AI intelligent shrimp food material processing system

Abstract

The invention discloses an AI intelligent shrimp food material processing system which mainly comprises an image acquisition unit, a database unit, a label downloading unit, a shrimp head removing unit and a classification unit. Wherein the image acquisition unit acquires an image of each shrimp, and transmits each independent image to the database unit; the database unit is used for storing data such as types, sizes, sizes of shrimp heads, lengths of shrimp bodies and the like of the shrimps so as to establish an exclusive tag for each shrimp after the data are compared with the images transmitted by the image acquisition unit; the label downloading unit is used for downloading label information from the database unit; the shrimp head removing unit adjusts the position of the cutter module according to the label to separate the shrimp head from the shrimp body; and the classification unit distinguishes shrimp bodies with different sizes according to the labels. Therefore, the production of the shrimp food materials is more efficient, the consistency of the quality is ensured, the labor cost is further reduced, and pollution caused by human factors is prevented.

Description

TECHNICAL FIELD

[0001] The present application relates to an AI intelligent shrimp food material processing system, in particular to a processing system for determining shrimp species and AI intelligent processing of shrimp food materials by establishing a unique label for each shrimp through image comparison, separating shrimp heads and bodies according to the label, and distinguishing shrimp bodies of different sizes according to the label. BACKGROUND

[0002] The current method for processing shrimp food materials generally includes classification of shrimp species (farmed shrimp, wild shrimp, sea shrimp, etc.), distinction of shrimp size, and removal of shrimp heads. These procedures are usually completed by manual work. However, manual work has the following shortcomings:

[0003] 1. A large amount of manpower is required for judgment and identification based on shrimp classification and size distinction, and removal of shrimp heads, resulting in high labor costs, which relatively affects the cost and selling price of products, and cannot meet market demand.

[0004] 2. The efficiency of manual work has its limits, and cannot improve productivity and yield. If production lines are added to improve productivity, it will relatively affect the operating cost.

[0005] 3. Human judgment and identification are prone to errors, which can reduce product yield and affect product reputation.

[0006] 4. It is difficult to control a large number of manual operations, which can increase the possibility of pollution caused by human factors during the operation process, and can relatively cause food safety problems.

[0007] Of course, manufacturers also consider replacing a large amount of manpower with machines to reduce operating costs and improve productivity. However, existing machines cannot adapt to different types of shrimp, and cannot identify the size and body shape of shrimp. Therefore, existing machines cannot completely replace manual work for processing shrimp food materials. In view of the above, the applicant has continuously researched and experimented, and has conceived and designed an AI intelligent shrimp food material processing system by establishing a unique label for each shrimp and performing food material processing operations based on label information to reduce labor costs and prevent pollution caused by human factors. SUMMARY

[0008] The main purpose of the present application is to provide an AI intelligent shrimp food material processing system, which establishes a unique label for each shrimp through image comparison, separates shrimp heads and bodies according to the label, and distinguishes shrimp bodies of different sizes according to the label to achieve the purpose of AI intelligent processing of shrimp food materials, thereby reducing labor costs, improving productivity efficiency, and preventing pollution caused by human factors.

[0009] The present application provides an AI intelligent shrimp food processing system, which comprises an image acquisition unit, a database unit, a label download unit, a shrimp head removal unit and a classification unit. The image acquisition unit acquires the image of each shrimp, and transmits each independent image to the database unit; the database unit stores the data of the species, volume and shrimp head size characteristics of the shrimp, and the shrimp body length, etc., to establish a special label (tag) for each shrimp after comparing with the image transmitted by the image acquisition unit; the label download unit downloads the information of the label from the database unit; the shrimp head removal unit separates the shrimp head from the shrimp body according to the position of the cutter module adjusted by the label; and the classification unit distinguishes the shrimp bodies of different sizes according to the label. In this way, the production of shrimp food is more efficient and the quality consistency is ensured, thereby reducing the labor cost and preventing pollution caused by human factors.

[0010] The aforementioned AI intelligent shrimp food processing system, wherein the image acquisition unit comprises a 3D camera and UV lamps, infrared lamps, LED white light, yellow light lamps, and RGB full wavelength lamps, etc., which continuously photograph or record the shrimp, and transmit the image data of the photographing or recording to the database unit.

[0011] The aforementioned AI intelligent shrimp food processing system, wherein the shrimp head removal unit comprises a lower knife alignment unit and a lower knife judgment unit, and a cutter module, which adjusts the position of the cutter module according to the information of the label through the lower knife alignment and judgment unit, so as to separate the shrimp head and the shrimp body of each shrimp by cutting.

