Food safety detection management and control method and system based on artificial intelligence

Abstract

The invention discloses a food safety detection management and control method and system based on artificial intelligence, and relates to the technical field of safety detection management and control. The method comprises the following steps: acquiring concentration data of an actual additive in a food sample, abnormal detection data and data of the additive under different environmental conditions, acquiring a first data set, a second data set and a third data set, preprocessing the acquired original data, and calculating to obtain a food safety index Spaq, Spaq, Spaq, Spaq, Spaq, Spaq, Spaq, Spaq, Spaq, Spaq and Spaq; the additive safety condition in the food production and processing process is monitored in real time, immediate feedback and early warning are provided, a report is automatically generated based on a grade evaluation result of a real-time monitoring module, processing suggestions for unqualified additives are provided, corresponding alarms are given out, and a detection algorithm and a detection model are periodically updated so as to adapt to new technologies and standards. And the detection accuracy and efficiency of the system are evaluated, and optimization and adjustment are carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of safety detection and control, and specifically to a food safety detection and control method and system based on artificial intelligence. Background Art

[0002] Food safety has always been a global concern, among which the use and management of food additives are particularly important. Food additives play a vital role in food production and can improve the taste, appearance and shelf life of food. However, with the development of the food industry, the types and usage of additives have continued to increase, bringing potential health risks. The safety testing of additives is particularly critical because the consumer groups of these foods are more sensitive to additives and have more stringent safety requirements. Therefore, the safety testing of food additives has become an important part of ensuring food safety.

[0003] The application of artificial intelligence in food safety testing and control systems provides a new path to solve many deficiencies in traditional testing methods. Traditional food safety testing often relies on manual operations, with low testing efficiency and difficulty in ensuring the accuracy and consistency of test results. Especially in the process of testing food additives, traditional testing methods are difficult to cope with complex ingredients and changing environmental conditions. In addition, traditional food safety testing systems lack real-time performance and are unable to detect and warn of potential safety risks in a timely manner.

[0004] The shortcomings of existing food safety testing systems are mainly reflected in the lack of timeliness and comprehensiveness of testing, which leads to the failure to detect potential food safety hazards in a timely manner. For example, due to the limitations of manual testing, some subtle concentration changes and environmental factors may be overlooked, which in turn leads to problems such as substandard additive concentrations or insufficient stability. When these problems occur, food quality may decline and even have adverse effects on consumers' health. If this situation is not monitored and handled in a timely manner during the production process, unqualified products will flow into the market, ultimately leading to more serious food safety incidents. Therefore, there is an urgent need for an efficient, accurate and comprehensive testing system to make up for the shortcomings of existing testing methods and ensure food safety. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a food safety detection and control method and system based on artificial intelligence, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a food safety detection and control system based on artificial intelligence, including a data acquisition module, a data preprocessing module, an intelligent detection module, a real-time monitoring module, a decision support module and a system maintenance module;

[0007] The data acquisition module is used to collect various data related to the safety of additives in food, obtain the concentration data of actual additives in food samples, abnormality detection data and data of additives under different environmental conditions, and obtain the first data group, the second data group and the third data group;

[0008] The data preprocessing module is used to clean, normalize and extract features from the collected raw data to ensure the accuracy and consistency of the data;

[0009] The intelligent detection module is used to classify and predict the type, concentration and safety risk of additives using machine learning algorithms, identify potential safety hazards using clustering algorithms and anomaly detection technology, train models based on historical data, and calculate and obtain: Food safety index Spaq;

[0010] The real-time monitoring module is used to monitor the safety of additives in food production and processing in real time, provide immediate feedback and early warning, display the current additive detection status, concentration and abnormal information, set threshold level evaluation, and issue an alarm to notify relevant personnel in a timely manner when an excess or abnormal situation is detected;

[0011] The decision support module is used to automatically generate a report containing additive concentration, stability evaluation and abnormal detection results based on the grade evaluation results of the real-time monitoring module, provide treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms;

[0012] The system maintenance module is used to regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and perform optimization and adjustments.

