A food material traceability method of a digital food safety management system

Abstract

This application relates to the field of process information management technology, specifically to a method for tracing the origin of ingredients in a digital food safety management system. The method includes: obtaining the quality deviation value of the ingredients; obtaining the storage condition similarity index of the ingredients; obtaining the usage process anomaly index of the ingredients based on the quality deviation value and the storage condition similarity index; and determining the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This application's embodiment analyzes the quality deviation value and storage condition similarity index of the ingredients to obtain the usage process anomaly index, and then determines the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This approach considers not only the warehousing stage but also the anomaly traceability during the usage process in the ingredient quality traceability process, thus improving the accuracy of the ingredient traceability results.

Description

Technical Field

[0001] This application belongs to the field of process information management technology, specifically relating to a method for tracing the source of ingredients in a digital food safety management system. Background Technology

[0002] The quality of ingredients directly impacts the overall quality of dishes. When ingredients are not fresh or even spoiled, it affects the operation of the catering industry. If this situation persists, it will severely damage the reputation of the catering industry, making it difficult to recover. Therefore, to ensure the healthy development of the catering industry, it is necessary to strictly control the quality of ingredients. This can be achieved by using IoT devices to record detailed data on the purchased ingredients at each stage of the catering process, facilitating traceability of ingredient quality and safety.

[0003] In the process of tracing the origin of ingredients, analysis of abnormal ingredients is often based solely on the procurement process data. However, analysis of the relationship between the storage process and batches of ingredients is relatively rare. Product process information management and product data management methods that only trace the batches of abnormal ingredients cannot achieve accurate traceability analysis of the factors that cause changes in the quality of ingredients. Summary of the Invention

[0004] To address the aforementioned issues, this application provides a method for tracing the source of ingredients in a digital food safety management system.

[0005] This application provides a method for tracing the source of ingredients in a digital food safety management system, the method comprising:

[0006] Obtain the quality deviation value of the ingredients;

[0007] Obtain the storage condition similarity index of the ingredients;

[0008] Based on the quality deviation value and the storage condition similarity index, the abnormality index of the food's usage process is obtained;

[0009] The cause of the food quality abnormality is determined based on the storage condition similarity index and the usage process abnormality index.

[0010] In one embodiment, obtaining the quality deviation value of the food ingredients includes:

[0011] Obtain the current taste quality evaluation value of the dish made from the ingredients;

[0012] To obtain empirical evaluation values ​​for the taste quality of dishes made from the ingredients;

[0013] Based on the current taste quality evaluation value of the dishes made from the ingredients, and the empirical evaluation value of the taste quality of the dishes made from the ingredients, the quality deviation value of the ingredients is obtained.

[0014] In one implementation, obtaining the storage condition similarity index of the ingredients includes:

[0015] Obtain the storage condition deviation coefficient of the food ingredient;

[0016] Based on the storage condition deviation coefficient, the storage condition similarity index of the food ingredients is obtained.

[0017] In one embodiment, obtaining the storage condition deviation coefficient of the food ingredient includes:

[0018] Obtain the total storage time of the ingredients;

[0019] Obtain the total number of storage conditions for the ingredients;

[0020] Obtain the storage condition monitoring values ​​of the food ingredients during the total storage time;

[0021] Obtain suggested values ​​for the storage conditions of the ingredients;

[0022] The storage condition deviation coefficient of the food ingredient is obtained based on the total storage time, the total number of storage conditions, the monitored value of the storage conditions, and the suggested value of the storage conditions.

[0023] In one implementation, obtaining the storage condition similarity index of the food ingredients based on the storage condition deviation coefficient includes:

[0024] Obtain the difference between the storage condition deviation coefficients of the current batch and historical batches of the ingredient;

[0025] Obtain the first fluctuation range of the current batch of the food ingredient's storage condition monitoring value;

[0026] Obtain the second fluctuation range of the historical batch storage condition monitoring values ​​of the food ingredients;

[0027] The storage condition similarity index of the food ingredients is obtained based on the difference in the storage condition deviation coefficient, the first fluctuation range, and the second fluctuation range.

[0028] In one implementation, obtaining the usage process abnormality index of the food ingredient based on the quality deviation value and the storage condition similarity index includes:

[0029] Obtain the first quality deviation value of abnormal ingredients in the same batch, wherein the abnormal ingredients are those with a quality deviation value less than zero;

[0030] Obtain the second quality deviation value of normal ingredients in the same batch, wherein the normal ingredients are ingredients whose quality deviation value is greater than or equal to zero;

[0031] Obtain the first storage condition similarity index of the abnormal ingredients in the same batch;

[0032] Obtain the second storage condition similarity index of the normal ingredients in the same batch;

[0033] The abnormal index of the usage process of the food ingredient is obtained based on the first quality deviation value, the second quality deviation value, the first storage condition similarity index, and the second storage condition similarity index.

