Method and device for determining freshness of food material, and electronic equipment

Abstract

This disclosure relates to a method, apparatus, and electronic device for determining the freshness of food ingredients. The method includes: determining target environmental indicators associated with at least one target food ingredient within a target storage device; determining a target sampling frequency corresponding to the target environmental indicators based on first preset mapping information and the target environmental indicators; collecting target environmental indicator data corresponding to the target environmental indicators within the storage space of the at least one target food ingredient within the target storage device based on the target sampling frequency; inputting the target environmental indicator data into a first preset analysis model to perform food freshness analysis, obtaining freshness grade indicator data corresponding to at least one target food ingredient; and inputting the freshness grade indicator data into a second preset analysis model to perform food freshness indicator data analysis, obtaining target freshness indicator data for at least one target food ingredient, wherein the target freshness indicator data characterizes the freshness of at least one target food ingredient. Utilizing embodiments of this disclosure can improve the accuracy of food freshness analysis.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus and electronic device for determining the freshness of food ingredients. Background Technology

[0002] In modern family life, the refrigerator, as the core appliance for food storage, directly affects food safety and nutrient retention. People typically judge freshness by observing the appearance, smelling the food, or checking the purchase date. However, these methods are subjective, inaccurate, and fail to accurately reflect the actual state of the food. Current technology includes some smart refrigerators that attempt to indirectly infer food freshness by monitoring the internal temperature with temperature sensors. However, temperature is only one factor influencing food spoilage; humidity and the food's own respiration also accelerate spoilage, making a single temperature reading insufficient for a comprehensive assessment. Furthermore, while some refrigerators are equipped with cameras that can capture images of food, they lack the ability to deeply analyze the image information, making it difficult to identify subtle signs of spoilage. These limitations of traditional methods and existing technologies make it difficult for users to accurately and in real-time monitor the freshness of food inside the refrigerator, leading to inaccurate freshness analysis and consequently affecting users' informed decisions regarding food storage and consumption. Summary of the Invention This disclosure provides a method, apparatus, and electronic device for determining the freshness of food ingredients, in order to at least solve the problems of inaccurate analysis of food ingredient freshness in related technologies.

[0003] According to a first aspect of the present disclosure, a method for determining the freshness of food ingredients is provided, comprising: Identify at least one target environmental indicator associated with a target food ingredient within the target storage device; Based on the first preset mapping information and the target environmental index, the target sampling frequency corresponding to the target environmental index is determined. The first preset mapping information represents the correspondence between the first preset environmental index of a variety of preset ingredients and the variety of preset sampling frequencies. Based on the target acquisition frequency, target environmental indicator data corresponding to the target environmental indicator are collected in the storage space where the at least one target ingredient is located within the target storage device; The target environmental index data is input into the first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to the at least one target food. The freshness grade index data represents the probability value of the at least one target food under multiple preset freshness grades. The freshness grade index data is input into the second preset analysis model to perform food freshness index data analysis, thereby obtaining target freshness index data for the at least one target food ingredient. The target freshness index data characterizes the freshness of the at least one target food ingredient.

[0004] According to a second aspect of the present disclosure, an apparatus for determining the freshness of food ingredients is provided, comprising: The target environmental indicator determination module is used to determine at least one target environmental indicator associated with a target food item within the target storage device. The target acquisition frequency determination module is used to determine the target acquisition frequency corresponding to the target environmental index based on the first preset mapping information and the target environmental index. The first preset mapping information represents the correspondence between the first preset environmental index of a variety of preset ingredients and the variety of preset acquisition frequencies. The target environmental indicator data acquisition module is used to acquire target environmental indicator data corresponding to the target environmental indicator in the storage space where the at least one target ingredient is located in the target storage device, based on the target acquisition frequency. The freshness grade index data determination module is used to input the target environmental index data into a first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to the at least one target food. The freshness grade index data represents the probability value of the at least one target food under multiple preset freshness grades. The target freshness index data determination module is used to input the freshness grade index data into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data for the at least one target food ingredient. The target freshness index data represents the freshness of the at least one target food ingredient.

[0005] 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.

[0006] 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.

[0007] The technical solutions provided by the embodiments of this disclosure bring at least the following beneficial effects: By identifying target environmental indicators associated with target ingredients within the target storage device and adaptively determining the optimal target acquisition frequency based on a first preset mapping information, the data acquisition process is optimized, significantly improving monitoring efficiency and reducing resource consumption. Subsequently, target environmental indicator data within the storage space where the target ingredients are located are acquired based on the target acquisition frequency, ensuring the accuracy and timeliness of data acquisition. After inputting the target environmental indicator data into the first preset analysis model, freshness grade indicator data representing the probability values ​​corresponding to at least one target ingredient under multiple preset freshness levels are obtained, providing a multi-dimensional probability distribution and greatly enhancing the comprehensiveness, reliability, and objectivity of freshness assessment. Further inputting the freshness grade indicator data into the second preset analysis model yields target freshness indicator data that efficiently represents the actual freshness of the ingredients, effectively improving the accuracy and reliability of ingredient freshness analysis.

[0008] 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

[0009] 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.

[0010] Figure 1 This is a flowchart illustrating a method for determining the freshness of food ingredients according to an exemplary embodiment; Figure 2 This is a flowchart illustrating a process for determining the target sampling frequency corresponding to a target environmental indicator based on the first preset mapping information and the target environmental indicator when the first preset mapping information does not record the preset sampling frequency corresponding to at least one target food ingredient associated with the target environmental indicator. Figure 3 This is a schematic diagram illustrating a process for obtaining target freshness index data according to an exemplary embodiment; Figure 4 This is a schematic diagram illustrating a process for determining a third environmental indicator according to an exemplary embodiment; Figure 5 This is a schematic diagram illustrating a process for determining a third target frequency corresponding to a third environmental indicator, according to an exemplary embodiment. Figure 6 This is a block diagram of a food freshness determination device according to an exemplary embodiment; Figure 7 This is a block diagram illustrating an electronic device for determining the freshness of food ingredients according to an exemplary embodiment. Detailed Implementation

[0011] 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.

[0012] 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.

[0013] 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.

