A method, system, and medium for improving beef quality

Abstract

The embodiment of the application provides a processing method, system and medium for improving beef quality, the method comprising: acquiring a beef cross-section image, preprocessing the beef cross-section image to obtain an optimized image; extracting the optimized image features, comparing the optimized image features with preset features, segmenting the target region and the background region of the optimized image to obtain the target region image grayscale value; judging whether the grayscale change rate is greater than or equal to the preset change rate threshold; if yes, generating a first processing environment according to the first processing strategy to process the beef quality; if no, generating a second processing strategy, generating a second processing environment according to the second processing strategy to process the beef quality; through image extraction on the beef cross-section, image feature analysis, target region grayscale value calculation, accurate analysis of the beef quality, different processing strategies are generated according to different beef qualities, and the processing precision is high.

Description

Technical Field

[0001] This application relates to the field of beef cattle quality treatment, and more specifically, to a treatment method, system, and medium for improving the quality of beef cattle. Background Technology

[0002] Beef cattle, also known as beef-producing cattle, are a type of cattle primarily raised for beef production. They are characterized by their full-bodied build, rapid weight gain, high feed conversion ratio, good meat production performance, and excellent meat quality and taste. Beef cattle not only provide meat products but also other by-products. However, the quality of beef cattle can decline during storage, and improper storage can harm consumers. To mitigate this impact, quality optimization treatments are needed to improve storage efficiency. Current beef cattle quality control methods rely on visually observing the color of the beef to determine quality and adjusting the storage environment accordingly. This approach is ineffective, and a robust technological solution is urgently needed to address these issues. Summary of the Invention

[0003] The purpose of this application is to provide a processing method, system, and medium for improving the quality of beef cattle. This method can accurately analyze the quality of beef cattle by extracting images from beef cattle cross-sections, analyzing image features, calculating the grayscale value of the target area, and generating different processing strategies based on different beef cattle qualities.

[0004] This application also provides a processing method for improving the quality of beef cattle, including:

[0005] Obtain cross-sectional images of beef cattle, and preprocess the cross-sectional images to obtain optimized images;

[0006] Extract optimized image features, compare the optimized image features with preset features, segment the target region and background region of the optimized image, and obtain the gray value of the target region image;

[0007] The weighted mean method was used to analyze the gray value changes in the target area image to obtain the gray value change rate;

[0008] Determine whether the grayscale change rate is greater than or equal to a preset change rate threshold;

[0009] If it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle.

[0010] If the value is less than the specified value, a second processing strategy is generated, and a second processing environment is created based on the second processing strategy to process the quality of the beef cattle.

[0011] Optionally, in the processing method for improving beef quality described in the embodiments of this application, the step of obtaining a cross-sectional image of the beef cattle and preprocessing the cross-sectional image to obtain an optimized image specifically includes:

[0012] Obtain cross-sectional images of beef cattle and perform binarization to remove image noise;

[0013] Extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points.

[0014] Obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points;

[0015] The connection trajectory between the contour breakpoint and the contour edge point is obtained based on the contour completion curvature.

[0016] The outline of the beef cattle is completed by connecting the breakpoints and edge points of the outline.

[0017] Optionally, in the processing method for improving beef cattle quality described in the embodiments of this application, after completing the beef cattle contour according to the connection trajectory of the contour breakpoint and the contour edge point, it further includes:

[0018] Obtain the outline of the beef cattle, and then segment the beef cattle into several sub-regions based on the outline.

[0019] Obtain cross-sectional images of beef cattle in each sub-region, and extract texture features of the sub-regions based on the cross-sectional images of beef cattle in each sub-region;

[0020] Calculate the similarity between the texture features of adjacent sub-regions and the preset texture features;

[0021] Determine whether the similarity is greater than or equal to a preset similarity threshold;

[0022] If the value is greater than or equal to the value, then adjacent sub-regions will be merged to obtain an optimized sub-region.

[0023] If the value is less than the specified value, the corresponding sub-region will be compared with the texture features of the next adjacent sub-region in turn.

[0024] Optionally, in the processing method for improving beef cattle quality described in the embodiments of this application, the step of analyzing the grayscale value changes of the target area image using the weighted average method to obtain the grayscale change rate is specifically as follows:

[0025] Obtain the texture features of beef cattle cross-section images, and obtain weighting coefficients based on the distribution of texture features of beef cattle cross-section images;

[0026] The grayscale value of the target region image is obtained by averaging the texture features located in the same sub-region.