[0013] Preferably, the data acquisition module includes an additive concentration data acquisition unit, an abnormality detection data acquisition unit and an additive stability data acquisition unit;

[0014] The additive concentration data acquisition unit is used to collect the actual concentration data of the additives in the food sample, and collects and records the actual concentration, allowable concentration range and concentration deviation of the additives in the food through high performance liquid chromatograph, gas chromatograph and mass spectrometer equipment to obtain: actual concentration data C act , Maximum allowed concentration data C max , allowable minimum concentration data C min and concentration compliance rate R com , forming a first data group;

[0015] The abnormal detection data acquisition unit is used to collect abnormal situation data occurring during food production and detection. Through intelligent sensors, real-time monitoring systems and data analysis platforms, real-time monitoring and recording of abnormal detection rate, number of abnormal types and system response related data are obtained: abnormal detection rate data R anom , abnormal type number data N anom , Detection sensitivity S sens , false alarm rate R false and abnormal response time data T resp , forming a second data group;

[0016] The additive stability data collection unit is used to collect the stability data of the additive under different environmental conditions, and collects and records the degradation rate, stability test results and environmental influencing factors of the additive through accelerated aging test equipment, high performance liquid chromatograph and environmental control system equipment to obtain: degradation rate data R deg , stability test result data S test 、Data of environmental impact factors E impact , Shelf Life life and the physical interaction value P interact , forming a third data group.

[0017] Preferably, the data preprocessing module includes a data cleaning unit and a feature extraction unit;

[0018] The data cleaning unit is used to clean the collected raw data, mainly including removing noise data, processing missing values ​​and outliers;

[0019] The feature extraction unit is used to normalize the cleaned data to eliminate the differences between different data scales and extract key feature parameters to provide an optimized feature data set for subsequent algorithm modeling.

[0020] Preferably, the intelligent detection module includes a prediction unit;

[0021] The prediction unit is used to classify and predict the type, concentration and safety risk of additives by applying machine learning algorithms, and generate food safety index Spaq, additive concentration coefficient K c , Safety standard compliance coefficient K s and additive stability factor K e .

[0022] Preferably, the real-time monitoring module includes an early warning unit;

[0023] The early warning unit is used to monitor the safety of additives in the food production and processing process in real time, compare the food safety index Spaq with the pre-set first qualified threshold M and second qualified threshold N, and grade the current detection status, concentration and abnormal information of the additives. Once an excess or abnormal situation is detected, an alarm is immediately issued to notify relevant personnel to ensure that corresponding measures are taken in time.

[0024] Preferably, the corresponding measures include:

[0025] The food safety index Spaq ≥ the second qualified threshold N, indicating that the safety of the food additive is more than 20% higher than the qualified state. The existing production and processing processes should continue to be maintained, and regular monitoring and testing should be carried out;

[0026] The first qualified threshold M ≤ Food Safety Index Spaq < the second qualified threshold N, indicating that the safety of food additives is qualified, but there is still room for improvement, such as increasing the monitoring frequency of concentration and stability, and timely discovering factors that affect the Food Safety Index Spaq;

[0027] If the safety index Spaq is less than the first qualified threshold M, it indicates that there are hidden dangers in the safety of food additives. It is judged as unqualified, and production is stopped immediately. A comprehensive production line inspection is carried out, the automatic early warning system is activated, relevant personnel are notified immediately, and the emergency plan is initiated.

[0028] Preferably, the decision support module includes a report generation unit and a processing suggestion unit;

[0029] The report generation unit is used to automatically generate a comprehensive report based on the grade evaluation results of the real-time monitoring module, the report content includes additive concentration, stability evaluation and abnormal detection results, and provides trend analysis.

[0030] Preferably, the processing suggestion unit is used to automatically evaluate whether further action is needed based on the data provided by the report generation unit, including adjusting the additive formula, suspending production or recalling the product. Once unqualified additives are detected, specific processing suggestions are immediately generated, and corresponding alarms are issued through the system to notify relevant personnel to take timely action.

[0031] Preferably, the system maintenance module includes a model updating unit;

[0032] The model updating unit is used to regularly update and optimize the detection algorithms and models, evaluate the detection accuracy and efficiency of the system, identify areas for improvement, and make adjustments to adapt to new technologies and standards.

