A quality inspection method and device for finished aluminum foil lunch box products

Abstract

The present invention discloses a quality inspection method and device for finished aluminum foil lunch boxes, relating to the field of chemical analysis and testing methods. The method comprises: receiving a food safety performance inspection instruction; obtaining a sampled aluminum foil lunch box; performing a trusted component safety inspection and a trusted microbial safety inspection on the sampled aluminum foil lunch box according to a lunch box component hazard detection model, generating a lunch box component safety inspection result and a lunch box microbial safety inspection result; performing a migration test and migration safety identification on the sampled aluminum foil lunch box, generating a lunch box migration safety inspection result; and drawing a lunch box safety performance inspection cloud map. This method solves the technical problem in the prior art that the food safety of the finished aluminum foil lunch box cannot be accurately inspected due to the low accuracy of the food safety performance inspection of the finished aluminum foil lunch box, resulting in harm to the health of the user. By introducing migration detection, the technical effect of accurately inspecting the food safety of the finished aluminum foil lunch box is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of chemical analysis and testing methods, and specifically to a quality inspection method and device for finished aluminum foil lunch box products. Background Art

[0002] Aluminum foil lunch boxes are common food packaging containers in the modern catering industry, and their quality and safety are of vital importance to consumers. Therefore, quality testing of finished aluminum foil lunch boxes is an important step in ensuring that they comply with relevant standards and regulations and protecting consumer rights. In terms of the development of quality testing technology, with the continuous breakthroughs in fields such as materials science, instrumental analysis, and information technology, the quality testing methods for finished aluminum foil lunch boxes have also been greatly improved. For example, advanced material analysis technology can accurately measure the chemical composition and physical properties of aluminum foil lunch boxes, thereby evaluating their quality and safety; high-precision instrumental analysis equipment can detect harmful substances that may be released by aluminum foil lunch boxes during food contact, ensuring that they meet food safety standards. Although the existing technical background provides strong support for the quality testing of finished aluminum foil lunch boxes, some challenges and needs still exist. Existing quality testing methods have low accuracy in testing the food safety performance of finished aluminum foil lunch boxes, resulting in the inability to accurately test the food safety of finished aluminum foil lunch boxes, which can easily lead to technical problems that endanger the health of users.

[0003] In summary, the prior art has a technical problem in that the food safety performance of the finished aluminum foil lunch box cannot be accurately tested due to low accuracy in testing the food safety performance of the finished aluminum foil lunch box, resulting in harm to the health of users. Summary of the Invention

[0004] Based on this, it is necessary to provide a quality inspection method and device for finished aluminum foil lunch boxes that can accurately detect the food safety of finished aluminum foil lunch boxes by introducing migration detection to address the above technical problems.

[0005] In a first aspect, a quality inspection method for finished aluminum foil lunch boxes is provided, the method comprising: receiving a food safety performance inspection instruction for a batch of finished aluminum foil lunch boxes, wherein the food safety performance inspection instruction comprises a batch full inspection instruction / batch random inspection instruction, and the batch random inspection instruction comprises a predetermined batch random inspection coefficient; when the food safety performance inspection instruction is a batch random inspection instruction, sampling and pre-processing the batch of finished aluminum foil lunch boxes according to the predetermined batch random inspection coefficient to obtain random inspection aluminum foil lunch boxes, wherein the pre-processing comprises a cleaning treatment; performing a confident component safety inspection on the random inspection aluminum foil lunch boxes according to a lunch box component harm detection model to generate a lunch box component safety inspection result; performing a confident microbial safety inspection on the random inspection aluminum foil lunch boxes to generate a lunch box microbial safety inspection result; performing a migration test and migration safety identification on the random inspection aluminum foil lunch boxes to generate a lunch box migration safety inspection result; and drawing a lunch box safety performance inspection cloud map based on the lunch box component safety inspection result, the lunch box microbial safety inspection result and the lunch box migration safety inspection result.

[0006] In a second aspect, a quality inspection device for finished aluminum foil lunch boxes is provided, the device comprising: a food safety performance inspection instruction receiving module, the food safety performance inspection instruction receiving module being used to receive food safety performance inspection instructions for batch aluminum foil lunch box finished products, wherein the food safety performance inspection instructions include batch full inspection instructions / batch sampling instructions, and the batch sampling instructions include a predetermined batch sampling coefficient; a sampling aluminum foil lunch box obtaining module, the sampling aluminum foil lunch box obtaining module being used to sample and pre-process the batch aluminum foil lunch box finished products according to the predetermined batch sampling coefficient when the food safety performance inspection instruction is a batch sampling instruction, to obtain the sampling aluminum foil lunch box, wherein the pre-processing includes a cleaning process; a confidence component safety detection module, the confidence component safety detection module being used to A confident component safety test is performed on the sampled aluminum foil lunch boxes according to the lunch box component hazard detection model to generate a lunch box component safety test result; a confident microbial safety detection module is used to perform a confident microbial safety test on the sampled aluminum foil lunch boxes to generate a lunch box microbial safety test result; a lunch box migration safety test result generation module is used to perform migration testing and migration safety identification on the sampled aluminum foil lunch boxes to generate a lunch box migration safety test result; a lunch box safety performance test cloud map drawing module is used to draw a lunch box safety performance test cloud map based on the lunch box component safety test result, the lunch box microbial safety test result and the lunch box migration safety test result.

[0007] The above-mentioned method and device for quality inspection of finished aluminum foil lunch boxes solve the technical problem in the prior art that the food safety performance inspection accuracy of finished aluminum foil lunch boxes is low, resulting in the inability to accurately inspect the food safety of finished aluminum foil lunch boxes, which endangers the health of users. By introducing migration detection, the technical effect of accurately inspecting the food safety of finished aluminum foil lunch boxes is achieved.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 1 is a flow chart of a quality inspection method for a finished aluminum foil lunch box according to an embodiment;

[0010] Figure 2 A schematic diagram of a process for conducting a food contact test for a quality inspection method of a finished aluminum foil lunch box according to one embodiment;

[0011] Figure 3 The figure is a structural block diagram of a quality inspection device for a finished aluminum foil lunch box in one embodiment.