[0014] Figure 1 This is a flowchart illustrating a method for determining the freshness of food ingredients according to an exemplary embodiment, such as... Figure 1 As shown, this method for determining the freshness of ingredients is used in electronic devices such as servers and includes the following steps.

[0015] In step S101, at least one target environmental indicator associated with the target food ingredient within the target storage device is determined.

[0016] In one specific embodiment, when at least one target ingredient is at least two target ingredients, and the at least two target ingredients include a first target ingredient and a second target ingredient, the target environmental indicators include a first environmental indicator associated with the first target ingredient and a second environmental indicator associated with the second target ingredient; the above determination of the target environmental indicators associated with at least one target ingredient in the target storage device includes: Based on the first target ingredient, the second target ingredient, and the third preset mapping information, the first environmental indicator and the second environmental indicator are determined.

[0017] In one specific embodiment, the third preset mapping information records the first preset environmental indicators corresponding to each of the various preset ingredients. The target environmental indicators include target temperature and target humidity data within the storage space where the target ingredient is located, as well as gas concentration data associated with the target ingredient.

[0018] For example, when the preset ingredient is banana, the corresponding first preset environmental indicator includes temperature and humidity, as well as at least one type of information such as ethylene concentration, oxygen concentration, and carbon dioxide concentration; when the preset ingredient is spinach, the corresponding first preset environmental indicator includes temperature and humidity, as well as at least one type of information such as carbon dioxide concentration, oxygen concentration, and ammonia concentration; when the preset ingredient is beef, the corresponding first preset environmental indicator includes temperature and humidity, as well as at least one type of information such as carbon dioxide concentration, oxygen concentration, hydrogen sulfide concentration, and carbon monoxide concentration. In the above embodiments, when there are at least two target ingredients, including a first target ingredient and a second target ingredient, the first environmental indicator and the second environmental indicator are determined based on the first target ingredient, the second target ingredient, and the third preset mapping information. This can accurately match the corresponding environmental indicators for the characteristics of the two target ingredients respectively. The established correspondence of the third preset mapping information ensures the standardization and basis of the determination process, effectively adapts to the needs of the two target ingredients, avoids confusion between the environmental indicators of different target ingredients, and thus improves the adaptability and effectiveness of determining the target environmental indicators associated with multiple target ingredients.

[0019] In a specific embodiment, when the third preset mapping information does not record at least one target environmental indicator associated with the target food ingredient, the above-mentioned determination of at least one target environmental indicator associated with the target food ingredient in the target storage device includes: Acquire target images of at least one target food ingredient; Food type identification is performed on the target image to determine the target food type of at least one target food. Based on the target ingredient type, a reference ingredient is determined from a variety of preset ingredients recorded in the third preset mapping information. Based on reference ingredients and third-preset mapping information, target environmental indicators are determined.

[0020] In one specific embodiment, the target image can be captured by a camera, which can be located in the storage space containing at least one target ingredient. The reference ingredient is an ingredient of the same type as the at least one target ingredient.

[0021] In a specific embodiment, the above-mentioned food type identification of the target image and determination of the target food type of at least one target food may include: extracting food features and performing type matching processing on the target image based on a preset food type identification model to determine the target food type of at least one target food.

[0022] In a specific embodiment, the aforementioned preset food type recognition model can be a deep learning network for recognizing food types in target images, such as convolutional neural networks and recurrent neural networks.

[0023] In one optional embodiment, the aforementioned preset food type recognition model can be trained based on sample images and corresponding preset food types. In a specific embodiment, the aforementioned sample images can be images of the food type to be recognized.

[0024] In a specific embodiment, determining a reference ingredient from a variety of preset ingredients recorded in the third preset mapping information based on the target ingredient type may include: retrieving a variety of preset ingredients recorded in the third preset mapping information based on the target ingredient type, locating a preset ingredient of the same type as the target ingredient through type matching, and determining it as a reference ingredient; if there are at least two reference ingredients, one may be selected randomly.

[0025] In a specific embodiment, the above-mentioned determination of the target environmental indicator based on the reference ingredient and the third preset mapping information may include: determining the environmental indicator associated with the reference ingredient based on the reference ingredient and the third preset mapping information, and using the environmental indicator associated with the reference ingredient as the target environmental indicator associated with the target ingredient.

[0026] In step S103, the target acquisition frequency corresponding to the target environmental index is determined based on the first preset mapping information and the target environmental index.

[0027] In one specific embodiment, the first preset mapping information represents the correspondence between a first preset environmental index of a variety of preset ingredients and a variety of preset collection frequencies.

[0028] For example, when the preset ingredient is bananas, and the corresponding first preset environmental indicators include temperature, humidity, ethylene concentration, oxygen concentration, and carbon dioxide concentration, the first preset mapping information records that the preset acquisition frequency for temperature is 500ms / time (bananas are extremely sensitive to temperature fluctuations and require ultra-high frequency monitoring to prevent chilling injury or accelerate ripening), the preset acquisition frequency for humidity is 2s / time (maintaining humidity at 95% RH can prevent water loss and shrinkage, but the rate of change is lower than that of temperature, so the frequency should be appropriately reduced to balance the system load), the preset acquisition frequency for ethylene concentration is 200ms / time (ethylene is the core indicator of banana ripeness and requires high frequency to capture sudden changes in concentration), the acquisition frequency for oxygen concentration is 5s / time (oxygen participates in respiration, but the change is relatively slow, so the acquisition frequency should be reduced as needed), and the acquisition frequency for carbon dioxide concentration is 3s / time (carbon dioxide accumulation accelerates spoilage, so medium frequency monitoring is required to prevent the concentration from exceeding the limit).

[0029] In one specific embodiment, if the first preset mapping information does not record the preset collection frequency corresponding to at least one target food ingredient-related target environmental indicator, such as Figure 2 As shown, the target acquisition frequency corresponding to the target environmental indicator, determined based on the first preset mapping information and the target environmental indicator, includes: In step S201, a target image of at least one target food ingredient is acquired.

[0030] In one specific embodiment, the target image can be captured by a camera, and correspondingly, the camera can be set in the storage space where at least one target ingredient is located.

