Health-care food production safety early warning method and system combined with environmental monitoring

Abstract

The application provides a health food production safety early warning method and system combined with environmental monitoring, relates to the technical field of food safety early warning, and comprises the following steps: obtaining a health food production cycle, performing neighborhood time zone aggregation, performing environmental element configuration on N time zone food ingredients, obtaining an N time zone environmental element threshold set, performing deviation analysis on the health food, obtaining an environmental deviation coefficient, and constructing an early warning mark for abnormal ingredients of the health food when the environmental deviation coefficient is greater than or equal to an environmental deviation coefficient threshold. The application can solve the technical problem of health food production safety caused by the lack of corresponding environmental control in the process of conveying products to each manufacturing link through different channels on the traditional food production line, and improve the product quality and safety of health food by real-time monitoring and deviation analysis and dynamic adjustment of the production environment.

Description

Technical Field

[0001] This application relates to the field of food safety early warning technology, and in particular to a method and system for early warning of health food production safety combined with environmental monitoring. Background Technology

[0002] With societal development and increasing consumer focus on the functionality and safety of food, the market demand for health food products continues to grow. The production process of health food involves multiple stages, including raw material handling, processing, packaging, storage, and transportation. Environmental factors in these stages, such as temperature, humidity, light, and air quality, have a significant impact on the quality and safety of health food products. Traditional quality control in health food production mainly relies on batch sampling, which can ensure product quality to a certain extent, but has significant limitations. Batch sampling cannot cover the specific circumstances of each product during production, transportation, and storage, making it difficult to detect and resolve subtle product quality changes caused by environmental factors. Furthermore, due to the lack of real-time monitoring and precise control of environmental factors at each stage of the production process, existing quality control methods cannot effectively prevent and resolve subtle product deterioration caused by transportation and storage environments.

[0003] In summary, existing technologies suffer from technical problems related to the production safety of health foods due to the lack of corresponding environmental control during the process of transporting products to various manufacturing stages through different channels on traditional food production lines. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for early warning of health food production safety that combines environmental monitoring, in order to solve the technical problems in the existing technology where the lack of corresponding environmental control during the process of transporting products to various manufacturing stages through different channels on traditional food production lines leads to production safety issues for health foods.

[0005] In view of the above problems, this application provides a method and system for early warning of health food production safety that combines environmental monitoring.

[0006] Firstly, this application provides a method for early warning of health food production safety combined with environmental monitoring. This method is implemented through a health food production safety early warning system combined with environmental monitoring. The method includes: obtaining the health food production cycle, where the production cycle includes component fluctuation time-series information; aggregating the component fluctuation time-series information across neighboring time zones to generate N time-zone food components; aggregating the N time-zone food components and configuring environmental elements to obtain a set of N time-zone environmental element thresholds; obtaining environmental element monitoring time-series information through environmental sensors; performing deviation analysis on the health food based on the environmental element monitoring time-series information and the set of N time-zone environmental element thresholds to obtain an environmental deviation coefficient; and constructing an early warning label for abnormal components in the health food when the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold, wherein abnormal components in the health food with the early warning label must pass a test before production can resume.

[0007] Secondly, this application also provides a health food production safety early warning system combined with environmental monitoring, used to execute the health food production safety early warning method combined with environmental monitoring as described in the first aspect, wherein the health food production safety early warning system combined with environmental monitoring includes: a production cycle acquisition module, which is used to obtain the health food production cycle, wherein the health food production cycle includes component fluctuation time series information; a neighborhood time zone aggregation module, which is used to traverse the component fluctuation time series information to perform neighborhood time zone aggregation and generate N time zone food components; and an environmental element configuration module, which is used to traverse the N time zones. The system includes: a food component analysis module, which analyzes environmental factors and obtains a set of threshold values ​​for environmental factors in N time zones; an environmental monitoring module, which obtains time-series monitoring information of environmental factors through environmental sensors; a deviation analysis module, which performs deviation analysis on the health food based on the time-series monitoring information of environmental factors and the set of threshold values ​​for environmental factors in N time zones to obtain an environmental deviation coefficient; and an early warning identification module, which constructs an early warning identification for abnormal components in the health food when the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold. Abnormal components in health food with the early warning identification must pass a test before production can resume.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By obtaining the production cycle of health food products, which includes the time-series information of component fluctuations, the system aggregates neighboring time zones from the time-series information of component fluctuations to generate N time-zone food components. It then iterates through the N time-zone food components and configures environmental elements to obtain a set of N time-zone environmental element thresholds. Environmental monitoring time-series information is obtained through environmental sensors. Based on the environmental element monitoring time-series information and the set of N time-zone environmental element thresholds, deviation analysis is performed on the health food products to obtain an environmental deviation coefficient. When the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold, an early warning label is constructed for abnormal components in the health food products. Abnormal components in health food products with the early warning label must pass a test before production can resume. In other words, through real-time monitoring and deviation analysis, the production environment is dynamically adjusted to improve the quality and safety of health food products.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the health food production safety early warning method that incorporates environmental monitoring, as described in this application.

[0013] Figure 2 This is a schematic diagram of the structure of the health food production safety early warning system that incorporates environmental monitoring, as described in this application.

[0014] Figure labeling: Production cycle acquisition module 11, Neighborhood time zone aggregation module 12, Environmental element configuration module 13, Environmental monitoring module 14, Deviation analysis module 15, Early warning sign construction module 16. Detailed Implementation

[0015] This application provides a method and system for early warning of health food production safety that integrates environmental monitoring. This addresses the technical problem in existing technologies where the lack of corresponding environmental control during the transport of products to various manufacturing stages via different channels in traditional food production lines leads to safety issues in health food production. Through real-time monitoring and deviation analysis, the production environment is dynamically adjusted to improve the quality and safety of health food products.

