A food safety supervision system based on cloud platform

Abstract

The present invention relates to the field of safety supervision, and in particular to a food safety supervision system based on a cloud platform. The system comprises: an acquisition module for collecting a contour image of a package and a morphological image of a target food; a fit analysis module for determining the contour overlap and edge distance based on the contour image and the morphological image, and determining whether the fit between the target food and the package is acceptable based on the contour overlap and edge distance; a sway risk analysis module for testing packages with acceptable fit to obtain the degree of deformation and distribution of package wrinkles during transportation to determine whether the sway risk is acceptable; a packaging material assessment module for performing a migration test on packages with acceptable sway risk to assess the material safety of the package based on the migration of substances in the packaging material into the food; and a safety determination module for determining whether the target food in the package is safe based on the fit, sway risk, and material safety. This system provides comprehensive protection for food safety.

Description

Technical Field

[0001] The present invention relates to the field of safety supervision, and in particular to a food safety supervision system based on a cloud platform. Background Art

[0002] In today's society, vacuum food packaging safety mainly has the following problems: fit, shaking risk and packaging material safety. These problems are directly related to food quality, shelf life and consumer health. In order to ensure food quality and consumer health, it is necessary to strengthen the selection and testing of packaging materials.

[0003] Patent document with publication number CN116165209A discloses a food vacuum small package defect detection system based on deep learning, which includes: an image acquisition module, an image processing module and a motion control module; the image acquisition module is used to obtain images of food vacuum small packages on a conveyor belt through an industrial camera; the image processing module is used to receive images transmitted by the image acquisition module, detect and locate defective packages in the image using a target detection algorithm based on deep learning, and transmit the location information of the defective packages to the motion control module after detecting the defective packages; the image processing module includes an image preprocessing submodule and a vacuum small package defect detection and positioning submodule; the image preprocessing submodule is used to preprocess the food vacuum small package image, including image size scaling and base Image denoising based on wavelet transform; the vacuum small package defect detection and positioning submodule includes a model pre-training process and a real-time detection and positioning process; the food vacuum small package defect detection model adopts an improved YOLOv5s network model, and the real-time detection and positioning process is as follows: the real-time image collected by the image acquisition module is input into the trained food vacuum small package defect detection model to obtain the detection result. When the defective package is detected, the position of the defective package on the image will be located. According to the mapping relationship between the position on the image and the actual position of the conveyor belt, and combined with the model reasoning consumption time and the conveyor belt transportation speed, the position information of the defective package is transmitted to the motion control module; the motion control module includes an industrial robot, which is used to eliminate the defective package detected by the image processing module.

[0004] In the prior art, defective vacuum packages detected by an image processing module are eliminated, but the remaining vacuum packages are not analyzed, which fails to provide quality assurance for food safety. Summary of the Invention

[0005] To this end, the present invention provides a food safety supervision system based on a cloud platform, which solves the quality assurance problem of food safety by detecting the fit, shaking risk and packaging material safety of vacuum-packaged food.

[0006] To achieve the above objectives, the present invention provides a food safety supervision system based on a cloud platform, comprising:

[0007] A collection module for collecting the outline of the packaging and the morphological image of the target food;

[0008] a fit analysis module, connected to the acquisition module, configured to determine a degree of outline overlap based on the contour lines of the contour image and the morphological image, determine an edge distance based on edge points of the contour image and the morphological image, and determine whether the fit between the target food and the packaging is acceptable based on the contour overlap and the edge distance;

[0009] a sway risk analysis module, connected to the fit analysis module, for testing packages with acceptable fit to determine the degree of deformation and distribution of wrinkles on the package surface during transportation, and based on the deformation degree and distribution, determining deformation data and movement range, thereby determining whether the target food has an acceptable sway risk within the package;

[0010] a packaging material assessment module, connected to the sway risk analysis module, for performing a migration test on the packaging that meets the sway risk requirements, so as to assess the material safety of the packaging based on the migration of substances in the packaging material into the food;

[0011] A safety determination module is connected to the fit analysis module, the shake risk analysis module and the packaging material evaluation module respectively, and is used to determine whether the target food in the package is safe food based on the fit, the shake risk and the material safety.

[0012] Furthermore, the fit analysis module includes:

[0013] a contour line extraction unit, configured to perform edge recognition on the contour image and the morphological image using edge detection, so as to extract the contour line of the target food and the contour line of the package;

[0014] The coincidence calculation unit is connected to the contour extraction subunit, aligns the target food contour line and the packaging contour line through image registration technology, obtains contour point positions respectively, and calculates contour coincidence according to the contour point positions.

