Patent ID: 11897648
Assignee: JIANGSU UNIVERSITY
Field: Handling (Mechanical engineering)
Classification: CPC B  A  G | IPC B

Claim 4:
5. The adaptive quantitative sub-packaging method for the fried rice with the multiple side dishes in the central kitchen according to claim 1, wherein a method for self-optimizing the optimal band λ_A and the optimal segmentation threshold C for key side dish recognition in the step I comprises: for images I_A&H_i_h_j of w fried rice samples A&H_i, images I_A-H_i_h_j of w fried rice samples A-H_i, and images I_O_h_j of w empty material boxes under a λh light source of an h-th band, generating a histogram of the images I_A&H_i_h_j respectively to obtain a peak gray value K_h_j corresponding to a peak value of the histogram, and calculating an average value K_h_mean of w peak gray values K_h_j;
according to a downlink optimization step ΔK_h− and an uplink optimization step ΔK_h+ of an h-th band segmentation threshold, determining p downlink optimization thresholds (K_h_mean)−c*ΔK_h− of the segmentation threshold in a downlink optimization direction and q uplink optimization thresholds (K_h_mean)+d*Δ K_h+ in an uplink optimization direction; obtaining p*q combined optimization thresholds C_λh_c_d=((K_h_mean)−c*ΔK_h−, (K_h_mean)+d*ΔK_h+) by taking any one downlink optimization threshold and any one uplink optimization threshold, wherein (K_h_mean)−c*ΔK_h− is used as a lower threshold for image processing, and (K_h_mean)+d*ΔK_h+ is used as an upper threshold for image processing; and sequentially processing, by the central control module, the images I_A&H_i_h_j of w fried rice samples A&H_i, the images I_A-H_i_h_j of w fried rice samples A-H_i, and the images I_O_h_j of the w empty material boxes according to the optimization threshold C_λh_c_d to obtain the recognition number of the key side dishes corresponding to each of the material boxes, comparing the recognition number of the key side dishes corresponding to each of the material boxes with the input number of the key side dishes corresponding to each of the material boxes, and calculating a corresponding modeling recognition rate D_λh_c_d; and
calculating the corresponding modeling recognition rate D_λh_c_d sequentially by taking the value of h as 1, 2, . . . , b−1, and b sequentially; and according to a corresponding h value, c value, and d value when a maximum modeling recognition rate D_λh_c_d is obtained, determining the optimal band to be λ_A=λh, the optimal segmentation threshold to be C=C_λh_c_d, and the optimal recognition rate for modeling to be D=D_λh_c_d, wherein c∈[1, p], d∈[1, q], and c, d, p, and q are positive integers, and
a method for determining the downlink optimization step ΔK_h− and the uplink optimization step ΔK_h+ comprises: obtaining, by the central control module, a number of optimization thresholds for a downlink optimization method p=Int(L*Rand(0, 1)) and q=Int(L*Rand(0, 1)) according to a number L of maximum unidirectional thresholds by means of a random number production function Rand(0, 1) and a rounding function Int with a value range in [0 1], and calculating the downlink optimization step ΔK_h−=Int((K_h_mean)/p) and the uplink optimization step ΔK_h+=Int((255−(K_h_mean))/q) accordingly.