[0012] The aforementioned AI intelligent shrimp food processing system, wherein the classification unit comprises an adjustment feedback unit, which compares the shrimp body with the preset size, and feeds back the comparison result to the database unit, so as to constitute the basis for adjusting the position of the cutter module of the shrimp head removal unit. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 Configuration diagram of the AI intelligent shrimp food processing system of the present application.

[0014] Figure 2 Block diagram of the AI intelligent shrimp food processing system of the present application.

[0015] Figure 3 First operation embodiment diagram of the AI intelligent shrimp food processing system of the present application.

[0016] Figure 4 Second operation embodiment diagram of the AI intelligent shrimp food processing system of the present application.

[0017] Figure 5 Third operation embodiment diagram of the AI intelligent shrimp food processing system of the present application.

[0018] Figure 6 This is a diagram of the fourth operational embodiment of the present invention applied to the AI ​​intelligent shrimp food processing system.

[0019] Figure 6A This is a flowchart of the cutting steps of the shrimp head removal unit of the present invention.

[0020] Figure 7 This is a diagram of the fifth operational embodiment of the present invention applied to the AI ​​intelligent shrimp food processing system.

[0021] Figure 7A This is a flowchart of the classification steps in the classification unit of the AI ​​intelligent shrimp food processing system of the present invention.

[0022] Figure labeling: 1-Image acquisition unit; 11-3D photography module; 12-UV lamp; 13-Infrared lamp; 14-Image recording unit; 2-Database unit; 3-Tag download unit; 4-Shrimp head removal unit; 41-Knife alignment unit; 42-Knife judgment unit; 43-Knife module; 44-Determine knife position; 45-Knife action; 46-Knife movement; 5-Classification unit; 51-Load data; 52-Determine weight; 53-Determine shrimp size; 54-Determine shrimp length; 55-Adjustment feedback unit; 6-Conveyor belt; 7-Shrimp. Detailed Implementation

[0023] Please also refer to Figure 1 and Figure 2 This is a configuration diagram and block diagram of the AI ​​intelligent shrimp food processing system of the present invention. As shown in the figure, the present invention includes an image capturing unit 1, a database unit 2, a tag downloading unit 3, a shrimp head removal unit 4, a classification unit 5, and other units. The image capturing unit 1 is used for continuous photography or video recording of shrimp, and transmits each independent image data from the photography or video recording to the database unit 2. In this embodiment, the image capturing unit 1 includes a 3D photography module 11 for capturing images, and UV lamps 12, infrared lamps 13, or LED white light, yellow light, and RGB full-wavelength lamps for auxiliary lighting. The image capturing unit 1 is connected to an image recording unit 14 for recording the photographed or video recorded image data.

[0024] The database unit 2 is connected to the image capturing unit 1 to store data such as the type, size, head size, body length, head-to-body ratio, and head and body color of the shrimp. This data is used to create a unique tag for each shrimp after comparison with the image transmitted by the image capturing unit 1.

[0025] The tag download unit 3 is connected to the database unit 2 to create a unique tag for each shrimp after the downloaded image is compared.

[0026] The shrimp head removing unit 4 is connected to the database unit 2 and the label downloading unit 3, and includes a lower cutter alignment unit 41 and a lower cutter judgment unit 42, and a cutter module 43 which can form upper and lower cutting actions. The lower cutter alignment unit 41 and the lower cutter judgment unit 42 adjust the position of the cutter module 43 according to the information of the label of the database unit 2, so that the lower cutter separates the shrimp head and the shrimp body of the individual shrimp. The separated shrimp body is continuously transported to the next operation process. In the embodiment, the shrimp head removing unit 4 includes a real-time recording unit 44 for recording the lower cutting action state of the cutter module 43 in real time.

[0027] The classification unit 5 is connected to the shrimp head removing unit 4 and the database unit 2, so that the shrimp body is classified into large shrimp and small shrimp according to the information of the label of the database unit 2. In the embodiment, the classification unit 5 includes an adjustment feedback unit 55 which compares the shrimp body with the preset size and feeds back the comparison result to the database unit 2, so as to form the basis for adjusting the position of the cutter module 43 of the shrimp head removing unit 4.

[0028] Please refer to Figures 3 to 6 for the operation embodiment of the application applied to shrimp food material preparation. As shown in the figure, the application is applied to shrimp food material preparation. The application sets the image capturing unit 1, the database unit 2, the label downloading unit 3, the shrimp head removing unit 4, the classification unit 5 and other units in connection with each other on a conveying belt 6 to form a shrimp food production line. The operation includes the following steps:

[0029] 1. The thawed shrimp 7 is arranged in order on the conveying belt 6 and is transported to the image capturing unit 1 (see Figure 3 ).