[0015] Furthermore, the fit analysis module further includes:

[0016] An edge point extraction unit, for respectively extracting target food edge points and packaging edge points by using edge detection;

[0017] The matching subunit is connected to the edge point extraction unit and is used to match the target food edge point with the package edge point to calculate the shortest distance between the matching points as the edge distance.

[0018] Furthermore, if the contour overlap is higher than a first qualified threshold and the edge distance is lower than a second qualified threshold, it is determined that the fit between the target food and the package is qualified.

[0019] Furthermore, the shaking risk analysis module includes:

[0020] The wrinkle assessment unit physically simulates the vibration and impact environment of the package during transportation and uses image processing to obtain the deformation degree and distribution of wrinkles on the package surface;

[0021] The shaking risk determination unit is connected to the wrinkle assessment unit and measures the deformation data and movement range of the target food in the package according to the deformation degree and the distribution, thereby determining whether the shaking risk of the target food in the package is qualified.

[0022] Furthermore, the shaking risk determination unit includes:

[0023] A monitoring subunit, used to monitor the deformation data of wrinkles and the movement range of the target food in the package in real time;

[0024] a data determination subunit, connected to the monitoring subunit, for determining a maximum deformation degree of the target food in the package based on the deformation data, and determining a maximum movement range of the target food in the package based on the movement range;

[0025] The shaking risk assessment subunit is connected to the data determination subunit. When the maximum deformation degree and the maximum movement range are both within the standard safety range, it is determined that the shaking risk of the target food in the package is qualified.

[0026] Furthermore, the packaging material evaluation module includes:

[0027] Environmental simulation unit, used to simulate the actual contact environment between the target food and packaging materials under test conditions;

[0028] a migrating substance detection unit connected to the environment simulation unit, for periodically sampling the target food in the actual contact environment and detecting migrating substances therein using gas chromatography;

[0029] a quantitative analysis unit connected to the migrating substance detection unit, for performing quantitative analysis on the migrating substance to determine its concentration and migration amount;

[0030] The safety assessment unit is connected to the quantitative analysis unit to determine whether the concentration and the migration amount are within the prescribed limit standard range, thereby evaluating the safety of the packaging material.

[0031] Furthermore, the quantitative analysis unit includes:

[0032] The pretreatment subunit pre-treats the target food containing migrating substances to ensure that the target food meets the requirements of quantitative analysis;

[0033] a concentration analysis subunit, connected to the pretreatment subunit, for injecting the pretreated target food into a quantitative analysis instrument, performing separation, detection, and quantification according to a spectroscopic method, recording the spectral absorbance output by the instrument, and converting the spectral absorbance into the concentration of the migrating substance using a regression equation;

[0034] The migration amount calculation subunit is connected to the concentration analysis subunit and calculates the migration amount according to the concentration and the contact time of the target food.

[0035] Furthermore, the regression equation is established using known concentrations of migrating substances and corresponding spectral absorbance data to reflect the quantitative relationship between spectral absorbance and the concentration of migrating substances. The concentration analysis subunit measures the spectral absorbance of the target food and detects the concentration value corresponding to the migrating substance in the regression equation to convert the spectral absorbance into the concentration of the substance.

[0036] Furthermore, the security determination module includes:

[0037] a data evaluation unit, configured to generate a food safety evaluation report based on the fit, the shaking risk, and the material safety;

[0038] The risk assessment unit is connected to the data assessment unit and assesses the food safety assessment report according to safety standards, thereby determining that the target food in the package is safe food.

[0039] Compared with the existing technology, the beneficial effect of the present invention lies in ensuring the accuracy and clarity of the packaging contour map and the target food morphology map through image acquisition technology, providing timely data support for subsequent analysis and processing, accurately evaluating the fit between the package and the target food through the analysis of the contour lines and edge points by the fit analysis module, ensuring the tightness and stability of the package, simulating the transportation environment through the shaking risk analysis module to evaluate the shaking risk of the package, ensuring the safety of food during transportation, reducing losses and return costs caused by transportation, using migration testing through the packaging material evaluation module to ensure that harmful substances in the packaging material will not migrate into the food to protect the health of consumers, and providing a comprehensive assessment of food safety through the comprehensive consideration of the fit, shaking risk and packaging material safety by the safety determination module.