[0031] In step S203, the target image is subjected to food type recognition to determine the target food type of at least one target food.

[0032] In a specific embodiment, the above step S203, which identifies the type of food in the target image and determines the type of at least one target food, can be further detailed in the above-described step of identifying the type of food in the target image and determining the type of at least one target food, and will not be repeated here.

[0033] In step S205, a reference ingredient is determined from a variety of preset ingredients recorded in the first preset mapping information, based on the target ingredient type.

[0034] In one specific embodiment, the reference ingredient is an ingredient of the same type as at least one target ingredient.

[0035] In a specific embodiment, the detailed steps of step S205, which determines the reference ingredient from a variety of preset ingredients recorded in the first preset mapping information based on the target ingredient type, can be found in the above description of determining the reference ingredient from a variety of preset ingredients recorded in the third preset mapping information based on the target ingredient type, and will not be repeated here.

[0036] In step S207, the target sampling frequency is determined based on the reference ingredients and the first preset mapping information.

[0037] In a specific embodiment, determining the target collection frequency based on the reference ingredient and the first preset mapping information may include: determining the data collection frequency corresponding to the environmental indicator associated with the reference ingredient based on the reference ingredient and the first preset mapping information, and using the data collection frequency corresponding to the environmental indicator associated with the reference ingredient as the target collection frequency corresponding to the target environmental indicator associated with the target ingredient.

[0038] In the above embodiments, when the first preset mapping information does not record the preset collection frequency corresponding to the target environmental index associated with the target food, the target food type is accurately located by collecting the target image and identifying the food type. Then, based on the type, the reference food of the same type is intelligently matched from the preset mapping information. Finally, the historical frequency configuration corresponding to the reference food is reused to determine the target collection frequency, effectively addressing the problem of missing monitoring needs in unknown or newly added food scenarios. This comprehensively ensures the integrity and accuracy of food freshness analysis in multiple scenarios.

[0039] In step S105, based on the target acquisition frequency, target environmental indicator data corresponding to the target environmental indicator in the storage space where at least one target ingredient is located in the target storage device are acquired.

[0040] In practical applications, the target temperature and humidity data in the target environmental index data can be collected by temperature sensors and humidity sensors, respectively, while the gas concentration data can be collected by an odor sensor array. Specifically, the odor sensor array, temperature sensor, and humidity sensor can be installed in the storage space where at least one target food ingredient is located.

[0041] In step S107, the target environmental index data is input into the first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to at least one target food.

[0042] In one specific embodiment, the freshness grade index data represents the probability value of at least one target ingredient under multiple preset freshness grades.

[0043] For example, the freshness grade index data can be a freshness probability vector of the target ingredient. Specifically, the freshness probability vector can be a probability vector of length 5, where each element in the freshness probability vector represents the probability that the ingredient is in one of five preset freshness grades (for example, fresh, relatively fresh, fairly fresh, not very fresh, spoiled), and the sum of the probability values ​​of all elements is always 1.

[0044] For example, the freshness grade index data of a certain ingredient can be [0.50, 0.30, 0.15, 0.05, 0.00], which means that the probability of the ingredient being fresh is 0.50, the probability of being relatively fresh is 0.3, the probability of being moderately fresh is 0.15, the probability of being not very fresh is 0.05, and the probability of being spoiled is 0.

[0045] In a specific embodiment, the first preset analysis model can be a deep learning network, such as a convolutional neural network and a recurrent neural network, that analyzes the freshness of food ingredients based on target environmental indicator data.

[0046] In an optional embodiment, the first preset analysis model described above can be trained based on the sample environmental index data of the sample ingredients and the corresponding preset freshness grade index data.

[0047] In step S109, the freshness grade index data is input into the second preset analysis model to perform freshness index data analysis of the ingredients, and to obtain the target freshness index data of at least one target ingredient.

[0048] In one specific embodiment, the target freshness index data characterizes the freshness of at least one target ingredient, and the target freshness index data is proportional to the freshness of the ingredient.

[0049] In a specific embodiment, the aforementioned second preset analysis model can be a deep learning network, such as a convolutional neural network or a recurrent neural network, that analyzes the freshness index data of food ingredients based on the freshness grade index data.

[0050] In an optional embodiment, the second preset analysis model described above can be trained based on the sample freshness grade index data of the sample ingredients and the corresponding preset freshness index data.

[0051] In one specific embodiment, the above method further includes: extracting features from the target environmental indicator data to obtain the target environmental features corresponding to the target environmental indicator data.

[0052] In a specific embodiment, the above-mentioned feature extraction of target environmental indicator data to obtain target environmental features corresponding to the target environmental indicator data may include: inputting the target environmental indicator data into a preset feature extraction model, performing feature extraction processing, and obtaining target environmental features corresponding to the target environmental indicator data.

[0053] In a specific embodiment, the aforementioned preset feature extraction model can be a deep learning network for extracting features from target environmental indicator data, such as a convolutional neural network and a recurrent neural network.

[0054] In an optional embodiment, the aforementioned preset feature extraction model can be trained based on sample environmental index data and corresponding preset environmental features.

[0055] In a specific embodiment, the above-mentioned inputting target environmental index data into a first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to at least one target food includes: inputting target environmental characteristics into a first preset analysis model to perform food freshness analysis and obtain freshness grade index data. In a specific embodiment, the above-mentioned inputting freshness grade index data into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data for at least one target food ingredient includes: inputting freshness grade index data and target environmental characteristics into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data.

[0056] In one specific embodiment, the target environmental indicator data includes target temperature indicator data and target humidity indicator data, such as... Figure 3 As shown, the above method also includes: In step S301, based on the freshness grade index data and the preset weights corresponding to multiple preset freshness grades, the basic freshness index data of at least one target ingredient is determined.

[0057] In a specific embodiment, the above-mentioned determination of the basic freshness index data of at least one target ingredient based on freshness grade index data and the preset weights corresponding to multiple preset freshness grades may include: weighting and summing the freshness grade index data based on the preset weights corresponding to multiple preset freshness grades to determine the basic freshness index data of at least one target ingredient.

[0058] In step S303, based on the target temperature index data, the target humidity index data, and the second preset mapping information, the target correction index data for the basic freshness index data is determined.