[0034] In one implementation, determining the cause of the food quality abnormality based on the storage condition similarity index and the usage process abnormality index includes:

[0035] If the storage condition similarity index is less than a first set threshold, the cause of the abnormal quality of the food is determined to be improper food storage;

[0036] If the storage condition similarity index is greater than or equal to the first set threshold, the cause of the food quality abnormality is determined based on the usage process abnormality index.

[0037] In one implementation, determining the cause of the food quality abnormality based on the usage process abnormality index when the storage condition similarity index is greater than or equal to the first preset threshold includes:

[0038] If the abnormality index during use is greater than zero, the cause of the abnormality in the quality of the food ingredient is determined to be improper use of the food ingredient.

[0039] In one embodiment, the method further includes:

[0040] If the cause of the abnormal quality of the food ingredient is determined to be improper use of the food ingredient, obtain the time interval between the time of the food ingredient's departure from the warehouse and the time of the food ingredient's preparation.

[0041] Based on the time interval, determine the cause of the abnormal quality of the food ingredients.

[0042] In one implementation, determining the cause of the food quality abnormality based on the time interval includes:

[0043] If the time interval is greater than or equal to the second preset threshold, the cause of the abnormal quality of the food is determined to be improper storage of the food after it leaves the warehouse;

[0044] If the time interval is less than the second set threshold, the cause of the abnormal quality of the ingredients is determined to be an abnormality in the ingredient preparation process.

[0045] This application offers the following advantages: It provides a method for tracing the source of ingredients in a digital food safety management system. The method includes: obtaining the quality deviation value of the ingredients; obtaining the storage condition similarity index of the ingredients; obtaining the usage process anomaly index of the ingredients based on the quality deviation value and the storage condition similarity index; and determining the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This application analyzes the quality deviation value and storage condition similarity index of the ingredients to obtain the usage process anomaly index, and then determines the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This approach considers not only the warehousing stage but also the anomaly traceability during the usage process in the ingredient quality traceability process, thus improving the accuracy of the ingredient traceability results. Attached Figure Description

[0046] To more clearly illustrate the implementation schemes of this application, the accompanying drawings used in the implementation schemes will be briefly introduced below. It should be understood that the accompanying drawings only show some implementation schemes of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from the accompanying drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating a food traceability method for a digital food safety management system according to an exemplary embodiment.

[0048] Figure 2 This is a flowchart illustrating a method for obtaining the quality deviation value of food ingredients according to an exemplary embodiment.

[0049] Figure 3 This is a flowchart illustrating a method for obtaining a storage condition similarity index of food ingredients according to an exemplary embodiment.

[0050] Figure 4 This is a flowchart illustrating a method for obtaining the storage condition deviation coefficient of food ingredients according to an exemplary embodiment.

[0051] Figure 5 This is a flowchart illustrating a method for obtaining a storage condition similarity index of food ingredients based on a storage condition deviation coefficient, according to an exemplary embodiment.

[0052] Figure 6 This is a flowchart illustrating a method for obtaining an abnormality index in the usage process of food ingredients based on a quality deviation value and a storage condition similarity index, according to an exemplary embodiment.

[0053] Figure 7 This is a flowchart illustrating a method for determining the cause of food quality abnormalities based on a storage condition similarity index and a process abnormality index, according to an exemplary embodiment.

[0054] Figure 8 This is a flowchart illustrating a method for determining the cause of food quality abnormalities based on a usage process abnormality index when the storage condition similarity index is greater than or equal to a first preset threshold, according to an exemplary embodiment.

[0055] Figure 9 This is a flowchart illustrating a method for tracing the source of ingredients in another digital food safety management system according to an exemplary embodiment.

[0056] Figure 10 This is a flowchart illustrating a method for determining the cause of abnormal food quality based on time intervals, according to an exemplary embodiment. Detailed Implementation

[0057] To clearly illustrate the technical features of this solution, the following detailed description, in conjunction with specific implementation methods and accompanying drawings, will provide a comprehensive explanation of this application.

[0058] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0059] It should be understood that the steps described in the method embodiments of this application may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of this application is not limited in this respect.

[0060] The term "comprising" and its variations as used herein are open-ended inclusions, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below.

[0061] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0062] It should be noted that the terms "one" and "multiple" used in this application are illustrative rather than restrictive. Those skilled in the art should understand that, unless explicitly stated in the context, they should be interpreted as "one or more". In the description of this application, unless otherwise stated, "multiple" refers to two or more than two, and other quantifiers are similar; "at least one item", "one item or multiple items", or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one item 'a' can represent any number of 'a's; as another example, one or more of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple; "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural.

