A food quality inspection system

Abstract

This invention relates to the field of food inspection technology, and more particularly to a food quality inspection system, comprising: a conveying module, a clamping module, a detection module, a raw material classification module, a display module, and an analysis module. The raw material classification module processes the data of each raw material and classifies them into different grades. The clamping module determines the freshness of the raw materials by lifting them. The analysis module determines whether a single batch of raw materials meets preset standards based on the grade ratio of each raw material, and adjusts the operating speed of the robotic arm to the corresponding value based on the judgment result, or specifically adjusts the maximum retention warning time for a single batch of raw materials, thereby improving the efficiency of raw material inspection.

Description

Technical Field

[0001] This invention relates to the field of food testing technology, and in particular to a food quality testing system. Background Technology

[0002] With the rapid development of the catering industry and the increasing demands of consumers for food safety and quality, traditional raw material quality testing methods are no longer sufficient to meet the needs of modern catering management. These methods often suffer from low testing efficiency. Therefore, developing an efficient and accurate food testing system is of paramount importance.

[0003] Chinese Patent Publication No. CN116148231 B discloses an online detection system and method for frozen and preserved pre-cooked vegetables, including an online display module, a detection point layout module, and a probe detection module. The online display module is used to store the freezing status of the vegetables online. The detection point layout module is used to arrange the detection points of the vegetables on the detection screen. The probe detection module is used to detect the freshness of the vegetables on the detection screen. The detection point layout module and the probe detection module are electrically connected. Both the detection point layout module and the probe detection module are electrically connected to the online display module. The online display module includes a vegetable archiving module, a data conversion module, and an information transmission module. The vegetable archiving module is used to store the raw material property data of different vegetables in the cloud. The data conversion module is electrically connected to the vegetable archiving module. It is evident that the above technical solution has the following problems: it does not consider classifying the grades of each raw material, nor does it consider automatically adjusting the detection parameters based on the proportion of each grade of raw material, affecting the accuracy of the detection results and thus affecting the detection efficiency. Summary of the Invention

[0004] To address this issue, the present invention provides a food quality inspection system to overcome the problems in the prior art that do not consider classifying the grades of each ingredient or automatically adjusting the inspection parameters based on the proportion of each grade of ingredient, thus affecting the accuracy of the inspection results and consequently the inspection efficiency.

[0005] To achieve the above objectives, the present invention provides a food quality inspection system, comprising:

[0006] A conveying module, comprising a conveyor belt for conveying raw materials to be processed;

[0007] A clamping module, comprising a robotic arm positioned above the conveyor belt to lift raw materials at predetermined clamping points;

[0008] The detection module includes a first visual detector disposed above the conveyor belt to acquire raw material image information and a second visual detector disposed on one side of the conveyor belt to acquire side image information.

[0009] The raw material classification module, which is connected to the detection module, is used to determine the doping degree of color blocks based on the raw material image information obtained by the visual detector, and classify each raw material grade in combination with the change in the curvature of the raw material.

[0010] The display module is used to output the maximum retention warning time for each batch of raw materials;

[0011] The analysis module, which is connected to the clamping module, the raw material division module and the display module respectively, is used to determine whether a batch of raw materials meets the preset standard based on the grade ratio of each raw material, and to adjust the running speed of the robotic arm to the corresponding value or adjust the maximum retention warning time for a single batch of raw materials to the corresponding value based on the judgment result.

[0012] Furthermore, the raw material segmentation module is used to determine the doping degree of color patches based on the raw material image information, including:

[0013] Used to identify and select grayscale regions based on the grayscale values ​​of raw material image information;

[0014] The ratio of the average area of ​​each grayscale region to the total area of ​​the raw material in the raw material image information is used to calculate the doping degree of the color block.

[0015] Furthermore, the raw material classification module is used to classify the grade of individual raw materials based on the doping degree of color patches, including:

[0016] If the color block impurity is greater than the second preset color block impurity, the raw material will be classified as a first-level raw material;

[0017] If the color block impurity degree is less than or equal to the second preset color block impurity degree and greater than the first preset color block impurity degree, then the grade of a single raw material is determined based on the change in the curvature of the raw material.