[0059] In one specific embodiment, the target correction index data characterizes the degree of influence of the target temperature index data and the target humidity index data on the basic freshness index data, and the second preset mapping information characterizes the preset correction index data corresponding to the preset environmental data differences of various preset ingredients. The preset environmental data differences include the difference between the target temperature index data and the preset temperature index data of at least one target ingredient, and the difference between the target humidity index data and the preset humidity index data of at least one target ingredient.

[0060] For example, in the second preset mapping information, for the case where the preset ingredient is spinach, the preset temperature index data can be 0℃, and the preset humidity index data can be 95%. The correspondence between the temperature difference and the preset correction index data is as follows: when the difference between the target temperature index data and the preset temperature index data is in the range of -1.5℃ to -0.5℃, the corresponding preset correction index data is +2, indicating that the temperature condition has a slight positive impact on the basic freshness index data of spinach; when the temperature difference is in the range of +0.5℃ to +1.5℃, the preset correction index data is -2, indicating that the temperature deviation has a slight negative impact; when the temperature difference is in the range of +2.5℃ to +3.5℃, the preset correction index data is -8, indicating that a larger temperature deviation has a more significant negative impact. The correspondence between humidity difference and preset correction index data is as follows: When the difference between the target humidity index data and the preset humidity index data is in the range of +2.5% to +3.5%, the corresponding preset correction index data is +1, indicating that the humidity condition has a slight positive impact; when the humidity difference is in the range of -2.5% to -1.5%, the preset correction index data is -2, indicating that the humidity deviation has a slight negative impact; when the humidity difference is in the range of -5.5% to -4.5%, the preset correction index data is -5, indicating that a larger humidity deviation has a more significant negative impact.

[0061] In step S305, based on the basic freshness index data and the target correction index data, the initial freshness index data of at least one target ingredient is determined.

[0062] In one specific embodiment, determining the initial freshness index data of at least one target ingredient based on the basic freshness index data and the target correction index data may include: using the sum of the basic freshness index data and the target correction index data as the initial freshness index data of at least one target ingredient.

[0063] In one specific embodiment, the above-mentioned inputting the freshness grade index data into the second preset analysis model to perform food freshness index data analysis and obtain target freshness index data for at least one target food ingredient includes: In step S307, the initial freshness index data is input into the second preset analysis model to perform food freshness index data analysis and obtain the target freshness index data.

[0064] In the above embodiments, when the target environmental index data includes target temperature index data and target humidity index data, basic freshness index data of at least one target ingredient is determined based on freshness grade index data and preset weights corresponding to multiple preset freshness grades. Then, a second preset mapping information is used to determine target correction index data representing the degree of influence of temperature and humidity on the basic freshness index data, combining the target temperature index data, target humidity index data, and preset environmental data differences representing multiple preset ingredients. Finally, initial freshness index data is determined based on the basic freshness index data and the target correction index data, and input into a second preset analysis model to obtain the target freshness index data. This approach leverages preset weights to make the determination of basic freshness index data more closely reflect the influence of each freshness grade. The target correction index data effectively incorporates the actual impact of temperature and humidity on freshness to compensate for the limitations of the basic data. The initial freshness index data integrates basic and correction information to improve its comprehensiveness. Finally, the results are further optimized through analysis by the second preset analysis model, thereby improving the accuracy and reliability of the target freshness index data and more accurately reflecting the freshness of the target ingredient.

[0065] In a specific embodiment, the above-mentioned inputting freshness grade index data and target environmental characteristics into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data includes: inputting initial freshness index data and target environmental characteristics into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data.

[0066] In one specific embodiment, when the first target ingredient and the second target ingredient are stored in the same storage space, and the first target ingredient is an ingredient that releases a preset volatile substance, and the preset volatile substance is a volatile substance that affects the freshness of the second target ingredient, the target environmental indicator further includes a third environmental indicator; the aforementioned determination of the target environmental indicator associated with at least one target ingredient in the target storage device further includes: Based on the first target ingredient, the second target ingredient, and the fourth preset mapping information, the third environmental indicator is determined.

[0067] In one specific embodiment, the fourth preset mapping information records the second preset environmental index corresponding to each of the various preset food combinations that have volatile interactions.

[0068] For example, when onions and potatoes are stored together, the hydrogen sulfide and propylthiol released by the onions will cause the potatoes to sprout and spoil. That is, the second preset environmental indicators corresponding to the preset food combination of onions and potatoes, which involves volatile interactions, recorded in the fourth preset mapping information include hydrogen sulfide gas concentration information and propylthiol gas concentration information.

[0069] In the above embodiments, when the first target ingredient and the second target ingredient are stored in the same storage space, and the first target ingredient is an ingredient that releases preset volatile substances that affect the freshness of the second target ingredient, a third environmental indicator is determined based on the fourth preset mapping information corresponding to the second preset environmental indicator of the first target ingredient, the second target ingredient, and multiple preset ingredient combinations that record the interaction of volatile substances. Incorporating the third environmental indicator into the target environmental indicator can specifically capture the special environmental impact caused by the interaction of volatile substances between the two target ingredients, effectively making up for the neglect of the interaction between ingredients when only considering the environmental indicator associated with a single ingredient, accurately adapting to storage scenarios with the effect of volatile substances, ensuring that the target environmental indicator fully covers the environmental factors brought about by the characteristics of the ingredients themselves and their interactions, thereby improving the comprehensiveness of the determination of the target environmental indicator.

[0070] In a specific embodiment, such as Figure 4 As shown, the above method also includes: In step S401, based on the first preset temperature data corresponding to the first target ingredient, the first preset humidity data corresponding to the first target ingredient, the second preset temperature data corresponding to the second target ingredient, and the second preset humidity data corresponding to the second target ingredient, the environmental requirement difference information of the first target ingredient and the second target ingredient is determined.

[0071] In one specific embodiment, the environmental requirement difference information characterizes the degree of conflict between the temperature and humidity requirements of the first target food ingredient and the second target food ingredient under ideal storage conditions. Specifically, the environmental requirement difference information may include temperature requirement difference information and humidity requirement difference information.