[0016] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0017] Example 1, please refer to the appendix. Figure 1 This application provides a method for early warning of health food production safety combined with environmental monitoring, wherein the method specifically includes the following steps:

[0018] Obtain the production cycle of health food products, wherein the production cycle of health food products includes information on the timing of component fluctuations.

[0019] Specifically, this involves monitoring the entire production process of health food products, from raw material procurement, processing, packaging to storage. During production, various factors (such as temperature, humidity, and mixing uniformity) can cause fluctuations or changes in component content. The health food production cycle includes information on the timing of these component fluctuations. Regular monitoring of the content changes of key components (such as active ingredients and nutrients) is conducted, and these changes over time are recorded. For example, if a key component of a health food product is vitamin E, samples need to be taken at different points in the production cycle (such as mixing, drying, and packaging stages) and the vitamin E content measured. By analyzing the timing of component fluctuations, the stability of the production process can be assessed, and fluctuations that may affect product quality can be detected promptly.

[0020] The temporal information of the component fluctuations is traversed to aggregate the neighboring time zones, generating N time zone food components.

[0021] Specifically, the temporal information of component fluctuations is divided into several time-series component type sets. For each pair of component type sets, adjacent time points are compared to obtain the repetition ratio. When there is complete repetition, adjacent time points are aggregated into the same time zone; when there are differences, adjacent time points are aggregated into different time zones. The system outputs food components from N time zones, including components from the first time zone, the second time zone, and so on up to the Nth time zone. By aggregating time points with similar component changes into time zones, production process management is simplified, and specific control strategies are developed for each time zone.

[0022] By iterating through the food components of the N time zones and configuring environmental factors, a set of environmental factor thresholds for the N time zones is obtained.

[0023] Specifically, based on the first food component of the first time zone, a correlation analysis is performed on the set of environmental element attributes (such as temperature, humidity, microbial content, pH value, pressure, etc.) to obtain a first-calibrated set of environmental element attributes. This identifies which environmental elements are closely related to changes in the first food component, thus obtaining the first-calibrated set of environmental element attributes. Based on the first food component, the first-calibrated set of environmental element attributes is jointly sorted to select the most relevant environmental elements, generating a threshold set of environmental elements for the first food component in the first time zone. This threshold set is then added to the first time zone's overall environmental element threshold set. This process is repeated to obtain N time zone environmental element threshold sets, including the first time zone threshold set, the second time zone threshold set, and so on up to the Nth time zone threshold set. These threshold ranges for environmental element attributes set for each time zone reflect which environmental conditions are suitable and which may adversely affect the stability of the food component in each time zone. By controlling environmental elements within suitable ranges, potential component changes during storage are reduced, thereby improving product quality.

[0024] Environmental sensors are used to obtain time-series information on environmental element monitoring.

[0025] Specifically, in the production process of health food products, various environmental sensors, such as temperature sensors, humidity sensors, and light sensors, are installed to monitor changes in environmental factors. These sensors automatically collect data at preset time intervals (e.g., every minute, every hour), forming a time-series information report on environmental factor monitoring, reflecting the changes in environmental factors over time during the production process. Through these environmental sensors, environmental changes during production can be monitored in real time, ensuring that the production environment meets predetermined quality standards.

[0026] Based on the monitoring time series information of the environmental elements and combined with the threshold set of the N time zones for environmental elements, deviation analysis is performed on health food products to obtain the environmental deviation coefficient.

[0027] Specifically, based on the temporal characteristics of environmental element monitoring time-series information, and combined with N time-zone environmental element threshold sets, for each time zone, the environmental element threshold most relevant to the current environmental element monitoring time-series information is selected to form a set of associated environmental element thresholds for that time zone. The N time-zone environmental element threshold sets include the first time-zone set, the second time-zone set, and so on up to the Nth time-zone set. Deviation analysis is performed on the integrated data, comparing the actual monitored environmental element values ​​with the thresholds for each time zone to identify whether environmental elements deviate from the set threshold range. The environmental element monitoring time-series information is compared with the associated environmental element threshold sets to calculate the proportion of environmental elements that do not meet the thresholds, which is set as the environmental deviation coefficient. Understanding the degree of deviation between environmental monitoring data and the set environmental element thresholds, the calculation of the environmental deviation coefficient helps to promptly identify potential quality problems caused by environmental changes, improving the accuracy of the early warning system.

[0028] When the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold, an early warning label is constructed for abnormal components in health food. The abnormal components of health food with the early warning label need to pass the test before they can be put into production again.

[0029] Furthermore, this application also includes the following steps: if the abnormal ingredient in the health food has already been put into production, the health food containing the abnormal ingredient is a product that must be tested.

[0030] Specifically, a preset threshold for the environmental deviation coefficient is used to determine the effectiveness of environmental control. When the environmental deviation coefficient is greater than or equal to the threshold, it indicates a problem with environmental control, and the relevant health food ingredients may have quality issues, requiring the establishment of an early warning label for abnormal ingredients in health foods. Abnormal ingredients in health foods refer to food ingredients that may be affected and lead to quality instability when the environmental deviation coefficient exceeds the threshold. For health foods with abnormal ingredients bearing an early warning label, they must be tested and confirmed to be qualified before being put into production again. If these abnormal ingredients have already been put into production, health foods containing abnormal ingredients will be marked as mandatory inspection products and subject to strict quality testing. Through early warning labels and mandatory inspection requirements, the circulation of health foods containing abnormal ingredients in the market can be prevented, thereby reducing potential quality problems.

