A method and device for preparing pig liver powder based on step-by-step processing

Abstract

The application relates to a pig liver powder preparation method and device based on a step-by-step process, and relates to the technical field of pig liver powder preparation.The application comprises the following steps: collecting the water content of pretreated pig liver; determining an initial thickness value according to the water content; controlling a preset cutting device to cut the pretreated pig liver based on the initial thickness value, and collecting a section detection image of the cut pig liver section; identifying and determining tissue characteristics according to the section detection image; determining a thickness adjustment value according to the tissue characteristics; calculating the sum value between the initial thickness value and the thickness adjustment value and taking the sum value as a cutting thickness value; and controlling the preset cutting device to cut the pretreated pig liver based on the cutting thickness value.The application has the effect of improving the quality of prepared pig liver powder.

Description

Technical Field

[0001] This invention relates to the field of pork liver powder preparation technology, and in particular to a method and apparatus for preparing pork liver powder based on stepwise processing. Background Technology

[0002] Pig liver powder is a powdered food or nutritional supplement made from pig liver as the main raw material, through processes such as cleaning, removing blood, cooking / steaming, drying, and pulverizing.

[0003] Currently, the preparation of pig liver powder generally involves slicing cleaned and de-blooded pig liver into slices of a preset thickness using a slicer, then putting the slices into a high-temperature and high-pressure cooking kettle to thoroughly cook, sterilize, and destroy anti-nutritional factors. The liver slices are then dried using a belt hot air dryer, and finally, the dried liver slices are pulverized in an ultra-micro pulverizer to obtain pig liver powder.

[0004] Currently, pig liver is sliced ​​using a preset thickness. However, different batches of pig liver have different water content, freshness, fascia distribution, and muscle tissue density. This can easily lead to the pig liver slices breaking or insufficient cooking during subsequent steaming or drying, resulting in a decrease in the quality of the prepared pig liver powder. Summary of the Invention

[0005] To improve the quality of prepared pork liver powder, this invention provides a method and apparatus for preparing pork liver powder based on stepwise processing.

[0006] In a first aspect, the present invention provides a method for preparing pork liver powder based on stepwise processing, employing the following technical solution: A method for preparing pork liver powder based on stepwise processing, comprising: S1: Moisture content of pretreated pig liver; S2: Determine the initial thickness value based on the moisture content; S3: Based on the initial thickness value, the preset cutting device cuts the pre-treated pig liver and acquires the cross-sectional detection image of the cut pig liver. S4: Determine tissue characteristics based on cross-section detection images; S5: Determine the thickness adjustment value based on the tissue characteristics; S6: Calculate the sum between the initial thickness value and the thickness adjustment value and use it as the cutting thickness value; S7: The pre-set cutting device, based on the cutting thickness value, cuts the pre-treated pig liver.

[0007] By adopting the above technical solution, the initial thickness value is determined by first collecting the moisture content of the pretreated pig liver, and then the thickness adjustment value is determined by combining the tissue characteristics of the cut surface detection image of the cut pig liver. Finally, the accurate cutting thickness value is calculated and the cutting is completed. This realizes step-by-step dynamic cutting based on the physicochemical properties of the pig liver itself, solves the problem of poor adaptability of fixed preset thickness slices, effectively improves the thickness uniformity of pig liver slices, and lays a stable material foundation for subsequent cooking, drying and pulverizing processes, thereby improving the quality of the prepared pig liver powder.

[0008] Optional methods for identifying organizational characteristics include: S41: Preprocess the cross-section detection image and convert it into a cross-section grayscale image; S42: Identify gray-level similar regions and gray-level abnormal regions based on cross-sectional gray-level images; S43: Identifying muscle fiber orientation based on grayscale similarity regions; S44: Determine muscle fiber type based on water content; S45: Determine the condition of muscle fibers by combining muscle fiber type and muscle fiber direction; S46: Eliminate detection holes based on grayscale anomaly areas to determine fascia distribution; S47: Combine fascia distribution with muscle fiber distribution as a tissue characteristic.

[0009] By adopting the above technical solution, the cross-sectional detection image is preprocessed and converted into a cross-sectional grayscale image, accurately identifying grayscale similar areas and grayscale abnormal areas, thereby determining the muscle fiber condition and fascia distribution, and finally combining them to obtain comprehensive and accurate tissue characteristics. This achieves quantitative identification of tissue characteristics, avoids the subjective error of manual identification, provides a reliable and scientific basis for determining the thickness adjustment value, and ensures the accuracy of subsequent cutting thickness values.

[0010] Optionally, methods for determining the thickness adjustment value include: S51: Retrieve muscle fiber type, muscle fiber direction and fascia distribution based on tissue characteristics; S52: Determine the type adjustment value based on the muscle fiber type; S53: Calculate the angle between the muscle fiber direction and the preset cutting direction and use it as the muscle fiber angle value; S54: Determine the angle adjustment value based on the muscle fiber angle value; S55: Determine the fascia distribution adjustment value based on the fascia distribution; S56: Combine the type adjustment value, angle adjustment value and fascia distribution adjustment value to determine the comprehensive adjustment value, and use the comprehensive adjustment value as the thickness adjustment value.

[0011] By adopting the above technical solution, the type, direction, and distribution of muscle fibers are retrieved based on tissue characteristics. The type adjustment value, angle adjustment value, and fascia distribution adjustment value are determined respectively, and then the thickness adjustment value is obtained by combining them. This achieves multi-dimensional parameter synergistic adjustment of thickness, so that the thickness adjustment value can be adapted to pig livers with different muscle fiber states and fascia distributions. This further improves the morphological consistency of pig liver slices and reduces the risk of slice fragmentation and adhesion caused by differences in tissue characteristics.

[0012] Optionally, methods for determining the fascia distribution adjustment value include: S551: Retrieve fascia distribution location points and single-point fascia area values ​​based on fascia distribution; S552: Calculate the distance between each fascia distribution location point and use it as the fascia distribution distance value; S553: ​​Determine the distribution distance reference value and area reference value based on the cross-section detection image; S554: Determine the distance adjustment value by combining the fascial distribution distance value with the distribution distance baseline value; S555: Determine the area adjustment value by combining the single-point area value of the fascia with the area benchmark value; S556: Combine the distance adjustment value and the area adjustment value to determine the distribution combination adjustment value, and use the distribution combination adjustment value as the fascia distribution adjustment value.