[0072] In a specific embodiment, determining the environmental requirement difference information of the first target ingredient and the second target ingredient based on the first preset temperature data corresponding to the first target ingredient, the first preset humidity data corresponding to the first target ingredient, the second preset temperature data corresponding to the second target ingredient, and the second preset humidity data corresponding to the second target ingredient may include: determining the temperature overlap range width and the total temperature span based on the first preset temperature data and the second preset temperature data, and then determining the temperature requirement difference information based on the formula: Temperature requirement difference information = 1 - (overlapping temperature width / total temperature span); determining the humidity overlap range width and the total humidity span based on the first preset humidity data and the second preset humidity data, and then determining the humidity requirement difference information based on the formula: Humidity requirement difference information = 1 - (overlapping humidity width / total humidity span).

[0073] In step S403, the impact analysis data is determined based on the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental requirement difference information.

[0074] In a specific embodiment, the preset volatility intensity characterizes the performance of the first target food ingredient in releasing preset volatile substances, the preset sensitivity coefficient characterizes the sensitivity of the second target food ingredient to the preset volatile substances, and the influence analysis data characterizes the degree to which the preset volatile substances released by the first target food ingredient affect the freshness of the second target food ingredient when the first target food ingredient and the second target food ingredient are stored in the same storage space.

[0075] In a specific embodiment, determining the impact analysis data based on the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental demand difference information may include: weighting and summing the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental demand difference information based on the preset weights corresponding to the preset weights of the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental demand difference information, to determine the impact analysis data.

[0076] In a specific embodiment, the determination of the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information includes: In step S405, if the impact analysis data is greater than or equal to the preset impact threshold, the operation of determining the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information is performed.

[0077] In the above embodiments, the environmental requirement difference information between the two target ingredients is determined based on the first preset temperature data, the first preset humidity data, and the second preset temperature data and the second preset humidity data of the second target ingredient. Combined with the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental requirement difference information, the influence analysis data characterizing the degree of influence of the preset volatile substances released by the first target ingredient on the freshness of the second target ingredient is determined. When the influence analysis data is greater than or equal to a preset influence threshold, the operation of determining a third environmental indicator based on the first target ingredient, the second target ingredient, and a fourth preset mapping information is performed. This approach can capture the differences in storage environment requirements between the two target ingredients by leveraging the environmental requirement difference information. The characteristics of volatile substance release and sensitivity are quantified by the preset volatility intensity and preset sensitivity coefficient, making the determination of influence analysis data more closely reflect actual interaction situations. Furthermore, using the preset influence threshold as a judgment standard, the third environmental indicator is only included when the influence reaches a certain level, avoiding unnecessary indicator redundancy. The fourth preset mapping information ensures the basis for determining the third environmental indicator, thereby accurately adapting to storage scenarios with significant volatile substance influence, improving the effectiveness of the third environmental indicator determination, and ensuring that the target environmental indicators are comprehensive without excessive redundancy.

[0078] In one specific embodiment, the target acquisition frequency includes a first target frequency corresponding to a first environmental indicator, a second target frequency corresponding to a second environmental indicator, and a third target frequency corresponding to a third environmental indicator.

[0079] In a specific embodiment, determining the target acquisition frequency corresponding to the target environmental indicator based on the first preset mapping information and the target environmental indicator includes: Based on the first preset mapping information, the first environmental indicator, the second environmental indicator, the third environmental indicator, and the impact analysis data, the first target frequency, the second target frequency, and the third target frequency are determined.

[0080] In the above embodiments, by determining the corresponding first target frequency, second target frequency, and third target frequency based on the first preset mapping information and by integrating the characteristics and impact analysis data of the first environmental indicator, the second environmental indicator, and the third environmental indicator, the collection frequency of different environmental indicators can be adapted to their impact logic on the freshness of the target ingredients. The first and second environmental indicators focus on the basic environmental requirements of a single ingredient, while the third environmental indicator relates to the interaction of volatiles between ingredients. Combined with dynamic adjustment based on impact analysis data, the frequency differentiation configuration of multiple indicators and multiple dimensions enables precise allocation of collection resources, avoids resource waste caused by excessive collection of basic indicators, and ensures the timeliness of monitoring the third environmental indicator. This allows the target collection frequency to not only conform to the storage rules of a single ingredient but also adapt to the dynamic interaction scenarios between ingredients, thereby improving the accuracy and comprehensiveness of monitoring the freshness of the target ingredients from the collection dimension.

[0081] In a specific embodiment, such as Figure 5 As shown, the determination of the first target frequency, the second target frequency, and the third target frequency based on the first preset mapping information, the first environmental indicator, the second environmental indicator, the third environmental indicator, and the impact analysis data includes: In step S501, based on the first preset mapping information, the first environmental index, and the second environmental index, the first basic acquisition frequency corresponding to the first environmental index and the second basic acquisition frequency corresponding to the second environmental index are determined.

[0082] In step S503, the first target frequency is determined based on the first basic acquisition frequency, the first preset correction coefficient corresponding to the first target ingredient, the influence analysis data, and the preset maximum influence data.

[0083] In one specific embodiment, the first preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target food ingredient on the first target frequency.

[0084] In a specific embodiment, determining the first target frequency based on the first basic acquisition frequency, the first preset correction coefficient corresponding to the first target ingredient, the impact analysis data, and the preset maximum impact data may include: determining the first target frequency based on the formula: first target frequency = first basic acquisition frequency * (1 + first preset correction coefficient * (impact analysis data / preset maximum impact data)).

[0085] In step S505, the second target frequency is determined based on the second basic acquisition frequency, the second preset correction coefficient corresponding to the second target ingredient, the influence analysis data, and the preset maximum influence data.

[0086] In one specific embodiment, the second preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target food ingredient on the second target frequency.

[0087] In a specific embodiment, determining the second target frequency based on the second basic acquisition frequency, the second preset correction coefficient corresponding to the second target ingredient, the impact analysis data, and the preset maximum impact data may include: determining the second target frequency based on the formula: second target frequency = second basic acquisition frequency * (1 + second preset correction coefficient * (impact analysis data / preset maximum impact data)).