[0018] If the color block impurity degree is less than or equal to the first preset color block impurity degree, the raw material is divided into three levels of raw material.

[0019] Furthermore, the raw material segmentation module is used to determine the curvature change of the raw material based on the side image information, including:

[0020] The robotic arm is used to lift the raw material after the raw material sorting module determines the grade of the individual raw material based on the curvature change. The second vision detector obtains the side image information after lifting.

[0021] The raw material segmentation module is used to obtain the raw material outline from the side image information;

[0022] The material segmentation module is used to obtain the height distance between the height of the lowest point in the material profile and the height of the end of the robotic arm, and to solve the ratio of this height distance to the length of the material profile to obtain the material curvature change.

[0023] The raw material classification module is used to classify individual raw materials into grades based on the curvature variation of the raw materials, including:

[0024] If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material, then the raw material is classified as a first-level raw material.

[0025] If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material and greater than the second preset change in curvature of the raw material, then the raw material is classified as a secondary raw material.

[0026] If the change in the curvature of the raw material is greater than the second preset change in the curvature of the raw material, the raw material will be classified as a secondary or tertiary raw material.

[0027] Furthermore, the analysis module is used to determine whether the operating parameters of the clamping module meet preset standards based on the proportion of secondary and tertiary raw materials to the total number of raw materials detected in a single detection cycle, including:

[0028] If the proportion of the second-third level is less than or equal to the preset proportion of the second-third level, then the quality of a single batch of raw materials is determined based on the proportion of the second-second level.

[0029] If the proportion of the secondary third level is greater than the preset proportion of the secondary third level, the running speed of the robotic arm will be adjusted to the corresponding value based on the corresponding reference weight of the raw materials in a single batch. Based on the proportion of the secondary third level within the detection cycle after adjustment, it will be determined whether the preset proportion of the secondary second level will be adjusted to the corresponding value, and based on the proportion of the secondary second level, it will be determined whether the quality of the raw materials in a single batch is qualified.

[0030] Furthermore, the analysis module is used to adjust the operating speed of the robotic arm to a corresponding value based on the corresponding reference weight of a single batch of raw materials, wherein:

[0031] The decrease in the operating speed of the robotic arm, determined based on the corresponding reference weight of a single batch of raw materials, is negatively correlated with the corresponding reference weight.

[0032] Furthermore, the analysis module is used to determine whether to adjust the preset secondary proportion to the corresponding value based on the secondary-tertiary proportion within the newly acquired detection cycle, after completing the adjustment of the robotic arm's operating speed, including:

[0033] The ratio of the newly acquired sub-tertiary level proportion to the sub-tertiary level proportion acquired in the previous detection cycle is calculated, and this ratio is determined as the adjustment parameter.

[0034] If the adjusted parameter is less than or equal to the preset adjusted parameter, the preset secondary ratio will be adjusted to the corresponding value, and the quality of a single batch of raw materials will be determined based on the secondary ratio.

[0035] If the adjusted parameter is greater than the preset adjustment parameter, the raw material quality of a single batch is determined to be unqualified.

[0036] Furthermore, the analysis module is used to adjust the preset secondary proportion to the corresponding value based on the parameter difference between the preset adjustment parameter and the adjustment parameter, wherein:

[0037] The increase in the preset secondary proportion determined by the parameter difference is positively correlated with the parameter difference.

[0038] Furthermore, the analysis module is used to determine whether the quality of a single batch of raw materials is qualified based on the secondary proportion, including:

[0039] If the proportion of secondary components is less than or equal to the preset proportion of secondary components, the quality of a single batch of raw materials is deemed to be qualified.

[0040] If the proportion of secondary components is greater than the preset proportion of secondary components, then the quality of a single batch of raw materials is deemed unqualified.

[0041] Furthermore, the analysis module is used to adjust the maximum retention warning duration to a corresponding value based on the secondary proportion when it is determined that the quality of a single batch of raw materials is unqualified, wherein:

[0042] The reduction in the maximum retention warning duration determined by the secondary proportion is positively correlated with the secondary proportion.