[0013] By adopting the above technical solution, the fascia distribution location points and the area value of a single fascia point are retrieved, the fascia distribution distance value is calculated, and the distance adjustment value and area adjustment value are determined by combining the distribution distance benchmark value and the area benchmark value, respectively. Finally, a precise fascia distribution adjustment value is obtained, which avoids the thickness adjustment deviation caused by uneven fascia distribution and improves the control accuracy of the influence of fascia distribution on thickness adjustment.

[0014] Optional methods for determining the distribution distance benchmark and area benchmark include: S5531: Determine the image area value and image contour location points based on the cross-section detection image; S5532: Calculate the product between the image area value and the preset area ratio coefficient and use it as the area reference value; S5533: Calculate and determine the vertical length value and the horizontal maximum distance value based on the image contour position points; S5534: Determine the distance selection value by combining the vertical length value and the horizontal maximum distance value; S5535: The product of the selected distance value and the preset distance ratio coefficient is used as the baseline value for the distribution distance.

[0015] By adopting the above technical solution, the image area value and image contour position point are determined based on the cross-sectional detection image. The area reference value and distribution distance reference value are calculated by preset scaling factors, providing a unified and objective reference standard for determining the distance adjustment value and area adjustment value, and ensuring the batch stability of the fascia distribution adjustment value.

[0016] Optionally, the methods for determining the distance selection value include: S55341: Calculate the average value of the vertical length and use it as the vertical average value; S55342: Calculate the ratio between the vertical average value and the horizontal maximum distance value and use it as the vertical-to-horizontal ratio. S55343: Determine the proportional influence value based on the vertical and horizontal proportional values; S55344: Select the maximum vertical length value and use it as the vertical maximum value; select the minimum vertical length value and use it as the vertical minimum value. S55345: Calculate the difference between the maximum and minimum vertical values ​​and use it as the vertical deviation value; S55346: Determine the influence value of vertical deviation based on the vertical deviation value; S55347: Determine the comprehensive impact value by combining the proportional impact value and the vertical deviation impact value; S55348: Calculate the product between the vertical average value and the comprehensive influence value and use it as the distance selection value.

[0017] By adopting the above technical solution, by calculating parameters such as the vertical average value, the vertical-to-horizontal ratio value, and the vertical deviation value, and combining them to obtain the comprehensive influence value, the final distance selection value is determined. This achieves accurate quantitative calculation of the distance selection value, fully considers the differences in the contour features of the pig liver cross-section, and makes the determination of the distribution distance benchmark value more in line with the actual shape of the pig liver, further improving the accuracy of the fascia distribution adjustment value.

[0018] Optionally, after cutting the pre-treated pig liver, the process may also include: S81: Determine cooking and drying control information by combining cutting thickness value and tissue characteristics; S82: Based on the cooking and drying control information, the preset cooking and drying device is controlled to cook and dry the pork liver slices, and images of the dried slices are acquired; S83: Generate the degree of dryness of the slice based on the image of the dried slice; S84: Determine the pulverization control information by combining the degree of dryness of the slices with the cutting thickness value; S85: Based on the crushing control information, the preset crushing device is controlled to crush the pig liver slices.

[0019] By adopting the above technical solution, after the cutting process, the cooking and drying control information is determined by combining the cutting thickness value and tissue characteristics. Then, based on the degree of drying of the slices identified by the dried slice image, the pulverization control information is determined by combining the cutting thickness value. This achieves parameter linkage of the entire process of cutting, cooking and drying, and pulverization, effectively improving the uniformity of cooking and drying of pork liver slices and the efficiency of pulverization, and ensuring the final product quality of pork liver powder.

[0020] Optional methods for determining cooking and drying control information include: S811: Determine the initial temperature parameters and initial spacing parameters based on the cutting thickness value; S812: Retrieve fascia distribution based on tissue characteristics; S813: Determine the temperature fascia adjustment parameters and spacing fascia adjustment parameters based on the fascia distribution; S814: Adjust the initial temperature parameters based on the temperature fascia adjustment parameters to obtain the final temperature parameters; S815: Adjust the initial spacing parameters based on the spacing fascia adjustment parameters to obtain the final spacing parameters; S816: Combine the final temperature parameter with the final spacing parameter and use it as cooking and drying control information.

[0021] By adopting the above technical solution, the initial temperature parameters and initial spacing parameters are determined by the cutting thickness value. The temperature and spacing adjustment parameters are determined by combining the fascia distribution. The final temperature parameters and final spacing parameters are then obtained and used as steaming and drying control information. This allows for adaptive adjustments to be made during steaming and drying to address the uneven steaming caused by the poor thermal conductivity of the fascia, thereby improving the steaming and drying effect of pork liver slices.

[0022] Optionally, methods for determining crushing control information include: S841: Determine the initial crushing parameters based on the cutting thickness value; S842: Determine drying adjustment parameters based on the degree of drying of the slices; S843: Determine the fascia content value based on the fascia distribution; S844: Determine fascia adjustment parameters based on fascia content values; S845: Combines drying adjustment parameters with fascia adjustment parameters and uses them as a comprehensive adjustment parameter; S846: Adjust the initial parameters of the crushing process based on the comprehensive adjustment parameters to serve as the final parameters of the crushing process, and use the final parameters of the crushing process as crushing control information.

[0023] By adopting the above technical solution, the initial parameters for pulverization are determined by the cutting thickness value. Combined with the drying adjustment parameters for the degree of dryness of the slices and the fascia adjustment parameters for the fascia content value, comprehensive adjustment parameters are obtained and the initial parameters for pulverization are optimized. This achieves multi-factor synergistic control of pulverization parameters, reduces the impact of uneven drying and fascia residue on the pulverization effect, improves the particle size uniformity of pork liver powder, and reduces the wear and tear on the pulverizer.

[0024] Secondly, the present invention provides a stepwise processing-based apparatus for preparing pork liver powder, which adopts the following technical solution: A stepwise processing-based pig liver powder preparation apparatus includes a memory and a processor. The memory stores a computer program that can be loaded and executed by the processor according to any one of the first aspects.