[0063] Although operations or steps are described in a specific order in the accompanying drawings in the embodiments of this application, this should not be construed as requiring these operations or steps to be performed in the specific order or serial order shown, or requiring all of the shown operations or steps to be performed to obtain the desired result. In the embodiments of this application, these operations or steps may be performed serially; they may be performed in parallel; or a portion of these operations or steps may be performed.

[0064] Meanwhile, it is understood that the data involved in this technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of relevant laws, regulations and related provisions.

[0065] First, the application scenario of this application will be explained. Existing methods, based on digital food safety management data such as restaurant reviews, can trace the quality attribute data of a single batch of ingredients and the abnormal data of the storage process. However, they often fail to effectively analyze abnormal phenomena in the preparation process of the corresponding dishes. For example, if ingredients are taken out in advance and not processed in time, they may be out of the effective storage environment and cause abnormalities. Even if the supplier is changed later, food quality problems will still recur. The root cause is that the traceability method for ingredient quality is not effective.

[0066] In view of this, this application provides a method for tracing the source of ingredients in a digital food safety management system, aiming to solve the above-mentioned problems. The application will be described below with reference to specific embodiments.

[0067] Figure 1 This is a flowchart illustrating a food traceability method for a digital food safety management system according to an exemplary embodiment. For example... Figure 1 As shown in the figure, this application provides a method for tracing the source of ingredients in a digital food safety management system, which may include the following steps:

[0068] In step S10, the quality deviation value of the ingredients is obtained.

[0069] In this step, the quality deviation value of the ingredients is obtained. For example, the current taste quality evaluation value of the dish made from the ingredients can be obtained first, then the empirical evaluation value of the taste quality of the dish made from the ingredients can be obtained, and then the quality deviation value of the ingredients can be obtained based on the current taste quality evaluation value and the empirical evaluation value of the taste quality of the dish made from the ingredients.

[0070] In step S20, the storage condition similarity index of the ingredients is obtained.

[0071] In this step, the storage condition similarity index of the ingredients is obtained. For example, the storage condition deviation coefficient of the ingredients can be obtained first, and then the storage condition similarity index of the ingredients can be obtained based on the storage condition deviation coefficient.

[0072] In step S30, the abnormal index of the usage process of the food ingredient is obtained based on the quality deviation value and the storage condition similarity index.

[0073] In this step, an abnormality index of the food's usage process is obtained based on the quality deviation value and the storage condition similarity index. For example, the first quality deviation value of abnormal food in the same batch (abnormal food is defined as food with a quality deviation value less than zero) can be obtained first. Then, the second quality deviation value of normal food in the same batch (normal food is food with a quality deviation value greater than or equal to zero) can be obtained. Next, the first storage condition similarity index of the abnormal food in the same batch is obtained, followed by the second storage condition similarity index of the normal food in the same batch. Finally, based on the first quality deviation value, the second quality deviation value, the first storage condition similarity index, and the second storage condition similarity index, the abnormality index of the food's usage process is obtained.

[0074] In step S40, the cause of the food quality abnormality is determined based on the storage condition similarity index and the usage process abnormality index.

[0075] In this step, the cause of the food quality abnormality is determined based on the storage condition similarity index and the usage process abnormality index. For example, if the storage condition similarity index is less than a first preset threshold, the cause of the food quality abnormality can be determined as improper storage; if the storage condition similarity index is greater than or equal to the first preset threshold, the cause of the food quality abnormality is determined based on the usage process abnormality index.

[0076] In summary, this application provides a method for tracing the source of ingredients in a digital food safety management system. The method includes: obtaining the quality deviation value of the ingredients; obtaining the storage condition similarity index of the ingredients; obtaining the usage process anomaly index of the ingredients based on the quality deviation value and the storage condition similarity index; and determining the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This application analyzes the quality deviation value and storage condition similarity index of the ingredients to obtain the usage process anomaly index, and then determines the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This approach considers not only the warehousing stage but also the anomaly traceability during the usage process in the ingredient quality traceability process, thus improving the accuracy of the ingredient traceability results.

[0077] Figure 2 This is a flowchart illustrating a method for obtaining quality deviation values ​​of food ingredients according to an exemplary embodiment. Figure 2 As shown, obtaining the quality deviation value of the ingredients may include the following steps:

[0078] In step S101, the current taste quality evaluation value of the dish made from the ingredients is obtained.