[0043] Compared with the prior art, the beneficial effects of the present invention are that the raw material classification module processes the data of each raw material and classifies each module into a grade; the clamping module determines the freshness of the raw material by lifting the raw material; the analysis module determines whether a single batch of raw material meets the preset standard based on the grade ratio of each raw material; and based on the judgment result, it determines to adjust the running speed of the robotic arm to the corresponding value, or to specifically adjust the maximum retention warning time for a single batch of raw material, thereby improving the detection efficiency of raw materials.

[0044] Furthermore, the raw materials are graded based on the degree of color patch impurity. Color patch impurity characterizes the degree of decay of the raw materials. When raw materials decay, there will be decayed areas with significantly lower grayscale values ​​than the surrounding areas. The more grayscale areas selected by the raw material classification module, the more decayed areas there will be, leading to a decrease in the average area of ​​each grayscale area. In other words, the degree of color patch impurity is inversely proportional to the degree of decay of the raw materials. When the color patch impurity is less than or equal to the second preset color patch impurity and greater than the first preset color patch impurity, to ensure the accuracy of data processing and the precision of raw material classification, in this case, [further details are needed]. The change in the curvature of the raw material is used to classify the grade of the individual raw material. The change in the curvature of the raw material represents its freshness. After the robotic arm completes the lifting of the raw material, the degree of bending of the raw material is negatively correlated with its freshness, and the change in the curvature of the raw material is also negatively correlated with its freshness. The proportion of the second-to-third grade within a single detection cycle is obtained. When the proportion of the second-to-third grade is greater than the preset proportion, there may be a situation where the raw material is damaged due to the excessive lifting speed of the robotic arm. In this case, the operating parameters of the robotic arm are adjusted accordingly to ensure the integrity of the raw material detection process. This improves the accuracy of the detection results and thus improves the detection efficiency.

[0045] Furthermore, after adjusting the operating speed of the robotic arm, adjustment parameters are obtained. These parameters characterize the impact of the robotic arm's operating speed adjustment on the raw material testing results. When the adjustment parameter is greater than the preset adjustment parameter, the robotic arm's operating speed has little impact on the raw material testing, and a large number of secondary and tertiary raw materials are present. In this case, the quality of a single batch of raw materials is determined to be unqualified. When the adjustment parameter is less than or equal to the preset adjustment parameter, the robotic arm's operating speed does affect the raw material testing results. Such raw materials are more prone to bending, and bending can damage the cell structure of vegetables, causing cell contents to leak out. The damaged areas provide a convenient channel for microbial invasion and reproduction, making these vegetables prone to rotting. In this case, the preset secondary proportion for determining whether a single batch of raw materials is qualified is adjusted to automatically adjust the data processing standards based on the actual situation and characteristics of the raw materials, effectively improving the efficiency of vegetable quality testing. Attached Figure Description

[0046] Figure 1 This is a block diagram of the food quality inspection system according to an embodiment of the present invention;

[0047] Figure 2 This is a schematic diagram of the food quality inspection system according to an embodiment of the present invention;

[0048] Figure 3 This is a logic diagram of the raw material classification module in an embodiment of the present invention, which classifies the grade of a single raw material based on the doping degree of color blocks.

[0049] Figure 4This is a logic diagram of the raw material classification module in an embodiment of the present invention, which classifies the grades of individual raw materials based on the curvature variation of the raw materials.

[0050] In the diagram: 1. Conveyor belt; 2. Robotic arm; 31. First vision detector; 32. Second vision detector. Detailed Implementation

[0051] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0052] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0053] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate 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 is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0054] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0055] Please see Figure 1 , Figure 2 , Figure 3 as well as Figure 4 The diagrams shown are, respectively, a block diagram of the food quality inspection system according to an embodiment of the present invention, a structural schematic diagram, a logic determination diagram of the raw material classification module for classifying the grade of a single raw material based on the degree of color patch impurity, and a logic determination diagram of the raw material classification module for classifying the grade of a single raw material based on the degree of curvature change of the raw material; an embodiment of the present invention provides a food quality inspection system, comprising:

[0056] A conveying module, which includes a conveyor belt 1 for conveying raw materials to be processed;

[0057] A clamping module, comprising a robotic arm 2 disposed above the conveyor belt 1 to lift raw materials at predetermined clamping points;

[0058] The detection module includes a first visual detector 31 disposed above the conveyor belt 1 to acquire raw material image information and a second visual detector 32 disposed on one side of the conveyor belt 1 to acquire side image information.