[0040] In particular, precise edge detection and calculation can accurately evaluate the fit of the target food, thus avoiding the influence of subjective judgment and realizing automatic analysis of the packaging contour map and the target food morphology map, thereby ensuring the stability and consistency of product quality.

[0041] In particular, through the collaborative work of the contour extraction subunit and the overlap calculation subunit, the contour lines of the target food and the packaging can be accurately extracted, and the overlap between them can be calculated, thereby improving the accuracy of fit detection. The calculation unit realizes the automatic extraction and alignment of the contour lines of the target food and the packaging, as well as the automatic calculation of the overlap, thereby improving the detection efficiency.

[0042] In particular, the matching subunit can match the edge points on the target food contour with the corresponding edge points on the packaging contour, providing accuracy for the subsequent calculation of the edge distance. Through an efficient matching algorithm, it can quickly find matching points and calculate the shortest distance between them, namely the edge distance, thereby improving the efficiency of the entire evaluation process.

[0043] In particular, by comprehensively considering the two indicators of contour overlap and edge distance, the fit between the packaging and the target food can be evaluated more comprehensively. The comprehensive evaluation method is more accurate than a single indicator and can more realistically reflect the actual matching between the packaging and the target food.

[0044] In particular, by precisely measuring wrinkle parameters and performing physical simulations, the performance of packaging during transportation can be more accurately assessed, helping to reduce cargo damage or transportation caused by improper packaging design. At the same time, reducing the risk of shaking can also reduce the additional costs caused by cargo damage.

[0045] In particular, real-time monitoring and data analysis can more accurately assess the risk of food shaking during transportation, reduce potential losses caused by inaccurate assessments, and enable targeted optimization of packaging design to improve packaging stability.

[0046] In particular, through environmental simulation and migration substance detection, the actual contact environment between packaging materials and food can be simulated and detected more accurately, thereby improving the accuracy of safety assessment. The results of quantitative analysis and safety assessment can provide valuable feedback for the production of packaging materials, promote the innovation and improvement of packaging materials to meet more stringent safety requirements.

[0047] In particular, the establishment of the regression equation is based on a large amount of experimental data, so it has high accuracy. Through this equation, the concentration of migrating substances in the target food can be accurately measured to reduce errors. Traditional detection methods may require complex chemical reactions or long experimental processes, while the use of regression equations for detection greatly simplifies the detection steps and improves detection efficiency.

[0048] In particular, by generating food safety assessment reports through the data evaluation unit, the safety of food can be comprehensively and accurately assessed, thereby protecting the health and safety of consumers. Food safety assessment helps to improve product quality and market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of the structure of a food safety supervision system based on a cloud platform provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the structure of a fit analysis module of a food safety supervision system based on a cloud platform provided by an embodiment of the present invention;

[0051] Figure 3 A schematic diagram of the structure of a shaking risk analysis module of a food safety supervision system based on a cloud platform provided by an embodiment of the present invention;

[0052] Figure 4 A schematic diagram of the packaging material evaluation module structure of a cloud-based food safety supervision system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0054] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0055] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be understood as a limitation on the present invention.

[0056] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0057] See also Figure 1 As shown, the present invention provides a food safety supervision system based on a cloud platform, comprising:

[0058] The acquisition module 10 is used to acquire the outline of the packaging and the morphology of the target food;

[0059] a fit analysis module 20, connected to the acquisition module, configured to determine a degree of outline overlap based on the contour lines of the contour image and the morphological image, determine an edge distance based on edge points of the contour image and the morphological image, and determine whether the fit between the target food and the package is acceptable based on the contour overlap and the edge distance;

[0060] The sway risk analysis module 30 is connected to the fit analysis module and is used to test packages with qualified fit to obtain the degree of deformation and distribution of wrinkles on the package surface during transportation. Based on the deformation degree and distribution, deformation data and movement range are determined to determine whether the target food has a qualified sway risk within the package.

[0061] a packaging material assessment module 40, connected to the sloshing risk analysis module, for performing a migration test on the packaging that meets the sloshing risk requirements, so as to assess the material safety of the packaging based on the migration of substances in the packaging material into the food;

[0062] The safety determination module 50 is connected to the fit analysis module, the shake risk analysis module and the packaging material evaluation module respectively, and is used to determine whether the target food in the package is safe food based on the fit, the shake risk and the material safety.