[0031] Furthermore, this application also includes the following steps: the component fluctuation time series information includes a first time-based component type set, a second time-based component type set, and a component type set up to the Mth time-based component type set; the first time-based component type set, the second time-based component type set, and the Mth time-based component type set are compared between adjacent time-based moments to obtain the component repetition ratio between adjacent time-based moments; when the component repetition ratio between adjacent time-based moments is equal to 1, the adjacent time-based moments are aggregated into the same time zone; when the component repetition ratio between adjacent time-based moments is not equal to 1, the adjacent time-based moments are aggregated into different time-zones; when the component repetition ratio between any two adjacent time-based moments is not equal to 1, the food components of the N time zones are output.

[0032] Specifically, the component data at each moment in the production cycle is divided into different component type sets. Each component type set contains all detected component types and their contents at that moment. For example, the component type set at the first moment might include all components and their contents at the start of production, the component type set at the second moment might include all components and their contents after production has been going on for a period of time, and so on, up to the component type set at the Mth moment.

[0033] The component type sets at consecutive time points are compared to calculate the component repetition rate. For example, the component type sets at time 1 and time 2 are compared, then the second and third time sets are compared, and so on, up to the (M-1)th and Mth time sets. By comparing the component type sets at two adjacent time points, the proportion of shared component types to the total number of their respective component types is calculated. For example, if the component type sets at time 1 and time 2 are completely identical, the component repetition rate is 100%. If the component type sets at two time points share only one component, the component repetition rate is 33.33% (assuming there are three components at each time point).

[0034] Based on the proportion of component repetition between adjacent time points, a decision is made whether to aggregate adjacent time points into the same time zone. If the proportion of component repetition between adjacent time points is equal to 1, meaning the component types at two time points are exactly the same, then they are aggregated into the same time zone. If the proportion of component repetition between adjacent time points is not equal to 1, meaning the component types at two time points are different, then they are aggregated into different time zones. When the proportion of component repetition between any two adjacent time points is not equal to 1, that is, there are no components of the same type in the M time point component type sets, the food components from the first time zone to the Nth time zone are output, resulting in N time zone food components. This means that the component types within each time zone are unique, or different from those in other time zones. For each time zone, the component type set for all time points within that time zone is provided. By comparing the component type sets of adjacent time points, component changes can be precisely monitored, thereby better controlling product quality.

[0035] Furthermore, this application also includes the following steps: Step 1: Based on the first food component of the first food component in the first time zone among the N time zone food components, perform correlation analysis on the set of environmental element attributes to obtain a first calibrated set of environmental element attributes; Step 2: Based on the first food component, perform joint sorting on the first calibrated set of environmental element attributes to generate a set of environmental element thresholds for the first food component in the first time zone; Step 3: Add the set of environmental element thresholds for the first food component in the first time zone to the set of environmental element thresholds for the first time zone; replace the data elements, and repeat steps 1 to 3 to obtain the set of environmental element thresholds for the N time zones.

[0036] Specifically, from N time zone food components, a first time zone food component is selected. From this first time zone food component, a specific food component is chosen, designated as the first food component. All environmental factors that may affect this food component are listed, such as temperature, humidity, microbial content, pH value, and pressure. The correlation between the first food component and environmental factor attributes is analyzed to determine which environmental factors have a significant impact on the food component, thus obtaining the first set of labeled element attributes. The first historical monitoring events of the first food component are collected, including datasets of food component change records and datasets of environmental factor attribute change records. These datasets are then normalized. Using grey relational analysis, the degree of correlation between food component changes and changes in various environmental factor attributes is assessed, and attributes with high correlation are labeled, forming the first set of labeled environmental factor attributes.

[0037] The second historical storage monitoring event for the first food component is collected under healthy storage conditions. This involves recording changes in the first food component and its calibrated environmental element attributes under healthy storage conditions. Healthy storage conditions refer to storing food under ideal or standard conditions, such as suitable temperature and humidity. The characteristic data of each set of calibrated environmental element attributes are normalized to construct corresponding location coordinates. Outliers in these location coordinates are identified and removed, constructing a threshold set for the first food component's environmental elements in the first time zone. Joint sorting refers to selecting suitable environmental conditions based on the characteristics of the first food component and the first set of calibrated environmental element attributes, considering the sensitivity of the food component to environmental elements and the interactions between environmental elements. The threshold set for the first food component's environmental elements in the first time zone refers to the threshold range of environmental element attributes set for the storage of the first food component within the first time zone, reflecting which environmental conditions are suitable and which may adversely affect the stability of the food component within this time zone.

[0038] The threshold set of environmental factors for the first food component in the first time zone is added to the threshold set of environmental factors for the first time zone. At this point, the threshold set of environmental factors for the first time zone contains all environmental factor thresholds related to the food component in the first time zone. For each time zone from the first to the Nth time zone, the above steps are repeated to obtain the threshold sets of environmental factors for the first time zone, the second time zone, and so on up to the Nth time zone, resulting in N threshold sets of environmental factors for each time zone. The threshold sets of food components and environmental factors for each time zone are analyzed and constructed based on the specific food component and environmental factor attributes of that time zone. Through correlation analysis and joint sorting, environmental factor thresholds can be accurately constructed for each time zone, reflecting the relationship between food components and environmental factors in that time zone.

