Beer yeast preservation method based on big data

Abstract

The present invention discloses a brewer's yeast preservation method based on big data. The position of clustered brewer's yeast is obtained by clustering, and the corresponding first yeast clustering circle and second yeast clustering circle are obtained with different first clustering radii and second clustering radii. It can be seen from the clustering characteristics that the density of brewer's yeast in the second yeast clustering circle is greater than the density of brewer's yeast in the first yeast clustering circle. In addition, through a seed preservation discrimination network, concentric circles are used to jointly judge the relationship between brewer's yeast and clustered groups, and the seed preservation status of brewer's yeast can be judged as a whole. Therefore, the change of clustered brewer's yeast can be more accurately judged at multiple time points. And based on the multiple seed preservation degree values ​​corresponding to the beer yeast image set of multiple preservation methods, the preservation method with the largest seed preservation degree value is found as the beer yeast preservation method.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a brewer's yeast preservation method based on big data. Background Art

[0002] Currently, improper storage of brewer's yeast can lead to contamination, degradation, and even mutation. Therefore, more appropriate storage methods are needed to prevent this. Furthermore, after storing the brewer's yeast, when it is needed again, the temperature and humidity can be adjusted to allow the yeast to grow again.

[0003] Because clusters of brewer's yeast can affect each other and reflect the sealing status of an area, it is necessary to use the clustering of brewer's yeast to determine the concentration of the yeast. However, because brewer's yeast can grow and change at different times, it is difficult to determine whether the yeast that gathered at the previous time point has left the cluster or whether new yeast has joined the cluster. This makes it difficult to determine the impact of changes in the yeast. Summary of the Invention

[0004] The purpose of the present invention is to provide a brewer's yeast preservation method based on big data to solve the above-mentioned problems existing in the prior art.

[0005] Acquire a beer yeast image set in multiple storage modes; the beer yeast image set includes beer yeast images at multiple time points; the beer yeast images include multiple sealed beer yeast images;

[0006] Based on the beer yeast image, detecting the position of the beer yeast to obtain a beer yeast binary image;

[0007] Clustering is performed based on the beer yeast binary image to obtain a first yeast clustering circle and a second yeast clustering circle; the second yeast clustering circle is an inner circle of the first yeast clustering circle;

[0008] Multiple first yeast cluster circles and second yeast cluster circles are obtained corresponding to the beer yeast binary images at multiple time points;

[0009] Based on the binary images of beer yeast at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles, the seed preservation status of the beer yeast is judged to obtain a seed preservation degree value;

[0010] A collection of brewer's yeast images in various preservation methods corresponds to multiple preservation degree values;

[0011] The preservation method corresponding to the preservation level value greater than the other preservation level values ​​among the multiple preservation level values ​​is used as the brewer's yeast preservation method.

[0012] Optionally, the seed preservation degree value is determined based on the beer yeast binary images at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles to obtain the seed preservation degree value, including:

[0013] Obtaining yeast identity values ​​based on the first yeast cluster circle and the second yeast cluster circle at multiple time points;

[0014] cutting the area of ​​the first yeast cluster circle of the beer yeast binary image to obtain a first circle image; obtaining a plurality of first circle images corresponding to a plurality of time points;

[0015] cutting the area of ​​the second yeast cluster circle of the beer yeast binary image to obtain a second circle image; and obtaining a plurality of second circle images corresponding to a plurality of time points;

[0016] Based on the multiple first circle images, the multiple second circle images and the yeast identity value, a seed conservation degree value is obtained through a seed conservation discrimination network.

[0017] Optionally, obtaining yeast identity values ​​based on the first yeast clustering circle and the second yeast clustering circle at multiple time points includes:

[0018] Obtain a first time point and a second time point; the first time point is adjacent to the second time point and the first time point is earlier than the second time point;

[0019] Calculating the overlapping area of ​​the first yeast cluster circle corresponding to the first time point and the first yeast cluster circle corresponding to the second time point to obtain a first overlapping area; the first overlapping area represents the movement of beer yeast in the first yeast cluster circles corresponding to adjacent time points;

[0020] Calculating the overlapping area of ​​the second yeast cluster circle corresponding to the first time point and the second yeast cluster circle corresponding to the second time point to obtain a second overlapping area; the second overlapping area represents the movement of beer yeast in the second yeast cluster circles corresponding to adjacent time points;

[0021] Calculating the distance between the center of the first yeast cluster circle corresponding to the first time point and the center of the first yeast cluster circle corresponding to the second time point to obtain a change distance; the change distance represents the degree of change of the center point of the beer yeast cluster;

[0022] A yeast identity value is obtained based on the first overlapping area, the second overlapping area, the first yeast clustering circle, the second yeast clustering circle, and the change distance; the yeast identity value indicates the possibility that the beer yeast in the first yeast clustering circle corresponding to the second time point and the beer yeast in the first yeast clustering circle corresponding to the first time point are the same beer yeast.