[0059] The raw material classification module (not shown in the figure) is connected to the detection module and is used to determine the doping degree of color blocks based on the raw material image information obtained by the visual detector, and classify each raw material grade in combination with the change in the curvature of the raw material.

[0060] The display module (not shown in the figure) is used to output the maximum retention warning time for each batch of raw materials;

[0061] The analysis module (not shown in the figure) is connected to the clamping module, the raw material division module and the display module respectively. It is used to determine whether a batch of raw materials meets the preset standard based on the grade ratio of each raw material, and to adjust the running speed of the robotic arm 2 to the corresponding value or adjust the maximum retention warning time for a single batch of raw materials to the corresponding value based on the judgment result.

[0062] Specifically, the predetermined clamping point is not limited; it can be the center point of the raw material obtained by the vision detector or the tail of the raw material, which will not be elaborated further.

[0063] Specifically, for tertiary raw materials, the clamping module can sort them into a waste bin (not shown in the figure) located on one side of the transmission module, which will not be elaborated further.

[0064] Specifically, the initial maximum retention warning duration for each batch of raw materials determined by the display module is not limited. It can be determined based on historical data. The retention duration corresponding to the proportion of Grade III raw materials greater than 5% in the test results of a single type of raw material is determined as the initial maximum retention warning duration for that type of raw material. This will not be elaborated further.

[0065] Specifically, the raw material classification module processes the data of each raw material and classifies them into different levels. The clamping module determines the freshness of the raw materials by lifting them. The analysis module determines whether a single batch of raw materials meets the preset standards based on the grade ratio of each raw material. Based on the judgment results, it determines to adjust the running speed of the robotic arm 2 to the corresponding value, or to adjust the maximum retention warning time for a single batch of raw materials, thereby improving the detection efficiency of raw materials.

[0066] Specifically, the raw material segmentation module is used to determine the doping degree of color patches based on raw material image information, including:

[0067] Used to identify and select grayscale regions based on the grayscale values ​​of raw material image information;

[0068] The ratio of the average area of ​​each grayscale region to the total area of ​​the raw material in the raw material image information is used to calculate the doping degree of the color block.

[0069] Specifically, the process of identifying and selecting grayscale regions based on the grayscale values ​​of the raw material image information is not limited. It can be done by using the region growing method, selecting a seed point, and gradually merging adjacent pixels according to the grayscale similarity criterion to form a region. Alternatively, it can be done by using deep learning models such as U-Net and FCN to segment the grayscale image and select different grayscale regions. These are existing technologies and will not be elaborated further.

[0070] Specifically, the raw material classification module is used to classify the grade of a single raw material based on the doping degree of the color patch, including:

[0071] If the color block impurity is greater than the second preset color block impurity, the raw material will be classified as a first-level raw material;

[0072] If the color block impurity degree is less than or equal to the second preset color block impurity degree and greater than the first preset color block impurity degree, then the grade of a single raw material is determined based on the change in the curvature of the raw material.

[0073] If the color block impurity degree is less than or equal to the first preset color block impurity degree, the raw material is divided into three levels of raw material.

[0074] Specifically, the first preset color block impurity C1 is selected within the range [0.61D0, 0.67D0]; the second preset color block impurity C2 is selected within the range [0.73D0, 0.82D0], where D0 is the average value of the color block impurity of each raw material for each type of raw material in the historical detection data.

[0075] Specifically, the raw materials to be put into storage are the initial fresh raw materials to be put into storage, which are obtained in order to determine the data parameters of fresh raw materials. It can be understood that in order to classify the specific situation of raw materials, the number of raw materials selected for storage should not be less than 1800, which will not be elaborated further.