[0012] Explanation of the accompanying drawings: food safety performance testing instruction receiving module 11, sampling aluminum foil lunch box obtaining module 12, trusted ingredient safety testing module 13, trusted microbial safety testing module 14, lunch box migration safety testing result generating module 15, lunch box safety performance testing cloud map drawing module 16. DETAILED DESCRIPTION

[0013] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0014] like Figure 1 As shown, the present application provides a method for quality inspection of finished aluminum foil lunch boxes, the method comprising:

[0015] Receiving a food safety performance test instruction for a batch of finished aluminum foil lunch boxes, wherein the food safety performance test instruction includes a batch full inspection instruction / batch random inspection instruction, and the batch random inspection instruction includes a predetermined batch random inspection coefficient;

[0016] A finished aluminum foil lunch box is a tableware made of high-quality aluminum foil material, commonly used in a variety of occasions such as food cooking, baking, freezing, and preserving. A quality inspection method refers to the means of inspecting the finished aluminum foil lunch box, covering multiple aspects to ensure its safety, hygiene, and functionality. This application provides a quality inspection method for finished aluminum foil lunch boxes. By conducting food safety performance inspections on finished aluminum foil lunch boxes and introducing migration tests for aluminum foil lunch boxes, it ensures that they will not have adverse effects on food during use, thereby improving the efficiency and accuracy of quality inspections and providing strong guarantees for the development of the aluminum foil lunch box industry.

[0017] Receive food safety performance test instructions for a batch of finished aluminum foil lunch boxes, where the batch of finished aluminum foil lunch boxes refers to multiple finished aluminum foil lunch boxes to be tested from the same production batch, wherein the food safety performance test instructions include a batch full inspection instruction / batch random inspection instruction, a batch full inspection instruction refers to an instruction for testing all the finished aluminum foil lunch boxes in the batch, a batch random inspection instruction refers to an instruction for obtaining representative samples from the finished aluminum foil lunch boxes in the batch for testing, and the batch random inspection instruction includes a predetermined batch random inspection coefficient, which is used to calculate the number of samples to be randomly inspected. The number of samples to be randomly inspected is calculated based on the predetermined batch random inspection coefficient and the number of finished aluminum foil lunch boxes in the batch, ensuring that the number of samples to be randomly inspected is sufficiently representative and can reflect the quality status of the entire batch of products. The above method provides a basis for subsequent processing of the finished aluminum foil lunch boxes in the batch.

[0018] When the food safety performance test instruction is a batch sampling instruction, sampling and pre-processing the finished aluminum foil lunch boxes in the batch according to the predetermined batch sampling coefficient to obtain the sampled aluminum foil lunch boxes, wherein the pre-processing includes cleaning;

[0019] When the food safety performance test instruction is a batch sampling instruction, the batch of finished aluminum foil lunch boxes is sampled according to the predetermined batch sampling coefficient. Specifically, the specific number of aluminum foil lunch boxes to be sampled is calculated based on the batch sampling coefficient and the total number of finished aluminum foil lunch boxes in the batch. This batch sampling coefficient can be determined based on previous experience, the product's historical pass rate, and the customer's specific requirements. Random sampling is performed within the entire batch of finished aluminum foil lunch boxes to ensure that each aluminum foil lunch box has an equal probability of being selected, thereby ensuring the representativeness and fairness of the sampling results. The batch of finished aluminum foil lunch boxes is pre-processed according to the predetermined batch sampling coefficient to obtain sampled aluminum foil lunch boxes. This pre-processing includes cleaning, i.e., the sampled aluminum foil lunch boxes are first cleaned. This includes removing surface impurities such as dust and oil to ensure the accuracy of subsequent test results. The sampled aluminum foil lunch boxes are the aluminum foil lunch boxes to be tested obtained after sampling and pre-processing the batch of finished aluminum foil lunch boxes, and are referred to as the sampled aluminum foil lunch boxes. This method ensures the accuracy and reliability of the test results. Through scientific sampling and pretreatment processes, the food safety performance of the entire batch of finished aluminum foil lunch boxes can be effectively evaluated, providing strong guarantees for product quality.

[0020] Performing a confidence component safety test on the sampled aluminum foil lunch boxes according to a lunch box component hazard detection model to generate a lunch box component safety test result;

[0021] A lunch box ingredient harm detection model that has been established and trained is selected. The lunch box ingredient harm detection model should be able to accurately identify the harmful substances present in the aluminum foil lunch box and evaluate its potential impact on food safety. The sampled aluminum foil lunch boxes are subjected to a confidence ingredient safety test based on the lunch box ingredient harm detection model, that is, the sampled aluminum foil lunch boxes are analyzed based on the lunch box ingredient harm detection model, which can identify and measure the chemical components in the aluminum foil lunch box. According to the test results, the harmful substances present in the aluminum foil lunch box are identified and quantitatively analyzed. Based on the test data and the model prediction results, a confidence assessment is made on the safety of the aluminum foil lunch box ingredients to generate a lunch box ingredient safety test result. The lunch box ingredient safety test result refers to the result obtained by integrating the lunch box ingredient harm identification result and the lunch box ingredient safety detection index of the sampled aluminum foil lunch box. Through the confidence ingredient safety test, the ingredient safety of the sampled aluminum foil lunch box can be comprehensively evaluated, providing a strong guarantee for product quality control and food safety.

[0022] Perform multiple component tests on the sampled aluminum foil lunch boxes to obtain multiple lunch box component test sets;

[0023] Performing standardization and central value calculation on the multiple lunch box component detection sets to obtain a confident lunch box component detection set;

[0024] Constructing the lunch box ingredient harmfulness detection model, wherein the lunch box ingredient harmfulness detection model includes an input layer, a lunch box ingredient harmfulness identification layer, a lunch box ingredient safety detection layer, and an output layer;

[0025] Inputting the confident lunch box ingredient detection set into the lunch box ingredient harmful identification layer, and outputting the lunch box ingredient harmful identification result;

[0026] Inputting the lunch box ingredient hazard identification result into the lunch box ingredient safety detection layer to obtain a lunch box ingredient safety detection index;

[0027] The lunch box ingredient hazard identification result and the lunch box ingredient safety detection index are added to the lunch box ingredient safety detection result.