[0023] Optionally, clustering is performed based on the beer yeast binary image to obtain a first yeast cluster circle and a second yeast cluster circle, including:

[0024] Obtaining a first cluster radius and a second cluster radius;

[0025] performing clustering based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point;

[0026] Taking the first yeast cluster center point as the cluster center, clustering is performed based on the beer yeast image and the second cluster radius to obtain a first yeast cluster circle.

[0027] Optionally, clustering based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point includes:

[0028] Obtain the position where the pixel value is 0 in the beer yeast image as the beer yeast position;

[0029] Taking the first cluster radius as the cluster radius, cluster the beer yeast positions to obtain the first yeast cluster center point;

[0030] The first yeast cluster circle is constructed with the center point of the first yeast cluster as the center and the radius of the first cluster as the radius.

[0031] Optionally, the seed conservation discrimination network includes a first variation neural network, a second variation neural network and a yeast position influence structure.

[0032] Optionally, obtaining the seed preservation degree value based on the plurality of first circle images, the plurality of second circle images and the yeast identity degree value through a seed preservation discrimination network includes:

[0033] Inputting the first circular images at two adjacent time points into a first variation neural network to obtain a first variation feature; the first variation feature represents a change in the beer yeast in the first circular image as time points change;

[0034] Inputting the second circular images at two adjacent time points into a second variation neural network to obtain a second variation feature; the second variation feature represents a change in the beer yeast in the second circular image as time points change;

[0035] Store the yeast identical degree value in the yeast position influence structure;

[0036] inputting the first change feature and the second change feature into a yeast position influence structure to obtain a discriminant feature;

[0037] The discriminant features corresponding to multiple time points are input into the temporal neural network to obtain the seed preservation degree value.

[0038] Optionally, the step of inputting the first change feature and the second change feature into the yeast position influence structure to obtain a discriminant feature includes:

[0039] multiplying each value in the first variation feature by the yeast identity value in the yeast position influence structure to obtain a first influence variation feature;

[0040] multiplying each value in the second variation feature by the product of the yeast identity value in the yeast position influence structure and multiplying by 2 to obtain a second influence variation feature;

[0041] The first influencing change feature and the second influencing change feature are input into the fusion network to perform feature fusion to obtain the discriminant feature.

[0042] Optionally, detecting the position of the beer yeast based on the beer yeast image to obtain the beer yeast binary image includes:

[0043] Inputting the beer yeast image into a beer yeast detection network to obtain beer yeast position features;

[0044] obtaining a background image; wherein the background image represents an image without brewer's yeast;

[0045] Inputting the background image into a beer yeast contrast network to obtain background features;

[0046] The beer yeast position features and background features are input into a reconstruction network, the value of the beer yeast position is set to 1 and the value of the position other than the beer yeast is set to 0, to obtain a beer yeast binary image.

[0047] Optionally, obtaining the yeast identity value according to the first overlapping area, the second overlapping area, the first yeast clustering circle, the second yeast clustering circle and the change distance includes:

[0048] Dividing the first overlapping area by the area of ​​the first yeast cluster circle to obtain a first movement value;

[0049] Dividing the second overlapping area by the area of ​​the second yeast cluster circle to obtain a second movement value;

[0050] Dividing the change distance by the radius of the first yeast clustering circle to obtain a third movement value;

[0051] The product of the first movement value multiplied by the second movement value is multiplied by the third movement value to obtain the yeast identity value.

[0052] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects:

[0053] The embodiment of the present invention also provides a brewer's yeast preservation method based on big data.