[0027] The percentage difference between the grayscale values ​​of the target region image and the grayscale values ​​of the target region image at a set time interval is calculated to obtain the grayscale change rate.

[0028] Optionally, in the processing method for improving beef cattle quality described in the embodiments of this application, if the value is greater than or equal to the first processing strategy, a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the beef cattle quality, specifically as follows:

[0029] If the grayscale change rate is greater than or equal to the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining rapidly, and the electron beam irradiation dose is generated based on the grayscale change rate.

[0030] Information on changes in standard bacterial colonies on the surface of beef cattle was obtained based on electron beam irradiation.

[0031] Obtain the current surface microbial colony information of beef cattle, compare the current surface microbial colony information of beef cattle with the standard microbial colony change information of beef cattle, and obtain the colony change rate;

[0032] If the colony change rate is greater than the preset change rate threshold, then adjust the electron beam radiation parameter.

[0033] If the colony change rate is less than the preset change rate threshold, a first radiation environment is generated based on the electron beam radiation amount, and the beef cattle are subjected to radiation treatment for a predetermined time.

[0034] Optionally, in the processing method for improving beef cattle quality described in the embodiments of this application, if the value is less than a certain threshold, a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the beef cattle quality. Specifically:

[0035] If the grayscale change rate is less than the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining slowly, and the cold storage temperature information of beef cattle is generated based on the grayscale change rate.

[0036] The temperature difference is obtained by comparing the refrigeration temperature information of beef cattle with the preset refrigeration temperature information.

[0037] If the temperature difference is greater than the first temperature value and less than the second temperature value, then first temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the first temperature adjustment information.

[0038] If the temperature difference is greater than the second temperature value, second temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the second temperature adjustment information.

[0039] Secondly, embodiments of this application provide a processing system for improving the quality of beef cattle. The system includes a memory and a processor. The memory includes a program for a processing method to improve the quality of beef cattle. When the processor executes the program for the processing method to improve the quality of beef cattle, it implements the following steps:

[0040] Obtain cross-sectional images of beef cattle, and preprocess the cross-sectional images to obtain optimized images;

[0041] Extract optimized image features, compare the optimized image features with preset features, segment the target region and background region of the optimized image, and obtain the gray value of the target region image;

[0042] The weighted mean method was used to analyze the gray value changes in the target area image to obtain the gray value change rate;

[0043] Determine whether the grayscale change rate is greater than or equal to a preset change rate threshold;

[0044] If it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle.

[0045] If the value is less than the specified value, a second processing strategy is generated, and a second processing environment is created based on the second processing strategy to process the quality of the beef cattle.

[0046] Optionally, in the processing system for improving beef cattle quality described in this application embodiment, the step of acquiring a cross-sectional image of the beef cattle and preprocessing the cross-sectional image to obtain an optimized image specifically includes:

[0047] Obtain cross-sectional images of beef cattle and perform binarization to remove image noise;

[0048] Extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points.

[0049] Obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points;

[0050] The connection trajectory between the contour breakpoint and the contour edge point is obtained based on the contour completion curvature.

[0051] The outline of the beef cattle is completed by connecting the breakpoints and edge points of the outline.

[0052] Optionally, in the processing system for improving beef cattle quality described in the embodiments of this application, after completing the beef cattle contour according to the connection trajectory of the contour breakpoint and the contour edge point, it further includes:

[0053] Obtain the outline of the beef cattle, and then segment the beef cattle into several sub-regions based on the outline.

[0054] Obtain cross-sectional images of beef cattle in each sub-region, and extract texture features of the sub-regions based on the cross-sectional images of beef cattle in each sub-region;

[0055] Calculate the similarity between the texture features of adjacent sub-regions and the preset texture features;

[0056] Determine whether the similarity is greater than or equal to a preset similarity threshold;

[0057] If the value is greater than or equal to the value, then adjacent sub-regions will be merged to obtain an optimized sub-region.

[0058] If the value is less than the specified value, the corresponding sub-region will be compared with the texture features of the next adjacent sub-region in turn.

[0059] Thirdly, embodiments of this application also provide a computer-readable storage medium, which includes a processing method program for improving the quality of beef cattle. When the processing method program for improving the quality of beef cattle is executed by a processor, it implements the steps of the processing method for improving the quality of beef cattle as described in any of the above claims.