[0039] Furthermore, this application also includes the following steps: based on the environmental element attribute set, collecting the first historical storage monitoring events of the first food component, wherein the first historical storage monitoring events include a first food component change record dataset, a first environmental element attribute change record dataset, a second environmental element attribute change record dataset up to the Qth environmental element attribute change record dataset; constructing a first normalized data sequence based on the first food component change record dataset; constructing a second normalized data sequence up to the Q+1th normalized data sequence based on the first environmental element attribute change record dataset, the second environmental element attribute change record dataset up to the Qth environmental element attribute change record dataset; using the first normalized data sequence as a reference sequence, performing grey relational analysis on the second normalized data sequence up to the Q+1th normalized data sequence to obtain the first environmental element attribute correlation degree, the second environmental element attribute correlation degree up to the Qth environmental element attribute correlation degree; labeling the environmental element attributes in the first environmental element attribute correlation degree, the second environmental element attribute correlation degree up to the Qth environmental element attribute correlation degree that are greater than or equal to the correlation degree threshold, to obtain the first labeled environmental element attribute set.

[0040] Specifically, the food composition of the first time zone refers to the set of all food composition types and their contents within the first time zone. The first food composition refers to the first composition in this set. Based on the set of all environmental factors affecting the stability of food compositions, historical monitoring data is collected, including changes in food composition and changes in environmental attribute properties. The first historical monitoring data includes a dataset of changes in the first food composition and datasets of changes in multiple environmental attribute properties related to the first food composition, including datasets of changes in the first environmental attribute property, datasets of changes in the second environmental attribute property, and so on up to the Qth environmental attribute property. Using the dataset of changes in the first food composition, normalization is performed to convert it into dimensionless numerical values ​​for easier comparison and analysis. Normalization typically scales the data to a fixed range, such as 0 to 1, to eliminate the influence of different units of measurement, allowing the data to be compared and analyzed on the same scale. Normalization is performed on each dataset of changes in environmental attribute properties to generate a corresponding normalized data sequence.

[0041] Using the first normalized data sequence (change in food composition) as the baseline sequence, grey relational analysis is performed on the normalized data sequences of other environmental element attributes to quantify the degree of correlation between different environmental elements and changes in food composition. Grey relational analysis is a multivariate analysis method used to compare the similarity and correlation between multiple time series, and is suitable for situations with limited data and incomplete information. The correlation threshold is a preset threshold used to determine whether the correlation between environmental element attributes and the first food composition change reaches a certain significant level. The correlation degrees of the first environmental element attributes, the second environmental element attributes, and up to the Qth environmental element attributes are obtained. Environmental element attributes greater than or equal to the correlation threshold are selected and calibrated to obtain the first calibrated set of environmental element attributes, which includes those environmental element attributes that have a significant impact on the first food composition change.

[0042] In a specific example, the first historical storage monitoring events include: a dataset of vitamin A change records: time point 1: 100 mg, time point 2: 95 mg, time point 3: 90 mg; a dataset of first environmental attribute (temperature) change records: time point 1: 20℃, time point 2: 22℃, time point 3: 25℃; and a dataset of second environmental attribute (humidity) change records: time point 1: 50%, time point 2: 55%, time point 3: 60%. Using the min-max normalization method, a first normalized data sequence is constructed as: time point 1: 1, time point 2: 0.5, time point 3: 0; normalized data sequences for temperature and humidity are constructed as follows: second normalized data sequence: time point 1: 0, time point 2: 0.2, time point 3: 0.5; and a third normalized data sequence: time point 1: 0, time point 2: 0.1667, time point 3: 0.25. A correlation threshold of 0.5 was set, and the calculated correlations were: Vitamin A and temperature: 0.6; Vitamin A and humidity: 0.75. Since the correlations of temperature and humidity are both greater than or equal to the correlation threshold of 0.5, they are designated as the first set of calibrated environmental element attributes. Therefore, the first set of calibrated environmental element attributes includes temperature and humidity. The study identifies which environmental element attributes have a significant impact on changes in the first food composition. Based on these calibrated environmental element attributes, environmental control during the production process is optimized to reduce the impact of these elements on composition changes.

[0043] Furthermore, this application also includes the following steps: based on the first set of calibrated environmental element attributes, collecting second historical storage monitoring events of healthy storage samples of the first food component, wherein the second historical storage monitoring events include a first set of calibrated environmental element attribute record feature data, a second set of calibrated environmental element attribute record feature data up to the Hth set of calibrated environmental element attribute record feature data; the first set of calibrated environmental element attribute record feature data includes several first calibrated environmental element attribute record features, and a first positioning coordinate is constructed based on several normalized values ​​of the several first calibrated environmental element attribute record features; up to the Hth set of calibrated environmental element attribute record feature data includes several Hth calibrated environmental element attribute record features, and an Hth positioning coordinate is constructed based on several normalized values ​​of the several Hth calibrated environmental element attribute record features; anomaly point deletion is performed based on the first positioning coordinate up to the Hth positioning coordinate to obtain several feature values ​​in the set of calibrated environmental element attributes; and a threshold set of environmental elements for the first food component in the first time zone is constructed based on the several feature values ​​in the set of calibrated environmental element attributes.

[0044] Specifically, the first set of calibrated environmental element attributes refers to the set of environmental element attributes that have a significant impact on the changes of the first food component, as previously calibrated through grey relational analysis. For the first food component, data from the second historical storage monitoring events of its healthy storage samples are collected under different storage conditions, including recorded feature data of calibrated environmental element attributes. The second historical storage monitoring events refer to the recorded change data of the first food component and its calibrated environmental element attributes under healthy storage conditions, including multiple sets of recorded feature data of calibrated environmental element attributes, such as the first set to the Hth set. The first set of recorded feature data of calibrated environmental element attributes includes several first-calibrated environmental element attribute recorded features, including multiple environmental element attributes, such as temperature and humidity. For each environmental element attribute, data is obtained through normalization processing. Normalized values ​​convert the raw data into a dimensionless standardized form, facilitating comparisons between different attributes. A coordinate system is constructed based on the normalized values ​​to represent the changes in environmental element attributes. Location coordinates are coordinate systems used to represent location or state, and can be used to represent the relationships or changes between different variables. For each set of recorded feature data of calibrated environmental element attributes, a location coordinate is constructed based on the normalized values ​​of the recorded features. For example, the first set of data constructs the first positioning coordinates, and the Hth set of data constructs the Hth positioning coordinates. These coordinates reflect the state under different combinations of environmental element attributes.