[0054] In the present invention, clustering is used to obtain the positions of clustered brewer's yeast, and corresponding first and second yeast clustering circles are obtained using different first and second clustering radii. Clustering characteristics show that the density of brewer's yeast in the second yeast clustering circle is greater than that in the first yeast clustering circle. Furthermore, a seed preservation discrimination network is used to jointly determine the relationship between brewer's yeast and the clustered population using concentric circles, enabling an overall determination of the state, amount of growth, and location of the brewer's yeast. This allows for more accurate determination of the state, amount of growth, and changes in the location of growth of the clustered brewer's yeast at multiple time points. Furthermore, based on the multiple seed preservation values ​​corresponding to the beer yeast image collections obtained for various preservation methods, the preservation method with the highest seed preservation value, i.e., a large amount of growth, an evenly distributed location of growth, and a good state of the beer yeast, is selected as the brewer's yeast preservation method. This achieves the technical effect of finding a more accurate and better method for preserving brewer's yeast. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 This is a flow chart of a big data-based brewer's yeast preservation method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The present invention will be described in detail below with reference to the accompanying drawings.

[0057] Example 1

[0058] like Figure 1 As shown, an embodiment of the present invention provides a method for preserving beer yeast based on big data, the method comprising:

[0059] S101: Acquire a beer yeast image set in multiple storage modes; the beer yeast image set includes beer yeast images at multiple time points; the beer yeast images include multiple sealed beer yeast images.

[0060] Among them, in this embodiment, the storage methods are three storage methods: paraffin sealing, liquid tube storage, and inclined surface storage.

[0061] S102: Based on the beer yeast image, the position of the beer yeast is detected to obtain a beer yeast binary image.

[0062] The beer yeast binary image is a binary image, wherein a value of 1 indicates that the image is beer yeast, and a value of 0 indicates that the image is not beer yeast.

[0063] S103: Clustering is performed based on the beer yeast binary image to obtain a first yeast clustering circle and a second yeast clustering circle; the second yeast clustering circle is an inner circle of the first yeast clustering circle.

[0064] S104: Obtaining a plurality of first yeast cluster circles and a plurality of second yeast cluster circles corresponding to the beer yeast binary images at a plurality of time points;

[0065] S105: Based on the binary images of the beer yeast at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles, the seed preservation status of the beer yeast is determined to obtain a seed preservation degree value.

[0066] During the storage of brewer's yeast, improper storage can lead to contamination, degeneration, or even mutation. After storage, when the yeast is needed again, the temperature and humidity are adjusted to allow the yeast to grow again. The seed preservation level is determined by the degree of change in the amount and location of yeast growth at multiple time points, as well as the degree to which the aggregated yeast affects contamination, degeneration, or even mutation.

[0067] S106: Obtaining multiple seed preservation degree values ​​corresponding to the brewer's yeast image sets in multiple preservation modes.

[0068] A beer yeast image set of a preservation method corresponds to a seed preservation degree value.

[0069] S107: Using a preservation method corresponding to a preservation level value greater than the other preservation level values ​​among the multiple preservation level values ​​as a brewer's yeast preservation method.

[0070] Optionally, the seed preservation degree value is determined based on the beer yeast binary images at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles to obtain the seed preservation degree value, including:

[0071] Obtaining yeast identity values ​​based on the first yeast cluster circle and the second yeast cluster circle at multiple time points;

[0072] The region of the first yeast cluster circle of the beer yeast binary image is cut to obtain a first circle image; a plurality of first circle images are obtained corresponding to a plurality of time points,

[0073] cutting the area of ​​the second yeast cluster circle of the beer yeast binary image to obtain a second circle image; and obtaining a plurality of second circle images corresponding to a plurality of time points;

[0074] Based on the multiple first circle images, the multiple second circle images and the yeast identity value, a seed conservation degree value is obtained through a seed conservation discrimination network.

[0075] Optionally, obtaining yeast identity values ​​based on the first yeast clustering circle and the second yeast clustering circle at multiple time points includes:

[0076] Obtain a first time point and a second time point; the first time point is adjacent to the second time point and the first time point is earlier than the second time point;

[0077] The overlapping area of ​​the first yeast cluster circle corresponding to the first time point and the first yeast cluster circle corresponding to the second time point is calculated to obtain a first overlapping area; the first overlapping area represents the movement of beer yeast in the first yeast cluster circles corresponding to adjacent time points.

[0078] The overlapping area is calculated according to the center and radius of the first yeast cluster circle corresponding to the first time point and the first yeast cluster circle corresponding to the second time point.