[0063] Specifically, after the production of vacuum food is completed, several target foods are randomly selected for safety testing. The packaging is used to wrap the target food. By analyzing the fit of the vacuum packaging, the risk of shaking and the safety of the packaging materials, it is determined whether the vacuum food in this production batch meets the industry's food vacuum packaging standards, ensuring the safety of food from packaging to consumption in the food packaging industry chain.

[0064] Specifically, the packaging contour map and food morphology map of the target food are collected according to the image sensor to capture the appearance characteristics of the packaging and food, image processing is performed on the packaging contour map and food morphology map to obtain contour lines and edge points, analysis is performed based on the contour lines and edge points to obtain contour coincidence and edge distance, so as to determine the fit of the target food, based on the packaging with qualified fit, the packaging wrinkle parameters are further detected, the vibration and impact during transportation are simulated, the deformation and movement of the packaging are observed, the deformation degree and distribution of the wrinkles are obtained, so as to determine the deformation data and movement range, so as to evaluate the shaking risk, based on the packaging with qualified shaking risk, a migration test is performed, and gas chromatography is used to detect the migration of substances in the packaging material to the food under the test conditions of the target food and packaging, so as to evaluate the safety of the packaging material, and the fit, shaking risk and packaging material safety are comprehensively considered to finally determine the safety of the food.

[0065] Specifically, gas chromatography is a highly sensitive analytical method that can be used to detect harmful substances in packaging materials that may migrate into food.

[0066] Specifically, image acquisition technology is used to ensure the accuracy and clarity of the packaging contour map and the target food morphology map, and timely data support is provided for subsequent analysis and processing. The fit analysis module analyzes the contour lines and edge points to accurately evaluate the fit between the packaging and the target food, ensuring the tightness and stability of the packaging. The shaking risk analysis module simulates the transportation environment to evaluate the shaking risk of the packaging, ensure the safety of food during transportation, and reduce losses and return costs caused by transportation. The packaging material evaluation module uses migration testing to ensure that harmful substances in the packaging materials will not migrate into the food to protect the health of consumers. The safety determination module comprehensively considers the fit, shaking risk and packaging material safety to provide a comprehensive assessment of food safety.

[0067] See also Figure 2 As shown, the fit analysis module 20 includes:

[0068] a contour line extraction unit 21 for performing edge recognition on the contour image and the morphological image using edge detection to extract the contour line of the target food and the contour line of the package;

[0069] The coincidence calculation unit 22 is connected to the contour extraction subunit, aligns the target food contour line and the package contour line through image registration technology, obtains contour point positions respectively, and calculates contour coincidence according to the contour point positions.

[0070] Specifically, the coincidence calculation subunit aligns the extracted target food with the packaging contour line. The coincidence calculation subunit can obtain the position of the corresponding point on the contour line and calculate the contour coincidence based on the position. The higher the coincidence, the better the fit between the target food and the packaging; conversely, the lower the coincidence, the worse the fit.

[0071] Specifically, the formula for measuring contour overlap is: J(A,B) = |A∩B| / |A∪B|, where A and B represent the contour point sets of the food and packaging, respectively. |A∩B| represents the number of elements in the intersection of the two sets (i.e., the number of overlapping contour points), and |A∪B| represents the number of elements in the union of the two sets (i.e., the total number of contour points). Values ​​range from 0 to 1, with values ​​closer to 1 indicating higher overlap and values ​​closer to 0 indicating lower overlap. For example, suppose that food contour point set A has 5 points and packaging contour point set B has 7 points, and there are 3 overlapping points between them. Then the overlap is: J(A,B) = 3 / (5+7-3) = 3 / 9≈0.333, indicating that the overlap between the food and packaging is approximately 33.3%.

[0072] Specifically, through the collaborative work of the contour extraction sub-unit and the overlap calculation sub-unit, the contour lines of the target food and the packaging can be accurately extracted, and the overlap between them can be calculated, thereby improving the accuracy of fit detection. The calculation unit realizes the automatic extraction and alignment of the contour lines of the target food and the packaging, as well as the automatic calculation of the overlap, thereby improving the detection efficiency.

[0073] Specifically, the fit analysis module also includes:

[0074] An edge point extraction unit, for respectively extracting target food edge points and packaging edge points by using edge detection;

[0075] The matching subunit is connected to the edge point extraction unit and is used to match the target food edge point with the package edge point to calculate the shortest distance between the matching points as the edge distance.