[0045] Outlier detection was performed on the positioning coordinates of all components to identify and remove data points that did not conform to normal variation patterns. These points were caused by measurement errors, data recording errors, or other abnormal factors. Data points that did not conform to normal distribution or expectations were excluded from the data, resulting in a set of characteristic values ​​representing the relationship between the first food component and key environmental element attributes under healthy storage conditions. Using the remaining characteristic values ​​of the calibrated environmental element attribute set, a threshold set of environmental elements for the first food component in the first time zone was constructed, setting a series of thresholds for environmental element attributes for the first food component within the first time zone. These thresholds reflect which environmental conditions are suitable and which may adversely affect the stability of the food component within the first time zone. By constructing positioning coordinates, the impact of different environmental element attributes on the food component is clearly represented, outliers are detected and removed, noise in the data is reduced, and the accuracy of the analysis is improved.

[0046] Furthermore, this application also includes the following steps: based on the first set of calibrated environmental element attributes, collecting second historical storage monitoring events of healthy storage samples of the first food component, wherein the second historical storage monitoring events include first set of calibrated environmental element attribute record feature data, second set of calibrated environmental element attribute record feature data up to the Hth set of calibrated environmental element attribute record feature data; obtaining a plurality of correlation degrees of calibrated environmental element attributes in the first set of calibrated environmental element attributes; traversing the plurality of correlation degrees of calibrated environmental element attributes, summing and comparing them with the plurality of correlation degrees of calibrated environmental element attributes to obtain the distribution weights of the plurality of calibrated environmental element attributes; the first set of calibrated environmental element attribute record feature data includes a plurality of first calibrated environmental element attribute record features, based on the plurality of first calibrated environmental element attribute record features... Several normalized values ​​of environmental element attribute record features are defined. These normalized values ​​are weighted according to the distribution weights of the defined environmental element attributes to construct a first positioning coordinate. This process continues until the first set of defined environmental element attribute record feature data includes several H-th defined environmental element attribute record features. Based on several normalized values ​​of these H-th defined environmental element attribute record features, these normalized values ​​are weighted according to the distribution weights of the defined environmental element attributes to construct an H-th positioning coordinate. Anomalies are deleted based on the first positioning coordinate up to the H-th positioning coordinate, resulting in several set feature values ​​of defined environmental element attributes. Based on these set feature values, a first food component environmental element threshold set for the first time zone is constructed.

[0047] Specifically, for the first food component, data from second historical storage monitoring events of its healthy storage samples are collected under different storage conditions, including recorded characteristic data of calibrated environmental element attributes. Second historical storage monitoring events refer to recorded changes in the first food component and its calibrated environmental element attributes under healthy storage conditions, including multiple sets of recorded characteristic data of calibrated environmental element attributes, such as sets one through H. For the first set of calibrated environmental element attributes, the correlation degree between each environmental element attribute and the changes in the first food component is calculated. The correlation degrees of several calibrated environmental element attributes are iterated through, summed, and compared to obtain the distribution weight of each environmental element attribute. The correlation degrees of all calibrated environmental element attributes are summed and then divided by the sum of these correlation degrees to obtain the distribution weight of each environmental element attribute.

[0048] The first set of calibrated environmental element attribute record feature data includes several first-stage calibrated environmental element attribute record features. These features are normalized to obtain several normalized values. Normalized values ​​convert the original data into a dimensionless, standardized form, facilitating comparisons between different attributes. Based on the distribution weights of the several calibrated environmental element attributes, weights are assigned to these normalized values ​​to construct the first positioning coordinates. Weighting refers to assigning different weights to different data points or features according to a certain standard or rule. Positioning coordinates refer to a coordinate system used to represent location or state, used to represent the relationship or change between different variables. Similarly, for each set of calibrated environmental element attribute record feature data, based on the normalized values ​​of the record features, and according to the distribution weights, a positioning coordinate is constructed. For example, the first set of data constructs the first positioning coordinate, and the Hth set of data constructs the Hth positioning coordinate, reflecting the state under different combinations of environmental element attributes.

[0049] Outliers are identified and removed from the first to the Hth positioning coordinates. These outliers are caused by measurement errors, data recording errors, or other abnormal factors. After outlier removal, a set of characteristic values ​​for the calibrated environmental element attributes is obtained, reflecting the stability of food components under different environmental conditions. Based on the characteristic values ​​of the calibrated environmental element attributes after removing outliers, a set of environmental element thresholds for the first food component in the first time zone is constructed. These thresholds reflect which environmental conditions are suitable within the first time zone and which may adversely affect the stability of the food component. Based on the weighted positioning coordinates, environmental control strategies can be optimized more effectively to ensure the stability of food components during storage. Constructing a set of environmental element thresholds allows for effective control and optimization of the production environment, ensuring product quality.

[0050] Furthermore, this application also includes the following steps: extracting a set of associated environmental element thresholds from the set of environmental element thresholds in the N time zones based on the time series characteristic information of the environmental element monitoring time series information; extracting the first component of the environmental element monitoring time series information of the environmental element monitoring time series information; extracting the first component of the environmental element thresholds set of the associated environmental element thresholds set; calculating the proportion of environmental elements in the first component of the environmental element monitoring time series information that do not belong to the first component of the environmental element thresholds set, and setting it as the environmental deviation coefficient.