[0079] The overlapping area of ​​the second yeast cluster circle corresponding to the first time point and the second yeast cluster circle corresponding to the second time point is calculated to obtain a second overlapping area; the second overlapping area represents the movement of beer yeast in the second yeast cluster circles corresponding to adjacent time points.

[0080] Calculating the distance between the center of the first yeast cluster circle corresponding to the first time point and the center of the first yeast cluster circle corresponding to the second time point to obtain a change distance; the change distance represents the degree of change of the center point of the beer yeast cluster;

[0081] A yeast identity value is obtained based on the first overlap area, the second overlap area, and the change distance. The yeast identity value indicates the likelihood that the brewer's yeast in the first yeast cluster circle corresponding to the second time point is the same brewer's yeast as the brewer's yeast in the first yeast cluster circle corresponding to the first time point.

[0082] Optionally, clustering is performed based on the beer yeast binary image to obtain a first yeast cluster circle and a second yeast cluster circle, including:

[0083] Get the first cluster radius and the second cluster radius.

[0084] In this embodiment, the first clustering radius is 16, and the second clustering radius is 8.

[0085] performing clustering based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point;

[0086] Taking the first yeast cluster center point as the cluster center, clustering is performed based on the beer yeast image and the second cluster radius to obtain a first yeast cluster circle.

[0087] Optionally, clustering based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point includes:

[0088] Obtain the position where the pixel value is 0 in the beer yeast image as the beer yeast position;

[0089] The first cluster radius is used as the cluster radius, and the positions of beer yeast are clustered to obtain the center point of the first yeast cluster.

[0090] In this embodiment, k-means is used for clustering.

[0091] The first yeast cluster circle is constructed with the center point of the first yeast cluster as the center and the radius of the first cluster as the radius.

[0092] Optionally, the seed conservation discrimination network includes a first variation neural network, a second variation neural network and a yeast position influence structure.

[0093] Optionally, obtaining the seed preservation degree value based on the plurality of first circle images, the plurality of second circle images and the yeast identity degree value through a seed preservation discrimination network includes:

[0094] The first circular images at two adjacent time points are input into a first variation neural network to obtain a first variation feature; the first variation feature represents the variation of the beer yeast in the first circular image as time points change.

[0095] The first variation neural network is a convolutional neural network (CNN) trained using the number of brewer's yeast added in the first circle image at two adjacent time points as labeled data.

[0096] The second circular images at two adjacent time points are input into a second variation neural network to obtain a second variation feature; the second variation feature represents the variation of the beer yeast in the second circular image as time points change.

[0097] The first variation neural network is a convolutional neural network (CNN) trained using the number of increased brewer's yeast in the second circle images at two adjacent time points as labeled data.

[0098] Store the yeast identity value in the yeast position influence structure.

[0099] The first change feature and the second change feature are input into the yeast position influence structure to obtain a discriminant feature.

[0100] The discriminant feature represents a feature that combines the changes in the state and quantity of yeast in the first circle image and the second circle image.

[0101] The discriminant features corresponding to multiple time points are input into the temporal neural network to obtain the seed preservation degree value.

[0102] In this embodiment, the temporal neural network (TCN) is trained using the labeled states and quantities of brewer's yeast.

[0103] The status of the brewer's yeast includes good quality and poor quality.

[0104] Optionally, the step of inputting the first change feature and the second change feature into the yeast position influence structure to obtain a discriminant feature includes:

[0105] multiplying each value in the first variation feature by the yeast identity value in the yeast position influence structure to obtain a first influence variation feature;

[0106] Each value in the second variation feature is multiplied by the product of the yeast identity value in the yeast position influence structure and multiplied by 2 to obtain the second influence variation feature.

[0107] By using the above method, it is possible to determine the characteristics of the change in the state of the beer yeast clustered in the first yeast cluster circle from good to poor quality and the change in the number of the beer yeast clustered in the first yeast cluster circle.

[0108] The first influencing change feature and the second influencing change feature are input into the fusion network to perform feature fusion to obtain the discriminant feature.

[0109] In this embodiment, the fusion network is a fully connected neural network (FCNN).

[0110] Optionally, detecting the position of the beer yeast based on the beer yeast image to obtain the beer yeast binary image includes:

[0111] Inputting the beer yeast image into a beer yeast detection network to obtain beer yeast position features;

[0112] The beer yeast detection network is trained using the marked positions of the beer yeast, so that the beer yeast detection network can detect the position of the beer yeast in the beer yeast image.