[0076] Specifically, matching the edge points on the target food contour with the corresponding edge points on the packaging contour is the basis for edge distance calculation. The shortest distance, i.e., the edge distance, is calculated by the nearest corresponding point of each food edge point on the packaging contour. For any edge point P(x1, y1) on the target food contour, the algorithm finds its corresponding point Q(x2, y2) on the packaging contour. The edge distance d between P and Q can be expressed as: The distance d is the edge distance between two points and is used to quantify the gap or misalignment between the package and the target food.

[0077] Specifically, both the food outline and the packaging outline consist of a series of discrete points;

[0078] An edge point P(3,4) on the food contour;

[0079] The corresponding point Q(5,6) on the package contour that matches point P;

[0080] Then the edge distance d between P and Q can be calculated as:

[0081] If the preset edge distance acceptance threshold is 3 mm, then in this example, the edge distance d (approximately 2.83 mm) is less than the acceptance threshold, so it can be considered that the edge distance at this point meets the requirements. At the same time, if the contour overlap is also higher than the acceptance threshold (e.g., 80%), then the target food can be comprehensively judged to have acceptable fit.

[0082] Specifically, the matching subunit can match the edge points on the target food contour with the corresponding edge points on the packaging contour, providing accuracy for the subsequent calculation of the edge distance. Through an efficient matching algorithm, it can quickly find matching points and calculate the shortest distance between them, namely the edge distance, thereby improving the efficiency of the entire evaluation process.

[0083] Specifically, if the contour overlap is higher than a first qualified threshold and the edge distance is lower than a second qualified threshold, it is determined that the fit between the target food and the package is qualified.

[0084] Specifically, by comparing the contour overlap with the first pass threshold and the edge distance with the second pass threshold, it can be determined whether the target food's fit meets the requirements. For example, if the overlap is higher than the first pass threshold (80%) and the edge distance is lower than the second pass threshold (2.5mm), the fit between the target food and the package is considered acceptable; otherwise, it is considered unacceptable.

[0085] Specifically, by comprehensively considering the two indicators of contour overlap and edge distance, the fit between the packaging and the target food can be evaluated more comprehensively. The comprehensive evaluation method is more accurate than a single indicator and can more truly reflect the actual matching between the packaging and the target food.

[0086] See also Figure 3 As shown, the sway risk analysis module 30 includes:

[0087] The wrinkle assessment unit 31 physically simulates the vibration and impact environment of the package during transportation and obtains the deformation degree and distribution of wrinkles on the package surface through image processing;

[0088] The shaking risk determination unit 32 is connected to the wrinkle assessment unit, and measures the deformation data and movement range of the target food in the package according to the deformation degree and the distribution, so as to determine whether the shaking risk of the target food in the package is qualified.

[0089] Specifically, the degree of deformation refers to the amount of deformation of the folds under vibration and impact, including the expansion, compression or twisting of the folds.

[0090] Specifically, distribution refers to the fact that if deformation is concentrated in a certain area of ​​the package, the food in that area may be subjected to greater impact force, thereby increasing the risk of shaking. Conversely, if deformation is evenly distributed, the movement of the food within the package may be relatively smooth, and the risk of shaking is relatively low.

[0091] Specifically, physical simulations are used to recreate the vibration and impact environments that packaging may encounter during transportation. During the simulation, the degree and distribution of deformation at the folds, which are typically the most vulnerable parts of the packaging, are monitored. Based on the monitoring results, the deformation data and movement range of the target food within the package are further measured. These deformation data and movement range can reflect whether the packaging can effectively secure the food during transportation, preventing it from shaking or shifting.

[0092] Specifically, by precisely measuring wrinkle parameters and performing physical simulations, the performance of packaging during transportation can be more accurately evaluated, helping to reduce cargo damage or transportation caused by improper packaging design. At the same time, reducing the risk of shaking can also reduce the additional costs caused by cargo damage.

[0093] Specifically, the shaking risk determination unit includes:

[0094] A monitoring subunit, used to monitor the deformation data of wrinkles and the movement range of the target food in the package in real time;

[0095] a data determination subunit, connected to the monitoring subunit, for determining a maximum deformation degree of the target food in the package based on the deformation data, and determining a maximum movement range of the target food in the package based on the movement range;

[0096] The shaking risk assessment subunit is connected to the data determination subunit. When the maximum deformation degree and the maximum movement range are both within the standard safety range, it is determined that the shaking risk of the target food in the package is qualified.