[0033] A food safety detection and control method based on artificial intelligence, comprising the following steps:

[0034] Step 1: By collecting various data related to the safety of additives in food, the concentration data of actual additives in food samples, abnormal detection data and data of additives under different environmental conditions are obtained to obtain the first data group, the second data group and the third data group;

[0035] Step 2: Clean, normalize and extract features of the collected raw data to ensure the accuracy and consistency of the data;

[0036] Step 3: Use machine learning algorithms to classify and predict additive types, concentrations, and safety risks, apply clustering algorithms and anomaly detection techniques to identify potential safety hazards, train models based on historical data, and calculate and obtain the food safety index Spaq;

[0037] Step 4: Real-time monitoring of the safety of additives in food production and processing, providing immediate feedback and early warning, displaying the current additive detection status, concentration and abnormal information, setting threshold level assessment, and issuing an alarm to notify relevant personnel in a timely manner when exceeding the standard or abnormal situation is detected;

[0038] Step 5: Based on the grade assessment results of the real-time monitoring module, a report containing additive concentration, stability assessment and abnormal detection results is automatically generated, providing treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms;

[0039] Step 6: Regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and make optimizations and adjustments.

[0040] The present invention provides a food safety detection and control method and system based on artificial intelligence, which has the following beneficial effects:

[0041] (1) When the system is running, it collects various data related to the safety of additives in food, obtains the concentration data of actual additives in food samples, abnormal detection data and data of additives under different environmental conditions, obtains the first data group, the second data group and the third data group, pre-processes the collected raw data, and obtains the food safety index Spaq after calculation. It monitors the safety of additives in food production and processing in real time, provides immediate feedback and early warning, automatically generates reports based on the level evaluation results of the real-time monitoring module, provides treatment suggestions for unqualified additives, and issues corresponding alarms. It regularly updates the detection algorithm and model to adapt to new technologies and standards, evaluates the detection accuracy and efficiency of the system, and optimizes and adjusts it.

[0042] (2) The AI-based food safety detection and control system significantly improves the efficiency and accuracy of food additive safety detection through the coordinated work of six modules. The data acquisition module is responsible for comprehensively collecting key additive concentrations, anomaly detection, and stability data in food samples to ensure that all relevant factors are fully considered. The data preprocessing module cleans and extracts features from these data, eliminating noise and outliers in the data, laying a solid foundation for subsequent algorithm analysis. The intelligent detection module uses advanced machine learning algorithms to accurately classify and predict the type, concentration, and potential safety risks of additives, and achieves accurate calculation of the food safety index Spaq.

[0043] (3) The real-time monitoring module monitors the safety of additives in the food production process in real time. Through the set threshold level assessment, it ensures that once an excess or abnormal situation is detected, an alarm can be issued in time and relevant personnel can be notified. Compared with traditional manual detection methods, this real-time and automated monitoring greatly shortens the response time and improves the agility of detection. Based on the monitoring results, the decision support module automatically generates a comprehensive report and makes targeted processing suggestions, such as adjusting the formula, suspending production or recalling products, thereby further ensuring food safety. The system maintenance module regularly updates the detection algorithms and models to ensure that the system can continue to adapt to new technical standards and market demands, and maintain the efficiency and reliability of the detection system.

[0044] (4) Compared with traditional technical means, the introduction of this system not only improves the comprehensiveness and accuracy of food additive safety testing, but also significantly enhances the capabilities of real-time monitoring and emergency response. Traditional testing methods often rely on manual operations, which are inefficient and susceptible to human factors. The automation and intelligent processing methods of this system effectively solve these shortcomings. In addition, by continuously updating and optimizing algorithms, the system can keep up with the latest technological developments and industry standards to ensure that food safety testing is always at the leading level. These improvements enable safety hazards in the food production process to be discovered and dealt with earlier, thereby effectively improving the overall level of food safety protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a block diagram of a food safety detection and control system based on artificial intelligence in the present invention;