[0088] In step S507, the third target frequency corresponding to the third environmental indicator is determined based on the first basic acquisition frequency, the second basic acquisition frequency, the preset enhancement coefficient corresponding to the third environmental indicator, the impact analysis data, the preset maximum impact data, and the preset impact threshold.

[0089] In one specific embodiment, the preset enhancement coefficient characterizes the degree of influence of the difference between the influence analysis data and the preset influence threshold on the third target frequency.

[0090] In a specific embodiment, determining the third target frequency corresponding to the third environmental indicator based on the first basic acquisition frequency, the second basic acquisition frequency, the preset enhancement coefficient corresponding to the third environmental indicator, the impact analysis data, the preset maximum impact data, and the preset impact threshold may include: determining the third target frequency corresponding to the third environmental indicator based on the formula: Third target frequency = max(first basic acquisition frequency, second basic acquisition frequency) * (1 + preset enhancement coefficient * max(0, (impact analysis data - preset impact threshold) / preset maximum impact data)).

[0091] In the above embodiments, a baseline logic for environmental indicator monitoring is constructed by first determining the first and second basic acquisition frequencies based on the first preset mapping information, the first environmental indicator, and the second environmental indicator. Then, a first preset correction coefficient (characterizing the influence of volatile substances on the first target frequency) and a second preset correction coefficient (characterizing the influence of volatile substances on the second target frequency) are introduced. Combined with the influence analysis data and the preset maximum influence data, the first and second target frequencies are dynamically adjusted to adapt the acquisition frequencies of the original environmental indicators to the interference caused by the interaction of volatile substances. Finally, for the third environmental indicator, based on the first and second basic acquisition frequencies, the frequency enhancement configuration of the third environmental indicator is realized through the preset enhancement coefficient, influence analysis data, preset maximum influence data, and preset influence threshold. This ensures the rationality of the basic environmental indicator monitoring and reflects the effect of volatile substances on the original indicators through the correction coefficient. The first, second, and third target frequencies not only conform to the characteristics of their respective environmental indicators but also collaboratively reflect the interaction influence of volatile substances between food ingredients. This achieves accurate and dynamic adaptation of the acquisition frequency between single characteristics and interactive scenarios, avoiding resource redundancy or monitoring gaps.

[0092] In one specific embodiment, the target environmental indicator data includes first environmental indicator data corresponding to a first environmental indicator, second environmental indicator data corresponding to a second environmental indicator, and third environmental indicator data corresponding to a third environmental indicator; the above-mentioned collection of target environmental indicator data corresponding to at least one target food ingredient in the storage space of the target storage device based on the target collection frequency includes: Based on the first target frequency, first environmental index data are collected from the storage space where the first target ingredient and the second target ingredient are located in the target storage device.

[0093] Based on the second target frequency, second environmental index data are collected from the storage space where the first target ingredient and the second target ingredient are located in the target storage device.

[0094] Based on the third target frequency, the third environmental index data of the storage space where the first target ingredient and the second target ingredient are located in the target storage device are collected.

[0095] Figure 6 This is a block diagram illustrating an apparatus for determining the freshness of food ingredients according to an exemplary embodiment. (Refer to...) Figure 6 The device includes: The target environmental indicator determination module 610 is used to determine at least one target environmental indicator associated with a target food ingredient within the target storage device. The target acquisition frequency determination module 620 is used to determine the target acquisition frequency corresponding to the target environmental index based on the first preset mapping information and the target environmental index. The first preset mapping information represents the correspondence between the first preset environmental index of a variety of preset ingredients and the variety of preset acquisition frequencies. The target environmental indicator data acquisition module 630 is used to acquire target environmental indicator data corresponding to the target environmental indicator in the storage space where at least one target ingredient is located in the target storage device, based on the target acquisition frequency. The freshness grade index data determination module 640 is used to input the target environmental index data into the first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to at least one target food. The freshness grade index data represents the probability value of at least one target food under multiple preset freshness grades. The target freshness index data determination module 650 is used to input freshness grade index data into a second preset analysis model to analyze the freshness index data of ingredients and obtain target freshness index data of at least one target ingredient. The target freshness index data represents the freshness of at least one target ingredient.

[0096] In an optional embodiment, the target environmental index data includes target temperature index data and target humidity index data, and the above-mentioned device further includes: The basic freshness index data determination module is used to determine the basic freshness index data of at least one target ingredient based on the freshness grade index data and the preset weights corresponding to multiple preset freshness grades. The target correction index data determination module is used to determine the target correction index data for the basic freshness index data based on the target temperature index data, the target humidity index data, and the second preset mapping information. The target correction index data characterizes the degree of influence of the target temperature index data and the target humidity index data on the basic freshness index data. The second preset mapping information characterizes the preset correction index data corresponding to the preset environmental data differences of various preset ingredients. The preset environmental data differences include the difference between the target temperature index data and the preset temperature index data of at least one target ingredient, and the difference between the target humidity index data and the preset humidity index data of at least one target ingredient. The initial freshness index data determination module is used to determine the initial freshness index data of at least one target ingredient based on the basic freshness index data and the target correction index data. The above-mentioned target freshness indicator data determination module 650 includes: The target freshness index data determination unit is used to input the initial freshness index data into the second preset analysis model, perform food freshness index data analysis, and obtain the target freshness index data.

[0097] In an optional embodiment, when at least one target ingredient is at least two target ingredients, and the at least two target ingredients include a first target ingredient and a second target ingredient, the target environmental indicators include a first environmental indicator associated with the first target ingredient and a second environmental indicator associated with the second target ingredient; the target environmental indicator determination module 610 includes: The first environmental indicator determination unit is used to determine the first environmental indicator and the second environmental indicator based on the first target ingredient, the second target ingredient, and the third preset mapping information. The third preset mapping information records the first preset environmental indicator corresponding to each of the various preset ingredients.

[0098] In an optional embodiment, where the first target ingredient and the second target ingredient are stored in the same storage space, and the first target ingredient is an ingredient that releases a preset volatile substance, and the preset volatile substance is a volatile substance that affects the freshness of the second target ingredient, the target environmental indicator further includes a third environmental indicator; the aforementioned target environmental indicator determination module 610 further includes: The second environmental indicator determination unit is used to determine the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information. The fourth preset mapping information records the second preset environmental indicator corresponding to each of the various preset ingredient combinations that have volatile interaction.