[0079] In this step, obtain the purchase batch The storage date is Ingredients Current dish rating for the prepared dish This dish evaluation score is also known as the taste quality evaluation score. For example, the cafeteria scans and stores all ingredient information purchased each day to create a product information form. This product information form can be shown in Table 1:

[0080] Table 1

[0081]

[0082] After the ingredients enter the storage warehouse, they are categorized and stored according to their optimal storage temperature and conditions. For example, carrots and radishes are stored in the same location. The storage process is monitored according to storage time, and monitoring data is collected to construct a storage process form based on storage conditions and storage time. The storage process form can be shown in Table 2.

[0083] Table 2

[0084]

[0085] For environmental monitoring data stored during the process, monitoring data is recorded at half-hour intervals. In the cafeteria food evaluation system, keywords (such as ingredients, taste, etc.) are extracted from food evaluations based on semantic recognition technology, and a statistical form of ingredient quality is generated accordingly. To facilitate quantitative analysis, the evaluation values ​​of dishes can be... It is divided into four levels: high, medium, low, and poor, corresponding to a scoring standard of 3 points, 2 points, 1 point, and 0 points respectively.

[0086] During storage, both the inherent quality of the food itself and changes in storage conditions can affect its final quality when used. Furthermore, the quality of food can vary depending on when it is consumed at different times. Therefore, analyzing food quality requires first analyzing any abnormal parameters observed during use at different times. Then, the storage conditions during the storage process must be compared with historical storage conditions to determine if changes in storage conditions have affected the quality, ultimately allowing for the tracing of the cause of food spoilage.

[0087] After purchasing ingredients, staff will categorize and store them according to their specific storage requirements, precisely controlling parameters such as temperature and humidity within the storage environment to maximize moisture retention and flavor. However, during storage, due to varying rates of moisture loss among different ingredients, even with optimized preservation efforts, oversights are inevitable, leading to a deterioration in the taste of some ingredients. Therefore, a systematic analysis of the usage status of different ingredients and an assessment of their quality changes at each storage stage are necessary to refine preservation strategies.

[0088] Obtain purchase batches from the cafeteria food evaluation system. The storage date is Ingredients Current taste quality rating of the prepared dish .

[0089] In step S102, an empirical evaluation value for the taste quality of the dish made from the ingredients is obtained.

[0090] In this step, empirical evaluation values ​​for the taste and quality of the dishes made from the ingredients are obtained. For example, when analyzing the differences in evaluations across different batches of ingredients, in addition to comparing and analyzing the monitoring process, the impact of storage time on ingredient quality must also be considered. This can be based on monitoring the purchase batches of different ingredients in a database. The corresponding dates and the usage dates of the ingredients are used to determine the storage days of the ingredients. Subsequently, dish ratings were obtained based on the storage dates of the ingredients. Constructing a relationship curve between dish evaluation and storage date Different users may rate dishes differently on the same day, and these ratings can vary significantly. To ensure that the food quality objectively reflects the general public's taste, a daily rating system will be implemented. After removing the maximum and minimum values, the mean value is used to obtain the daily batch of ingredients. The rating of the dishes prepared ,in, To round down, Daily dish rating The mean after removing the maximum and minimum values.

[0091] Similarly, we can obtain the purchase batch The next storage date is Ingredients Experience-based assessment of the taste and quality of prepared dishes - date relationship curve Based on this curve, empirical evaluation values ​​for the taste and quality of dishes made from the ingredients are obtained. .

[0092] In step S103, the quality deviation value of the ingredients is obtained based on the current taste quality evaluation value of the dish made from the ingredients and the experience evaluation value of the taste quality of the dish made from the ingredients.

[0093] In this step, the current taste and quality evaluation value of the dish prepared from the ingredients is used. And the experience-based assessment values ​​of the taste and quality of dishes made from ingredients. Obtain the quality deviation value of the ingredients. For example, the quality deviation value of ingredients. It can be obtained from the following formula:

[0094] Formula 1

[0095] Figure 3 This is a flowchart illustrating a method for obtaining a storage condition similarity index for food ingredients according to an exemplary embodiment. Figure 3 As shown, obtaining the storage condition similarity index of the ingredients may include the following steps:

[0096] In step S201, the storage condition deviation coefficient of the food ingredient is obtained.

[0097] In this step, the deviation coefficient of the food's storage conditions is obtained. For example, the total storage time of the food can be obtained first, then the total number of storage conditions of the food can be obtained, then the storage condition monitoring value of the food within the total storage time can be obtained, then the suggested value of the storage conditions of the food can be obtained, and finally the deviation coefficient of the food's storage conditions can be obtained based on the total storage time, the total number of storage conditions, the storage condition monitoring value, and the suggested value of the storage conditions.

[0098] In step S202, the storage condition similarity index of the food ingredients is obtained based on the storage condition deviation coefficient.