[0025] In summary, the present invention has at least one of the following beneficial technical effects: 1. The initial thickness value is determined by first collecting the moisture content of the pre-treated pig liver, and then the thickness adjustment value is determined by combining the tissue characteristics of the cut surface detection image of the cut pig liver. Finally, the accurate cutting thickness value is calculated and the cutting is completed. This realizes step-by-step dynamic cutting based on the physicochemical properties of the pig liver itself, solves the problem of poor adaptability of fixed preset thickness slices, effectively improves the thickness uniformity of pig liver slices, and lays a stable material foundation for subsequent cooking, drying and pulverizing processes, thereby improving the quality of the prepared pig liver powder. 2. By retrieving muscle fiber type, muscle fiber direction, and fascia distribution based on tissue characteristics, adjustment values ​​for type, angle, and fascia distribution were determined, and then the thickness adjustment value was obtained by combining them. This enabled multi-dimensional parameter synergistic adjustment of thickness, allowing the thickness adjustment value to be adapted to pig livers with different muscle fiber states and fascia distributions. This further improved the morphological consistency of pig liver slices and reduced the risk of slice fragmentation and adhesion caused by differences in tissue characteristics. 3. After the cutting process, the cooking and drying control information is determined by combining the cutting thickness value and tissue characteristics. Then, based on the degree of drying of the slices identified by the dried slice image, the pulverization control information is determined by combining the cutting thickness value. This realizes the parameter linkage of the entire process of cutting, cooking and drying, and pulverization, which effectively improves the uniformity of cooking and drying of pork liver slices and the efficiency of pulverization, and ensures the final product quality of pork liver powder. Attached Figure Description

[0026] Figure 1 This is a flowchart of a step-by-step process for preparing pork liver powder. Figure 2 This is a flowchart illustrating the method for identifying organizational characteristics; Figure 3 This is a flowchart illustrating the method for determining thickness adjustment values; Figure 4 This is a flowchart illustrating the process of cutting pre-treated pig liver. Detailed Implementation

[0027] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0028] A step-by-step method for preparing pork liver powder involves determining the initial thickness value by collecting the moisture content of pretreated pork liver, obtaining cross-sectional images after preliminary cutting, identifying muscle fiber characteristics (combining moisture content and fiber orientation) and fascia distribution through grayscale processing, forming tissue characteristics, and then determining multi-dimensional adjustment values ​​by retrieving muscle fiber type, orientation angle, and fascia distribution (refined distance and area benchmark quantification), comprehensively obtaining the cutting thickness value to complete precise cutting. Subsequently, the cutting thickness and tissue characteristics are linked to control the cooking and drying parameters (temperature and spacing), and the pulverization parameters are adjusted based on the dried slice images, cutting thickness, and fascia content, achieving synergistic operation of the entire process of cutting, cooking, drying, and pulverization, thereby improving the quality of the prepared pork liver powder.

[0029] Reference Figure 1 This invention discloses a method for preparing pork liver powder based on a step-by-step processing method, comprising: S1: Moisture content of pretreated pig liver.

[0030] Pre-treated pig liver refers to raw pig liver that has undergone preliminary treatments such as removing blood, impurities, and some surface moisture, thus meeting the basic requirements for subsequent processing. Moisture content refers to the percentage of water mass in the pre-treated pig liver relative to its total mass.

[0031] Moisture content was measured using a capacitive moisture meter, by inserting a probe directly into different parts of the pig liver and then calculating the average value.

[0032] S2: Determine the initial thickness value based on the moisture content.

[0033] The initial thickness value refers to the thickness value corresponding to the initial cutting of the pig liver.

[0034] Different moisture contents correspond to different initial thickness values; the higher the moisture content, the smaller the initial thickness value.

[0035] The initial thickness value is obtained by inputting the moisture content into a preset initial thickness database, which facilitates subsequent use.

[0036] The initial thickness database contains a table that maps different moisture contents to their corresponding initial thickness values. The initial thickness database is set in advance by the operator according to actual needs.

[0037] For example, the initial thickness database can be set to an initial thickness of 0.8 cm when the moisture content is greater than 0.75, 1 cm when the moisture content is between 0.7 and 0.75, and 1.2 cm when the moisture content is less than 0.7.

[0038] S3: Based on the initial thickness value, the preset cutting device cuts the pre-processed pig liver and acquires the cross-sectional detection image of the cut pig liver.

[0039] The cutting device refers to a slicer used to slice pig liver. The cross-sectional inspection image refers to a visual image of the cross-section of the pig liver after it has been cut, acquired under standardized lighting conditions.

[0040] The pre-processed pig liver is initially cut using a preset cutting device with an initial thickness value. Then, the cut surface detection image is detected and acquired by an image acquisition device preset on the cutting device, which facilitates further cutting in the future.

[0041] S4: Determine tissue characteristics based on cross-sectional detection images.

[0042] Tissue characteristics refer to the key features of the tissue state on the surface of a pig liver slice. Tissue characteristics include the distribution of fascia and the condition of muscle fibers.

[0043] By identifying and analyzing the cross-sectional images, tissue characteristics can be determined, facilitating subsequent use.

[0044] To further ensure the rationality of organizational characteristics, it is necessary to perform further separate analysis and calculation of organizational characteristics, which will be explained in detail through the steps shown below.

[0045] Reference Figure 2 The method for identifying organizational characteristics includes the following steps: S41: Preprocess the cross-section detection image and convert it into a cross-section grayscale image.

[0046] Among them, a cross-sectional grayscale image refers to a single-channel image that contains only grayscale values.

[0047] By sequentially performing Gaussian filtering noise reduction (filtering out granular noise in the image), region cropping (selecting and retaining the effective area of ​​the liver slice while removing the background), and histogram equalization (improving image brightness uniformity and eliminating slice reflections) on the slice detection image, the image clarity and accuracy of subsequent feature recognition are improved. The preprocessed image is then converted from the RGB color space to the grayscale color space to generate a grayscale image of the slice for later use.

[0048] S42: Identify gray-scale similar regions and gray-scale abnormal regions based on cross-sectional gray-scale images.

[0049] Among them, gray-level similar regions refer to continuous pixel regions in a cross-sectional gray-level image whose gray-level values ​​are within the same preset range. Gray-level abnormal regions refer to pixel regions in a cross-sectional gray-level image whose gray-level values ​​deviate from those of the gray-level similar regions.

[0050] By first setting the range to 120-180, and then using a threshold segmentation algorithm to process the grayscale image of the cross section, pixels whose grayscale values ​​fall within the preset range are classified as grayscale similar regions, and pixels that do not fall within the preset range are classified as grayscale abnormal regions, which facilitates subsequent use.

[0051] S43: Identify muscle fiber orientation based on grayscale similarity regions.

[0052] Among them, muscle fiber orientation refers to the direction of extension and arrangement of the muscle fiber tissue in the cut surface of pig liver.

[0053] The texture direction parameters of gray-level similar regions are calculated by using the gray-level co-occurrence matrix algorithm. Then, the calculated direction parameters are statistically analyzed, and the direction with the highest frequency is selected as the main direction of the muscle fiber. If there are two or more high-frequency directions, it is determined to be a mesh-like interlaced direction, thus obtaining the direction of the muscle fiber for subsequent use.

[0054] S44: Determine the muscle fiber type based on water content.

