the food processing preference information of the target object. The step of determining at least one food processing information based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food ingredient association data, and feeding back the at least one food processing information to the target object, includes: Based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food ingredient association data, the processing information of the multiple food ingredients is determined; Based on the food processing preference information, the various food processing information is sorted to obtain the sorted second target processing information, and the second target processing information is fed back to the target object.

6. The method according to claim 1, characterized in that, The first ingredient association data includes the inventory data corresponding to each of the multiple preset ingredients and the theoretical content of the first element corresponding to each of the multiple preset nutritional elements contained in each of the multiple preset ingredients. The method further includes: Based on the inventory data and the theoretical content of the first element, determine the theoretical content of the second element corresponding to each of the various preset nutritional elements in the preset storage device; Based on the preset health data and the real-time physiological data, determine at least one second element to be ingested and the corresponding second intake amount of the at least one second element to be ingested for the target object within a second preset time period. Based on the multiple preset nutritional elements, the theoretical content of the second element, the at least one second element to be ingested, and the amount of the second element to be ingested, the information of the first supplementary food corresponding to at least one food to be supplemented is determined, and the information of the first supplementary food is fed back to the target object so that the target object can supplement the food. The at least one food to be supplemented includes the at least one second element to be ingested.

7. The method according to claim 6, characterized in that, The method further includes: Obtain historical food processing information; The historical food processing information is input into a preset preference analysis network to analyze the processing preferences of the target object and obtain the food processing preference information of the target object. Based on the food processing preference information, second supplementary food information for the target supplementary food is determined, wherein the at least one supplementary food includes the target supplementary food; Obtain the target address of the target object and the supplementary ingredient association information of the target supplementary ingredient from at least one preset ingredient provider; Based on the second supplementary ingredient information, the target address, and the supplementary ingredient association information, a target provider is determined, and the second supplementary ingredient information and the target address are fed back to the target provider so that the target provider can provide the target supplementary ingredient to the target address. The at least one preset ingredient provider includes the target provider.

8. A food processing device, characterized in that, include: The first data acquisition module is used to acquire the target object's preset health data, real-time physiological data, and the first ingredient association data corresponding to each of the multiple preset ingredients in the preset storage device, as well as the storage environment data of the multiple preset ingredients; The first intake data determination module is used to determine, based on the preset health data and the real-time physiological data, at least one first element to be ingested by the target object within a first preset time period and the first amount to be ingested corresponding to the at least one first element to be ingested. The food processing information determination module is used to determine at least one food processing information based on the storage environment data, the at least one first element to be ingested, the first amount to be ingested, and the first food-related data, and to feed back the at least one food processing information to the target object. A processing module is used to control a preset processing device to run according to a preset processing program corresponding to the first target processing information in response to a selection instruction for the first target processing information, wherein the at least one ingredient processing information includes the first target processing information.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the food processing method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the food processing method as described in any one of claims 1 to 7.

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