[0099] In this step, the storage condition similarity index of the ingredients is obtained based on the storage condition deviation coefficient. For example, the difference between the storage condition deviation coefficients of the current batch and historical batches of the ingredients can be obtained first, then the first fluctuation range of the storage condition monitoring value of the current batch of the ingredients can be obtained, then the second fluctuation range of the storage condition monitoring value of the historical batches of the ingredients can be obtained, and finally the storage condition similarity index of the ingredients is obtained based on the difference in storage condition deviation coefficients, the first fluctuation range, and the second fluctuation range.

[0100] Figure 4 This is a flowchart illustrating a method for obtaining the deviation coefficient of storage conditions for food ingredients according to an exemplary embodiment. Figure 4 As shown, obtaining the storage condition deviation coefficient of the food ingredient may include the following steps:

[0101] In step S2011, the total storage time of the ingredients is obtained.

[0102] In this step, the total storage time of the ingredients is obtained, and the total number of moments within that total storage time is calculated. .

[0103] In step S2012, the total number of storage conditions for the ingredients is obtained.

[0104] In this step, the total number of storage conditions for the ingredients is obtained. For example, the storage conditions for food ingredients may include temperature, humidity, etc.

[0105] In step S2013, the storage condition monitoring values ​​of the food ingredients are obtained within the total storage time.

[0106] In this step, the first [item's] number within the total storage time is obtained. The monitored value of storage condition i at time moment .

[0107] In step S2014, recommended values ​​for the storage conditions of the ingredients are obtained.

[0108] In this step, the ingredients are obtained in the first... Suggested value for storage condition i at time step For example, the ingredients in the first... Suggested value for storage condition i at time step It can be experience points.

[0109] In step S2015, the storage condition deviation coefficient of the food ingredient is obtained based on the total storage time, the total number of storage conditions, the storage condition monitoring value, and the suggested value of the storage conditions.

[0110] In this step, the total number of moments in the total storage duration is used as a basis. Total number of storage conditions Storage condition monitoring values And recommended values ​​for storage conditions. Obtain the deviation coefficient of food storage conditions For example, the deviation coefficient of food storage conditions. It can be obtained from the following formula:

[0111] Formula 2

[0112] in, It is the ratio of the absolute value of the difference between the monitored value and the recommended value of the storage condition to the recommended value of the storage condition, representing the relative magnitude of the difference between the monitored state and the recommended state of the storage condition. It represents the average value of the overall changes in monitoring data during the food storage process, and can indicate the overall deviation between the actual and recommended values ​​of the food storage conditions.

[0113] Based on the monitoring data of a single ingredient in the storage warehouse, the deviation coefficients between the actual storage conditions and the recommended conditions (optimal preservation conditions) of the ingredient are compared. Multiple storage data analyses are performed on the storage parameters to obtain the deviation of the actual storage conditions of the ingredient from the standard (recommended) storage conditions of the ingredient.

[0114] Figure 5 This is a flowchart illustrating a method for obtaining a storage condition similarity index of food ingredients based on a storage condition deviation coefficient, according to an exemplary embodiment. Figure 5 As shown, obtaining the storage condition similarity index of the food ingredients based on the storage condition deviation coefficient may include the following steps:

[0115] In step S2021, the difference between the storage condition deviation coefficients of the current batch and the historical batch of the food ingredient is obtained.

[0116] In this step, the current batch of ingredients is obtained. Storage condition deviation coefficient Compared with historical batches Storage condition deviation coefficient The difference .

[0117] In step S2022, the first fluctuation range of the storage condition monitoring value of the current batch of the food ingredient is obtained.

[0118] In this step, the current batch of ingredients is obtained. The first fluctuation range of the monitored value of storage condition i For example, the first fluctuation range Indicates the current batch of ingredients The difference between the maximum and minimum values ​​of the monitoring data under storage condition i during storage, i.e., the current batch of ingredients. The fluctuation range of the monitored value of storage condition i.

[0119] In step S2023, the second fluctuation range of the storage condition monitoring values ​​of the historical batches of the food ingredient is obtained.

[0120] In this step, the historical batches of the ingredients are obtained. The second fluctuation range of the monitored value of storage condition i For example, the second fluctuation range Indicates the historical batch of ingredients The difference between the maximum and minimum values ​​of the monitoring data under storage condition i during storage, i.e., the historical batch value of the food ingredients. The fluctuation range of the monitored value of storage condition i. Among them, the batch of ingredients used in the dish whose storage conditions are closest to the recommended values ​​is selected as the historical batch of the ingredients. .

[0121] In step S2024, the storage condition similarity index of the food ingredient is obtained based on the difference of the storage condition deviation coefficient, the first fluctuation range, and the second fluctuation range.