[0055] Among them, muscle fiber type refers to the classification of muscle fiber structural state based on the water content of pretreated pig liver. Muscle fiber types include dense and loose types.

[0056] By setting up a reference table that corresponds to a dense type when the moisture content is less than 0.7%, a medium-dense type when the moisture content is 0.7 to 0.75%, and a loose type when the moisture content is greater than 0.75%, the collected moisture content values ​​of pretreated pig liver are directly matched with this reference table to determine the muscle fiber type, which is convenient for subsequent use.

[0057] S45: Determine the condition of muscle fibers by combining muscle fiber type and muscle fiber direction.

[0058] Among them, muscle fiber condition refers to the overall state of porcine liver muscle fibers obtained after integrating muscle fiber type and muscle fiber direction.

[0059] By combining muscle fiber type with muscle fiber orientation, a dataset containing both muscle fiber type and orientation is created and merged into a single muscle fiber profile for easier subsequent use.

[0060] S46: Eliminate detection holes based on grayscale anomalies to determine fascia distribution.

[0061] Among these, the detection pores refer to the pore-like areas created when collecting moisture content data. Fascia distribution refers to the location and area of ​​the fascia within the pig liver.

[0062] By comparing the gray-scale abnormal areas with the preset jack features, the areas that meet the jack features are selected, and the remaining areas are marked and their areas are statistically analyzed, thereby obtaining the fascia distribution.

[0063] Socket features refer to the grayscale range, shape, and other characteristics corresponding to the socket. Socket features are obtained after pre-input by the operator.

[0064] S47: Combine fascia distribution with muscle fiber distribution as a tissue characteristic.

[0065] In this process, by combining the distribution of fascia with the distribution of muscle fibers, a dataset containing both fascia distribution and muscle fiber information is created and merged as tissue characteristics, which facilitates subsequent use.

[0066] S5: Determine the thickness adjustment value based on the tissue characteristics.

[0067] The thickness adjustment value refers to the compensation value used to correct the initial thickness value.

[0068] By analyzing the tissue characteristics, the thickness adjustment value can be determined to facilitate subsequent use.

[0069] To further ensure the rationality of the thickness adjustment value, it is necessary to perform a further separate analysis and calculation on the thickness adjustment value, which will be explained in detail through the steps shown below.

[0070] Reference Figure 3 The method for determining the thickness adjustment value includes the following steps: S51: Retrieves muscle fiber type, muscle fiber direction, and fascia distribution based on tissue characteristics.

[0071] Among them, the muscle fiber type, muscle fiber direction and fascia distribution can be retrieved by tissue characteristics to facilitate subsequent use.

[0072] S52: Determine the type adjustment value based on the muscle fiber type.

[0073] Among them, the type adjustment value refers to the specific thickness correction parameter set for different muscle fiber types.

[0074] By inputting the muscle fiber type into a preset type adjustment database, a type adjustment value is obtained for easy subsequent use.

[0075] The type adjustment database pre-stores a table of different muscle fiber types and their corresponding type adjustment values, which is obtained after the operator pre-inputs the values.

[0076] For example, the type adjustment database can be set to a type adjustment value of 0.2 for dense muscle fibers, 0.1 for moderately dense muscle fibers, and -0.1 for loose muscle fibers.

[0077] S53: Calculate the angle between the muscle fiber direction and the preset cutting direction and use it as the muscle fiber angle value.

[0078] The cutting direction refers to the direction in which the pig liver is cut. This cutting direction is obtained through pre-input by the operator. The muscle fiber angle value refers to the angle between the muscle fiber direction and the preset cutting direction.

[0079] Calculating the angle values ​​of muscle fibers facilitates subsequent use.

[0080] S54: Determine the angle adjustment value based on the muscle fiber angle value.

[0081] Among them, the angle adjustment value refers to the specific thickness correction parameter determined based on the muscle fiber angle value.

[0082] By inputting the muscle fiber angle value into a preset angle adjustment database, an angle adjustment value is obtained for easy subsequent use.

[0083] The angle adjustment database has a pre-stored table of different muscle fiber angle values ​​and their corresponding angle adjustment values. The angle adjustment database is obtained after the operator pre-inputs the values.

[0084] For example, the angle adjustment database can be set to 0.15 when the muscle fiber angle is between 0 and 30 degrees (cut along the fiber, the slice is not easy to break, and the thickness can be appropriately increased), 0.05 when the muscle fiber angle is between 30 and 60 degrees, and -0.1 when the muscle fiber angle is between 60 and 90 degrees (cut across the fiber, the slice is easy to break, and the thickness needs to be reduced).

[0085] S55: Determine the fascia distribution adjustment value based on the fascia distribution.

[0086] Among them, the fascia distribution adjustment value refers to a specific thickness correction parameter set based on the distribution of pig liver fascia.

[0087] By analyzing the distribution of fascia, adjustment values ​​for fascia distribution can be determined to facilitate subsequent use.

[0088] To further ensure the rationality of the fascia distribution adjustment value, it is necessary to perform a further separate analysis and calculation of the fascia distribution adjustment value, which will be explained in detail through the steps shown below.

[0089] The method for determining the fascia distribution adjustment value includes the following steps: S551: Retrieve the location points of the fascia distribution and the area value of a single fascia point based on the fascia distribution.

[0090] In this context, the fascia distribution location point refers to the geometric center coordinate point corresponding to each independent fascial region in the cross-sectional grayscale image, and the fascial single-point area value refers to the area value corresponding to each independent fascial region in the cross-sectional grayscale image. The fascial distribution includes both the fascial distribution location points and the fascial single-point area values.

[0091] The location points of the fascia and the area value of a single fascia point can be retrieved by analyzing the fascia distribution information, which will facilitate subsequent use.

[0092] S552: Calculate the distance between each fascia distribution location point and use it as the fascia distribution distance value.

[0093] The fascia distribution distance value refers to the distance between each fascia distribution location point.

[0094] Calculating the fascia distribution distance value facilitates subsequent use.

[0095] S553: ​​Determine the distribution distance reference value and area reference value based on the cross-section detection image.

[0096] The distribution distance reference value refers to a reference threshold determined based on the contour features of the pig liver cross-section in the cross-section detection image. The area reference value refers to a reference threshold determined based on the overall area of ​​the pig liver cross-section in the cross-section detection image.

[0097] By analyzing the cross-section detection image, the distribution distance benchmark value and area benchmark value are determined, which facilitates subsequent use.

[0098] To further ensure the rationality of the distribution distance benchmark and area benchmark, it is necessary to perform further separate analysis and calculation on the distribution distance benchmark and area benchmark, which will be explained in detail through the steps shown below.