[0113] In this embodiment, the brewer's yeast detection network is a convolutional neural network (CNN).

[0114] A background image is obtained; the background image represents an image without brewer's yeast.

[0115] The background image and the beer yeast image are images acquired by a device at the same location.

[0116] The background image is input into a beer yeast contrast network to obtain background features.

[0117] In this embodiment, the background image is a convolutional neural network (CNN).

[0118] The beer yeast position features and background features are input into a reconstruction network, the value of the beer yeast position is set to 1 and the value of the position other than the beer yeast is set to 0, to obtain a beer yeast binary image.

[0119] In this embodiment, the reconstruction network is a convolutional neural network (CNN) that performs convolution with a deconvolution kernel.

[0120] The reconstruction network is trained using a binary image in which the value of the position of the marked beer yeast is set to 1 and the values ​​of other positions are set to 0.

[0121] Optionally, obtaining the yeast identity value according to the first overlapping area, the second overlapping area, the first yeast clustering circle, the second yeast clustering circle and the change distance includes:

[0122] The first overlap area is divided by the area of ​​the first yeast cluster circle to obtain a first movement value.

[0123] The first movement value indicates the possibility that the beer yeasts judged at two adjacent times are the same beer yeast, represented by the first overlapping area.

[0124] The second overlap area is divided by the area of ​​the second yeast cluster circle to obtain a second movement value.

[0125] The first movement value indicates the possibility that the beer yeast determined at two adjacent times are the same beer yeast, represented by the second overlapping area.

[0126] The change distance is divided by the radius of the first yeast clustering circle to obtain a third movement value.

[0127] The first movement value indicates the possibility that the beer yeast determined at two adjacent times are the same beer yeast, represented by a change distance.

[0128] The product of the first movement value multiplied by the second movement value is multiplied by the third movement value to obtain the yeast identity value.

[0129] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

Claims

1. A brewer's yeast preservation method based on big data, characterized in that: include: Acquire a beer yeast image set in multiple storage modes; the beer yeast image set includes beer yeast images at multiple time points; the beer yeast images include multiple sealed beer yeast images; Based on the beer yeast image, detecting the position of the beer yeast to obtain a beer yeast binary image; Clustering is performed based on the beer yeast binary image to obtain a first yeast clustering circle and a second yeast clustering circle; the second yeast clustering circle is an inner circle of the first yeast clustering circle; Multiple first yeast cluster circles and second yeast cluster circles are obtained corresponding to the beer yeast binary images at multiple time points; The seed preservation degree value is based on the beer yeast binary images at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles to judge the beer yeast seed preservation status and obtain the seed preservation degree value; A collection of brewer's yeast images in various preservation methods corresponds to multiple preservation degree values; The preservation method corresponding to the preservation level value greater than the other preservation level values ​​among the multiple preservation level values ​​is used as the brewer's yeast preservation method.

2. The method for preserving brewer's yeast based on big data according to claim 1, characterized in that: The seed preservation degree value is obtained by judging the seed preservation status of the beer yeast based on the beer yeast binary images at multiple time points and the corresponding first yeast cluster circles and second yeast cluster circles, including: Obtaining yeast identity values ​​based on the first yeast cluster circle and the second yeast cluster circle at multiple time points; cutting the area of ​​the first yeast cluster circle of the beer yeast binary image to obtain a first circle image; obtaining a plurality of first circle images corresponding to a plurality of time points; cutting the area of ​​the second yeast cluster circle of the beer yeast binary image to obtain a second circle image; and obtaining a plurality of second circle images corresponding to a plurality of time points; Based on the multiple first circle images, the multiple second circle images and the yeast identity value, a seed conservation degree value is obtained through a seed conservation discrimination network.