[0060] As can be seen from the above, the processing method, system, and medium for improving beef quality provided in this application embodiment involves acquiring a beef cattle cross-section image, preprocessing the beef cattle cross-section image to obtain an optimized image, extracting optimized image features, comparing the optimized image features with preset features, segmenting the target area and background area of ​​the optimized image to obtain the grayscale value of the target area image, analyzing the grayscale value change of the target area image using a weighted average method to obtain the grayscale change rate, determining whether the grayscale change rate is greater than or equal to a preset change rate threshold, generating a first processing strategy if it is greater than or equal to, and generating a first processing environment to process the beef cattle quality according to the first processing strategy, and generating a second processing environment to process the beef cattle quality if it is less than, generating a second processing strategy, and generating a second processing environment to process the beef cattle quality according to the second processing strategy. By extracting and analyzing image features from the beef cattle cross-section image, calculating the grayscale value of the target area, and accurately analyzing the beef cattle quality, different processing strategies are generated according to different beef cattle qualities, resulting in a technology with high processing accuracy.

[0061] Other features and advantages of this application will be set forth in the following description, and the purposes and advantages of this application can be realized and obtained by means of the structures specifically pointed out in the written description, claims and drawings. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 A flowchart illustrating a processing method for improving beef cattle quality provided in an embodiment of this application;

[0064] Figure 2 A flowchart of the beef cattle cross-section image optimization processing method for improving beef cattle quality provided in the embodiments of this application;

[0065] Figure 3 This is a flowchart of the adjacent sub-region fusion optimization process of the processing method for improving beef quality provided in the embodiments of this application;

[0066] Figure 4 This is a schematic diagram of the processing system for improving the quality of beef cattle provided in an embodiment of this application. Detailed Implementation

[0067] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0068] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0069] Please refer to Figure 1 , Figure 1 This is a flowchart of a processing method for improving beef cattle quality according to some embodiments of this application. The processing method for improving beef cattle quality is used in a terminal device and includes the following steps:

[0070] S101, Obtain a cross-sectional image of beef cattle, preprocess the cross-sectional image of beef cattle to obtain an optimized image;

[0071] S102, extract optimized image features, compare the optimized image features with preset features, segment the target area and background area of ​​the optimized image, and obtain the gray value of the target area image.

[0072] S103, The weighted average method is used to analyze the gray value changes of the target area image to obtain the gray value change rate;

[0073] S104, determine whether the grayscale change rate is greater than or equal to the preset change rate threshold;

[0074] S105, if it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle; if it is less than, then a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the quality of beef cattle.

[0075] It should be noted that by binarizing the cross-sectional image of beef cattle to remove noise and enhancing it, the accuracy of feature extraction is improved during the feature extraction process, preventing large errors in image feature analysis. The grayscale change rate is obtained by analyzing the grayscale values ​​of the target area image, and different processing strategies are generated based on the grayscale change rate to establish different processing environments, thereby improving the quality of beef cattle under different conditions.

[0076] Please refer to Figure 2 , Figure 2 This is a flowchart of a beef cattle cross-section image optimization processing method for improving beef cattle quality, as described in some embodiments of this application. According to an embodiment of the present invention, a beef cattle cross-section image is acquired, and the image is preprocessed to obtain an optimized image, specifically as follows:

[0077] S201, acquire a cross-sectional image of beef cattle, and perform binarization processing on the image to remove image noise;

[0078] S202, extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points.

[0079] S203, obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points;

[0080] S204, the connection trajectory between the contour breakpoint and the contour edge point is obtained according to the contour completion curvature;

[0081] S205, complete the outline of the beef cattle by connecting the outline breakpoints and the outline edge points.

[0082] It should be noted that by analyzing and processing the image, the outline of the beef cattle is extracted, and the outline is analyzed to determine whether the outline is complete. When the outline is incomplete, in order to ensure the accuracy of the analysis, the breakpoints of the outline are first filled in. During the incomplete process, the curvature changes of the outline points on both sides of the breakpoint are analyzed to improve the accuracy of the completed outline and reduce the image analysis error.