[0097] Specifically, by real-time monitoring of wrinkle deformation data and the movement range of the target food in the package, it is possible to capture the wrinkle deformation and target food movement caused by various external forces (such as vibration, impact, etc.) exerted on the package during transportation, so as to obtain the maximum deformation degree and maximum movement range of the target food in the package. Combined with industry standards, the shaking risk of the target food during transportation can be evaluated.

[0098] Specifically, for example, the fold height of the vacuum packaging of cooked meat products is 5 mm, the fold spacing is 4 cm, and the folds are evenly distributed. The vibration and impact environment of the packaging with these fold parameters during transportation is physically simulated. The simulation conditions are set as follows: vibration frequency of 5 Hz, amplitude of 2 cm, impact acceleration of 50 g, and duration of 10 milliseconds. During the simulation, it was monitored that the maximum deformation of the target food in the package reached 2 cm, and the maximum movement range exceeded 15% of the package size (relative to the original size). According to industry experience, for vacuum-packed cooked meat products, the maximum deformation allowed is usually no more than 1 cm, and the maximum movement range is no more than 10% of the package size. Obviously, the results of this measurement exceeded these two safety ranges.

[0099] Specifically, real-time monitoring and data analysis can more accurately assess the risk of food shaking during transportation, reduce potential losses caused by inaccurate assessments, and make targeted optimizations to packaging design to improve packaging stability.

[0100] See also Figure 4 As shown, the packaging material evaluation module 40 further includes:

[0101] an environmental simulation unit 41 for simulating the actual contact environment between the target food and the packaging material under test conditions;

[0102] a migrating substance detection unit 42, connected to the environment simulation unit, for periodically sampling the target food in the actual contact environment and detecting migrating substances therein using gas chromatography;

[0103] The quantitative analysis unit 43 is connected to the migration substance detection unit and performs quantitative analysis on the migration substance to determine its concentration and migration amount;

[0104] The safety assessment unit 44 is connected to the quantitative analysis unit to determine whether the concentration and the migration amount are within the prescribed limit standard range, thereby evaluating the safety of the packaging material.

[0105] Specifically, the test conditions are the temperature, humidity, and light test conditions during the transportation of vacuum food, which aim to restore the actual usage scenario to the greatest extent possible.

[0106] Specifically, based on the actual contact environment created by the environmental simulation unit, food is sampled regularly (24 hours) and tested by gas chromatography (GC). This method has the characteristics of high sensitivity, high resolution and rapid analysis, and is suitable for the detection of a variety of volatile and non-volatile organic compounds. The detection data provided by the migration substance detection unit is quantitatively analyzed to determine the concentration and migration amount of the migration substance. By comparing the data at different time points or under different test conditions, we can gain an in-depth understanding of how the migration behavior changes with time, temperature and other factors, and compare the actual concentration of the migration amount with the prescribed limit standard. The limit standard is usually formulated by government regulatory agencies or industry associations to ensure the safety of food packaging materials.

[0107] Specifically, through environmental simulation and migration substance detection, the actual contact environment between packaging materials and food can be simulated and detected more accurately, thereby improving the accuracy of safety assessment. The results of quantitative analysis and safety assessment can provide valuable feedback for the production of packaging materials, promote the innovation and improvement of packaging materials to meet more stringent safety requirements.

[0108] Specifically, the quantitative analysis unit includes:

[0109] The pretreatment subunit pre-treats the target food containing migrating substances to ensure that the target food meets the requirements of quantitative analysis;

[0110] a concentration analysis subunit, connected to the pretreatment subunit, for injecting the pretreated target food into a quantitative analysis instrument, performing separation, detection, and quantification according to a spectroscopic method, recording the spectral absorbance output by the instrument, and converting the spectral absorbance into the concentration of the migrating substance using a regression equation;

[0111] The migration amount calculation subunit is connected to the concentration analysis subunit and calculates the migration amount according to the concentration and the contact time of the target food.

[0112] Specifically, a target food containing migrating substances is obtained and subjected to necessary pretreatment operations, including filtration, dilution, and concentration. The pretreated target food is injected into a quantitative analysis instrument, which is usually separated, detected, and quantified based on the light absorption characteristics of different substances in the target food using spectroscopy. During the concentration analysis process, the instrument will output spectral absorbance data, which is a direct reflection of the concentration of the substance in the target food.

