Method for detecting and analyzing the traits of sea shrimp based on vector graph digitization

Abstract

The detection and analysis method of sea shrimp traits based on vector graph digitization comprises the following steps: S1, a detection and analysis system adopts a geometric scanning form to take a photo of a sea shrimp to be detected, and a vector graph composed of lines and tracks is obtained; S2, the obtained vector graph is screened and verified, and vector graphs with large repetition, large coincidence degree or large deviation are removed; S3, the remaining vector graphs are stored in a set, and a basic data set is formed; S4, the basic data set is subjected to digitization translation processing, and sea shrimp traits are evaluated and judged according to digitized data and specific color elements of the sea shrimp; and S5, the evaluation and judgment result of the sea shrimp traits is transmitted to a user host computer, and remote detection and analysis of the sea shrimp traits is realized.

Description

Technical fields:

[0001] This invention relates to a method for detecting and analyzing the traits of shrimp based on vector graphic digitization. Background technology:

[0002] Sea shrimp, also known as red shrimp, red prawn, and large green shrimp, is a general term for the meat or whole body of aquatic and marine shrimp. Sea shrimp is rich in nutrients and has a delicious taste. It can be used in cooking and as a medicinal material. Sea shrimp is mainly distributed in the Yellow Sea, Bohai Sea and sea areas north of the Yangtze River estuary, and is a specialty of China.

[0003] The shrimp is elongated and laterally compressed. Females are about 18–24 cm long, while males are slightly shorter. Their bodies are transparent; females are brownish-blue, while males are slightly yellowish. The entire body is covered in a carapace. The thorax is relatively hard and broad, with a long, pointed rostrum extending from the center of the frontal region. The upper edge has 7–9 teeth, and the lower edge has 3–5 teeth. Below the rostrum, on either side, are a pair of stalked eyes. The head has five pairs of appendages; the first and second pairs form two pairs of whip-like antennae, with the second pair being very long. There are three other pairs of legs, forming one pair of mandibles and two pairs of maxillae, which are part of the mouthparts. The thorax has eight pairs of appendages, three pairs of which are pedipalps, part of the mouthparts, and five pairs of walking legs. The first three pairs of walking legs have pincer-like ends, with the third pair being the longest. The last two pairs end in claws; the abdomen has 7 segments, clearly segmented and flexible; there are 6 pairs of abdominal appendages; the first pair of female endopods is extremely small, while in males they become genitalia; the sixth pair is the caudal appendage, short and stout, which merges with the seventh abdominal caudal segment to form the caudal fin.

[0004] To select superior products from a large quantity of shrimp, it is necessary to test and compare the characteristics of the shrimp to obtain the shrimp products required by users. Generally, the relevant indicators that need to be tested for shrimp include shrimp shape, size, and specific color. However, most existing testing and analysis methods involve collecting images of shrimp, reading and recognizing information from the images, and then analyzing the characteristics of the shrimp to obtain superior shrimp products. Since the images are mostly taken directly by cameras, when there are many shrimp, there is an inevitable phenomenon of blurry images or overlap, which can lead to defective shrimp products during the testing process. Furthermore, the images are analyzed and judged subjectively by staff, lacking a unified quantitative judgment standard, thus failing to accurately analyze and judge the characteristics of the shrimp and obtain the required superior shrimp products. Summary of the Invention:

[0005] This invention provides a method for detecting and analyzing the traits of shrimp based on vector graphics digitization. The method is rationally designed, and through the combined action of multiple vector graphics calculation methods, it digitizes the acquired shrimp vector graphics according to predetermined calculation steps and information transmission directions, ensuring that the collected image information can be accurately and effectively identified. At the same time, it sets a unified evaluation standard, eliminating reliance on subjective judgment by staff, and can accurately identify and judge each type of shrimp product. This allows for the selection of shrimp products with superior traits from a large number of shrimp and the screening out of unqualified shrimp products, thus solving the problems existing in the prior art.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] A method for detecting and analyzing the traits of shrimp based on vector graphics digitization, the method comprising the following steps:

[0008] S1, the detection and analysis system uses geometric scanning to photograph the shrimp to be detected, and obtains a vector graphic composed of lines and trajectories;

[0009] S2, filter and verify the obtained vector graphics, and remove vector graphics that are duplicated, have a high degree of overlap or have large deviations;

[0010] S3, the remaining vector graphics are summarized and stored to form the basic dataset;

[0011] S4, digitizes the basic dataset, and evaluates and judges the characteristics of the shrimp based on the digitized data and the specific color elements of the shrimp.