[0076] Specifically, the raw material segmentation module is used to determine the curvature change of the raw material based on the side image information, including:

[0077] The robotic arm 2 is used to lift the raw material under the condition that the raw material division module determines the grade of the individual raw material based on the curvature change of the raw material, and the second vision detector 32 obtains the side image information after lifting.

[0078] The raw material segmentation module is used to obtain the raw material outline from the side image information;

[0079] The material segmentation module is used to obtain the height distance between the height of the lowest point in the material profile and the height of the end of the robotic arm 2, and to solve the ratio of this height distance to the length of the material profile to obtain the material curvature change.

[0080] Specifically, the raw material classification module is used to classify individual raw materials into grades based on the amount of curvature variation, including:

[0081] If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material, then the raw material is classified as a first-level raw material.

[0082] If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material and greater than the second preset change in curvature of the raw material, then the raw material is classified as a secondary raw material.

[0083] If the change in the curvature of the raw material is greater than the second preset change in the curvature of the raw material, the raw material will be classified as a secondary or tertiary raw material.

[0084] Specifically, there is no limitation on the specific method for obtaining the outline of the raw material in the side image information. Image processing algorithms and edge detection algorithms, such as Canny edge detection and Sobe L operator, can be used to detect areas with drastic changes in pixel values ​​in the side image, thereby determining the edge outline of the vegetable. It can be understood that the outline of the raw material in the image information can be selected. This is existing technology and will not be elaborated further.

[0085] Specifically, the first preset raw material curvature change amount B1 is selected within the range [1.27H0, 1.67H0]; the second preset raw material curvature change amount B2 is selected within the range [3.61H0, 4.62H0], where H0 is the average value of the curvature change amount of each raw material for each type of raw material in the historical detection data.

[0086] Specifically, the analysis module is used to determine whether the operating parameters of the clamping module meet preset standards based on the proportion of secondary and tertiary raw materials in the total number of raw materials detected in a single detection cycle, including:

[0087] If the proportion of the second-third level is less than or equal to the preset proportion of the second-third level, then the quality of a single batch of raw materials is determined based on the proportion of the second-second level.

[0088] If the proportion of the secondary third level is greater than the preset proportion of the secondary third level, the running speed of the robotic arm 2 will be adjusted to the corresponding value based on the corresponding reference weight of the raw materials in a single batch. Based on the proportion of the secondary third level within the detection cycle after adjustment, it will be determined whether the preset proportion of the secondary second level will be adjusted to the corresponding value, and based on the proportion of the secondary second level, it will be determined whether the quality of the raw materials in a single batch is qualified.

[0089] Specifically, the pre-defined proportion Z0 of the second-to-third level is selected within the range [0.68, 0.81].

[0090] Specifically, there is no limitation on the method for determining the corresponding reference weight for a single type of raw material. It can be the average weight of each raw material of a single type that has been put into storage in the historical test data. This will not be elaborated further.

[0091] Specifically, a single detection cycle is the time taken by the raw material classification module to classify the grade of a single raw material based on the change in the curvature of the raw material.

[0092] Specifically, the grade of each raw material is determined based on the degree of color patch impurity. Color patch impurity characterizes the degree of decay of the raw material. When raw material decays, there will be decayed areas with significantly lower grayscale values ​​than the surrounding areas. The more grayscale areas selected by the raw material classification module, the more decayed areas there will be, leading to a decrease in the average area of ​​each grayscale area. In other words, the degree of color patch impurity is inversely proportional to the degree of decay of the raw material. When the color patch impurity is less than or equal to the second preset color patch impurity and greater than the first preset color patch impurity, to ensure the accuracy of data processing and the precision of raw material classification, in this case, the raw material... The curvature change is used to classify the grade of a single raw material; the curvature change of the raw material represents its freshness. After the robotic arm 2 completes the lifting of the raw material, the degree of bending of the raw material is negatively correlated with its freshness, and the curvature change of the raw material is also negatively correlated with its freshness. The proportion of the second-to-third grade within a single detection cycle is obtained. When the proportion of the second-to-third grade is greater than the preset proportion, there may be a situation where the raw material is damaged due to the excessive lifting speed of the robotic arm 2. In this case, the operating parameters of the robotic arm 2 are adjusted accordingly to ensure the integrity of the raw material detection process. This improves the accuracy of the detection results and thus improves the detection efficiency.