[0028] According to a predetermined sampling plan, a certain number of samples, referred to as the sampled aluminum foil lunch boxes, are randomly selected from the entire batch. Each sampled aluminum foil lunch box undergoes multiple component tests to ensure the stability and reliability of the test results. This may include testing at different time points using different testing methods. Each test generates a lunch box component test set, which is a collection of test results for lunch box ingredients, including volatile organic compound (VOC) and heavy metal tests. After multiple tests, multiple lunch box component test sets are generated. The collected multiple lunch box component test sets are cleaned to remove outliers, duplicate data, or missing values. The cleaned data is then normalized to eliminate dimensional differences between the different test sets and ensure data comparability. Centralized values, such as mean and median, are calculated for each component across multiple test sets to obtain more representative data. After normalization and centralized value calculation, a confident lunch box component test set is generated, which is used for subsequent model testing. A lunch box ingredient hazard detection model is constructed, which includes an input layer, a lunch box ingredient hazard identification layer, a lunch box ingredient safety detection layer and an output layer; a confident lunch box ingredient detection set is received as input data of the model, and in the lunch box ingredient hazard identification layer, harmful substances or ingredients present in the lunch box are identified through a machine learning algorithm; in the lunch box ingredient safety detection layer, the safety of the lunch box ingredients is evaluated based on the identified harmful substances or ingredients in combination with safety standards or thresholds, and a lunch box ingredient safety detection index is calculated. The confident lunch box ingredient detection set is input into the lunch box ingredient harm identification layer, and the model will output the lunch box ingredient harm identification result. The lunch box ingredient harm identification result refers to the assessment result of the potential harm that these ingredients may cause to human health or the environment after testing the aluminum foil lunch box. The assessment result is usually based on multiple testing items and standards, including chemical composition testing, heavy metal testing, and environmental protection and degradation performance testing. The lunch box ingredient harm identification result is input into the lunch box ingredient safety detection layer, and the model will calculate the lunch box ingredient safety detection index. The lunch box ingredient harm identification result and the lunch box ingredient safety detection index are added to the lunch box ingredient safety detection result to form a complete detection result report, which is recorded as the lunch box ingredient safety detection result. Through the above method, the lunch box ingredient harm detection model is constructed using multiple ingredient detection data of sampled aluminum foil lunch boxes, and the lunch box ingredient safety detection result is obtained, providing a strong guarantee for food safety.

[0029] Building a lunch box ingredient harmfulness detection model architecture, wherein the lunch box ingredient harmfulness detection model architecture includes an input layer, a first hidden layer, a second hidden layer, and an output layer;

[0030] Load the records of the confident lunch box ingredient detection set, the records of the harmful identification results of the lunch box ingredients, and the records of the lunch box ingredient safety detection index;

[0031] Using the confident lunch box ingredient detection set records as input data and the lunch box ingredient harmfulness identification result records as output data, supervised learning is performed on the heterogeneous machine learning channel. When the harmfulness identification learning coefficient satisfies the harmfulness identification learning constraint, a lunch box ingredient harmfulness identifier is generated, wherein the heterogeneous machine learning channel includes multiple machine learning methods;

[0032] Performing supervised learning on the lunch box ingredient hazard identification result record and the lunch box ingredient safety detection index record based on the heterogeneous machine learning channel, and generating a lunch box ingredient safety detector when the safety detection learning coefficient satisfies the safety detection learning constraint;

[0033] The lunch box ingredient harmfulness identifier and the lunch box ingredient safety detector are used in combination with the lunch box ingredient harmfulness detection model architecture to generate the lunch box ingredient harmfulness detection model.

[0034] Build a lunch box ingredient harmfulness detection model architecture, wherein the lunch box ingredient harmfulness detection model architecture includes an input layer, a first hidden layer, a second hidden layer and an output layer. The input layer is responsible for receiving the trusted lunch box ingredient detection set records as the input data of the model. According to the number of features of the input data, the number of neurons in the input layer is designed. The first hidden layer is used to extract the features of the input data and convert it into a higher-level representation. The appropriate activation function and number of neurons are selected to ensure that the model can learn the complex patterns of the data. In this application, the lunch box ingredient harmfulness identifier is embedded in the first hidden layer, and the second hidden layer further processes the output of the first hidden layer to extract deeper features. It is also necessary to select an appropriate activation function and number of neurons. In this application, the lunch box ingredient safety detector is embedded in the second hidden layer, and the output layer is responsible for generating the final lunch box ingredient harmfulness identification results and lunch box ingredient safety detection index. According to the task requirements, the output layer can be designed as two independent output nodes, corresponding to the harmfulness identification results and the safety detection index respectively. Load the confident lunch box ingredient test set records, lunch box ingredient harmfulness identification result records, and lunch box ingredient safety testing index records. These records represent the results of past tests on aluminum foil lunch boxes of the same specifications and can be retrieved through queries. This constructs a heterogeneous machine learning pipeline that incorporates multiple machine learning methods. These machine learning methods can include traditional machine learning algorithms such as support vector machines and decision trees, as well as deep learning algorithms such as neural networks and convolutional neural networks. The heterogeneous machine learning pipeline performs supervised learning training using the confident lunch box ingredient test set records as input and the harmfulness identification result records as output. By adjusting the model's parameters and structure, the model accurately identifies harmful ingredients in lunch boxes. During training, a harmfulness identification learning coefficient is introduced to evaluate the model's learning performance. When this coefficient meets the preset harmfulness identification learning constraints, the model is considered to have learned sufficient harmfulness identification capabilities, and a lunch box ingredient harmfulness identifier can be generated. Based on the heterogeneous machine learning pipeline, further supervised learning training is performed using the harmfulness identification result records and the food box ingredient safety testing index records. A safety detection learning coefficient is also introduced to evaluate the model's learning performance in safety detection. When the coefficient meets the preset safety detection learning constraints, the model is considered to have sufficient safety detection capabilities and a lunch box ingredient safety detector can be generated. The generated lunch box ingredient harmful identifier and lunch box ingredient safety detector are embedded in the first hidden layer and the second hidden layer, and integrated with the lunch box ingredient harmful detection model architecture to obtain the lunch box ingredient harmful detection model. Ensure that each part can work together to jointly realize the function of lunch box ingredient harmful detection. Through the above method, a lunch box ingredient harmful detection model based on heterogeneous machine learning channels is built to achieve effective detection of the safety and harmfulness of lunch box ingredients.

[0035] Performing a reliable microbial safety test on the sampled aluminum foil lunch boxes to generate a lunch box microbial safety test result;

[0036] Confidential microbial safety testing is performed on sampled aluminum foil lunch boxes, generating test results. The detected microbial species and counts are compared with relevant food safety standards to assess the microbial safety of the aluminum foil lunch boxes. A confidence analysis is conducted on the test results, taking into account various factors. The results of microbial culture, enumeration, identification, and safety assessment are compiled to form a detailed test report, namely the lunch box microbial safety test results. This method allows for confident microbial safety testing of sampled aluminum foil lunch boxes and generates accurate and reliable test results, helping to ensure the microbial safety of aluminum foil lunch boxes.