[0099] In an optional embodiment, the above-described apparatus further includes: The environmental demand difference information determination module is used to determine the environmental demand difference information between the first target ingredient and the second target ingredient based on the first preset temperature data corresponding to the first target ingredient, the first preset humidity data corresponding to the first target ingredient, the second preset temperature data corresponding to the second target ingredient, and the second preset humidity data corresponding to the second target ingredient. The impact analysis data determination module is used to determine impact analysis data based on the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and environmental demand difference information. The preset volatility intensity characterizes the performance of the first target ingredient in releasing preset volatile substances, the preset sensitivity coefficient characterizes the sensitivity of the second target ingredient to the preset volatile substances, and the impact analysis data characterizes the degree to which the preset volatile substances released by the first target ingredient affect the freshness of the second target ingredient when the first and second target ingredients are stored in the same storage space. The second determination unit for the aforementioned environmental indicators includes: The second environmental indicator determination subunit is used to perform the operation of determining the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information when the impact analysis data is greater than or equal to the preset impact threshold.

[0100] In an optional embodiment, the target acquisition frequency includes a first target frequency corresponding to a first environmental indicator, a second target frequency corresponding to a second environmental indicator, and a third target frequency corresponding to a third environmental indicator; the target acquisition frequency determination module 620 includes: The target acquisition frequency determination unit is used to determine the first target frequency, the second target frequency, and the third target frequency based on the first preset mapping information, the first environmental indicator, the second environmental indicator, the third environmental indicator, and the impact analysis data.

[0101] In an optional embodiment, the target acquisition frequency determination unit includes: The basic acquisition frequency determination subunit is used to determine the first basic acquisition frequency corresponding to the first environmental indicator and the second basic acquisition frequency corresponding to the second environmental indicator based on the first preset mapping information, the first environmental indicator and the second environmental indicator. The first target frequency determination subunit is used to determine the first target frequency based on the first basic acquisition frequency, the first preset correction coefficient corresponding to the first target food ingredient, the influence analysis data, and the preset maximum influence data; the first preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target food ingredient on the first target frequency; The second target frequency determination subunit is used to determine the second target frequency based on the second basic acquisition frequency, the second preset correction coefficient corresponding to the second target food ingredient, the influence analysis data, and the preset maximum influence data; the second preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target food ingredient on the second target frequency; The third target frequency determination subunit is used to determine the third target frequency corresponding to the third environmental indicator based on the first basic acquisition frequency, the second basic acquisition frequency, the preset enhancement coefficient corresponding to the third environmental indicator, the impact analysis data, the preset maximum impact data, and the preset impact threshold. The preset enhancement coefficient characterizes the degree of influence of the difference between the impact analysis data and the preset impact threshold on the third target frequency.

[0102] In an optional embodiment, if the first preset mapping information does not record a preset collection frequency corresponding to at least one target food ingredient-related target environmental indicator, the target collection frequency determination module 620 includes: A target image acquisition unit is used to acquire a target image of at least one target food ingredient. The target ingredient type determination unit is used to identify the ingredient type of the target image and determine the target ingredient type of at least one target ingredient. The reference ingredient determination unit is used to determine a reference ingredient from a variety of preset ingredients recorded in the first preset mapping information based on the target ingredient type. The reference ingredient is an ingredient of the same type as at least one target ingredient. The target acquisition frequency determination unit is used to determine the target acquisition frequency based on the reference ingredients and the first preset mapping information.

[0103] 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.

[0104] Figure 7 This is a block diagram illustrating an electronic device for determining the freshness of food ingredients according to an exemplary embodiment. The electronic device may be a server, and its internal structure diagram may be as follows: Figure 7 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 method for determining the freshness of food ingredients. 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.

[0105] Those skilled in the art will understand that Figure 7 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 freshness determination method as described in the embodiments of this disclosure.

[0106] In an exemplary embodiment, a computer-readable storage medium is also provided, which, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the food freshness determination method in the embodiments of this disclosure.

[0107] In an exemplary embodiment, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the method for determining the freshness of ingredients in this disclosure.

[0108] 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.

[0109] 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.

[0110] 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 determining the freshness of food ingredients, characterized in that, The method includes: Identify at least one target environmental indicator associated with a target food ingredient within the target storage device; Based on the first preset mapping information and the target environmental index, the target sampling frequency corresponding to the target environmental index is determined. The first preset mapping information represents the correspondence between the first preset environmental index of a variety of preset ingredients and the variety of preset sampling frequencies. Based on the target acquisition frequency, target environmental indicator data corresponding to the target environmental indicator are collected in the storage space where the at least one target ingredient is located within the target storage device; The target environmental index data is input into the first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to the at least one target food. The freshness grade index data represents the probability value of the at least one target food under multiple preset freshness grades. The freshness grade index data is input into the second preset analysis model to perform food freshness index data analysis, thereby obtaining target freshness index data for the at least one target food ingredient. The target freshness index data characterizes the freshness of the at least one target food ingredient.

2. The method according to claim 1, characterized in that, The target environmental index data includes target temperature index data and target humidity index data, and the method further includes: Based on the freshness grade index data and the preset weights corresponding to the multiple preset freshness grades, the basic freshness index data of the at least one target ingredient is determined. Based on the target temperature index data, the target humidity index data, and the second preset mapping information, target correction index data is determined for the basic freshness index data. The target correction index data characterizes the degree of influence of the target temperature index data and the target humidity index data on the basic freshness index data. The second preset mapping information characterizes the preset correction index data corresponding to the preset environmental data differences of various preset ingredients. The preset environmental data differences include the difference between the target temperature index data and the preset temperature index data of at least one target ingredient, and the difference between the target humidity index data and the preset humidity index data of at least one target ingredient. Based on the basic freshness index data and the target correction index data, the initial freshness index data of the at least one target ingredient is determined; The step of inputting the freshness grade index data into a second preset analysis model to perform food freshness index data analysis and obtain the target freshness index data of the at least one target food ingredient includes: The initial freshness index data is input into the second preset analysis model to perform food freshness index data analysis and obtain the target freshness index data.