[0093] Specifically, the analysis module is used to adjust the operating speed of the robotic arm 2 to a corresponding value based on the corresponding reference weight of a single batch of raw materials, wherein:

[0094] The decrease in the operating speed of robotic arm 2, determined based on the corresponding reference weight of a single batch of raw materials, is negatively correlated with the corresponding reference weight.

[0095] In this embodiment, optionally:

[0096] Compare the corresponding reference weight of a single batch of raw materials with the first preset reference weight and the second preset reference weight;

[0097] If the corresponding reference weight is less than or equal to the first preset reference weight, the running speed of the robotic arm 2 is determined as the first running speed, which is 0.72 times the initial running speed.

[0098] If the corresponding reference weight is less than or equal to the second preset reference weight and greater than the first preset reference weight, then the operating speed of the robotic arm 2 is determined as the second operating speed, which is 0.84 times the initial operating speed.

[0099] If the corresponding reference weight is greater than the second preset reference weight, the operating speed of the robotic arm 2 will be determined as the third operating speed, which is 0.93 times the initial operating speed.

[0100] The first preset reference weight G1 is selected within the interval [110, 130], and the second preset reference weight G2 is selected within the interval [480, 500].

[0101] Specifically, the analysis module is used to determine whether to adjust the preset secondary proportion to the corresponding value based on the secondary-tertiary proportion within the newly acquired detection cycle, after completing the adjustment of the operating speed of the robotic arm 2, including:

[0102] The ratio of the newly acquired sub-tertiary level proportion to the sub-tertiary level proportion acquired in the previous detection cycle is calculated, and this ratio is determined as the adjustment parameter.

[0103] If the adjusted parameter is less than or equal to the preset adjusted parameter, the preset secondary ratio will be adjusted to the corresponding value, and the quality of a single batch of raw materials will be determined based on the secondary ratio.

[0104] If the adjusted parameter is greater than the preset adjustment parameter, the raw material quality of a single batch is determined to be unqualified.

[0105] Specifically, the preset adjustment parameter T0 is selected within the interval [0.63, 0.71].

[0106] Specifically, after adjusting the operating speed of robotic arm 2, adjustment parameters are obtained. These parameters characterize the impact of adjusting the operating speed of robotic arm 2 on the raw material testing results. When the adjustment parameter is greater than the preset adjustment parameter, the operating speed of robotic arm 2 has little impact on the raw material testing, and a large number of secondary and tertiary raw materials are present. In this case, the quality of a single batch of raw materials is determined to be unqualified. When the adjustment parameter is less than or equal to the preset adjustment parameter, the operating speed of robotic arm 2 affects the raw material testing results. Such raw materials are more prone to bending, and bending can damage the cell structure of vegetables, causing cell contents to leak out. The damaged parts provide a convenient channel for the invasion and reproduction of microorganisms, making these vegetables prone to rotting. In this case, the preset secondary proportion for determining whether a single batch of raw material is qualified is adjusted to automatically adjust the data processing standards based on the actual situation and characteristics of the raw materials, effectively improving the efficiency of vegetable quality testing.

[0107] Specifically, the analysis module is used to adjust the preset secondary proportion to a corresponding value based on the parameter difference between the preset adjustment parameter and the adjustment parameter, wherein:

[0108] The increase in the preset secondary proportion determined by the parameter difference is positively correlated with the parameter difference.

[0109] In this embodiment, optionally:

[0110] Compare the parameter difference with the first preset parameter difference and the second preset parameter difference;

[0111] If the parameter difference is less than or equal to the first preset parameter difference, the preset secondary ratio is adjusted to the corresponding value using the first preset ratio adjustment coefficient. The preset secondary ratio after adjustment using the first preset ratio adjustment coefficient is 1.12 times the initial preset secondary ratio.