[0037] Perform multiple microbial tests on the sampled aluminum foil lunch boxes to obtain multiple lunch box microbial test sets;

[0038] Performing standardization and central value calculation on the multiple lunch box microbial detection sets to obtain a confident lunch box microbial detection set;

[0039] Performing microbial damage detection on the confident lunch box microbial detection set according to the lunch box microbial damage identification sub-model to generate a lunch box microbial damage identification result;

[0040] Performing a microbial safety evaluation on the lunch box microbial hazard identification result according to the lunch box microbial safety detection sub-model to generate a lunch box microbial safety evaluation coefficient;

[0041] The lunch box microbial hazard identification result and the lunch box microbial safety evaluation coefficient are added to the lunch box microbial safety detection result.

[0042] Multiple microbial tests are conducted on the sampled aluminum foil lunch boxes to ensure the stability and reliability of the test results. Each test will conduct a detailed analysis of the types and quantities of microorganisms in the lunch boxes; each microbial test will generate a lunch box microbial test set, including the test results of various microorganisms. Through multiple tests, multiple lunch box microbial test sets will be obtained. The collected multiple lunch box microbial test sets are cleaned, outliers or duplicate data are removed, and standardized to eliminate the dimensional differences between different test sets. The central value of each microbial species in multiple test sets is calculated to obtain more representative data. This helps to reduce the randomness of single test results and improve the reliability of the overall data. After standardization and central value calculation, a confident lunch box microbial test set is obtained. The confident lunch box microbial test set will serve as input data for subsequent microbial hazard identification and safety evaluation. A sub-model for identifying microbial hazards in lunch boxes is constructed. Microbial hazard detection is performed on a confident microbial detection set. This identifies microbial species that pose a risk to human health and outputs a microbial hazard identification result, which includes the species of microorganisms posing a risk of harm. Based on this microbial hazard identification result, a microbial safety assessment is performed using a sub-model for detecting microbial safety in lunch boxes. For example, the microbial species, number, and thresholds of relevant safety standards are used to quantitatively assess the microbial safety of the lunch boxes. Based on the output of the safety detection sub-model, a microbial safety evaluation coefficient is generated. This coefficient reflects the performance of the lunch box in terms of microbial safety and can serve as an important indicator for evaluating the overall safety of the lunch box. For example, by setting safety standard thresholds for the number and type of microorganisms and weighting the types of microbial safety tests, a microbial safety evaluation coefficient can be generated. The microbial hazard identification result and the microbial safety evaluation coefficient are integrated to form a complete microbial safety test result. This method enables confident microbial safety testing of sampled aluminum foil lunch boxes and generates comprehensive and accurate microbial safety test results, helping to improve the quality and safety of aluminum foil lunch boxes.

[0043] Performing migration testing and migration safety identification on the sampled aluminum foil lunch boxes to generate lunch box migration safety test results;

[0044] Migration testing of aluminum foil lunch boxes is an important step to ensure that they will not have adverse effects on food during use. This involves testing substances released into food after the lunch box comes into contact with food. Migration testing involves contacting a pretreated aluminum foil lunch box sample with a test solution under set temperature, time and other conditions to simulate migration during actual use. After the contact is completed, substances that may migrate into the lunch box are extracted from the test solution. Appropriate analytical methods are used to qualitatively and quantitatively analyze the extracted migrants to determine their type and content. Migration safety identification involves comparing the test results of migrants with relevant food safety standards to evaluate the safety of the migrants. Taking into account factors such as the nature of the migrants and usage conditions, a risk assessment of the migration safety of aluminum foil lunch boxes is conducted to determine whether there are potential health risks. The migration test data, qualitative and quantitative analysis results of migrants, safety standard comparison results and risk assessment results are collated to form a detailed test report, namely the lunch box migration safety test results. Through the above method, migration testing and migration safety identification can be carried out on sampled aluminum foil lunch boxes, and accurate and reliable lunch box migration safety test results can be generated, which helps to ensure that aluminum foil lunch boxes do not pose potential threats to human health during use.

[0045] Establish A migration test scenarios based on the multidimensional migration test scenario indicators, wherein the multidimensional migration test scenario indicators include simulated food, simulated contact time, simulated contact temperature, simulated contact area, and simulated contact environment, and A is a positive integer greater than 1;

[0046] Extracting an ath migration test scenario according to the A migration test scenarios, and reading the detection information of the simulated food in the ath migration test scenario to obtain an ath pre-food detection data, wherein a belongs to A and a is a positive integer;

[0047] Performing the food contact test on the sampled aluminum foil lunch box according to the a migration test scenario to generate a post-food test data;

[0048] Performing substance migration testing on the ath post-food testing data based on the ath pre-food testing data to generate ath food migration testing data;

[0049] Performing a substance migration safety evaluation based on the a-th food migration test data to generate a a-th food migration safety evaluation coefficient;

[0050] The ath food migration detection data and the ath food migration safety evaluation coefficient are added to the lunch box migration safety detection result.

[0051] Determine multidimensional migration test scenario metrics, including simulated food, simulated contact time, simulated contact temperature, simulated contact area, and simulated contact environment. The simulated food includes the food that the aluminum foil lunch box would actually come into contact with. These multidimensional migration test scenario metrics will be used to simulate the various conditions encountered during actual use. Based on the determined metrics, construct A different migration test scenarios. Each scenario will simulate different food, contact time, temperature, area, and environmental conditions. From the A migration test scenarios, select the ath scenario as the current test subject. Read the test information for the simulated food in the ath migration test scenario. This information will serve as pre-test food data for comparison with subsequent test data. Select a representative aluminum foil lunch box sample for food contact testing. Under the conditions of the ath migration test scenario, place the aluminum foil lunch box sample in contact with the simulated food, simulating actual use. After the contact is complete, test the simulated food to generate post-test food data. This post-test food data will reflect the potential migration of substances that may occur after the aluminum foil lunch box comes into contact with the food. Based on the a-th pre-food test data and the a-th post-food test data, a substance migration test is performed. The substance migration test identifies the types and quantities of substances that may migrate by comparing the before and after data. Based on the results of the migration test, in combination with relevant food safety standards and regulations, a substance migration safety assessment is performed. The a-th food migration safety assessment coefficient is generated through calculation or scoring. The a-th food migration safety assessment coefficient will reflect the migration safety level of the aluminum foil lunch box in this test scenario. The a-th food migration test data and the a-th food migration safety assessment coefficient are added to the lunch box migration safety test results, and the integrated lunch box migration safety test results are output. This can understand the migration safety performance of the aluminum foil lunch box in different test scenarios, providing a basis for subsequent decision-making and measures. According to the above method, the sampled aluminum foil lunch boxes are subjected to comprehensive migration testing and migration safety identification based on multi-dimensional migration test scenario indicators. This helps to ensure the safety of the lunch boxes during actual use.