[0099] The methods for determining the distribution distance benchmark value and the area benchmark value include the following steps: S5531: Determine the image area value and image contour location points based on the cross-section detection image.

[0100] Here, the image area value refers to the total pixel area corresponding to the effective region of the pig liver cross-section in the cross-section detection image. The image contour location points refer to the set of pixel coordinates of the edge of the pig liver cross-section.

[0101] The edge contours of the cross-section are extracted from the cross-section detection image using the Canny edge detection algorithm. Then, the complete contour pixel coordinates of the cross-section are identified and obtained using a contour detection algorithm (such as OpenCV's findContours function), which are the image contour location points. Finally, the number of pixels in the area enclosed by the contour is counted, or the contour area calculation function is called to obtain the image area value, which is convenient for subsequent use.

[0102] S5532: Calculate the product between the image area value and the preset area ratio coefficient and use it as the area reference value.

[0103] The area ratio factor refers to the reference threshold used to convert image area values ​​into suitable fascia area assessments. The area ratio factor is preset by the operator according to actual needs.

[0104] By calculating the product between the image area value and a preset area ratio coefficient, and using the calculation result as the area reference value, the accuracy of the obtained area reference value is improved.

[0105] S5533: Calculate and determine the vertical length value and the horizontal maximum distance value based on the position points of the image contour.

[0106] The vertical length value refers to the vertical distance of the pig liver cross-section outline. The horizontal maximum distance value refers to the maximum horizontal distance of the pig liver cross-section outline.

[0107] By extracting the x-coordinate (horizontal direction) and y-coordinate (vertical direction) of all points based on the image contour location points, the maximum and minimum values ​​of the x-coordinate are found and the difference is calculated as the horizontal maximum distance value. Then, based on the same x-coordinate, the difference between the maximum and minimum values ​​of the corresponding y-coordinate is calculated as the vertical length value, which is convenient for subsequent use.

[0108] S5534: Determine the distance selection value by combining the vertical length value and the horizontal maximum distance value.

[0109] The distance selection value refers to the selected reference distance parameter.

[0110] By combining and analyzing the vertical length value and the maximum horizontal distance value, the distance selection value is determined to facilitate subsequent use.

[0111] To further ensure the rationality of the selected distance value, it is necessary to perform a further separate analysis and calculation on the selected distance value, which will be explained in detail through the steps shown below.

[0112] The method for determining the distance selection value includes the following steps: S55341: Calculate the average value of the vertical length and use it as the vertical average value.

[0113] Among them, the vertical average value refers to the average value of the vertical length.

[0114] Calculating the vertical average value makes it easier to use later.

[0115] S55342: Calculate the ratio between the vertical average value and the horizontal maximum distance value and use it as the vertical-horizontal ratio value.

[0116] The vertical-to-horizontal ratio refers to the ratio between the average vertical value and the furthest horizontal distance.

[0117] Calculating the vertical and horizontal ratios facilitates subsequent use.

[0118] S55343: Determine the proportional influence value based on the vertical and horizontal proportional values.

[0119] The proportional influence value refers to the parameter that corrects the reference length based on the length in the horizontal direction.

[0120] The larger the vertical and horizontal scale values, the greater the scale influence. The scale influence values ​​are obtained by inputting the vertical and horizontal scale values ​​into a preset scale database for convenient subsequent use.

[0121] The proportion database has a pre-set table of different vertical and horizontal proportion values ​​and their corresponding proportion influence values. The proportion database is obtained after the operator pre-inputs the values.

[0122] S55344: Select the maximum vertical length value and use it as the vertical maximum value; select the minimum vertical length value and use it as the vertical minimum value.

[0123] The vertical maximum value refers to the maximum value of the vertical length. The vertical minimum value refers to the minimum value of the vertical length.

[0124] By selecting and defining the maximum and minimum vertical values, it becomes easier to use them later.

[0125] S55345: Calculate the difference between the maximum and minimum vertical values ​​and use it as the vertical deviation value.

[0126] The vertical deviation value refers to the difference between the maximum vertical value and the minimum vertical value.

[0127] Calculating the vertical deviation value facilitates subsequent use.

[0128] S55346: Determine the influence value of vertical deviation based on the vertical deviation value.

[0129] Among them, the vertical deviation influence value refers to the parameter that corrects the reference length based on the length deviation in the vertical direction.

[0130] The larger the vertical deviation value, the greater the impact of the vertical deviation. The vertical deviation value is entered into a preset vertical deviation database to obtain the vertical deviation impact value, which is convenient for subsequent use.

[0131] The vertical deviation database pre-stores a table showing the correspondence between different vertical deviation values ​​and their respective influence values. The vertical deviation database is obtained after the operator has pre-entered the data.

[0132] S55347: Determine the comprehensive impact value by combining the proportional influence value and the vertical deviation influence value.

[0133] Among them, the comprehensive influence value refers to the composite correction parameter obtained by integrating the proportional influence value and the vertical deviation influence value.

[0134] The influence values ​​of the proportional and vertical deviations are weighted and calculated, and the result is used as the comprehensive influence value for subsequent use. The specific weights for the weighting calculation are preset by the operator according to actual needs.

[0135] S55348: Calculate the product between the vertical average value and the comprehensive influence value and use it as the distance selection value.

[0136] Specifically, the product of the vertical average value and the comprehensive impact value is calculated, and the calculation result is used as the distance selection value for convenient subsequent use.

[0137] S5535: The product of the selected distance value and the preset distance ratio coefficient is used as the baseline value for the distribution distance.

[0138] The distance ratio factor is a reference threshold used to convert selected distance values ​​into appropriate fascia area assessments. This distance ratio factor is preset by the operator based on actual needs.

[0139] By calculating the product between the selected distance value and the preset distance ratio coefficient, and using the calculation result as the distribution distance benchmark value, the accuracy of the obtained distribution distance benchmark value is improved.

[0140] S554: Determine the distance adjustment value by combining the fascial distribution distance value with the distribution distance benchmark value.

[0141] Among them, the distance adjustment value refers to the adjustment value corresponding to the cutting thickness when adjusting the fascia distribution distance value. The distance adjustment value is used to compensate for the influence of the density of fascia distribution on the cutting process.

[0142] The distance adjustment value is obtained by using a preset distance adjustment calculation formula to calculate the fascial distribution distance value and the distribution distance benchmark value, which is convenient for subsequent use.

[0143] The distance adjustment calculation formula is: Distance adjustment value = (fascia distribution distance value / distribution distance baseline value - 1) * K1. K1 is a preset correction coefficient, and the value range of K1 is usually from 0.05 to 0.2. K1 is pre-selected and set by the operator according to different cutting equipment and types of pig liver.