[0112] If the parameter difference is less than or equal to the second preset parameter difference and greater than the first preset parameter difference, the preset secondary ratio is adjusted to the corresponding value using the second preset ratio adjustment coefficient. The preset secondary ratio after adjustment using the second preset ratio adjustment coefficient is 1.22 times the initial preset secondary ratio.

[0113] If the parameter difference is greater than the second preset parameter difference, the third preset percentage adjustment coefficient is used to adjust the preset secondary percentage to the corresponding value. The preset secondary percentage after adjustment using the third preset percentage adjustment coefficient is 1.32 times the initial preset secondary percentage.

[0114] The first preset parameter difference is set to 0.2T0, and the second preset parameter difference is set to 0.8T0.

[0115] Specifically, the analysis module is used to determine whether the quality of a single batch of raw materials is qualified based on the secondary proportion, including:

[0116] If the proportion of secondary components is less than or equal to the preset proportion of secondary components, the quality of a single batch of raw materials is deemed to be qualified.

[0117] If the proportion of secondary components is greater than the preset proportion of secondary components, then the quality of a single batch of raw materials is deemed unqualified.

[0118] The preset secondary proportion R0 is selected within the interval [0.23, 0.33].

[0119] Specifically, the analysis module is used to adjust the maximum retention warning duration to a corresponding value based on the secondary proportion when it is determined that the quality of a single batch of raw materials is unqualified, wherein:

[0120] The reduction in the maximum retention warning duration determined by the secondary proportion is positively correlated with the secondary proportion.

[0121] In this embodiment, optionally:

[0122] Compare the secondary proportion with the first preset proportion verification threshold and the second preset proportion verification threshold;

[0123] If the proportion of secondary level is less than or equal to the first preset proportion verification threshold, the maximum retention warning time will be determined as the first maximum retention warning time, which is 0.82 times the initial maximum retention warning time.

[0124] If the proportion of secondary level is less than or equal to the second preset proportion verification threshold and greater than the first preset proportion verification threshold, then the maximum retention warning duration is determined as the second maximum retention warning duration, which is 0.62 times the initial maximum retention warning duration.

[0125] If the proportion of secondary level is greater than the second preset proportion verification threshold, the maximum retention warning time will be determined as the third maximum retention warning time, which is 0.41 times the initial maximum retention warning time.

[0126] The first preset percentage calibration threshold is set to 1.4R0, and the second preset percentage calibration threshold is set to 2.2R0.

[0127] The technical solution of the present invention has been described above with reference to 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 can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A food quality inspection system, characterized in that, include: A conveying module, comprising a conveyor belt for conveying raw materials to be processed; A clamping module, comprising a robotic arm positioned above the conveyor belt to lift raw materials at predetermined clamping points; The detection module includes a first visual detector disposed above the conveyor belt to acquire raw material image information and a second visual detector disposed on one side of the conveyor belt to acquire side image information. The raw material classification module, which is connected to the detection module, is used to determine the doping degree of color blocks based on the raw material image information obtained by the visual detector, and classify each raw material grade in combination with the change in the curvature of the raw material. The raw material segmentation module is used to determine the doping degree of color patches based on raw material image information, including: Used to identify and select grayscale regions based on the grayscale values ​​of raw material image information; The ratio of the average area of ​​each grayscale region to the total area of ​​the raw material in the raw material image information is used to calculate the doping degree of the color block; The raw material segmentation module is used to determine the curvature change of the raw material based on the side image information, including: The robotic arm is used to lift the raw material after the raw material sorting module determines the grade of the individual raw material based on the curvature change. The second vision detector obtains the side image information after lifting. The raw material segmentation module is used to obtain the raw material outline from the side image information; The material segmentation module is used to obtain the height distance between the height of the lowest point in the material profile and the height of the end of the robotic arm, and to solve the ratio of this height distance to the length of the material profile to obtain the material curvature change. The display module is used to output the maximum retention warning time for each batch of raw materials; The analysis module, which is connected to the clamping module, the raw material division module and the display module respectively, is used to determine whether a batch of raw materials meets the preset standard based on the grade ratio of each raw material, and to adjust the running speed of the robotic arm to the corresponding value or adjust the maximum retention warning time for a single batch of raw materials to the corresponding value based on the judgment result.