[0052] like Figure 2 As shown, after completing the food contact test of the a-1 migration test scenario, the image data of the sampled aluminum foil lunch box is collected in real time to obtain the monitoring data of the lunch box after the a-1 test;

[0053] Performing a lunch box cleaning demand analysis based on the lunch box monitoring data after the a-1 test to generate a lunch box cleaning demand coefficient;

[0054] Determining whether the lunch box cleaning requirement coefficient is less than a lunch box cleaning requirement threshold;

[0055] If the lunch box cleaning requirement coefficient is greater than / equal to the lunch box cleaning requirement threshold, generating a lunch box cleaning instruction;

[0056] The sampled aluminum foil lunch box is cleaned according to the lunch box cleaning instruction, and a food contact test is performed on the sampled aluminum foil lunch box after the cleaning process according to the ath migration test scenario.

[0057] Immediately after completing the food contact test for the migration test scenario a-1, use an image acquisition device, such as a high-definition camera, to capture real-time images of the sampled aluminum foil lunch boxes. Ensure that the captured images are clear, comprehensive, and reflect the internal and external surface conditions of the lunch boxes. The captured image data is then imported into a computer and pre-processed using image processing software, such as noise reduction and contrast enhancement, to better extract the characteristic information of the lunch boxes. After image processing, key information such as stains and residues on the lunch box surface is extracted, generating post-a-1 lunch box monitoring data for subsequent analysis of lunch box cleaning needs. Based on this post-a-1 lunch box monitoring data, analyze the extent of surface stains, the type and amount of residue, and assess the cleanliness of the lunch boxes. Based on the analysis results and in combination with pre-set cleaning needs assessment criteria, calculate a lunch box cleaning needs coefficient. This coefficient reflects the degree of cleaning requirements required for the lunch boxes in their current state. The calculated lunch box cleaning requirement coefficient is compared with a preset lunch box cleaning requirement threshold. The threshold is typically determined based on factors such as food safety standards and lunch box usage scenarios. If the lunch box cleaning requirement coefficient is greater than or equal to the cleaning requirement threshold, the lunch box requires cleaning. A lunch box cleaning instruction is then generated, and the sampled aluminum foil lunch box is cleaned according to the instruction, using appropriate cleaning equipment and methods. To ensure that no new sources of contamination are introduced during the cleaning process and that the cleaning effect meets food safety requirements, the sampled aluminum foil lunch box undergoes a food contact test according to the requirements of the migration test scenario (a). Test conditions are maintained consistent with the previous test to facilitate accurate comparative analysis. This method monitors the cleanliness of the sampled aluminum foil lunch box in real time and performs timely cleaning according to cleaning requirements. This helps ensure that the lunch box is in a clean condition before each test, thereby improving the accuracy of migration testing and migration safety identification. It also provides safety assurance for the subsequent use of the lunch box.

[0058] Performing a substance migration comparison on the ath post-food test data according to the ath pre-food test data to generate an ath food migration comparison result;

[0059] Performing a lunch box association analysis based on the a-th food migration comparison result to generate a a-th association migration comparison result that satisfies the association constraint;

[0060] Reading the lunch box migration data of the sampled aluminum foil lunch box according to the a-1 migration test scenario to generate a-1 lunch box migration data;

[0061] performing migration impact identification on the a-th associated migration comparison result according to the a-1th lunch box migration data to generate a lunch box migration impact identification result;

[0062] The a-th associated migration comparison result is corrected according to the lunch box migration impact identification result to obtain the a-th food migration detection data.

[0063] Using the pre-test data from the first food scenario as a reference, a substance migration comparison is performed against the post-test data from the first food scenario. To identify which substances migrated from the aluminum foil lunch box into the food during the food contact test, the comparison results generate the first food migration comparison results, detailing the types and quantities of the migrating substances. A lunch box association analysis is then performed on the first food migration comparison results. This step aims to identify associations between migrating substances and aluminum foil lunch box characteristics, such as material and production process. This association analysis can identify migrating substances that are closely related to lunch box characteristics and meet association constraints, generating the first association migration comparison results. Before performing migration impact identification, the lunch box migration data from the first migration test scenario, including the migration performance of the aluminum foil lunch box in the previous test scenario, is read. This is crucial for analyzing the impact of migrating substances in the current test scenario. Based on the migration data from the first lunch box, a migration impact identification is performed on the first association migration comparison results. This step analyzes the impact of the migration behavior in the previous test scenario on the migrating substances in the current test scenario, such as whether the same migrating substances appear and the trend of the migration amount. Through migration impact identification, a lunch box migration impact identification result is generated. According to the lunch box migration impact identification result, the a-th associated migration comparison result is corrected. In this application, the correction includes adjusting the estimation of the migration amount, considering factors such as the interaction between different foods and lunch box materials, and adjusting the a-th associated migration comparison result to obtain the a-th food migration detection data with higher accuracy. This step is to more accurately reflect the migration performance of the aluminum foil lunch box in the current test scenario. Through data correction, more accurate and reliable a-th food migration detection data can be obtained. Through the above method, a comprehensive migration comparison, correlation analysis and impact identification can be performed on the sampled aluminum foil lunch boxes, thereby obtaining more accurate and comprehensive migration detection data.

[0064] A lunch box safety performance test cloud map is drawn based on the lunch box ingredient safety test results, the lunch box microbial safety test results and the lunch box migration safety test results.

[0065] A cloud chart is created based on the results of the lunch box ingredient safety test, the lunch box microbiological safety test, and the lunch box migration safety test. First, because the units and dimensions of different safety test results may vary, the data needs to be standardized to facilitate comparison and presentation on a consistent scale. Second, each safety test result is categorized and rated according to safety standards or regulations, for example, into levels such as excellent, good, qualified, and unqualified. Then, using a professional data visualization tool, the cloud chart's three coordinate axes, representing the three key safety dimensions of a lunch box, are used to represent ingredient safety, microbiological safety, and migration safety. Based on the processed data, the position of each lunch box sample within the cloud chart is determined. The closer the position is to the origin of the coordinate axis, the worse the sample's performance on the corresponding safety dimension; conversely, the closer the position is to the origin of the coordinate axis, the better the performance. Different colors and sizes can be used to distinguish different levels of safety test results. For example, a color gradient can be used to represent safety levels from excellent to unqualified, and the size can reflect the severity of the test result. Necessary labels and descriptions are added to the cloud chart to clearly explain the safety dimension represented by each coordinate axis and the meaning of the data point. Using this method, the distribution of data points in the cloud chart allows for a quick understanding of the ingredient safety, microbiological safety, and migration safety of individual lunch box samples. Furthermore, by comparing positional differences between samples, the overall safety performance can be assessed. This helps manufacturers and consumers gain a more intuitive understanding of the safety performance of lunch boxes, providing a reference for improving production processes and selecting safe and reliable lunch box products.