[0083] Please refer to Figure 3 , Figure 3 This is a flowchart of the adjacent sub-region fusion optimization process of a method for improving beef cattle quality according to some embodiments of this application. According to an embodiment of the present invention, after completing the beef cattle contour based on the connection trajectory between contour breakpoints and contour edge points, the process further includes:

[0084] S301, Obtain the outline of the beef cattle, and divide the beef cattle into regions based on the outline to obtain several sub-regions;

[0085] S302, Obtain the beef cattle cross-section image of each sub-region, and extract the texture features of the sub-region based on the beef cattle cross-section image of the sub-region;

[0086] S303, calculate the similarity between the texture features of adjacent sub-regions and the preset texture features;

[0087] S304, Determine whether the similarity is greater than or equal to the preset similarity threshold;

[0088] S305, if it is greater than or equal to, then the adjacent sub-regions are merged to obtain the optimized sub-region; if it is less than, then the corresponding sub-region is compared with the next adjacent sub-region in turn for texture features.

[0089] It should be noted that the beef cattle outline region is segmented into sub-regions, each sub-region is analyzed separately, and the analysis results are then merged. When the texture features of the sub-regions are highly similar, adjacent sub-regions are merged to simplify the analysis process. If the similarity is low, the sub-region is compared with other adjacent sub-regions to achieve analysis and judgment of each sub-region, thereby optimizing the segmentation of the beef cattle outline region.

[0090] According to an embodiment of the present invention, the weighted mean method is used to analyze the grayscale value changes of the target region image to obtain the grayscale change rate, specifically:

[0091] Obtain the texture features of beef cattle cross-section images, and obtain weighting coefficients based on the distribution of texture features of beef cattle cross-section images;

[0092] The grayscale value of the target region image is obtained by averaging the texture features located in the same sub-region.

[0093] The percentage difference between the grayscale values ​​of the target region image and the grayscale values ​​of the target region image at a set time interval is calculated to obtain the grayscale change rate.

[0094] It should be noted that the grayscale change rate is calculated by analyzing the grayscale values ​​of the target area image and the grayscale values ​​at a set time interval. During the calculation process, the grayscale change rate is more closely related to the actual change by weighting the distribution of texture features of the beef cattle cross-section.

[0095] According to an embodiment of the present invention, if the value is greater than or equal to the specified value, a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle, specifically as follows:

[0096] If the grayscale change rate is greater than or equal to the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining rapidly, and the electron beam irradiation dose is generated based on the grayscale change rate.

[0097] Information on changes in standard bacterial colonies on the surface of beef cattle was obtained based on electron beam irradiation.

[0098] Obtain the current surface microbial colony information of beef cattle, compare the current surface microbial colony information of beef cattle with the standard microbial colony change information of beef cattle, and obtain the colony change rate;

[0099] If the colony change rate is greater than the preset change rate threshold, then adjust the electron beam radiation parameter.

[0100] If the colony change rate is less than the preset change rate threshold, a first radiation environment is generated based on the electron beam radiation amount, and the beef cattle are subjected to radiation treatment for a predetermined time.

[0101] It should be noted that by judging the grayscale change rate, electron beam irradiation is applied to the surface of beef cattle after the grayscale change rate meets the conditions, thereby achieving the effect of sterilization on the surface of beef cattle, reducing the rapid deterioration of beef cattle quality, and improving beef cattle quality. In addition, by detecting the change rate of bacterial colonies on the surface of beef cattle in real time, the electron beam irradiation dose is dynamically adjusted in real time to improve the utilization rate of electron beam irradiation.

[0102] According to an embodiment of the present invention, if the value is less than a certain threshold, a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the quality of the beef cattle, specifically as follows:

[0103] If the grayscale change rate is less than the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining slowly, and the cold storage temperature information of beef cattle is generated based on the grayscale change rate.

[0104] The temperature difference is obtained by comparing the refrigeration temperature information of beef cattle with the preset refrigeration temperature information.

[0105] If the temperature difference is greater than the first temperature value and less than the second temperature value, then first temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the first temperature adjustment information.

[0106] If the temperature difference is greater than the second temperature value, second temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the second temperature adjustment information.

[0107] It should be noted that when the quality of beef cattle changes slowly, the quality of beef cattle can be optimized by gradually adjusting the refrigeration temperature in a gradient manner, thereby achieving the optimal refrigeration environment to improve the quality of beef cattle.

[0108] According to an embodiment of the present invention, optimized image features are extracted, and the target region and background region of the optimized image are segmented by comparing the optimized image features with preset features to obtain the grayscale value of the target region image. Specifically:

[0109] Obtain optimized image features, compare the optimized image features with the mean of the target features, and obtain the target feature similarity.