[0051] Specifically, based on the time-series characteristics of environmental element monitoring data, including time-series features such as trends, periodicity, and abrupt changes, environmental element thresholds related to these time characteristics are extracted from the environmental element threshold sets of N time zones, forming associated environmental element threshold sets for each time zone. The first-component environmental element monitoring time-series information extracted from the environmental element monitoring time-series information, i.e., the data on the changes of relevant environmental elements over time, reflects the changes of environmental elements (such as temperature and humidity) upon which the first food component depends over time. The first-component environmental element threshold set extracted from the associated environmental element threshold sets defines the range of environmental conditions required for the first food component during storage and transportation, ensuring its quality and stability. The proportion of environmental elements in the first-component environmental element monitoring time-series information that do not belong to the first-component environmental element threshold set is calculated out of the total number of monitored environmental elements. This proportion is used as a deviation indicator for environmental control. A higher environmental deviation coefficient indicates a greater deviation between the environmental monitoring data and the set environmental element thresholds, resulting in poorer environmental control effectiveness. By extracting associated environmental element threshold sets and calculating environmental deviation coefficients, the production environment can be monitored and controlled more effectively, ensuring product quality.

[0052] In summary, the health food production safety early warning method combined with environmental monitoring provided in this application has the following technical effects:

[0053] By obtaining the production cycle of health food products, which includes the time-series information of component fluctuations, the system aggregates the component fluctuation time-series information across neighboring time zones to generate N time-zone food components. It then iterates through these N time-zone food components and configures environmental factors to obtain a set of N time-zone environmental factor thresholds. Environmental monitoring time-series information is obtained through environmental sensors. Based on this monitoring time-series information and the N time-zone environmental factor thresholds, deviation analysis is performed on the health food products to obtain an environmental deviation coefficient. When the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold, an early warning indicator is constructed for abnormal components in the health food products. These abnormal components require passing a test before production can resume. In other words, through real-time monitoring and deviation analysis, the production environment is dynamically adjusted to improve the quality and safety of health food products.

[0054] Example 2: Based on the same inventive concept as the health food production safety early warning method combined with environmental monitoring in the foregoing examples, this application also provides a health food production safety early warning system combined with environmental monitoring. Please refer to the appendix. Figure 2 The health food production safety early warning system combined with environmental monitoring includes:

[0055] The production cycle acquisition module 11 is used to obtain the production cycle of health food products, wherein the production cycle of health food products includes the time sequence information of component fluctuations.

[0056] The neighborhood time zone aggregation module 12 is used to traverse the component fluctuation time series information to perform neighborhood time zone aggregation and generate N time zone food components.

[0057] The environmental element configuration module 13 is used to traverse the food components of the N time zones and configure environmental elements to obtain a set of environmental element thresholds for the N time zones.

[0058] The environmental monitoring module 14 is used to obtain environmental element monitoring time sequence information through environmental sensors.

[0059] Deviation analysis module 15 is used to perform deviation analysis on health food based on the environmental element monitoring time series information and the threshold set of environmental elements in N time zones, and obtain the environmental deviation coefficient.

[0060] The warning label construction module 16 is used to construct a warning label for abnormal components in health food when the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold. The abnormal components in health food with the warning label need to pass the test before they can be put into production again.

[0061] Furthermore, the neighborhood time zone aggregation module 12 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0062] The component fluctuation time series information includes a first time-based component type set, a second time-based component type set, and so on up to the Mth time-based component type set; adjacent time-based component type sets are compared to obtain the component repetition ratio of adjacent time-based components; when the component repetition ratio of adjacent time-based components is equal to 1, adjacent time-based components are aggregated into the same time zone; when the component repetition ratio of adjacent time-based components is not equal to 1, adjacent time-based components are aggregated into different time zones; when the component repetition ratio of any two adjacent time-based components is not equal to 1, the food components of the N time zones are output.

[0063] Furthermore, the environmental element configuration module 13 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0064] Step 1: Based on the first food component of the first food component in the first time zone among the N time zone food components, perform correlation analysis on the set of environmental element attributes to obtain a first calibrated set of environmental element attributes; Step 2: Based on the first food component, perform joint sorting on the first calibrated set of environmental element attributes to generate a set of environmental element thresholds for the first food component in the first time zone; Step 3: Add the set of environmental element thresholds for the first food component in the first time zone to the set of environmental element thresholds for the first time zone; replace the data elements, and repeat steps 1 to 3 to obtain the set of environmental element thresholds for the N time zones.

[0065] Furthermore, the environmental element configuration module 13 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0066] Based on the set of environmental element attributes, the first historical storage monitoring events of the first food component are collected, wherein the first historical storage monitoring events include a dataset of changes in the first food component, a dataset of changes in the first environmental element attributes, a dataset of changes in the second environmental element attributes up to the Qth environmental element attribute change dataset; a first normalized data sequence is constructed based on the first food component change dataset; a second normalized data sequence up to the Q+1th normalized data sequence is constructed based on the first environmental element attribute change dataset, the second environmental element attribute change dataset up to the Qth environmental element attribute change dataset; using the first normalized data sequence as a reference sequence, grey relational analysis is performed on the second normalized data sequence up to the Q+1th normalized data sequence to obtain the correlation degree of the first environmental element attributes, the correlation degree of the second environmental element attributes up to the Qth environmental element attributes; environmental element attributes with correlation degrees greater than or equal to the correlation degree threshold among the first environmental element attribute correlation degrees, the second environmental element attribute correlation degrees up to the Qth environmental element attribute correlation degrees are labeled to obtain the first labeled environmental element attribute set.