3. The brewer's yeast preservation method based on big data according to claim 1, characterized in that: The obtaining of yeast identity values ​​based on the first yeast clustering circle and the second yeast clustering circle at multiple time points includes: Obtain a first time point and a second time point; the first time point is adjacent to the second time point and the first time point is earlier than the second time point; Calculating the overlapping area of ​​the first yeast cluster circle corresponding to the first time point and the first yeast cluster circle corresponding to the second time point to obtain a first overlapping area; the first overlapping area represents the movement of beer yeast in the first yeast cluster circles corresponding to adjacent time points; Calculating the overlapping area of ​​the second yeast cluster circle corresponding to the first time point and the second yeast cluster circle corresponding to the second time point to obtain a second overlapping area; the second overlapping area represents the movement of beer yeast in the second yeast cluster circles corresponding to adjacent time points; Calculating the distance between the center of the first yeast cluster circle corresponding to the first time point and the center of the first yeast cluster circle corresponding to the second time point to obtain a change distance; the change distance represents the degree of change of the center point of the beer yeast cluster; A yeast identity value is obtained based on the first overlapping area, the second overlapping area, the first yeast clustering circle, the second yeast clustering circle, and the change distance; the yeast identity value indicates the possibility that the beer yeast in the first yeast clustering circle corresponding to the second time point and the beer yeast in the first yeast clustering circle corresponding to the first time point are the same beer yeast.

4. The method for preserving brewer's yeast based on big data according to claim 1, characterized in that: The method of clustering the beer yeast binary image to obtain a first yeast cluster circle and a second yeast cluster circle includes: Obtaining a first cluster radius and a second cluster radius; performing clustering based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point; Taking the first yeast cluster center point as the cluster center, clustering is performed based on the beer yeast image and the second cluster radius to obtain a first yeast cluster circle.

5. The method for preserving brewer's yeast based on big data according to claim 3, characterized in that: The clustering is performed based on the beer yeast image and the first cluster radius to obtain a first yeast cluster circle and a first yeast cluster center point, comprising: Obtain the position where the pixel value is 0 in the beer yeast image as the beer yeast position; Taking the first cluster radius as the cluster radius, cluster the beer yeast positions to obtain the first yeast cluster center point; The first yeast cluster circle is constructed with the center point of the first yeast cluster as the center and the radius of the first cluster as the radius.

6. The method for preserving brewer's yeast based on big data according to claim 2, characterized in that: The seed conservation discrimination network includes a first variation neural network, a second variation neural network and a yeast position influence structure.

7. The method for preserving brewer's yeast based on big data according to claim 6, characterized in that: The method of obtaining a seed preservation degree value based on the plurality of first circle images, the plurality of second circle images and the yeast identical degree value through a seed preservation discrimination network includes: Inputting the first circular images at two adjacent time points into a first variation neural network to obtain a first variation feature; the first variation feature represents a change in the beer yeast in the first circular image as time points change; Inputting the second circular images at two adjacent time points into a second variation neural network to obtain a second variation feature; the second variation feature represents a change in the beer yeast in the second circular image as time points change; Store the yeast identical degree value in the yeast position influence structure; inputting the first change feature and the second change feature into a yeast position influence structure to obtain a discriminant feature; The discriminant features corresponding to multiple time points are input into the temporal neural network to obtain the seed preservation degree value.

8. The method for preserving brewer's yeast based on big data according to claim 7, characterized in that: The step of inputting the first and second variation characteristics into the yeast position influence structure to obtain a discriminant characteristic comprises: multiplying each value in the first variation feature by the yeast identity value in the yeast position influence structure to obtain a first influence variation feature; multiplying each value in the second variation feature by the product of the yeast identity value in the yeast position influence structure and multiplying by 2 to obtain a second influence variation feature; The first influencing change feature and the second influencing change feature are input into the fusion network to perform feature fusion to obtain the discriminant feature.

9. The method for preserving brewer's yeast based on big data according to claim 1, characterized in that: The method of detecting the position of the beer yeast based on the beer yeast image to obtain the beer yeast binary image includes: Inputting the beer yeast image into a beer yeast detection network to obtain beer yeast position features; obtaining a background image; wherein the background image represents an image without brewer's yeast; Inputting the background image into a beer yeast contrast network to obtain background features; The beer yeast position features and background features are input into a reconstruction network, the value of the beer yeast position is set to 1 and the value of the position other than the beer yeast is set to 0, to obtain a beer yeast binary image.

10. The method for preserving brewer's yeast based on big data according to claim 3, characterized in that: Obtaining a yeast identity value according to the first overlapping area, the second overlapping area, the first yeast clustering circle, the second yeast clustering circle, and the change distance includes: Dividing the first overlapping area by the area of ​​the first yeast cluster circle to obtain a first movement value; Dividing the second overlapping area by the area of ​​the second yeast cluster circle to obtain a second movement value; Dividing the change distance by the radius of the first yeast clustering circle to obtain a third movement value; The product of the first movement value multiplied by the second movement value is multiplied by the third movement value to obtain the yeast identity value.

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