[0110] If the similarity of the target feature is greater than the preset similarity threshold, then the corresponding optimized image feature is determined to be the target feature;

[0111] If the similarity of the target features is less than the preset similarity threshold, the corresponding optimized image features are determined to be background features.

[0112] If the similarity of the target feature is equal to the preset similarity threshold, then the corresponding optimized image feature is determined to be a blank feature.

[0113] It should be noted that by analyzing the optimized image features, different regions of the image can be accurately extracted and separated. The region corresponding to the target feature is the target region, i.e., the beef cattle region; the region corresponding to the background feature is the background region after the image is optimized; the blank region is the other regions of the optimized image besides the target region and the background region. The blank region will not have any impact on the analysis results. By analyzing and searching for blank regions and discarding them, the processing speed can be improved.

[0114] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the structure of a processing system for improving beef cattle quality according to some embodiments of this application. Secondly, embodiments of this application provide a processing system 4 for improving beef cattle quality. The system includes a memory 41 and a processor 42. The memory includes a program for a processing method for improving beef cattle quality. When the processor executes the program for the processing method for improving beef cattle quality, it implements the following steps:

[0115] Obtain cross-sectional images of beef cattle, and preprocess the cross-sectional images to obtain optimized images;

[0116] Extract optimized image features, compare the optimized image features with preset features, segment the target region and background region of the optimized image, and obtain the gray value of the target region image;

[0117] The weighted mean method was used to analyze the gray value changes in the target area image to obtain the gray value change rate;

[0118] Determine whether the grayscale change rate is greater than or equal to a preset change rate threshold;

[0119] If it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle.

[0120] If the value is less than the specified value, a second processing strategy is generated, and a second processing environment is created based on the second processing strategy to process the quality of the beef cattle.

[0121] It should be noted that by binarizing the cross-sectional image of beef cattle to remove noise and enhancing it, the accuracy of feature extraction is improved during the feature extraction process, preventing large errors in image feature analysis. The grayscale change rate is obtained by analyzing the grayscale values ​​of the target area image, and different processing strategies are generated based on the grayscale change rate to establish different processing environments, thereby improving the quality of beef cattle under different conditions.

[0122] According to an embodiment of the present invention, a cross-sectional image of beef cattle is acquired, and the cross-sectional image of beef cattle is preprocessed to obtain an optimized image, specifically as follows:

[0123] Obtain cross-sectional images of beef cattle and perform binarization to remove image noise;

[0124] Extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points.

[0125] Obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points;

[0126] The connection trajectory between the contour breakpoint and the contour edge point is obtained based on the contour completion curvature.

[0127] The outline of the beef cattle is completed by connecting the breakpoints and edge points of the outline.

[0128] It should be noted that by analyzing and processing the image, the outline of the beef cattle is extracted, and the outline is analyzed to determine whether the outline is complete. When the outline is incomplete, in order to ensure the accuracy of the analysis, the breakpoints of the outline are first filled in. During the incomplete process, the curvature changes of the outline points on both sides of the breakpoint are analyzed to improve the accuracy of the completed outline and reduce the image analysis error.

[0129] According to an embodiment of the present invention, after completing the outline of the beef cattle based on the connection trajectory between the outline breakpoints and the outline edge points, the method further includes:

[0130] Obtain the outline of the beef cattle, and then segment the beef cattle into several sub-regions based on the outline.

[0131] Obtain cross-sectional images of beef cattle in each sub-region, and extract texture features of the sub-regions based on the cross-sectional images of beef cattle in each sub-region;

[0132] Calculate the similarity between the texture features of adjacent sub-regions and the preset texture features;

[0133] Determine whether the similarity is greater than or equal to a preset similarity threshold;

[0134] If the value is greater than or equal to the value, then adjacent sub-regions will be merged to obtain an optimized sub-region.

[0135] If the value is less than the specified value, the corresponding sub-region will be compared with the texture features of the next adjacent sub-region in turn.

[0136] It should be noted that the beef cattle outline region is segmented into sub-regions, each sub-region is analyzed separately, and the analysis results are then merged. When the texture features of the sub-regions are highly similar, adjacent sub-regions are merged to simplify the analysis process. If the similarity is low, the sub-region is compared with other adjacent sub-regions to achieve analysis and judgment of each sub-region, thereby optimizing the segmentation of the beef cattle outline region.