3. The method according to claim 1, characterized in that, When the at least one target ingredient is at least two target ingredients, and the at least two target ingredients include a first target ingredient and a second target ingredient, the target environmental indicator includes a first environmental indicator associated with the first target ingredient and a second environmental indicator associated with the second target ingredient. The target environmental indicators associated with at least one target food ingredient within the target storage device include: Based on the first target ingredient, the second target ingredient, and the third preset mapping information, the first environmental indicator and the second environmental indicator are determined, and the third preset mapping information records the first preset environmental indicator corresponding to each of the various preset ingredients.

4. The method according to claim 3, characterized in that, When the first target ingredient and the second target ingredient are stored in the same storage space, and the first target ingredient is an ingredient that releases a preset volatile substance, and the preset volatile substance is a volatile substance that affects the freshness of the second target ingredient, the target environmental indicator also includes a third environmental indicator. The determination of the target environmental indicators associated with at least one target food ingredient within the target storage device also includes: Based on the first target ingredient, the second target ingredient, and the fourth preset mapping information, the third environmental indicator is determined. The fourth preset mapping information records the second preset environmental indicator corresponding to each of the various preset ingredient combinations that have volatile interaction.

5. The method according to claim 4, characterized in that, The method further includes: Based on the first preset temperature data corresponding to the first target ingredient, the first preset humidity data corresponding to the first target ingredient, the second preset temperature data corresponding to the second target ingredient, and the second preset humidity data corresponding to the second target ingredient, the environmental requirement difference information between the first target ingredient and the second target ingredient is determined; Based on the preset volatility intensity of the first target ingredient, the preset sensitivity coefficient of the second target ingredient, and the environmental requirement difference information, impact analysis data is determined. The preset volatility intensity characterizes the performance of the first target ingredient in releasing the preset volatile substances, the preset sensitivity coefficient characterizes the sensitivity of the second target ingredient to the preset volatile substances, and the impact analysis data characterizes the degree to which the preset volatile substances released by the first target ingredient affect the freshness of the second target ingredient when the first target ingredient and the second target ingredient are stored in the same storage space. The step of determining the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information includes: If the impact analysis data is greater than or equal to a preset impact threshold, the operation of determining the third environmental indicator based on the first target ingredient, the second target ingredient, and the fourth preset mapping information is performed.

6. The method according to claim 5, characterized in that, The target acquisition frequency includes a first target frequency corresponding to the first environmental indicator, a second target frequency corresponding to the second environmental indicator, and a third target frequency corresponding to the third environmental indicator; The step of determining the target acquisition frequency corresponding to the target environmental indicator based on the first preset mapping information and the target environmental indicator includes: Based on the first preset mapping information, the first environmental indicator, the second environmental indicator, the third environmental indicator, and the impact analysis data, the first target frequency, the second target frequency, and the third target frequency are determined.

7. The method according to claim 6, characterized in that, The step of determining the first target frequency, the second target frequency, and the third target frequency based on the first preset mapping information, the first environmental indicator, the second environmental indicator, the third environmental indicator, and the impact analysis data includes: Based on the first preset mapping information, the first environmental indicator and the second environmental indicator, the first basic acquisition frequency corresponding to the first environmental indicator and the second basic acquisition frequency corresponding to the second environmental indicator are determined. Based on the first basic acquisition frequency, the first preset correction coefficient corresponding to the first target ingredient, the influence analysis data, and the preset maximum influence data, the first target frequency is determined; the first preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target ingredient on the first target frequency; Based on the second basic acquisition frequency, the second preset correction coefficient corresponding to the second target ingredient, the influence analysis data, and the preset maximum influence data, the second target frequency is determined; the second preset correction coefficient characterizes the degree of influence of the preset volatile substances released by the first target ingredient on the second target frequency; Based on the first basic acquisition frequency, the second basic acquisition frequency, the preset enhancement coefficient corresponding to the third environmental indicator, the impact analysis data, the preset maximum impact data, and the preset impact threshold, the third target frequency corresponding to the third environmental indicator is determined; the preset enhancement coefficient characterizes the degree of influence of the difference between the impact analysis data and the preset impact threshold on the third target frequency.

8. The method according to claim 1, characterized in that, When the first preset mapping information does not record the preset collection frequency corresponding to the target environmental indicator associated with the at least one target food ingredient, determining the target collection frequency corresponding to the target environmental indicator based on the first preset mapping information and the target environmental indicator includes: Acquire target images of at least one of the target ingredients; The target image is subjected to food type recognition to determine the target food type of the at least one target food. Based on the target ingredient type, a reference ingredient is determined from the multiple preset ingredients recorded in the first preset mapping information. The reference ingredient is an ingredient of the same type as the at least one target ingredient. Based on the reference ingredients and the first preset mapping information, the target sampling frequency is determined.

9. A device for determining the freshness of food ingredients, characterized in that, include: The target environmental indicator determination module is used to determine at least one target environmental indicator associated with a target food item within the target storage device. The target acquisition frequency determination module is used to determine the target acquisition frequency corresponding to the target environmental index based on the first preset mapping information and the target environmental index. The first preset mapping information represents the correspondence between the first preset environmental index of a variety of preset ingredients and the variety of preset acquisition frequencies. The target environmental indicator data acquisition module is used to acquire target environmental indicator data corresponding to the target environmental indicator in the storage space where the at least one target ingredient is located in the target storage device, based on the target acquisition frequency. The freshness grade index data determination module is used to input the target environmental index data into a first preset analysis model to perform food freshness analysis and obtain freshness grade index data corresponding to the at least one target food. The freshness grade index data represents the probability value of the at least one target food under multiple preset freshness grades. The target freshness index data determination module is used to input the freshness grade index data into a second preset analysis model to perform food freshness index data analysis and obtain target freshness index data for the at least one target food ingredient. The target freshness index data represents the freshness of the at least one target food ingredient.

10. 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 method for determining the freshness of ingredients as described in any one of claims 1 to 8.

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