[0067] Furthermore, the environmental element configuration module 13 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0068] Based on the first set of calibrated environmental element attributes, second historical storage monitoring events of healthy storage samples of the first food component are collected. These second historical storage monitoring events include a first set of calibrated environmental element attribute record feature data, a second set of calibrated environmental element attribute record feature data, and so on up to the Hth set of calibrated environmental element attribute record feature data. The first set of calibrated environmental element attribute record feature data includes several first calibrated environmental element attribute record features. Based on several normalized values ​​of these first calibrated environmental element attribute record features, a first positioning coordinate is constructed. Up to the Hth set of calibrated environmental element attribute record feature data, several Hth calibrated environmental element attribute record features are included. Based on several normalized values ​​of these Hth calibrated environmental element attribute record features, an Hth positioning coordinate is constructed. Anomaly point deletion is performed based on the first positioning coordinates up to the Hth positioning coordinates to obtain several feature values ​​from the calibrated environmental element attribute set. Based on these feature values, a threshold set of environmental elements for the first food component in the first time zone is constructed.

[0069] Furthermore, the environmental element configuration module 13 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0070] Based on the first set of calibrated environmental element attributes, second historical storage monitoring events of the healthy storage samples of the first food component are collected. These second historical storage monitoring events include first set of calibrated environmental element attribute record feature data, second set of calibrated environmental element attribute record feature data up to the Hth set of calibrated environmental element attribute record feature data. The correlation degrees of several calibrated environmental element attributes in the first set of calibrated environmental element attributes are obtained. These correlation degrees are iterated through, summed, and compared with other correlation degrees to obtain the distribution weights of several calibrated environmental element attributes. The first set of calibrated environmental element attribute record feature data includes several first calibrated environmental element attribute record features. Based on these several first calibrated environmental element attribute record features... Several normalized values ​​of the recorded features are weighted according to the distribution weights of the several calibrated environmental element attributes to construct a first positioning coordinate; until the first set of calibrated environmental element attribute record feature data includes several H-th calibrated environmental element attribute record features, several normalized values ​​of the several H-th calibrated environmental element attribute record features are weighted according to the distribution weights of the several calibrated environmental element attributes to construct an H-th positioning coordinate; based on the first positioning coordinate up to the H-th positioning coordinate, outlier points are deleted to obtain several calibrated environmental element attribute set feature values; based on the several calibrated environmental element attribute set feature values, a first food component environmental element threshold set for the first time zone is constructed.

[0071] Furthermore, the deviation analysis module 15 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0072] Based on the time-series characteristics of the environmental element monitoring time-series information, an associated environmental element threshold set is extracted from the N time zone environmental element threshold sets; the first component of the environmental element monitoring time-series information is extracted; the first component of the associated environmental element threshold set is extracted; the proportion of environmental elements whose monitoring time-series information does not belong to the first component of the environmental element threshold set is calculated and set as the environmental deviation coefficient.

[0073] Furthermore, the early warning identifier construction module 16 in the health food production safety early warning system combined with environmental monitoring is also used for:

[0074] If the abnormal ingredient in the health food has already been put into production, the health food containing the abnormal ingredient is a product that must be tested.

[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The method and specific examples for early warning of health food production safety combined with environmental monitoring in Example 1 are also applicable to the early warning system for early warning of health food production safety combined with environmental monitoring in this embodiment. Through the foregoing detailed description of the method for early warning of health food production safety combined with environmental monitoring, those skilled in the art can clearly understand the early warning system for early warning of health food production safety combined with environmental monitoring in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the system disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0076] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0077] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for early warning of health food production safety combined with environmental monitoring, characterized in that, include: Obtain the production cycle of health food products, wherein the production cycle of health food products includes the time sequence information of component fluctuations; The temporal information of the component fluctuations is traversed to aggregate neighboring time zones, generating N time zone food components, including: The component fluctuation time series information includes the component type set at the first time moment, the component type set at the second time moment, and the component type set up to the Mth time moment; The component type set at the first time point, the component type set at the second time point, and the component type set up to the Mth time point are compared between adjacent time points to obtain the component repetition ratio at adjacent time points; When the repetition ratio of adjacent time components is equal to 1, adjacent time points are aggregated into the same time zone; When the repetition ratio of adjacent time components is not equal to 1, adjacent time points are aggregated into different time zones; When the repetition ratio of components between any two adjacent time points is not equal to 1, output the food components of the N time zones; Iterate through the food components of the N time zones and configure environmental factors to obtain a set of threshold values ​​for the environmental factors of the N time zones, including: Step 1: Based on the first food component of the first time zone among the food components of the N time zones, perform a correlation analysis on the set of environmental element attributes to obtain the first set of calibrated environmental element attributes; Step 2: Based on the first food component, perform joint sorting on the first set of calibrated environmental element attributes to generate a set of environmental element thresholds for the first food component in the first time zone; Step 3: Add the first food component environmental element threshold set of the first time zone to the first time zone environmental element threshold set; Replace the data elements and repeat steps one through three to obtain the threshold set of the N time zone environmental elements. Environmental sensors are used to obtain time-series information on environmental element monitoring. Based on the monitoring time series information of the environmental elements and combined with the threshold sets of the N time zones for environmental elements, deviation analysis is performed on health food products to obtain environmental deviation coefficients, including: Based on the temporal characteristic information of the environmental element monitoring time series information, extract the associated environmental element threshold set from the N time zone environmental element threshold sets; The first component of the environmental element monitoring time series information is extracted from the environmental element monitoring time series information. Extract the first component of the associated environmental element threshold set; The proportion of environmental elements whose monitoring time series information of the first component environmental elements does not belong to the threshold set of the first component environmental elements is calculated and set as the environmental deviation coefficient. When the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold, an early warning label is constructed for abnormal components in health food. The abnormal components of health food with the early warning label need to pass the test before they can be put into production again.