[0137] According to an embodiment of the present invention, the weighted mean method is used to analyze the grayscale value changes of the target region image to obtain the grayscale change rate, specifically:

[0138] Obtain the texture features of beef cattle cross-section images, and obtain weighting coefficients based on the distribution of texture features of beef cattle cross-section images;

[0139] The grayscale value of the target region image is obtained by averaging the texture features located in the same sub-region.

[0140] The percentage difference between the grayscale values ​​of the target region image and the grayscale values ​​of the target region image at a set time interval is calculated to obtain the grayscale change rate.

[0141] It should be noted that the grayscale change rate is calculated by analyzing the grayscale values ​​of the target area image and the grayscale values ​​at a set time interval. During the calculation process, the grayscale change rate is more closely related to the actual change by weighting the distribution of texture features of the beef cattle cross-section.

[0142] According to an embodiment of the present invention, if the value is greater than or equal to the specified value, a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle, specifically as follows:

[0143] If the grayscale change rate is greater than or equal to the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining rapidly, and the electron beam irradiation dose is generated based on the grayscale change rate.

[0144] Information on changes in standard bacterial colonies on the surface of beef cattle was obtained based on electron beam irradiation.

[0145] Obtain the current surface microbial colony information of beef cattle, compare the current surface microbial colony information of beef cattle with the standard microbial colony change information of beef cattle, and obtain the colony change rate;

[0146] If the colony change rate is greater than the preset change rate threshold, then adjust the electron beam radiation parameter.

[0147] If the colony change rate is less than the preset change rate threshold, a first radiation environment is generated based on the electron beam radiation amount, and the beef cattle are subjected to radiation treatment for a predetermined time.

[0148] It should be noted that by judging the grayscale change rate, electron beam irradiation is applied to the surface of beef cattle after the grayscale change rate meets the conditions, thereby achieving the effect of sterilization on the surface of beef cattle, reducing the rapid deterioration of beef cattle quality, and improving beef cattle quality. In addition, by detecting the change rate of bacterial colonies on the surface of beef cattle in real time, the electron beam irradiation dose is dynamically adjusted in real time to improve the utilization rate of electron beam irradiation.

[0149] According to an embodiment of the present invention, if the value is less than a certain threshold, a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the quality of the beef cattle, specifically as follows:

[0150] If the grayscale change rate is less than the preset grayscale change rate threshold, it is determined that the quality of beef cattle is declining slowly, and the cold storage temperature information of beef cattle is generated based on the grayscale change rate.

[0151] The temperature difference is obtained by comparing the refrigeration temperature information of beef cattle with the preset refrigeration temperature information.

[0152] If the temperature difference is greater than the first temperature value and less than the second temperature value, then first temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the first temperature adjustment information.

[0153] If the temperature difference is greater than the second temperature value, second temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the second temperature adjustment information.

[0154] It should be noted that when the quality of beef cattle changes slowly, the quality of beef cattle can be optimized by gradually adjusting the refrigeration temperature in a gradient manner, thereby achieving the optimal refrigeration environment to improve the quality of beef cattle.

[0155] According to an embodiment of the present invention, optimized image features are extracted, and the target region and background region of the optimized image are segmented by comparing the optimized image features with preset features to obtain the grayscale value of the target region image. Specifically:

[0156] Obtain optimized image features, compare the optimized image features with the mean of the target features, and obtain the target feature similarity.

[0157] If the similarity of the target feature is greater than the preset similarity threshold, then the corresponding optimized image feature is determined to be the target feature;

[0158] If the similarity of the target features is less than the preset similarity threshold, the corresponding optimized image features are determined to be background features.

[0159] If the similarity of the target feature is equal to the preset similarity threshold, then the corresponding optimized image feature is determined to be a blank feature.

[0160] It should be noted that by analyzing the optimized image features, different regions of the image can be accurately extracted and separated. The region corresponding to the target feature is the target region, i.e., the beef cattle region; the region corresponding to the background feature is the background region after the image is optimized; the blank region is the other regions of the optimized image besides the target region and the background region. The blank region will not have any impact on the analysis results. By analyzing and searching for blank regions and discarding them, the processing speed can be improved.

[0161] A third aspect of the present invention provides a computer-readable storage medium including a processing method program for improving the quality of beef cattle. When the processing method program for improving the quality of beef cattle is executed by a processor, it implements the steps of the processing method for improving the quality of beef cattle as described above.