[0144] S555: Determine the area adjustment value by combining the single-point area value of the fascia with the area benchmark value.

[0145] Among them, the area adjustment value refers to the adjustment value corresponding to the cutting thickness when adjusting the cutting thickness based on the area value of a single point of the fascia. The area adjustment value is used to compensate for the influence of the size of the fascia distribution on the cutting process.

[0146] The area adjustment value is obtained by using a preset area adjustment calculation formula to calculate the area value of a single point of fascia and the area benchmark value, which facilitates subsequent use.

[0147] The formula for calculating the area adjustment is: Area adjustment value = (Single point area value of fascia / Base area value - 1) * K2. K2 is a preset correction coefficient, and the value of K2 is usually between 0.03 and 0.1. K2 is pre-selected and set by the operator according to different cutting equipment and types of pig liver.

[0148] S556: Combine the distance adjustment value and the area adjustment value to determine the distribution combination adjustment value, and use the distribution combination adjustment value as the fascia distribution adjustment value.

[0149] Among them, the distribution combined adjustment value refers to the composite correction parameter obtained by integrating the distance adjustment value and the area adjustment value.

[0150] By weighting the distance adjustment value and the area adjustment value, and using the result as the distribution-combined adjustment value, and then using the distribution-combined adjustment value as the fascia distribution adjustment value, the accuracy of the obtained fascia distribution adjustment value is improved. The weights in the weighting calculation are preset by the operator according to actual needs.

[0151] S56: Combine the type adjustment value, angle adjustment value and fascia distribution adjustment value to determine the comprehensive adjustment value, and use the comprehensive adjustment value as the thickness adjustment value.

[0152] Among them, the comprehensive adjustment value refers to the composite correction parameter obtained by integrating the type adjustment value, angle adjustment value and fascia distribution adjustment value.

[0153] By weighting the type adjustment value, angle adjustment value, and fascia distribution adjustment value, and using the result as the comprehensive adjustment value, and then using the comprehensive adjustment value as the thickness adjustment value, the accuracy of the obtained thickness adjustment value is improved. The weights in the weighting calculation are preset by the operator according to actual needs.

[0154] S6: Calculate the sum between the initial thickness value and the thickness adjustment value and use it as the cutting thickness value.

[0155] The cutting thickness value refers to the thickness value required for the current cutting operation.

[0156] The sum of the initial thickness value and the thickness adjustment value is calculated, and the result is used as the cutting thickness value for convenient subsequent use.

[0157] S7: The pre-set cutting device, based on the cutting thickness value, cuts the pre-treated pig liver.

[0158] In this process, the pre-treated pig liver is cut to a specific thickness by controlling a pre-set cutting device. This achieves stepwise dynamic cutting based on the physicochemical properties of the pig liver itself, solving the problem of poor adaptability of fixed-thickness slices. It effectively improves the uniformity of the pig liver slice thickness, laying a stable material foundation for subsequent cooking, drying, and pulverizing processes, thereby improving the quality of the prepared pig liver powder.

[0159] To further ensure the rationality of cutting the pre-treated pig liver, it is necessary to perform further separate analysis and calculations on the pre-treated pig liver after cutting, which will be explained in detail through the following steps.

[0160] Reference Figure 4 After the pre-treated pig liver is cut, the following steps are also included: S81: Determine the cooking and drying control information by combining the cutting thickness value and the microstructure characteristics.

[0161] The cooking and drying control information refers to the control information used to operate the cooking and drying equipment. The cooking and drying equipment includes a steam oven for cooking pork liver slices and a belt hot air dryer for drying the cooked pork liver slices. The cooking and drying control information includes cooking temperature, cooking time, cooking interval, drying temperature, and drying time.

[0162] By combining the cutting thickness value with the tissue characteristics, the cooking and drying control information can be determined, which will facilitate subsequent use.

[0163] To further ensure the rationality of the cooking and drying control information, it is necessary to perform further separate analysis and calculation on the cooking and drying control information, which will be explained in detail through the following steps.

[0164] The method for determining cooking and drying control information includes the following steps: S811: Determine the initial temperature parameters and initial spacing parameters based on the cutting thickness value.

[0165] The initial temperature parameter refers to the basic temperature parameters of the cutting process required based on the cutting thickness value. The initial temperature parameters include the cooking temperature and cooking time. The initial spacing parameter refers to the basic spacing parameters of the cutting process required based on the cutting thickness value.

[0166] The cutting thickness values ​​are averaged to obtain the average thickness, which is then input into a preset thickness temperature database to match the initial temperature parameters. The cutting thickness values ​​are then input into a preset thickness spacing database to match the initial spacing parameters, facilitating subsequent use.

[0167] The thickness-temperature database pre-stores a lookup table of different average thicknesses and their corresponding initial temperature parameters, while the thickness-spacing database pre-stores a lookup table of different cutting thicknesses and their corresponding initial spacing parameters. Both the thickness-temperature database and the thickness-spacing database are obtained by the operator after steaming pork liver slices of different thicknesses, selecting the optimal steaming temperature, steaming time, and steaming spacing for the best steaming effect.

[0168] S812: Retrieve fascia distribution based on tissue characteristics.

[0169] Among these methods, the distribution of fascia can be retrieved based on tissue characteristics, making it easier to use later.

[0170] S813: Determine the temperature fascia adjustment parameters and spacing fascia adjustment parameters based on the fascia distribution.

[0171] Among them, the temperature fascia adjustment parameter refers to the parameter corresponding to adjusting the basic temperature parameter of the cutting process based on the fascia distribution. The spacing fascia adjustment parameter refers to the parameter corresponding to adjusting the basic spacing parameter of the cutting process based on the fascia distribution.

[0172] By retrieving the fascia distribution distance value and the fascia single point area value based on the fascia distribution condition, the fascia area ratio and concentration are determined by the fascia distribution distance value and the fascia single point area value. The larger the fascia area ratio and the denser the concentration, the greater the temperature and duration corresponding to the temperature fascia adjustment parameter, and the larger the spacing fascia adjustment parameter.

[0173] By inputting the fascia distribution distance value and the fascia single-point area value into the preset fascia adjustment database, temperature fascia adjustment parameters and spacing fascia adjustment parameters are obtained for convenient subsequent use.

[0174] The fascia adjustment database pre-stores different fascia distribution distance values, fascia single-point area values, and corresponding temperature and spacing fascia adjustment parameters.

[0175] S814: Adjust the initial temperature parameters based on the temperature fascia adjustment parameters to obtain the final temperature parameters.