[0066] When the food safety performance test instruction is the batch full inspection instruction, the food safety performance test is performed on the batch of finished aluminum foil lunch boxes according to the batch full inspection instruction to generate a full inspection lunch box safety performance test cloud map.

[0067] When the food safety performance test instruction is a batch full inspection instruction, it means that a comprehensive food safety performance test is required for the entire batch of finished aluminum foil lunch boxes. The food safety performance test can be carried out according to the above method to ensure that the safety performance of all products in the batch meets the relevant standards and requirements, thereby protecting the food safety of consumers. All data obtained from the traversal test are sorted, classified and standardized for visual display. The processed data are plotted into a full inspection lunch box safety performance test cloud map using data visualization tools or programming languages. The full inspection lunch box safety performance test cloud map should be able to clearly display the performance of each sample in the three dimensions of ingredient safety, microbial safety and migration safety. By traversing the food safety performance test and generating a full inspection lunch box safety performance test cloud map, the safety performance status of the batch of finished aluminum foil lunch boxes can be fully understood, providing an important reference for producers and consumers.

[0068] like Figure 3As shown, the embodiment of the present application includes a quality inspection device for a finished aluminum foil lunch box, the device comprising:

[0069] A food safety performance test instruction receiving module 11 is used to receive a food safety performance test instruction for a batch of finished aluminum foil lunch boxes, wherein the food safety performance test instruction includes a batch full inspection instruction / batch random inspection instruction, and the batch random inspection instruction includes a predetermined batch random inspection coefficient;

[0070] The sampling aluminum foil lunch box obtaining module 12 is used to sample and pre-process the batch of finished aluminum foil lunch boxes according to the predetermined batch sampling coefficient to obtain the sampling aluminum foil lunch boxes when the food safety performance test instruction is a batch sampling instruction, wherein the pre-processing includes cleaning;

[0071] A trusted ingredient safety detection module 13 is configured to perform a trusted ingredient safety detection on the sampled aluminum foil lunch boxes according to a lunch box ingredient hazard detection model, and generate a lunch box ingredient safety detection result;

[0072] A trusted microbial safety detection module 14 is used to perform a trusted microbial safety detection on the sampled aluminum foil lunch boxes and generate a lunch box microbial safety detection result;

[0073] A lunch box migration safety test result generating module 15 is used to perform migration testing and migration safety identification on the sampled aluminum foil lunch boxes to generate a lunch box migration safety test result;

[0074] The lunch box safety performance detection cloud map drawing module 16 is used to draw a lunch box safety performance detection cloud map based on the lunch box ingredient safety detection results, the lunch box microbial safety detection results and the lunch box migration safety detection results.

[0075] Furthermore, the embodiment of the present application also includes:

[0076] A component detection module, configured to perform multiple component detections on the sampled aluminum foil lunch boxes to obtain multiple lunch box component detection sets;

[0077] a lunch box component detection set standardization processing module, the lunch box component detection set standardization processing module being used to perform standardization processing and central value calculation based on the multiple lunch box component detection sets to obtain a confident lunch box component detection set;

[0078] A lunch box ingredient harmfulness detection model construction module, wherein the lunch box ingredient harmfulness detection model construction module is used to construct the lunch box ingredient harmfulness detection model, wherein the lunch box ingredient harmfulness detection model includes an input layer, a lunch box ingredient harmfulness identification layer, a lunch box ingredient safety detection layer, and an output layer;

[0079] a lunch box ingredient harmfulness identification result output module, the lunch box ingredient harmfulness identification result output module being used to input the confident lunch box ingredient detection set into the lunch box ingredient harmfulness identification layer and output a lunch box ingredient harmfulness identification result;

[0080] a lunch box ingredient harmfulness identification result input module, the lunch box ingredient harmfulness identification result input module being used to input the lunch box ingredient harmfulness identification result into the lunch box ingredient safety detection layer to obtain a lunch box ingredient safety detection index;

[0081] A lunch box ingredient adding module is used to add the lunch box ingredient harmful identification result and the lunch box ingredient safety detection index to the lunch box ingredient safety detection result.

[0082] Furthermore, the embodiment of the present application also includes:

[0083] A model architecture building module, wherein the model architecture building module is used to build a lunch box ingredient harmfulness detection model architecture, wherein the lunch box ingredient harmfulness detection model architecture includes an input layer, a first hidden layer, a second hidden layer, and an output layer;

[0084] A record loading module, which is used to load the records of the confident lunch box ingredient detection set, the records of the harmful identification results of the lunch box ingredients, and the records of the safety detection index of the lunch box ingredients;

[0085] A heterogeneous machine learning channel supervised learning module is configured to use the confident lunch box ingredient detection set records as input data and the lunch box ingredient harmful identification result records as output data to perform supervised learning on the heterogeneous machine learning channel, and generate a lunch box ingredient harmful identifier when the harmful identification learning coefficient satisfies the harmful identification learning constraint, wherein the heterogeneous machine learning channel includes multiple machine learning methods;

[0086] a lunch box ingredient safety detector generation module, the module being configured to perform supervised learning on the lunch box ingredient hazard identification result records and the lunch box ingredient safety detection index records based on the heterogeneous machine learning channel, and to generate a lunch box ingredient safety detector when a safety detection learning coefficient satisfies a safety detection learning constraint;

[0087] A lunch box ingredient harmfulness detection model generation module is used to generate the lunch box ingredient harmfulness detection model based on the lunch box ingredient harmfulness identifier and the lunch box ingredient safety detector, combined with the lunch box ingredient harmfulness detection model architecture.