[0162] This invention discloses a processing method, system, and medium for improving beef cattle quality. The method involves acquiring a cross-sectional image of beef cattle, preprocessing the image to obtain an optimized image, extracting features from the optimized image, comparing these features with preset features, segmenting the target and background regions of the optimized image to obtain the grayscale values ​​of the target region image, and analyzing the grayscale value changes of the target region image using a weighted average method to obtain the grayscale change rate. The method then determines whether the grayscale change rate is greater than or equal to a preset change rate threshold. If it is greater than or equal to, a first processing strategy is generated, and a first processing environment is created based on the first processing strategy to process the beef cattle quality. If it is less than, a second processing strategy is generated, and a second processing environment is created based on the second processing strategy to process the beef cattle quality. By extracting and analyzing image features from the beef cattle cross-section image and calculating the grayscale values ​​of the target region, the method accurately analyzes the beef cattle quality and generates different processing strategies based on different beef cattle qualities, achieving high processing accuracy.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0164] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0166] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0167] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A treatment method for improving the quality of beef cattle, characterized in that, include: Obtain cross-sectional images of beef cattle, and preprocess the cross-sectional images to obtain optimized images; Extract optimized image features, compare the optimized image features with preset features, segment the target region and background region of the optimized image, and obtain the gray value of the target region image; The weighted mean method was used to analyze the grayscale value changes in the target region image to obtain the grayscale change rate, specifically: Obtain the texture features of beef cattle cross-section images, and obtain weighting coefficients based on the distribution of texture features of beef cattle cross-section images; The grayscale value of the target region image is obtained by averaging the texture features located in the same sub-region. Calculate the percentage difference between the gray values ​​of the target region image and the gray values ​​of the target region image at a set time interval to obtain the gray value change rate; Determine whether the grayscale change rate is greater than or equal to a preset change rate threshold; If it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle. The first processing strategy is as follows: if the gray change rate is greater than or equal to the preset gray change rate threshold, it is determined that the quality of beef cattle is declining rapidly, and the electron beam irradiation amount is generated according to the gray change rate. Information on changes in standard bacterial colonies on the surface of beef cattle was obtained based on electron beam irradiation. Obtain the current surface microbial colony information of beef cattle, compare the current surface microbial colony information of beef cattle with the standard microbial colony change information of beef cattle, and obtain the colony change rate; If the colony change rate is greater than the preset change rate threshold, then adjust the electron beam radiation parameter. If the colony change rate is less than the preset change rate threshold, a first radiation environment is generated based on the electron beam radiation amount, and the beef cattle are subjected to radiation treatment for a predetermined time. If it is less than, a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the quality of beef cattle. The second processing strategy is as follows: if the grayscale change rate is less than the preset grayscale change rate threshold, it is determined that the beef quality is declining slowly, and beef refrigeration temperature information is generated based on the grayscale change rate. The temperature difference is obtained by comparing the refrigeration temperature information of beef cattle with the preset refrigeration temperature information. If the temperature difference is greater than the first temperature value and less than the second temperature value, then first temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the first temperature adjustment information. If the temperature difference is greater than the second temperature value, second temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the second temperature adjustment information.

2. The treatment method for improving beef cattle quality according to claim 1, characterized in that, The process of obtaining a cross-sectional image of beef cattle and preprocessing it to obtain an optimized image involves the following steps: Obtain cross-sectional images of beef cattle and perform binarization to remove image noise; Extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points. Obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points; The connection trajectory between the contour breakpoint and the contour edge point is obtained based on the contour completion curvature. The outline of the beef cattle is completed by connecting the breakpoints and edge points of the outline.

3. The treatment method for improving beef cattle quality according to claim 1, characterized in that, After completing the outline of the beef cattle based on the connection trajectory between the outline breakpoints and the outline edge points, the process also includes: Obtain the outline of the beef cattle, and then segment the beef cattle into several sub-regions based on the outline. Obtain cross-sectional images of beef cattle in each sub-region, and extract texture features of the sub-regions based on the cross-sectional images of beef cattle in each sub-region; Calculate the similarity between the texture features of adjacent sub-regions and the preset texture features; Determine whether the similarity is greater than or equal to a preset similarity threshold; If the value is greater than or equal to the value, then adjacent sub-regions will be merged to obtain an optimized sub-region. If the value is less than the specified value, the corresponding sub-region will be compared with the texture features of the next adjacent sub-region in turn.