[0176] The final temperature parameter refers to the temperature parameter obtained after adjusting the initial temperature parameter. The process involves retrieving the initial temperature value and duration from the initial temperature parameter, adjusting the temperature fascia parameter to the adjusted temperature value and duration, calculating the sum of the initial and adjusted temperature values ​​as the final temperature value, and calculating the sum of the adjusted and initial temperature durations as the final duration value. Finally, the final temperature value and the final duration value are combined to form the final temperature parameter for convenient subsequent use.

[0177] S815: Adjust the initial spacing parameters based on the spacing fascia adjustment parameters to obtain the final spacing parameters.

[0178] Specifically, the sum of the spacing fascia adjustment parameters and the initial spacing parameters is calculated, and the calculation result is used as the final spacing parameter for convenient subsequent use.

[0179] S816: Combine the final temperature parameter with the final spacing parameter and use it as cooking and drying control information.

[0180] In this way, the accuracy of the cooking and drying control information is improved by combining the final temperature parameter with the final spacing parameter and using it as cooking and drying control information.

[0181] S82: Based on the cooking and drying control information, the preset cooking and drying device is controlled to cook and dry the pig liver slices, and images of the dried slices are acquired.

[0182] Among them, the dried slice image refers to the image corresponding to the sliced ​​pig liver after steaming and drying.

[0183] The pig liver slices are steamed and dried by controlling the preset steaming and drying device with steaming and drying control information. Then, the dried slice images are acquired by the image acquisition device preset on the belt hot air dryer for subsequent use.

[0184] S83: Generate the degree of dryness of the slice based on the image of the dried slice.

[0185] Among them, the degree of drying of the slices refers to the drying standard level corresponding to the steaming and drying of pig liver slices.

[0186] By preprocessing and extracting grayscale values ​​and features such as cracks, wrinkles, and textures from the dried slice images, and determining the texture smoothing value based on the number of cracks, wrinkles, and textures, the grayscale values ​​and texture smoothing values ​​are then input into a preset drying database to match and obtain the degree of drying of the slices for subsequent use.

[0187] The drying database contains a pre-stored table of different grayscale values, texture smoothness values, and corresponding slice drying degrees. The drying database is obtained after the operator pre-inputs the data.

[0188] S84: Determine the pulverization control information by combining the degree of drying of the slices with the cutting thickness value.

[0189] Among these, the pulverization control information refers to the control information used to control the operation of the pulverizing device. The pulverizing device refers to a pulverizer used to pulverize slices of pig liver.

[0190] By combining the analysis of the degree of drying of the slices with the cutting thickness value, the crushing control information can be determined to facilitate subsequent use.

[0191] To further ensure the rationality of the crushing control information, it is necessary to perform further separate analysis and calculation on the crushing control information, which will be explained in detail through the steps shown below.

[0192] The method for determining crushing control information includes the following steps: S841: Determine the initial crushing parameters based on the cutting thickness value.

[0193] The initial crushing parameters refer to the basic process parameters of the crushing process required based on the cutting thickness value. These initial crushing parameters include crushing speed, crushing time, and screen aperture.

[0194] The initial crushing parameters are obtained by inputting the cutting thickness value into a preset crushing database, which facilitates subsequent use.

[0195] The crushing database has a pre-stored table of different cutting thickness values ​​and their corresponding initial crushing parameters, which is obtained after the operator pre-inputs the data.

[0196] S842: Determine the drying adjustment parameters based on the degree of drying of the slices.

[0197] Among them, the drying adjustment parameter refers to the pulverization parameter compensation parameter determined based on the degree of drying of pig liver slices (over-dry / meeting standards / under-dry).

[0198] The drier the slices, the lower the pulverizing speed, the shorter the pulverizing time, and the larger the screen aperture corresponding to the drying adjustment parameters.

[0199] By inputting the degree of dryness of the slices into a preset drying adjustment database, drying adjustment parameters can be obtained for convenient subsequent use.

[0200] The drying adjustment database contains a pre-stored table of different slice drying degrees and their corresponding drying adjustment parameters, which are obtained after being pre-entered by the operator.

[0201] S843: Determine the fascia content value based on the fascia distribution.

[0202] Among them, the fascia content value refers to the quantitative indicator of the proportion of fascia in pig liver slices.

[0203] By retrieving the area value of a single fascia point based on the distribution of the fascia, and calculating the ratio between the area value of the single fascia point and the area value of the image, the fascia content value is obtained for convenient subsequent use.

[0204] S844: Determine the fascia adjustment parameters based on the fascia content value.

[0205] Among them, the fascia adjustment parameter refers to the pulverization parameter compensation parameter determined based on the fascia content value.

[0206] By inputting the fascia content value into a preset fascia content database, a matching fascia content value can be obtained, which facilitates subsequent use.

[0207] The fascia content database has a pre-stored table of different fascia content values ​​and their corresponding fascia adjustment parameters. The fascia content database is obtained after the operator pre-inputs the data.

[0208] S845: Combines the drying adjustment parameters with the fascia adjustment parameters and uses them as a comprehensive adjustment parameter.

[0209] Among them, the comprehensive adjustment parameters refer to the adjustment parameters that are required when the crushing parameters need to be adjusted in the end.

[0210] By adjusting the drying and fascia adjustment parameters and their corresponding pulverizing speed, pulverizing time, and screen aperture, and calculating the sum of these parameters, a comprehensive adjustment parameter is obtained for convenient subsequent use.

[0211] S846: Adjust the initial parameters of the crushing process based on the comprehensive adjustment parameters to serve as the final parameters of the crushing process, and use the final parameters of the crushing process as crushing control information.

[0212] Among them, the final pulverization parameter refers to the final parameter corresponding to the pulverization of pig liver slices.

[0213] By extracting the corresponding crushing speed, crushing time, and screen aperture from the comprehensive adjustment parameters and the initial crushing parameters, and calculating the sum of these parameters, the results are combined to form the final crushing parameters. These final crushing parameters are then used as crushing control information, thereby improving the accuracy of the obtained crushing control information.

[0214] S85: Based on the crushing control information, the preset crushing device is controlled to crush the pig liver slices.

[0215] The process involves controlling a pre-set pulverizing device to pulverize pork liver slices using pulverizing control information. This achieves parameter linkage across the entire process of cutting, steaming, drying, and pulverizing, effectively improving the uniformity of steaming and drying of pork liver slices and the efficiency of pulverizing, thus ensuring the final product quality of pork liver powder.