[0088] Furthermore, the embodiment of the present application also includes:

[0089] A microbial detection module, configured to perform multiple microbial detections on the sampled aluminum foil lunch boxes to obtain multiple lunch box microbial detection sets;

[0090] A module for obtaining a confident lunch box microbial detection set, configured to perform standardization processing and central value calculation on the plurality of lunch box microbial detection sets to obtain a confident lunch box microbial detection set;

[0091] a module for generating a result of identifying microbial damage in lunch boxes, the module being configured to perform a microbial damage detection on the confident lunch box microbial detection set according to a lunch box microbial damage identification sub-model, and generate a lunch box microbial damage identification result;

[0092] a lunch box microbial safety evaluation coefficient generation module, the lunch box microbial safety evaluation coefficient generation module being used to perform a microbial safety evaluation on the lunch box microbial hazard identification result according to the lunch box microbial safety detection sub-model, and generate a lunch box microbial safety evaluation coefficient;

[0093] A lunch box microbial safety evaluation coefficient adding module is used to add the lunch box microbial harm identification result and the lunch box microbial safety evaluation coefficient to the lunch box microbial safety detection result.

[0094] Furthermore, the embodiment of the present application also includes:

[0095] A migration test scenario building module, wherein the migration test scenario building module is used to build A migration test scenarios based on multidimensional migration test scenario indicators, wherein the multidimensional migration test scenario indicators include simulated food, simulated contact time, simulated contact temperature, simulated contact area, and simulated contact environment, and A is a positive integer greater than 1;

[0096] a detection information reading module, the detection information reading module being configured to extract an ath migration test scenario based on the A migration test scenarios, and read detection information of the simulated food in the ath migration test scenario to obtain an ath pre-placed food detection data, where a belongs to A and is a positive integer;

[0097] a food contact test execution module, configured to execute the food contact test on the sampled aluminum foil lunch box according to the a migration test scenario, and generate a post-food detection data;

[0098] a substance migration detection module, configured to perform substance migration detection on the ath post-food detection data based on the ath pre-food detection data to generate ath food migration detection data;

[0099] a migration safety evaluation coefficient generation module, the migration safety evaluation coefficient generation module being used to perform a substance migration safety evaluation based on the a-th food migration detection data and generate a a-th food migration safety evaluation coefficient;

[0100] A food migration safety evaluation coefficient adding test result module is used to add the ath food migration test data and the ath food migration safety evaluation coefficient to the lunch box migration safety test result.

[0101] Furthermore, the embodiment of the present application also includes:

[0102] An image data acquisition module, which is used to collect image data of the sampled aluminum foil lunch boxes in real time after completing the food contact test of the a-1 migration test scenario, and obtain monitoring data of the lunch boxes after the a-1 test;

[0103] a lunch box cleaning requirement coefficient generating module, the lunch box cleaning requirement coefficient generating module being used to perform lunch box cleaning requirement analysis based on the lunch box monitoring data after the a-1th test, and generate a lunch box cleaning requirement coefficient;

[0104] a cleaning requirement coefficient judgment module, the cleaning requirement coefficient judgment module being used to judge whether the lunch box cleaning requirement coefficient is less than a lunch box cleaning requirement threshold;

[0105] a lunch box cleaning instruction generating module, configured to generate a lunch box cleaning instruction if the lunch box cleaning requirement coefficient is greater than / equal to the lunch box cleaning requirement threshold;

[0106] A food contact testing module is used to clean the sampled aluminum foil lunch box according to the lunch box cleaning instruction, and to perform a food contact test on the sampled aluminum foil lunch box after the cleaning process according to the ath migration test scenario.

[0107] Furthermore, the embodiment of the present application also includes:

[0108] a food migration comparison result generating module, configured to perform a substance migration comparison on the ath post-food detection data based on the ath pre-food detection data to generate an ath food migration comparison result;

[0109] a lunch box association analysis module, configured to perform a lunch box association analysis based on the first food migration comparison result to generate a first association migration comparison result that satisfies association constraints;

[0110] a lunch box migration data reading module, configured to read the lunch box migration data of the sampled aluminum foil lunch boxes according to the a-1 migration test scenario, and generate a-1 lunch box migration data;

[0111] a migration impact identification module, configured to perform migration impact identification on the a-th associated migration comparison result based on the a-th lunch box migration data, and generate a lunch box migration impact identification result;

[0112] A comparison result correction module is used to correct the a-th associated migration comparison result according to the lunch box migration impact identification result to obtain the a-th food migration detection data.

[0113] Furthermore, the embodiment of the present application also includes:

[0114] A food safety performance testing module is used to perform food safety performance testing on the batch of finished aluminum foil lunch boxes according to the batch full inspection instruction when the food safety performance testing instruction is the batch full inspection instruction, and generate a full inspection lunch box safety performance testing cloud map.

[0115] Each module in the above-mentioned quality inspection device for finished aluminum foil lunch boxes can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0116] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0117] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the scope of the present application, and such modifications and improvements are all within the scope of protection of the present application.