[0113] Specifically, migration amount refers to the amount of substances that migrate from packaging materials into food per unit time.

[0114] Specifically, the migration amount (M) can be calculated using the formula: M = C × V / T. For example, if the concentration of the migrating substance X is C = 10 mg / L, the volume of the food is V = 1 L, and the contact time is T = 24 h, the calculated migration amount is: M = 10 mg / L × 1 L / 24 h = 0.42 mg / h (round the result to two decimal places). Therefore, the migration amount of the migrating substance X is 0.42 mg / h, indicating that the amount of the migrating substance X that migrates from the packaging material into the food during the 24-hour contact time is 0.42 mg.

[0115] Specifically, the target food after pretreatment is more in line with the requirements of quantitative analysis instruments, can reduce instrument failures and errors, and extend the service life of the instrument. The concentration analysis subunit uses spectral methods for separation, detection and quantification, and can accurately determine the concentration of migrating substances in the target food. The migration amount calculation subunit calculates the migration amount based on the concentration of the migrating substance and the contact time with the target food, and can accurately assess the potential risks of migrating substances to food, and can understand the migration laws and trends of migrating substances under different conditions.

[0116] Specifically, the regression equation is established using known concentrations of migrating substances and corresponding spectral absorbance data to reflect the quantitative relationship between spectral absorbance and the concentration of migrating substances. The concentration analysis subunit measures the spectral absorbance of the target food and detects the concentration value corresponding to the migrating substance in the regression equation to convert the spectral absorbance into the concentration of the substance.

[0117] Specifically, in actual operation, a series of migration substance samples with known concentrations are first selected, and the spectral absorbance is measured using a spectrometer. Statistical regression models (such as multiple linear regression, nonlinear regression, etc.) are used to establish a mathematical relationship between spectral absorbance and migration substance concentration based on these data, namely the regression equation.

[0118] Specifically, the establishment of the regression equation is based on a large amount of experimental data, so it has high accuracy. Through this equation, the concentration of migrating substances in the target food can be accurately measured to reduce errors. Traditional detection methods may require complex chemical reactions or long experimental processes, while the use of regression equations for detection greatly simplifies the detection steps and improves detection efficiency.

[0119] Specifically, the security determination module includes:

[0120] a data evaluation unit, configured to generate a food safety evaluation report based on the fit, the shaking risk, and the material safety;

[0121] The risk assessment unit is connected to the data assessment unit and assesses the food safety assessment report according to safety standards, thereby determining that the target food in the package is safe food.

[0122] Specifically, the safety standard has a first qualified threshold value (80%) for contour overlap in fit, a second qualified threshold value (2.5mm) for edge distance, and the safety standard for shaking risk generally allows a maximum deformation of no more than 1 cm, and a maximum movement range of no more than 10% of the packaging size. The safety standard for packaging materials is based on the limit values ​​specified in the national food safety standards. Different types of food may have different sensitivities and acceptances to migrating substances. For example, high-risk foods such as infant food and food for special medical purposes usually have stricter migration limits.

[0123] Specifically, by generating a food safety assessment report through the data evaluation unit, the safety of food can be comprehensively and accurately assessed, thereby protecting the health and safety of consumers. Food safety assessment helps to improve product quality and market competitiveness.