[0122] In this step, the difference in storage condition deviation coefficients is used. First fluctuation range and the second fluctuation range Obtain the similarity index of food storage conditions For example, the similarity index of food storage conditions. It can be obtained from the following formula:

[0123] Formula 3

[0124] in, Indicates the current batch of ingredients Compared with historical batches Overall differences in storage conditions. Indicates the current batch of ingredients Compared with historical batches The difference in the extreme range of the storage conditions. The mean of the extreme value differences in the fluctuation range of all storage condition monitoring data during food storage is used as a supplementary factor to the local data on overall similarity during food storage, in order to improve the accuracy of data comparison. By combining overall differences and local differences, the similarity of the storage process of different batches of the same ingredient is analyzed. Adding 0.1 to the denominator is to prevent the denominator from being zero.

[0125] This formula is used to characterize the changes (similarity) in storage conditions between the current batch and previous batches of food during storage. It analyzes the consistency of food storage conditions when the canteen manages food, and obtains the interference factors of changes in storage conditions on the quality of food storage. If the similarity between the storage process data of the current batch and the historical batch is low, it indicates that the abnormal storage conditions of the current batch of food may be the main factor causing the abnormal quality of the current batch of food.

[0126] use The activation function is related to the similarity index of the storage conditions of the ingredients. Normalization is performed, and the threshold is set to 0.8. If the data shows low similarity between the current batch and historical batches of the food during storage, it can be determined that the quality abnormality is caused by abnormal storage conditions. If the data from the current batch of ingredients is highly similar to the storage process data of historical batches, then the cause of the current ingredient quality anomaly needs further analysis.

[0127] Furthermore, if only the ingredients used on a single day from the same batch exhibit abnormalities, while the other ingredients received normal evaluations on different days, the cause of the abnormality on that day may not be due to poor quality of the ingredients themselves or improper storage. Rather, it could be due to improper handling by the chef during ingredient processing. Therefore, when analyzing ingredient quality data, a batch-wide abnormality index analysis can be performed on ingredients with abnormal evaluations to obtain an abnormality index during ingredient usage.

[0128] Figure 6 This is a flowchart illustrating a method for obtaining an abnormality index in the usage process of food ingredients based on a quality deviation value and a storage condition similarity index, according to an exemplary embodiment. Figure 6 As shown, obtaining the usage process abnormality index of the food ingredient based on the quality deviation value and the storage condition similarity index may include the following steps:

[0129] In step S301, the first quality deviation value of abnormal ingredients in the same batch is obtained, wherein the abnormal ingredients are those whose quality deviation value is less than zero.

[0130] In this step, abnormal ingredients from the same batch are obtained. First quality deviation value Abnormal ingredients Ingredients with a quality deviation value of less than zero.

[0131] In step S302, a second quality deviation value of normal ingredients in the same batch is obtained, wherein the normal ingredients are ingredients whose quality deviation value is greater than or equal to zero.

[0132] In this step, obtain normal ingredients from the same batch. Second quality deviation value Normal ingredients For ingredients with a quality deviation value greater than or equal to zero.

[0133] In step S303, the first storage condition similarity index of the abnormal ingredients in the same batch is obtained.

[0134] In this step, abnormal ingredients from the same batch are obtained. First storage condition similarity index .

[0135] In step S304, the second storage condition similarity index of the normal ingredients in the same batch is obtained.

[0136] In this step, obtain normal ingredients from the same batch. Second storage condition similarity index .

[0137] In step S305, the abnormal index of the usage process of the food ingredient is obtained based on the first quality deviation value, the second quality deviation value, the first storage condition similarity index, and the second storage condition similarity index.

[0138] In this step, based on the first quality deviation value Second quality deviation value First storage condition similarity index and the second storage condition similarity index Obtain the abnormal index of the food usage process. For example, the abnormal index of food usage process. It can be obtained from the following formula:

[0139] Formula 4

[0140] in, Normal ingredients with abnormal ingredients The relative ratio of quality deviation values ​​indicates the quality difference of the same type and batch of ingredients; Indicates abnormal ingredients With normal ingredients The deviation in storage condition similarity; the larger this value, the more likely the ingredients are abnormal. With normal ingredients The greater the difference in storage condition parameters during the storage process, the better this value should be, serving as an adjustment factor to improve the performance of abnormal ingredients. With normal ingredients The reliability of the deviation value, and thus the Perform inverse proportional processing Adding 0.1 to the denominator is to prevent the denominator from being zero.

[0141] Figure 7 This is a flowchart illustrating a method for determining the cause of quality abnormalities in food ingredients based on a storage condition similarity index and a process abnormality index, according to an exemplary embodiment. Figure 7 As shown, determining the cause of the food quality abnormality based on the storage condition similarity index and the usage process abnormality index may include the following steps:

[0142] In step S401, if the storage condition similarity index is less than a first set threshold, the cause of the abnormal quality of the food ingredient is determined to be improper storage of the food ingredient.