[0046] Figure 2 This is a schematic diagram of the steps of an artificial intelligence-based food safety detection and control method of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] The present invention provides a food safety detection and control system based on artificial intelligence, please refer to Figure 1 , including data acquisition module, data preprocessing module, intelligent detection module, real-time monitoring module, decision support module and system maintenance module;

[0050] The data acquisition module is used to collect various data related to the safety of additives in food, obtain the concentration data of actual additives in food samples, abnormality detection data and data of additives under different environmental conditions, and obtain the first data group, the second data group and the third data group;

[0051] The data preprocessing module is used to clean, normalize and extract features from the collected raw data to ensure the accuracy and consistency of the data;

[0052] The intelligent detection module is used to classify and predict the type, concentration and safety risk of additives using machine learning algorithms, identify potential safety hazards using clustering algorithms and anomaly detection technology, train models based on historical data, and calculate and obtain: Food safety index Spaq;

[0053] The real-time monitoring module is used to monitor the safety of additives in food production and processing in real time, provide immediate feedback and early warning, display the current additive detection status, concentration and abnormal information, set threshold level evaluation, and issue an alarm to notify relevant personnel in a timely manner when an excess or abnormal situation is detected;

[0054] The decision support module is used to automatically generate a report containing additive concentration, stability evaluation and abnormal detection results based on the grade evaluation results of the real-time monitoring module, provide treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms;

[0055] The system maintenance module is used to regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and perform optimization and adjustments.

[0056] In this embodiment, by collecting various data related to the safety of additives in food, obtaining the concentration data of actual additives in food samples, abnormal detection data and data of additives under different environmental conditions, obtaining the first data group, the second data group and the third data group, pre-processing the collected raw data, and obtaining after calculation: the food safety index Spaq, real-time monitoring of the safety of additives in food production and processing, providing instant feedback and early warning, and automatically generating a report including additive concentration, stability evaluation and abnormal detection results based on the grade evaluation results of the real-time monitoring module, providing treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms, regularly updating the detection algorithm and model to adapt to new technologies and standards, evaluating the detection accuracy and efficiency of the system, and optimizing and adjusting.

[0057] Example 2

[0058] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the data acquisition module includes an additive concentration data acquisition unit, an abnormality detection data acquisition unit and an additive stability data acquisition unit;

[0059] The additive concentration data acquisition unit is used to collect the actual concentration data of the additives in the food sample, and collects and records the actual concentration, allowable concentration range and concentration deviation of the additives in the food through high performance liquid chromatograph, gas chromatograph and mass spectrometer equipment to obtain: actual concentration data C act , Maximum allowed concentration data C max , allowable minimum concentration data C min and concentration compliance rate R com , forming a first data group;

[0060] The abnormal detection data acquisition unit is used to collect abnormal situation data occurring during food production and detection. Through intelligent sensors, real-time monitoring systems and data analysis platforms, real-time monitoring and recording of abnormal detection rate, number of abnormal types and system response related data are obtained: abnormal detection rate data R anom , abnormal type number data N anom , Detection sensitivity S sens , false alarm rate R false and abnormal response time data T resp , forming a second data group;

[0061] The additive stability data collection unit is used to collect the stability data of the additive under different environmental conditions, and collects and records the degradation rate, stability test results and environmental influencing factors of the additive through accelerated aging test equipment, high performance liquid chromatograph and environmental control system equipment to obtain: degradation rate data Rdeg , stability test result data S test 、Data of environmental impact factors E impact , Shelf Life life and the physical interaction value P interact , forming a third data group.

[0062] The data preprocessing module includes a data cleaning unit and a feature extraction unit;

[0063] The data cleaning unit is used to clean the collected raw data, mainly including removing noise data, processing missing values ​​and outliers;

[0064] The feature extraction unit is used to normalize the cleaned data to eliminate the differences between different data scales and extract key feature parameters to provide an optimized feature data set for subsequent algorithm modeling.