[0124] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0125] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A food safety supervision system based on a cloud platform, characterized in that: include: A collection module for collecting the outline of the packaging and the morphological image of the target food; a fit analysis module, connected to the acquisition module, configured to determine a degree of outline overlap based on the contour lines of the contour image and the morphological image, determine an edge distance based on edge points of the contour image and the morphological image, and determine whether the fit between the target food and the packaging is acceptable based on the contour overlap and the edge distance; a sway risk analysis module, connected to the fit analysis module, for testing packages with acceptable fit to determine the degree of deformation and distribution of wrinkles on the package surface during transportation, and based on the deformation degree and distribution, determining deformation data and movement range, thereby determining whether the target food has an acceptable sway risk within the package; a packaging material assessment module, connected to the sway risk analysis module, for performing a migration test on the packaging that meets the sway risk requirements, so as to assess the material safety of the packaging based on the migration of substances in the packaging material into the food; a safety determination module, connected to the fit analysis module, the shake risk analysis module, and the packaging material evaluation module, respectively, for determining whether the target food in the package is safe food based on the fit, the shake risk, and the material safety; The fit analysis module includes: a contour line extraction unit, configured to perform edge recognition on the contour image and the morphological image using edge detection, so as to extract the contour line of the target food and the contour line of the package; a coincidence calculation unit connected to the contour extraction unit, aligning the target food contour and the packaging contour using image registration technology, obtaining contour point positions, and calculating contour coincidence based on the contour point positions; The fit analysis module also includes: An edge point extraction unit, for respectively extracting target food edge points and packaging edge points by using edge detection; a matching subunit, connected to the edge point extraction unit, for matching the target food edge points with the package edge points to calculate the shortest distance between the matching points as the edge distance; If the contour overlap is higher than the first qualified threshold and the edge distance is lower than the second qualified threshold, it is determined that the fit between the target food and the package is qualified; The formula for measuring contour overlap is: J(A,B) = |A∩B| / |A∪B|, where A and B represent the contour point sets of food and packaging, respectively, |A∩B| represents the number of overlapping contour points, and |A∪B| represents the total number of contour points. The value range is between 0 and 1. The closer the value is to 1, the higher the overlap is, and the closer the value is to 0, the lower the overlap is. Matching the edge points on the target food contour with the corresponding edge points on the packaging contour is the basis for edge distance calculation. The shortest distance between each food edge point and the nearest corresponding point on the packaging contour is calculated as the edge distance.

2. The food safety supervision system based on the cloud platform according to claim 1 is characterized in that: The sloshing risk analysis module includes: The wrinkle assessment unit physically simulates the vibration and impact environment of the package during transportation and uses image processing to obtain the deformation degree and distribution of wrinkles on the package surface; The shaking risk determination unit is connected to the wrinkle assessment unit and measures the deformation data and movement range of the target food in the package according to the deformation degree and the distribution, thereby determining whether the shaking risk of the target food in the package is qualified.

3. The food safety supervision system based on the cloud platform according to claim 2 is characterized in that: The shaking risk determination unit includes: A monitoring subunit, used to monitor the deformation data of wrinkles and the movement range of the target food in the package in real time; a data determination subunit, connected to the monitoring subunit, for determining a maximum deformation degree of the target food in the package based on the deformation data, and determining a maximum movement range of the target food in the package based on the movement range; The shaking risk assessment subunit is connected to the data determination subunit. When the maximum deformation degree and the maximum movement range are both within the standard safety range, it is determined that the shaking risk of the target food in the package is qualified.

4. The food safety supervision system based on the cloud platform according to claim 3 is characterized in that: The packaging material evaluation module includes: Environmental simulation unit, used to simulate the actual contact environment between the target food and packaging materials under test conditions; a migrating substance detection unit connected to the environment simulation unit, for periodically sampling the target food in the actual contact environment and detecting migrating substances therein using gas chromatography; a quantitative analysis unit connected to the migrating substance detection unit, for performing quantitative analysis on the migrating substance to determine its concentration and migration amount; The safety assessment unit is connected to the quantitative analysis unit to determine whether the concentration and the migration amount are within the prescribed limit standard range, thereby evaluating the safety of the packaging material.

5. The food safety supervision system based on the cloud platform according to claim 4 is characterized in that: The quantitative analysis unit includes: The pretreatment subunit pre-treats the target food containing migrating substances to ensure that the target food meets the requirements of quantitative analysis; a concentration analysis subunit, connected to the pretreatment subunit, for injecting the pretreated target food into a quantitative analysis instrument, performing separation, detection, and quantification according to a spectroscopic method, recording the spectral absorbance output by the instrument, and converting the spectral absorbance into the concentration of the migrating substance using a regression equation; The migration amount calculation subunit is connected to the concentration analysis subunit and calculates the migration amount according to the concentration and the contact time of the target food.

6. The food safety supervision system based on the cloud platform according to claim 5 is characterized in that: The regression equation is established using known concentrations of migrating substances and corresponding spectral absorbance data to reflect the quantitative relationship between spectral absorbance and the concentration of migrating substances. The concentration analysis subunit measures the spectral absorbance of the target food and detects the concentration value corresponding to the migrating substance in the regression equation to convert the spectral absorbance into the concentration of the substance.

7. The food safety supervision system based on the cloud platform according to claim 6 is characterized in that: The security determination module includes: a data evaluation unit, configured to generate a food safety evaluation report based on the fit, the shaking risk, and the material safety; The risk assessment unit is connected to the data assessment unit and assesses the food safety assessment report according to safety standards, thereby determining that the target food in the package is safe food.

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