2. The food quality inspection system according to claim 1, characterized in that, The raw material classification module is used to classify the grade of a single raw material based on the doping degree of the color patch, including: If the color block impurity is greater than the second preset color block impurity, the raw material will be classified as a first-level raw material; If the color block impurity is less than or equal to the second preset color block impurity and greater than the first preset color block impurity, then the grade of a single raw material is determined based on the change in the curvature of the raw material. If the color block impurity degree is less than or equal to the first preset color block impurity degree, the raw material is divided into three levels of raw material.

3. The food quality inspection system according to claim 2, characterized in that, The raw material classification module is used to classify individual raw materials into grades based on the curvature variation of the raw materials, including: If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material, then the raw material is classified as a first-level raw material. If the change in curvature of the raw material is less than or equal to the first preset change in curvature of the raw material and greater than the second preset change in curvature of the raw material, then the raw material is classified as a secondary raw material. If the change in the curvature of the raw material is greater than the second preset change in the curvature of the raw material, the raw material will be classified as a secondary or tertiary raw material.

4. The food quality inspection system according to claim 3, characterized in that, The analysis module is used to determine whether the operating parameters of the clamping module meet preset standards based on the proportion of secondary and tertiary raw materials to the total number of raw materials detected in a single detection cycle, including: If the proportion of the second-third level is less than or equal to the preset proportion of the second-third level, then the quality of a single batch of raw materials is determined based on the proportion of the second-second level. If the proportion of the secondary third level is greater than the preset proportion of the secondary third level, the running speed of the robotic arm will be adjusted to the corresponding value based on the corresponding reference weight of the raw materials in a single batch. Based on the proportion of the secondary third level within the detection cycle after adjustment, it will be determined whether the preset proportion of the secondary second level will be adjusted to the corresponding value, and based on the proportion of the secondary second level, it will be determined whether the quality of the raw materials in a single batch is qualified.

5. The food quality inspection system according to claim 4, characterized in that, The analysis module is used to adjust the operating speed of the robotic arm to a corresponding value based on the corresponding reference weight of a single batch of raw materials, wherein: The decrease in the operating speed of the robotic arm, determined based on the corresponding reference weight of a single batch of raw materials, is negatively correlated with the corresponding reference weight.

6. The food quality inspection system according to claim 5, characterized in that, The analysis module is used to determine whether to adjust the preset secondary proportion to the corresponding value based on the secondary-tertiary proportion within the newly acquired detection cycle, after completing the adjustment of the robotic arm's operating speed, including: The ratio of the newly acquired sub-tertiary level proportion to the sub-tertiary level proportion acquired in the previous detection cycle is calculated, and this ratio is determined as the adjustment parameter. If the adjusted parameter is less than or equal to the preset adjusted parameter, the preset secondary ratio will be adjusted to the corresponding value, and the quality of a single batch of raw materials will be determined based on the secondary ratio. If the adjusted parameter is greater than the preset adjustment parameter, the raw material quality of a single batch is determined to be unqualified.

7. The food quality inspection system according to claim 6, characterized in that, The analysis module is used to adjust the preset secondary proportion to the corresponding value based on the parameter difference between the preset adjustment parameter and the adjustment parameter, wherein: The increase in the preset secondary proportion determined by the parameter difference is positively correlated with the parameter difference.

8. The food quality inspection system according to claim 7, characterized in that, The analysis module is used to determine whether the quality of a single batch of raw materials is qualified based on the secondary proportion, including: If the proportion of secondary components is less than or equal to the preset proportion of secondary components, the quality of a single batch of raw materials is deemed to be qualified. If the proportion of secondary components is greater than the preset proportion of secondary components, then the quality of a single batch of raw materials is deemed unqualified.

9. The food quality inspection system according to claim 8, characterized in that, The analysis module is used to adjust the maximum retention warning duration to a corresponding value based on the secondary proportion when a single batch of raw materials is determined to be substandard. The reduction in the maximum retention warning duration determined by the secondary proportion is positively correlated with the secondary proportion.

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