[0065] In this embodiment, the data acquisition module can comprehensively and accurately collect key data related to the safety of food additives through the additive concentration data acquisition unit, the abnormal detection data acquisition unit and the additive stability data acquisition unit. The actual concentration data obtained by the high-performance liquid chromatograph, gas chromatograph and mass spectrometer equipment and the abnormal detection data collected by the intelligent sensor and real-time monitoring system enable the system to accurately evaluate the safety of additives. This comprehensive data acquisition improves the system's ability to identify food safety hazards and provides a solid foundation for subsequent detection and analysis. The data preprocessing module effectively removes noise and outliers in the original data through the data cleaning unit and the feature extraction unit, and normalizes the data. This preprocessing ensures the accuracy and consistency of the data and avoids analysis deviations caused by data quality problems. By extracting key feature parameters, the module also provides an optimized feature data set for subsequent algorithm modeling, improves the efficiency and accuracy of the intelligent detection algorithm, and ensures the reliability of the final test results.

[0066] Example 3

[0067] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the intelligent detection module includes a prediction unit;

[0068] The prediction unit is used to classify and predict the type, concentration and safety risk of additives by applying machine learning algorithms, and generate food safety index Spaq, additive concentration coefficient K c , Safety standard compliance coefficient K s and additive stability factor K e ;

[0069] The food safety index Spaq is calculated by the following formula:

[0070] Spaq=α*K c +β*K s +γ*K e ;

[0071] In the formula, K c Indicates the additive concentration coefficient, K s Indicates the safety standard compliance coefficient, K e represents the additive stability coefficient, α, β and γ represent the additive concentration coefficient K c , Safety standard compliance coefficient K s and additive stability factor K e The proportionality coefficient of

[0072] The additive concentration coefficient K c Calculated by the following formula:

[0073]

[0074] In the formula, C act Indicates actual concentration data, C max Indicates the maximum allowable concentration data, C min Indicates the minimum allowable concentration data, R com Indicates the concentration compliance rate.

[0075] The concentration compliance rate R com The calculation is done by the following formula:

[0076]

[0077] The safety standard compliance coefficient K s Calculated by the following formula:

[0078]

[0079] In the formula, R anom Represents the anomaly detection rate data, N anom Indicates the number of abnormal types, S sens Indicates the detection sensitivity, R false represents the false alarm rate, T resp Indicates abnormal response time data.

[0080] The anomaly detection rate data R anom The calculation is done by the following formula:

[0081] The false alarm rate R false The calculation is done by the following formula:

[0082] The additive stability factor K e Calculated by the following formula:

[0083]

[0084] In the formula, R deg Degradation rate data, S test Indicates the stability test result data, E impact Indicates environmental impact factor data, P life Indicates shelf life, P interact Represents the physical interaction value.

[0085] The degradation rate data R deg The calculation is done by the following formula:

[0086] In this embodiment, the intelligent detection module uses the prediction unit to effectively use the machine learning algorithm to classify and predict the type, concentration and safety risk of additives. The generated food safety index Spaq and additive concentration coefficient K c , Safety standard compliance coefficient K s and additive stability factor K e It can fully reflect the safety status of additives in food. This comprehensive evaluation method based on multi-dimensional data is more accurate than traditional single parameter testing and significantly improves the ability to predict and manage food safety risks. The system calculates the additive concentration coefficient K through a series of complex algorithm formulas. c , Safety standard compliance coefficient K s and additive stability factor K e Able to draw conclusions more quickly. These algorithms can not only process large amounts of data in real time, but also improve detection accuracy by continuously optimizing models. This efficient and accurate algorithm-driven detection method significantly shortens the detection cycle, allowing food production and supervision to respond in a shorter time, thereby improving the efficiency of overall food safety management.