[0030] 2. The image capturing unit 1 continuously photographs or records the shrimp 7, and transmits the independent image data of each shrimp 7 to the database unit 2 for comparison with the data of the shrimp 7 stored in the database unit 2, such as the species, volume, head size, body length, head and body proportion, head and body color and the like, so as to establish the exclusive label of each shrimp 7 (see Figure 4 ).

[0031] 3. The label downloading unit 3 downloads the exclusive label of each shrimp 7 established after comparison from the database unit 2 (see Figure 5 ).

[0032] 4. The shrimp head removing unit 4 adjusts the position of the cutter module 43 according to the information of the label of the database unit 2, so that the lower cutter separates the shrimp head and the shrimp body of the individual shrimp 7. The shelled shrimp body is peeled by a shelling machine to complete the production operation of shrimp food (see Figure 6 ). The lower cutter is as followsFigure 6A As shown, at least the following steps are included:

[0033] a. Determine the falling knife position 44, according to different shrimp body shape and area, length, size, plus photo comparison, determine the falling knife position of shrimp head.

[0034] b. Knife action 45, the tool of fixed knife and knife act simultaneously.

[0035] c. Knife 46, by the knife simulator master's hands, arc down, the shrimp head and shrimp body of shrimp 7 are separated.

[0036] 5. According to the information of the label of the database unit 2, the shrimp body is divided into large and small shrimp by the classification unit 5 (see Figure 7 ). The classification is shown in Figure 7A , at least the following steps are included:

[0037] a. Load data 51, the standard data of the shrimp species is loaded by the database unit 15 to improve the accuracy of comparison.

[0038] b. Discriminate weight 52, discriminate shrimp weight by real-time weighing device.

[0039] c. Discriminate shrimp area size 53.

[0040] d. Discriminate shrimp length 54

[0041] 6. By adjusting the feedback unit 55, the shrimp body is compared with the preset size, and the comparison result is fed back to the database unit 2, so as to adjust the correct position of the knife module 43 of the shrimp head removing unit 4. In this way, the production of shrimp food is more efficient and the quality consistency is ensured, thereby reducing labor cost and preventing pollution caused by human factors.

[0042] The foregoing embodiments are only for illustrating the preferred embodiments of the present application, not for limiting the scope of the present application. Any slight modification or change does not deviate from the spirit and scope of the present application.

[0043] In summary, the present application comprises image acquisition unit, database unit and label download unit, shrimp head removing unit, classification unit and other units, which constitute an AI intelligent shrimp food processing system. Through image comparison, a special label is established for each shrimp, and the shrimp head and shrimp body are separated according to the label, and different sizes of shrimp body are distinguished according to the label, so as to achieve the purpose of AI intelligent processing of shrimp food, thereby reducing labor cost, improving production efficiency and preventing pollution caused by human factors.

Claims

1. An AI intelligent shrimp food material processing system, characterized in that, Comprising: a database unit for storing data of shrimp species, volume, head size, body length, and head-to-body ratio, head and body color; an image capturing unit connected to the database unit for continuously photographing or filming the shrimp and transmitting each independent image data to the database unit for comparison with the transmitted image to establish a unique tag for each shrimp; a tag downloading unit connected to the database unit for downloading the unique tag established for each shrimp after image comparison; a head removal unit connected to the database unit and the tag downloading unit, comprising a cutter module capable of performing upper and lower cutting actions, and separating the head and body of the shrimp according to the tag information from the database unit; a classification unit connected to the head removal unit and the database unit for classifying the shrimp body into large and small shrimp according to the tag information from the database unit.

2. The smart shrimp food material processing system of claim 1, wherein, The image capturing unit comprises a 3D camera module for capturing images, and UV and infrared lamps for auxiliary lighting.

3. The smart shrimp food material processing system of claim 1, wherein, The image capturing unit is connected to an image recording unit for recording image data.

4. The smart shrimp food material processing system of claim 1, wherein, The head removal unit comprises a lower cutter alignment unit and a lower cutter judgment unit for adjusting the position of the cutter module according to the tag information from the database unit.

5. The smart shrimp food material processing system of claim 1, wherein, The classification unit comprises an adjustment feedback unit for comparing the shrimp body with the removed head with preset size and dimension values, and feeding back the comparison results to the database unit to adjust the position of the cutter module of the head removal unit.

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