[0143] In this step, if the storage condition similarity index is less than a first preset threshold, the cause of the food quality abnormality is determined to be improper food storage. For example, the first preset threshold can be 0.8.

[0144] In step S402, if the storage condition similarity index is greater than or equal to the first set threshold, the cause of the abnormal quality of the food ingredient is determined according to the usage process abnormality index.

[0145] In this step, if the storage condition similarity index is greater than or equal to a first preset threshold, the cause of the food quality abnormality is determined based on the usage process abnormality index. For example, if the storage condition similarity index is greater than or equal to the first preset threshold, the cause of the food quality abnormality can be determined to be improper use of the food if the usage process abnormality index is greater than zero; otherwise, the food quality can be determined to be normal.

[0146] Figure 8 This is a flowchart illustrating a method for determining the cause of food quality abnormalities based on a usage process abnormality index when a storage condition similarity index is greater than or equal to a first preset threshold, according to an exemplary embodiment. Figure 8As shown, when the storage condition similarity index is greater than or equal to the first preset threshold, determining the cause of the food quality abnormality based on the usage process abnormality index may include the following steps:

[0147] In step S4021, if the abnormality index during use is greater than zero, the cause of the abnormality in the quality of the food ingredient is determined to be improper use of the food ingredient.

[0148] In this step, the process anomaly index is used. If the value is greater than zero, the cause of the abnormal food quality is determined to be improper use of the food.

[0149] Figure 9 This is a flowchart illustrating a method for food traceability in a digital food safety management system according to an exemplary embodiment. For example... Figure 9 As shown, the method may further include the following steps:

[0150] In step S50, if it is determined that the cause of the abnormal quality of the ingredient is improper use of the ingredient, the time interval between the time of the ingredient's release from storage and the time of the ingredient's preparation is obtained.

[0151] In this step, if the cause of the abnormal quality of the ingredients is determined to be improper use of the ingredients, the time interval between the time the ingredients were taken out of the warehouse and the time they were prepared is obtained.

[0152] In step S60, the cause of the abnormal quality of the food ingredient is determined based on the time interval.

[0153] In this step, the cause of the food quality abnormality is determined based on the time interval. For example, if the time interval is greater than or equal to a second set threshold (e.g., 12 hours), the cause of the food quality abnormality can be determined to be improper storage of the food after it leaves the warehouse; if the time interval is less than the second set threshold, the cause of the food quality abnormality can be determined to be an abnormality in the food preparation process.

[0154] Figure 10 This is a flowchart illustrating a method for determining the cause of abnormal food quality based on time intervals, according to an exemplary embodiment. Figure 10 As shown, determining the cause of the food quality abnormality based on the time interval may include the following steps:

[0155] In step S601, if the time interval is greater than or equal to the second set threshold, the cause of the abnormal quality of the food ingredient is determined to be improper storage of the food ingredient after it leaves the warehouse.

[0156] In this step, if the time interval is greater than or equal to a second preset threshold, the cause of the food quality abnormality is determined to be improper storage of the food after it leaves the warehouse. For example, the second preset threshold can be 12 hours.

[0157] In step S602, if the time interval is less than the second set threshold, the cause of the abnormal quality of the food ingredient is determined to be an abnormality in the food ingredient preparation process.

[0158] In this step, if the time interval is less than the second set threshold, the cause of the abnormal quality of the ingredients is determined to be an abnormality in the ingredient preparation process.

[0159] After determining the cause of the abnormal food quality, the food flow information retrieved from the food safety management system—which is obtained by scanning the RFID electronic tags of the food (the tags are linked to the food's entire process data)—can be used to extract monitoring data of the food during storage and preparation, thus completing the traceability of food quality issues.

[0160] In summary, this application provides a method for tracing the source of ingredients in a digital food safety management system. The method includes: obtaining the quality deviation value of the ingredients; obtaining the storage condition similarity index of the ingredients; obtaining the usage process anomaly index of the ingredients based on the quality deviation value and the storage condition similarity index; and determining the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This application analyzes the quality deviation value and storage condition similarity index of the ingredients to obtain the usage process anomaly index, and then determines the cause of the quality anomaly based on the storage condition similarity index and the usage process anomaly index. This approach considers not only the warehousing stage but also the anomaly traceability during the usage process in the ingredient quality traceability process, thus improving the accuracy of the ingredient traceability results.

[0161] This application also provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the steps of the food traceability method of the digital food safety management system provided in this application.

[0162] In another exemplary embodiment, a computer program product is also provided, which includes a computer program executable by a programmable electronic device, the computer program having a code portion for performing the food traceability method of the digital food safety management system described above when executed by the programmable electronic device.