[0087] Example 4

[0088] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the real-time monitoring module includes an early warning unit;

[0089] The early warning unit is used to monitor the safety of additives in the food production and processing process in real time, and compares the food safety index Spaq with the pre-set first qualified threshold M and second qualified threshold N to grade the current detection status, concentration and abnormal information of the additives. Once an excess or abnormal situation is detected, an alarm is immediately issued to notify relevant personnel to ensure that corresponding measures are taken in time;

[0090] The food safety index Spaq ≥ the second qualified threshold N, indicating that the safety of food additives is more than 20% higher than the qualified state. Continue to maintain the existing production and processing processes, conduct regular monitoring and testing, and further optimize the automated control of the additive delivery system to ensure that the additive concentration is more stable between Cmax and Cmin. You can install more precise flow control valves or use a real-time concentration monitoring system, and regularly evaluate and adjust the production environment, such as controlling environmental factors such as temperature and humidity, to further improve the stability of the additive Stest;

[0091] The first qualified threshold M≤food safety index Spaq<the second qualified threshold N, indicating that the safety of food additives is qualified, but there is still room for improvement. The monitoring frequency of concentration and stability should be increased, and factors affecting the food safety index Spaq should be discovered in time. The production of each batch should be monitored and recorded more carefully, especially the key process parameters such as temperature and pressure should be optimized to ensure the stability of each batch of products. The storage conditions of additives should be gradually improved during the production and storage process, such as controlling light and oxygen content, to reduce the degradation rate Rdeg;

[0092] If the safety index Spaq is less than the first qualified threshold M, it indicates that there are hidden dangers in the safety of food additives. It is judged as unqualified, and production is immediately suspended. The production line is fully checked, the equipment or raw materials with problems are adjusted or replaced, the concentration control system is recalibrated, the automatic early warning system is activated, the relevant personnel are notified immediately, and the emergency plan is activated.

[0093] The decision support module includes a report generation unit and a processing suggestion unit;

[0094] The report generation unit is used to automatically generate a comprehensive report based on the grade evaluation results of the real-time monitoring module, the report content includes additive concentration, stability evaluation and abnormal detection results, and provides trend analysis;

[0095] The processing suggestion unit is used to automatically evaluate whether further action is needed based on the data provided by the report generation unit, including adjusting the additive formula, suspending production or recalling the product. Once unqualified additives are detected, specific processing suggestions are immediately generated, and corresponding alarms are issued through the system to notify relevant personnel to take timely action.

[0096] The system maintenance module includes a model updating unit;

[0097] The model updating unit is used to regularly update and optimize the detection algorithms and models, evaluate the detection accuracy and efficiency of the system, identify areas for improvement, and make adjustments to adapt to new technologies and standards.

[0098] In this embodiment, the early warning unit in the real-time monitoring module realizes all-round dynamic monitoring of the food production process by comparing the food safety index Spaq with the preset threshold in real time. When the safety of the additive is lower than the early warning value, the system will immediately issue an alarm to notify relevant personnel to take emergency measures quickly. This immediate early warning and response mechanism effectively prevents potential food safety hazards, provides stronger protection for food safety, and significantly reduces the probability of accidents. Through the decision support module, the system can automatically generate detailed monitoring reports and provide specific processing suggestions based on the test results. Whether it is adjusting the formula, suspending production, or recalling products, the system can give the best action plan. This intelligent decision support not only improves the efficiency of food safety management, but also makes the production process more accurate and controllable, ensuring the high quality and safety of the final product.

[0099] Example 5

[0100] A food safety detection and control method based on artificial intelligence, please refer to Figure 2 , specifically: including the following steps:

[0101] Step 1: By collecting various data related to the safety of additives in food, the concentration data of actual additives in food samples, abnormal detection data and data of additives under different environmental conditions are obtained to obtain the first data group, the second data group and the third data group;

[0102] Step 2: Clean, normalize and extract features of the collected raw data to ensure the accuracy and consistency of the data;

[0103] Step 3: Use machine learning algorithms to classify and predict additive types, concentrations, and safety risks, apply clustering algorithms and anomaly detection techniques to identify potential safety hazards, train models based on historical data, and calculate and obtain the food safety index Spaq;

[0104] Step 4: Real-time monitoring of the safety of additives in food production and processing, providing immediate feedback and early warning, displaying the current additive detection status, concentration and abnormal information, setting threshold level assessment, and issuing an alarm to notify relevant personnel in a timely manner when exceeding the standard or abnormal situation is detected;

[0105] Step 5: Based on the grade assessment results of the real-time monitoring module, a report containing additive concentration, stability assessment and abnormal detection results is automatically generated, providing treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms;

[0106] Step 6: Regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and make optimizations and adjustments.