2. The method for early warning of health food production safety combined with environmental monitoring as described in claim 1, characterized in that, Based on the first food component of the first time zone, a correlation analysis is performed on the set of environmental element attributes to obtain the first calibrated set of environmental element attributes, including: Based on the set of environmental element attributes, the first historical storage monitoring events of the first food component are collected, wherein the first historical storage monitoring events include the dataset of changes in the first food component, the dataset of changes in the first environmental element attributes, the dataset of changes in the second environmental element attributes, up to the dataset of changes in the Qth environmental element attributes. Based on the first dataset of changes in food components, a first normalized data sequence is constructed. Based on the first environmental element attribute change record dataset, the second environmental element attribute change record dataset up to the Qth environmental element attribute change record dataset, construct a second normalized data sequence up to the Q+1th normalized data sequence; Using the first normalized data sequence as the baseline sequence, grey relational analysis is performed on the second normalized data sequence up to the Q+1th normalized data sequence to obtain the first environmental element attribute correlation degree, the second environmental element attribute correlation degree up to the Qth environmental element attribute correlation degree. The environmental element attributes whose correlation degree is greater than or equal to the correlation degree threshold among the first environmental element attribute correlation degree, the second environmental element attribute correlation degree, and up to the Qth environmental element attribute correlation degree are labeled to obtain the first labeled environmental element attribute set.

3. The method for early warning of health food production safety combined with environmental monitoring as described in claim 1, characterized in that, Based on the first food component, the first set of calibrated environmental element attributes is jointly sorted to generate a set of environmental element thresholds for the first food component in the first time zone, including: Based on the first set of calibrated environmental element attributes, the second historical storage monitoring events of the healthy storage samples of the first food component are collected, wherein the second historical storage monitoring events include the first set of calibrated environmental element attribute record feature data, the second set of calibrated environmental element attribute record feature data up to the Hth set of calibrated environmental element attribute record feature data; The first set of calibration environmental element attribute record feature data includes several first calibration environmental element attribute record features. Based on several normalized values ​​of the several first calibration environmental element attribute record features, a first positioning coordinate is constructed. Until the Hth group of calibrated environmental element attribute record feature data includes several Hth calibrated environmental element attribute record features, the Hth positioning coordinates are constructed based on several normalized values ​​of the several Hth calibrated environmental element attribute record features; Based on the first positioning coordinates up to the Hth positioning coordinates, abnormal points are deleted to obtain feature values ​​in a set of calibration environmental element attributes. Based on the characteristic values ​​of the aforementioned set of calibrated environmental element attributes, a threshold set of environmental elements for the first food component in the first time zone is constructed.

4. The method for early warning of health food production safety combined with environmental monitoring as described in claim 2, characterized in that, Based on the first food component, the first set of calibrated environmental element attributes is jointly sorted to generate a set of environmental element thresholds for the first food component in the first time zone, including: Based on the first set of calibrated environmental element attributes, the second historical storage monitoring events of the healthy storage samples of the first food component are collected, wherein the second historical storage monitoring events include the first set of calibrated environmental element attribute record feature data, the second set of calibrated environmental element attribute record feature data up to the Hth set of calibrated environmental element attribute record feature data; Obtain the correlation degree of several calibration environmental element attributes in the first calibration environmental element attribute set; By iterating through the correlation degrees of the several calibrated environmental element attributes, summing and comparing them with the correlation degrees of the several calibrated environmental element attributes, the distribution weights of the several calibrated environmental element attributes are obtained. The first set of calibration environmental element attribute record feature data includes several first calibration environmental element attribute record features. Based on several normalized values ​​of the several first calibration environmental element attribute record features, the several normalized values ​​are weighted according to the distribution weight of the several calibration environmental element attributes to construct the first positioning coordinates. Until the first set of calibrated environmental element attribute record feature data includes several Hth calibrated environmental element attribute record features, based on several normalized values ​​of the several Hth calibrated environmental element attribute record features, the several normalized values ​​are weighted according to the distribution weight of the several calibrated environmental element attributes to construct the Hth positioning coordinates; Based on the first positioning coordinates up to the Hth positioning coordinates, abnormal points are deleted to obtain feature values ​​in a set of calibration environmental element attributes. Based on the characteristic values ​​of the aforementioned set of calibrated environmental element attributes, a threshold set of environmental elements for the first food component in the first time zone is constructed.

5. The method for early warning of health food production safety combined with environmental monitoring as described in claim 1, characterized in that, If the abnormal ingredient in the health food has already been put into production, the health food containing the abnormal ingredient is a product that must be tested.

6. A health food production safety early warning system combined with environmental monitoring, characterized in that: The step of implementing the health food production safety early warning method combined with environmental monitoring according to any one of claims 1 to 5, wherein the health food production safety early warning system combined with environmental monitoring includes: A production cycle acquisition module is used to obtain the production cycle of health food products, wherein the production cycle of health food products includes component fluctuation time sequence information. The neighborhood time zone aggregation module is used to traverse the component fluctuation time series information to aggregate neighborhood time zones and generate N time zone food components; An environmental element configuration module is used to traverse the food components of the N time zones and configure environmental elements to obtain a set of environmental element thresholds for the N time zones. An environmental monitoring module is used to obtain time-series information on environmental element monitoring through environmental sensors. The deviation analysis module is used to perform deviation analysis on health food based on the environmental element monitoring time series information and the threshold set of environmental elements in N time zones, and obtain the environmental deviation coefficient. The warning label construction module is used to construct a warning label for abnormal components in health food when the environmental deviation coefficient is greater than or equal to the environmental deviation coefficient threshold. The abnormal components of health food with the warning label need to pass the test before they can be put into production again.

Images