[0163] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application.

Claims

1. A method for tracing the origin of food ingredients in a digital food safety management system, characterized in that, The method includes: Obtain the quality deviation value of the ingredients; Obtain the storage condition similarity index of the ingredients; Based on the quality deviation value and the storage condition similarity index, the abnormality index of the food's usage process is obtained; The cause of the food quality abnormality is determined based on the storage condition similarity index and the usage process abnormality index.

2. The food traceability method of the digital food safety management system according to claim 1, characterized in that, The process of obtaining the quality deviation value of the ingredients includes: Obtain the current taste quality evaluation value of the dish made from the ingredients; To obtain empirical evaluation values ​​for the taste quality of dishes made from the ingredients; Based on the current taste quality evaluation value of the dishes made from the ingredients, and the empirical evaluation value of the taste quality of the dishes made from the ingredients, the quality deviation value of the ingredients is obtained.

3. The food traceability method of the digital food safety management system according to claim 1, characterized in that, The process of obtaining the storage condition similarity index of the ingredients includes: Obtain the storage condition deviation coefficient of the food ingredient; Based on the storage condition deviation coefficient, the storage condition similarity index of the food ingredients is obtained.

4. The food traceability method of the digital food safety management system according to claim 3, characterized in that, The process of obtaining the storage condition deviation coefficient of the food ingredients includes: Obtain the total storage time of the ingredients; Obtain the total number of storage conditions for the ingredients; Obtain the storage condition monitoring values ​​of the food ingredients during the total storage time; Obtain suggested values ​​for the storage conditions of the ingredients; The storage condition deviation coefficient of the food ingredient is obtained based on the total storage time, the total number of storage conditions, the monitored value of the storage conditions, and the suggested value of the storage conditions.

5. The food traceability method of the digital food safety management system according to claim 3, characterized in that, The step of obtaining the storage condition similarity index of the food ingredients based on the storage condition deviation coefficient includes: Obtain the difference between the storage condition deviation coefficients of the current batch and historical batches of the ingredient; Obtain the first fluctuation range of the current batch of the food ingredient's storage condition monitoring value; Obtain the second fluctuation range of the historical batch storage condition monitoring values ​​of the food ingredients; The storage condition similarity index of the food ingredients is obtained based on the difference in the storage condition deviation coefficient, the first fluctuation range, and the second fluctuation range.

6. The food traceability method of the digital food safety management system according to claim 1, characterized in that, The step of obtaining the usage process abnormality index of the food ingredient based on the quality deviation value and the storage condition similarity index includes: Obtain the first quality deviation value of abnormal ingredients in the same batch, wherein the abnormal ingredients are those with a quality deviation value less than zero; Obtain the second quality deviation value of normal ingredients in the same batch, wherein the normal ingredients are ingredients whose quality deviation value is greater than or equal to zero; Obtain the first storage condition similarity index of the abnormal ingredients in the same batch; Obtain the second storage condition similarity index of the normal ingredients in the same batch; The abnormal index of the usage process of the food ingredient is obtained based on the first quality deviation value, the second quality deviation value, the first storage condition similarity index, and the second storage condition similarity index.

7. The food traceability method of the digital food safety management system according to claim 1, characterized in that, The step of determining the cause of the food quality abnormality based on the storage condition similarity index and the usage process abnormality index includes: If the storage condition similarity index is less than a first set threshold, the cause of the abnormal quality of the food is determined to be improper food storage; If the storage condition similarity index is greater than or equal to the first set threshold, the cause of the food quality abnormality is determined based on the usage process abnormality index.

8. The food traceability method of the digital food safety management system according to claim 7, characterized in that, When the storage condition similarity index is greater than or equal to the first preset threshold, determining the cause of the food quality abnormality based on the usage process abnormality index includes: If the abnormality index during use is greater than zero, the cause of the abnormality in the quality of the food ingredient is determined to be improper use of the food ingredient.

9. The food traceability method of the digital food safety management system according to claim 8, characterized in that, The method further includes: If the cause of the abnormal quality of the food ingredient is determined to be improper use of the food ingredient, obtain the time interval between the time of the food ingredient's departure from the warehouse and the time of the food ingredient's preparation. Based on the time interval, determine the cause of the abnormal quality of the food ingredients.

10. The food traceability method of the digital food safety management system according to claim 9, characterized in that, The step of determining the cause of the food quality abnormality based on the time interval includes: If the time interval is greater than or equal to the second preset threshold, the cause of the abnormal quality of the food is determined to be improper storage of the food after it leaves the warehouse; If the time interval is less than the second set threshold, the cause of the abnormal quality of the ingredients is determined to be an abnormality in the ingredient preparation process.

Images