4. A processing system for improving the quality of beef cattle, characterized in that, The system includes a memory and a processor. The memory contains a program for a processing method to improve the quality of beef cattle. When the processor executes the program for the processing method to improve the quality of beef cattle, it performs the following steps: Obtain cross-sectional images of beef cattle, and preprocess the cross-sectional images to obtain optimized images; Extract optimized image features, compare the optimized image features with preset features, segment the target region and background region of the optimized image, and obtain the gray value of the target region image; The weighted mean method was used to analyze the grayscale value changes in the target region image to obtain the grayscale change rate, specifically: Obtain the texture features of beef cattle cross-section images, and obtain weighting coefficients based on the distribution of texture features of beef cattle cross-section images; The grayscale value of the target region image is obtained by averaging the texture features located in the same sub-region. Calculate the percentage difference between the gray values ​​of the target region image and the gray values ​​of the target region image at a set time interval to obtain the gray value change rate; Determine whether the grayscale change rate is greater than or equal to a preset change rate threshold; If it is greater than or equal to, then a first processing strategy is generated, and a first processing environment is generated according to the first processing strategy to process the quality of beef cattle. The first processing strategy is as follows: if the gray change rate is greater than or equal to the preset gray change rate threshold, it is determined that the quality of beef cattle is declining rapidly, and the electron beam irradiation amount is generated according to the gray change rate. Information on changes in standard bacterial colonies on the surface of beef cattle was obtained based on electron beam irradiation. Obtain the current surface microbial colony information of beef cattle, compare the current surface microbial colony information of beef cattle with the standard microbial colony change information of beef cattle, and obtain the colony change rate; If the colony change rate is greater than the preset change rate threshold, then adjust the electron beam radiation parameter. If the colony change rate is less than the preset change rate threshold, a first radiation environment is generated based on the electron beam radiation amount, and the beef cattle are subjected to radiation treatment for a predetermined time. If it is less than, a second processing strategy is generated, and a second processing environment is generated according to the second processing strategy to process the quality of beef cattle. The second processing strategy is as follows: if the grayscale change rate is less than the preset grayscale change rate threshold, it is determined that the beef quality is declining slowly, and beef refrigeration temperature information is generated based on the grayscale change rate. The temperature difference is obtained by comparing the refrigeration temperature information of beef cattle with the preset refrigeration temperature information. If the temperature difference is greater than the first temperature value and less than the second temperature value, then first temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the first temperature adjustment information. If the temperature difference is greater than the second temperature value, second temperature adjustment information is generated, and the temperature of the refrigeration environment is adjusted according to the second temperature adjustment information.

5. The processing system for improving beef cattle quality according to claim 4, characterized in that, The process of obtaining a cross-sectional image of beef cattle and preprocessing it to obtain an optimized image involves the following steps: Obtain cross-sectional images of beef cattle and perform binarization to remove image noise; Extract the outline of the beef cattle and obtain the outline edge points. Obtain the outline breakpoint information based on the outline and the outline edge points. Obtain two contour edge points on both sides of the contour breakpoint, and calculate the contour completion curvature based on the two contour edge points; The connection trajectory between the contour breakpoint and the contour edge point is obtained based on the contour completion curvature. The outline of the beef cattle is completed by connecting the breakpoints and edge points of the outline.

6. The processing system for improving beef cattle quality according to claim 5, characterized in that, After completing the outline of the beef cattle based on the connection trajectory between the outline breakpoints and the outline edge points, the process also includes: Obtain the outline of the beef cattle, and then segment the beef cattle into several sub-regions based on the outline. Obtain cross-sectional images of beef cattle in each sub-region, and extract texture features of the sub-regions based on the cross-sectional images of beef cattle in each sub-region; Calculate the similarity between the texture features of adjacent sub-regions and the preset texture features; Determine whether the similarity is greater than or equal to a preset similarity threshold; If the value is greater than or equal to the value, then adjacent sub-regions will be merged to obtain an optimized sub-region. If the value is less than the specified value, the corresponding sub-region will be compared with the texture features of the next adjacent sub-region in turn.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a processing method program for improving the quality of beef cattle, which, when executed by a processor, implements the steps of the processing method for improving the quality of beef cattle as described in any one of claims 1 to 3.

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