[0216] Based on the same inventive concept, this invention provides a step-by-step processing apparatus for preparing pork liver powder, including a memory and a processor. The memory stores a computer program that can be loaded and executed by the processor as described above for a step-by-step processing method for preparing pork liver powder.

[0217] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0218] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for preparing pork liver powder based on stepwise processing, characterized in that, include: S1: Moisture content of pretreated pig liver; S2: Determine the initial thickness value based on the moisture content; S3: Based on the initial thickness value, the preset cutting device cuts the pre-treated pig liver and acquires the cross-sectional detection image of the cut pig liver. S4: Determine tissue characteristics based on cross-section detection images; S5: Determine the thickness adjustment value based on the tissue characteristics; S6: Calculate the sum between the initial thickness value and the thickness adjustment value and use it as the cutting thickness value; S7: The pre-set cutting device, based on the cutting thickness value, cuts the pre-treated pig liver.

2. The method for preparing pork liver powder based on stepwise processing according to claim 1, characterized in that, Methods for identifying organizational characteristics include: S41: Preprocess the cross-section detection image and convert it into a cross-section grayscale image; S42: Identify gray-level similar regions and gray-level abnormal regions based on cross-sectional gray-level images; S43: Identifying muscle fiber orientation based on grayscale similarity regions; S44: Determine muscle fiber type based on water content; S45: Determine the condition of muscle fibers by combining muscle fiber type and muscle fiber direction; S46: Eliminate detection holes based on grayscale anomaly areas to determine fascia distribution; S47: Combine fascia distribution with muscle fiber distribution as a tissue characteristic.

3. The method for preparing pork liver powder based on stepwise processing according to claim 2, characterized in that, The methods for determining the thickness adjustment value include: S51: Retrieve muscle fiber type, muscle fiber direction and fascia distribution based on tissue characteristics; S52: Determine the type adjustment value based on the muscle fiber type; S53: Calculate the angle between the muscle fiber direction and the preset cutting direction and use it as the muscle fiber angle value; S54: Determine the angle adjustment value based on the muscle fiber angle value; S55: Determine the fascia distribution adjustment value based on the fascia distribution; S56: Combine the type adjustment value, angle adjustment value and fascia distribution adjustment value to determine the comprehensive adjustment value, and use the comprehensive adjustment value as the thickness adjustment value.

4. The method for preparing pork liver powder based on stepwise processing according to claim 3, characterized in that, Methods for determining fascia distribution adjustment values ​​include: S551: Retrieve fascia distribution location points and single-point fascia area values ​​based on fascia distribution; S552: Calculate the distance between each fascia distribution location point and use it as the fascia distribution distance value; S553: ​​Determine the distribution distance reference value and area reference value based on the cross-section detection image; S554: Determine the distance adjustment value by combining the fascial distribution distance value with the distribution distance baseline value; S555: Determine the area adjustment value by combining the single-point area value of the fascia with the area benchmark value; S556: Combine the distance adjustment value and the area adjustment value to determine the distribution combination adjustment value, and use the distribution combination adjustment value as the fascia distribution adjustment value.

5. The method for preparing pork liver powder based on stepwise processing according to claim 4, characterized in that, The methods for determining the distribution distance benchmark value and area benchmark value include: S5531: Determine the image area value and image contour location points based on the cross-section detection image; S5532: Calculate the product between the image area value and the preset area ratio coefficient and use it as the area reference value; S5533: Calculate and determine the vertical length value and the horizontal maximum distance value based on the image contour position points; S5534: Determine the distance selection value by combining the vertical length value and the horizontal maximum distance value; S5535: The product of the selected distance value and the preset distance ratio coefficient is used as the baseline value for the distribution distance.

6. The method for preparing pork liver powder based on stepwise processing according to claim 5, characterized in that, The methods for determining the distance selection value include: S55341: Calculate the average value of the vertical length and use it as the vertical average value; S55342: Calculate the ratio between the vertical average value and the horizontal maximum distance value and use it as the vertical-to-horizontal ratio. S55343: Determine the proportional influence value based on the vertical and horizontal proportional values; S55344: Select the maximum vertical length value and use it as the vertical maximum value; select the minimum vertical length value and use it as the vertical minimum value. S55345: Calculate the difference between the maximum and minimum vertical values ​​and use it as the vertical deviation value; S55346: Determine the influence value of vertical deviation based on the vertical deviation value; S55347: Determine the comprehensive impact value by combining the proportional impact value and the vertical deviation impact value; S55348: Calculate the product between the vertical average value and the comprehensive influence value and use it as the distance selection value.

7. The method for preparing pork liver powder based on stepwise processing according to claim 2, characterized in that, After the pre-treated pig liver is cut, the following steps are also included: S81: Determine cooking and drying control information by combining cutting thickness value and tissue characteristics; S82: Based on the cooking and drying control information, the preset cooking and drying device is controlled to cook and dry the pork liver slices, and images of the dried slices are acquired; S83: Generate the degree of dryness of the slice based on the image of the dried slice; S84: Determine the pulverization control information by combining the degree of dryness of the slices with the cutting thickness value; S85: Based on the crushing control information, the preset crushing device is controlled to crush the pig liver slices.

8. The method for preparing pork liver powder based on stepwise processing according to claim 7, characterized in that, Methods for determining cooking and drying control information include: S811: Determine the initial temperature parameters and initial spacing parameters based on the cutting thickness value; S812: Retrieve fascia distribution based on tissue characteristics; S813: Determine the temperature fascia adjustment parameters and spacing fascia adjustment parameters based on the fascia distribution; S814: Adjust the initial temperature parameters based on the temperature fascia adjustment parameters to obtain the final temperature parameters; S815: Adjust the initial spacing parameters based on the spacing fascia adjustment parameters to obtain the final spacing parameters; S816: Combine the final temperature parameter with the final spacing parameter and use it as cooking and drying control information.

9. The method for preparing pork liver powder based on stepwise processing according to claim 7, characterized in that, Methods for determining crushing control information include: S841: Determine the initial crushing parameters based on the cutting thickness value; S842: Determine drying adjustment parameters based on the degree of drying of the slices; S843: Determine the fascia content value based on the fascia distribution; S844: Determine fascia adjustment parameters based on fascia content values; S845: Combines drying adjustment parameters with fascia adjustment parameters and uses them as a comprehensive adjustment parameter; S846: Adjust the initial parameters of the crushing process based on the comprehensive adjustment parameters to serve as the final parameters of the crushing process, and use the final parameters of the crushing process as crushing control information.

10. A stepwise processing apparatus for preparing pork liver powder, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program and can be loaded and executed by the processor, as described in any one of claims 1 to 9, a method for preparing pig liver powder based on stepwise processing.

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