[0107] In this embodiment, in the first and second steps, through systematic data collection and preprocessing, it is ensured that all key data related to the safety of additives in food samples are accurately recorded and processed. The concentration data, abnormal detection data and stability data of additives under different environmental conditions obtained by various advanced equipment have built a detailed and reliable data foundation. Subsequent cleaning and normalization processing further improves the accuracy and consistency of the data, laying a solid foundation for the intelligent analysis of the system. In the third and fourth steps, the system uses machine learning algorithms and clustering techniques to accurately classify and predict the type, concentration and potential safety risks of additives. The real-time monitoring module realizes dynamic monitoring and risk management of the entire food production process by calculating and setting thresholds for the food safety index Spaq. When an abnormality or exceeding the standard is detected, the system can immediately issue an early warning and notify relevant personnel to ensure that food safety hazards are identified and handled in a timely manner. In the fifth and sixth steps, the system can not only generate detailed test reports based on real-time monitoring data, but also provide specific processing suggestions based on the report to ensure that every step in the production process meets safety standards. In addition, the model update function of the system can continuously optimize the detection algorithm and model to adapt to new technologies and industry standards. This continuous optimization mechanism enables the system to maintain efficient and accurate detection capabilities during long-term operation, improving the overall safety of food production and product quality.

[0108] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A food safety detection and control system based on artificial intelligence, characterized by: It includes data acquisition module, data preprocessing module, intelligent detection module, real-time monitoring module, decision support module and system maintenance module; The data acquisition module is used to collect various data related to the safety of additives in food, obtain the concentration data of actual additives in food samples, abnormality detection data and data of additives under different environmental conditions, and obtain the first data group, the second data group and the third data group; The data preprocessing module is used to clean, normalize and extract features from the collected raw data to ensure the accuracy and consistency of the data; The intelligent detection module is used to classify and predict the type, concentration and safety risk of additives using machine learning algorithms, identify potential safety hazards using clustering algorithms and anomaly detection technology, train models based on historical data, and calculate and obtain: Food safety index Spaq; The real-time monitoring module is used to monitor the safety of additives in food production and processing in real time, provide immediate feedback and early warning, display the current additive detection status, concentration and abnormal information, set threshold level evaluation, and issue an alarm to notify relevant personnel in a timely manner when an excess or abnormal situation is detected; The decision support module is used to automatically generate a report containing additive concentration, stability evaluation and abnormal detection results based on the grade evaluation results of the real-time monitoring module, provide treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms; The system maintenance module is used to regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and perform optimization and adjustments.

2. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The data acquisition module includes an additive concentration data acquisition unit, an abnormality detection data acquisition unit, and an additive stability data acquisition unit; The additive concentration data acquisition unit is used to collect the actual concentration data of the additives in the food sample, and collects and records the actual concentration, allowable concentration range and concentration deviation of the additives in the food through high performance liquid chromatograph, gas chromatograph and mass spectrometer equipment to obtain: actual concentration data C act , Maximum allowed concentration data C max , allowable minimum concentration data C min and concentration compliance rate R com , forming a first data group; The abnormal detection data acquisition unit is used to collect abnormal situation data occurring during food production and detection. Through intelligent sensors, real-time monitoring systems and data analysis platforms, real-time monitoring and recording of abnormal detection rate, number of abnormal types and system response related data are obtained: abnormal detection rate data R anom , abnormal type number data N anom , Detection sensitivity S sens , false alarm rate R false and abnormal response time data T resp , forming a second data group; The additive stability data collection unit is used to collect the stability data of the additive under different environmental conditions, and collects and records the degradation rate, stability test results and environmental influencing factors of the additive through accelerated aging test equipment, high performance liquid chromatograph and environmental control system equipment to obtain: degradation rate data R deg , stability test result data S test 、Data of environmental impact factors E impact , Shelf Life life and the physical interaction value P interact , forming a third data group.

3. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The data preprocessing module includes a data cleaning unit and a feature extraction unit; The data cleaning unit is used to clean the collected raw data, mainly including removing noise data, processing missing values ​​and outliers; The feature extraction unit is used to normalize the cleaned data to eliminate the differences between different data scales and extract key feature parameters to provide an optimized feature data set for subsequent algorithm modeling.

4. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The intelligent detection module includes a prediction unit; The prediction unit is used to classify and predict the type, concentration and safety risk of additives by applying machine learning algorithms, and generate food safety index Spaq, additive concentration coefficient K c , Safety standard compliance coefficient K s and additive stability factor K e .

5. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The real-time monitoring module includes an early warning unit; The early warning unit is used to monitor the safety of additives in the food production and processing process in real time, compare the food safety index Spaq with the pre-set first qualified threshold M and second qualified threshold N, and grade the current detection status, concentration and abnormal information of the additives. Once an excess or abnormal situation is detected, an alarm is immediately issued to notify relevant personnel to ensure that corresponding measures are taken in time.

6. The food safety detection and control system based on artificial intelligence according to claim 5 is characterized in that: The corresponding measures include: The food safety index Spaq ≥ the second qualified threshold N, indicating that the safety of the food additive is more than 20% higher than the qualified state. The existing production and processing processes should continue to be maintained, and regular monitoring and testing should be carried out; The first qualified threshold M ≤ Food Safety Index Spaq < the second qualified threshold N, indicating that the safety of food additives is qualified, but there is still room for improvement, such as increasing the monitoring frequency of concentration and stability, and timely discovering factors that affect the Food Safety Index Spaq; If the safety index Spaq is less than the first qualified threshold M, it indicates that there are hidden dangers in the safety of food additives. It is judged as unqualified, and production is stopped immediately. A comprehensive production line inspection is carried out, the automatic early warning system is activated, relevant personnel are notified immediately, and the emergency plan is initiated.

7. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The decision support module includes a report generation unit and a processing suggestion unit; The report generation unit is used to automatically generate a comprehensive report based on the grade evaluation results of the real-time monitoring module, the report content includes additive concentration, stability evaluation and abnormal detection results, and provides trend analysis.

8. The food safety detection and control system based on artificial intelligence according to claim 7 is characterized in that: The processing suggestion unit is used to automatically evaluate whether further action is needed based on the data provided by the report generation unit, including adjusting the additive formula, suspending production or recalling the product. Once unqualified additives are detected, specific processing suggestions are immediately generated, and corresponding alarms are issued through the system to notify relevant personnel to take timely action.

9. The food safety detection and control system based on artificial intelligence according to claim 1 is characterized in that: The system maintenance module includes a model updating unit; The model updating unit is used to regularly update and optimize the detection algorithms and models, evaluate the detection accuracy and efficiency of the system, identify areas for improvement, and make adjustments to adapt to new technologies and standards.

10. A food safety detection and control method based on artificial intelligence, applied to a food safety detection and control system based on artificial intelligence according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: By collecting various data related to the safety of additives in food, the concentration data of actual additives in food samples, abnormal detection data and data of additives under different environmental conditions are obtained to obtain the first data group, the second data group and the third data group; Step 2: Clean, normalize and extract features of the collected raw data to ensure the accuracy and consistency of the data; Step 3: Use machine learning algorithms to classify and predict additive types, concentrations, and safety risks, apply clustering algorithms and anomaly detection techniques to identify potential safety hazards, train models based on historical data, and calculate and obtain the food safety index Spaq; Step 4: Real-time monitoring of the safety of additives in food production and processing, providing immediate feedback and early warning, displaying the current additive detection status, concentration and abnormal information, setting threshold level assessment, and issuing an alarm to notify relevant personnel in a timely manner when exceeding the standard or abnormal situation is detected; Step 5: Based on the grade assessment results of the real-time monitoring module, a report containing additive concentration, stability assessment and abnormal detection results is automatically generated, providing treatment suggestions for unqualified additives, including adjusting the formula, suspending production or recalling products, and issuing corresponding alarms; Step 6: Regularly update detection algorithms and models to adapt to new technologies and standards, evaluate the detection accuracy and efficiency of the system, and make optimizations and adjustments.

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