Claims

1. A quality inspection method for finished aluminum foil lunch boxes, characterized in that: The method comprises: Receiving a food safety performance test instruction for a batch of finished aluminum foil lunch boxes, wherein the food safety performance test instruction includes a batch full inspection instruction / batch random inspection instruction, and the batch random inspection instruction includes a predetermined batch random inspection coefficient; When the food safety performance test instruction is a batch sampling instruction, sampling and pre-processing the finished aluminum foil lunch boxes in the batch according to the predetermined batch sampling coefficient to obtain the sampled aluminum foil lunch boxes, wherein the pre-processing includes cleaning; The sampled aluminum foil lunch boxes are subjected to a confidence ingredient safety test based on the lunch box ingredient hazard detection model to generate a lunch box ingredient safety test result, including: Perform multiple component tests on the sampled aluminum foil lunch boxes to obtain multiple lunch box component test sets; Performing standardization and central value calculation on the multiple lunch box component detection sets to obtain a confident lunch box component detection set; Constructing the lunch box ingredient harmfulness detection model, wherein the lunch box ingredient harmfulness detection model includes an input layer, a lunch box ingredient harmfulness identification layer, a lunch box ingredient safety detection layer, and an output layer; Inputting the confident lunch box ingredient detection set into the lunch box ingredient harmful identification layer, and outputting the lunch box ingredient harmful identification result; Inputting the lunch box ingredient hazard identification result into the lunch box ingredient safety detection layer to obtain a lunch box ingredient safety detection index; Adding the lunch box ingredient hazard identification result and the lunch box ingredient safety detection index to the lunch box ingredient safety detection result; Conduct a reliable microbial safety test on the sampled aluminum foil lunch boxes to generate a lunch box microbial safety test result, including: Perform multiple microbial tests on the sampled aluminum foil lunch boxes to obtain multiple lunch box microbial test sets; Performing standardization and central value calculation on the multiple lunch box microbial detection sets to obtain a confident lunch box microbial detection set; Performing microbial damage detection on the confident lunch box microbial detection set according to the lunch box microbial damage identification sub-model to generate a lunch box microbial damage identification result; Performing a microbial safety evaluation on the lunch box microbial hazard identification result according to the lunch box microbial safety detection sub-model to generate a lunch box microbial safety evaluation coefficient; Adding the lunch box microbial hazard identification result and the lunch box microbial safety evaluation coefficient to the lunch box microbial safety test result; The sampled aluminum foil lunch boxes are subjected to migration testing and migration safety identification to generate lunch box migration safety test results, including: Establish A migration test scenarios based on the multidimensional migration test scenario indicators, wherein the multidimensional migration test scenario indicators include simulated food, simulated contact time, simulated contact temperature, simulated contact area, and simulated contact environment, and A is a positive integer greater than 1; Extracting an ath migration test scenario according to the A migration test scenarios, and reading the detection information of the simulated food in the ath migration test scenario to obtain an ath pre-food detection data, wherein a belongs to A and a is a positive integer; Performing the food contact test on the sampled aluminum foil lunch box according to the a migration test scenario to generate a post-food test data; Performing substance migration testing on the ath post-food testing data based on the ath pre-food testing data to generate ath food migration testing data; Performing a substance migration safety evaluation based on the a-th food migration test data to generate a a-th food migration safety evaluation coefficient; Adding the a-th food migration test data and the a-th food migration safety evaluation coefficient to the lunch box migration safety test result; The method of performing substance migration detection on the a-th post-food detection data based on the a-th pre-food detection data to generate the a-th food migration detection data includes: Performing a substance migration comparison on the ath post-food test data according to the ath pre-food test data to generate an ath food migration comparison result; Performing a lunch box association analysis based on the a-th food migration comparison result to generate a a-th association migration comparison result that satisfies the association constraint; Read the lunch box migration data of the sampled aluminum foil lunch box according to the a-1 migration test scenario to generate a-1 lunch box migration data; performing migration impact identification on the a-th associated migration comparison result according to the a-1th lunch box migration data to generate a lunch box migration impact identification result; Correcting the first associated migration comparison result according to the lunch box migration impact identification result to obtain the first food migration detection data; A lunch box safety performance test cloud map is drawn based on the lunch box ingredient safety test results, the lunch box microbial safety test results and the lunch box migration safety test results.

2. The method according to claim 1, wherein Constructing the lunch box ingredient harmfulness detection model includes: Building a lunch box ingredient harmfulness detection model architecture, wherein the lunch box ingredient harmfulness detection model architecture includes an input layer, a first hidden layer, a second hidden layer, and an output layer; Load the records of the confident lunch box ingredient detection set, the records of the harmful identification results of the lunch box ingredients, and the records of the lunch box ingredient safety detection index; Using the confident lunch box ingredient detection set records as input data and the lunch box ingredient harmfulness identification result records as output data, supervised learning is performed on the heterogeneous machine learning channel. When the harmfulness identification learning coefficient satisfies the harmfulness identification learning constraint, a lunch box ingredient harmfulness identifier is generated, wherein the heterogeneous machine learning channel includes multiple machine learning methods; Performing supervised learning on the lunch box ingredient hazard identification result record and the lunch box ingredient safety detection index record based on the heterogeneous machine learning channel, and generating a lunch box ingredient safety detector when the safety detection learning coefficient satisfies the safety detection learning constraint; The lunch box ingredient harmfulness identifier and the lunch box ingredient safety detector are used in combination with the lunch box ingredient harmfulness detection model architecture to generate the lunch box ingredient harmfulness detection model.

3. The method according to claim 1, wherein The food contact test of the sampled aluminum foil lunch box is performed according to the migration test scenario a, including: After completing the food contact test of the a-1 migration test scenario, image data of the sampled aluminum foil lunch box is collected in real time to obtain monitoring data of the lunch box after the a-1 test; Performing a lunch box cleaning demand analysis based on the lunch box monitoring data after the a-1 test to generate a lunch box cleaning demand coefficient; Determining whether the lunch box cleaning requirement coefficient is less than a lunch box cleaning requirement threshold; If the lunch box cleaning requirement coefficient is greater than / equal to the lunch box cleaning requirement threshold, generating a lunch box cleaning instruction; The sampled aluminum foil lunch box is cleaned according to the lunch box cleaning instruction, and a food contact test is performed on the sampled aluminum foil lunch box after the cleaning process according to the ath migration test scenario.

4. The method according to claim 1, wherein When the food safety performance test instruction is the batch full inspection instruction, the food safety performance test is performed on the batch of finished aluminum foil lunch boxes according to the batch full inspection instruction to generate a full inspection lunch box safety performance test cloud map.

5. A quality inspection device for finished aluminum foil lunch boxes, characterized in that: The device is used to perform the method according to any one of claims 1 to 4, and the device comprises: A food safety performance test instruction receiving module, the food safety performance test instruction receiving module is used to receive a food safety performance test instruction for a batch of finished aluminum foil lunch boxes, wherein the food safety performance test instruction includes a batch full inspection instruction / batch random inspection instruction, and the batch random inspection instruction includes a predetermined batch random inspection coefficient; a sampling aluminum foil lunch box obtaining module, wherein when the food safety performance test instruction is a batch sampling instruction, the sampling aluminum foil lunch box obtaining module is used to sample and pre-process the batch of finished aluminum foil lunch boxes according to the predetermined batch sampling coefficient to obtain the sampling aluminum foil lunch boxes, wherein the pre-processing includes cleaning; A trusted ingredient safety detection module, configured to perform a trusted ingredient safety test on the sampled aluminum foil lunch boxes according to a lunch box ingredient hazard detection model, and generate a lunch box ingredient safety test result; A trusted microbial safety detection module is used to perform a trusted microbial safety detection on the sampled aluminum foil lunch boxes and generate a lunch box microbial safety detection result; A lunch box migration safety test result generation module is used to perform migration testing and migration safety identification on the sampled aluminum foil lunch boxes to generate a lunch box migration safety test result; A lunch box safety performance detection cloud map drawing module is used to draw a lunch box safety performance detection cloud map based on the lunch box ingredient safety detection results, the lunch box microbial safety detection results and the lunch box migration safety detection results.

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