[0012] S5 transmits the evaluation results of shrimp traits to the user's host computer, enabling remote detection and analysis of shrimp traits.

[0013] The detection and analysis system uses geometric scanning to photograph the shrimp to be detected, obtaining a vector graphic composed of lines and trajectories. The steps include:

[0014] S1.1, Set the scanning area, divide the scanning area into equal parts, and obtain multiple equally divided discrimination partitions;

[0015] S1.2, each discrimination partition is marked and numbered, and multiple standard coordinate points are set on the discrimination partition at the center position;

[0016] S1.3, the geometric trajectory of the shrimp to be detected is placed in the scanning and imaging area close to the standard coordinate point, thereby obtaining the standard vector image of the shrimp.

[0017] The process of filtering and verifying the obtained vector graphics, removing duplicates, those with high overlap, or those with large deviations, includes the following steps:

[0018] S2.1, set the standard area percentage of the standard vector diagram corresponding to the superior traits of the shrimp in each discrimination partition;

[0019] S2.2, compare the actual area ratio of the standard shrimp vector map to the standard area ratio in each discrimination partition;

[0020] S2.3, set correction parameters, establish a linear regression equation based on the relative difference between the actual area ratio and the standard area ratio of all shrimp products, and quickly remove vector graphics with duplication, high overlap or large deviation based on the linear regression trend.

[0021] The basic dataset undergoes digital translation processing. The evaluation and judgment of shrimp traits based on the digitized data and the specific color elements of the shrimp include the following steps:

[0022] S4.1 Define vector graphics files within the basic dataset, and convert the vector graphics files into plaintext format data based on the translation function. The plaintext format data includes punctuation files, trajectory files, and region files.

[0023] S4.2, combine plaintext data with different positions in the vector graphic file, and extract the plaintext data according to certain subdivision criteria;

[0024] S4.3 uses a correction function to quantitatively describe and analyze the uncertainty of corrected plaintext format data in order to achieve digital translation processing of vector graphics.

[0025] Define the vector graphics files within the base dataset, and convert the vector graphics into plaintext data based on the translation function, including the following steps:

[0026] S4.1.1, Establish a polar coordinate system associated with the trajectory of the vector graphic file;

[0027] S4.1.2, generalize the trajectory and curvature changes of the vector graphic file, and read the corresponding change coordinate parameters;

[0028] S4.1.3 converts vector graphics files into plaintext format.

[0029] Combining plaintext data with different locations in a vector graphic file and extracting the plaintext data according to certain subdivision criteria includes the following steps:

[0030] S4.2.1, Set the subdivision parameters for two intersecting directions;

[0031] S4.2.2, set the angle between the two intersecting directions and the polar coordinate system accordingly;

[0032] S4.2.3, the vector graphic file is meshed by discrimination partitioning, and plaintext data is extracted by combining subdivision parameters and standard coordinate points.

[0033] Using correction functions to quantitatively describe and analyze the uncertainty of corrected plaintext format data includes the following steps:

[0034] S4.3.1, group plaintext data into multiple arrays;

[0035] S4.3.2, Calculate the corresponding probability interval to determine the uncertainty of the array;

[0036] S4.3.3 outputs relevant uncertain data in a digital format.

[0037] The detection and analysis system includes a controller, on which an instruction input device, a wireless transceiver, and a driver are connected. The instruction input device is used to transmit control instructions to the controller for shrimp morphology detection. The wireless transceiver is used to establish remote communication between the controller and the user's host computer. The driver is used to drive the camera to perform geometric scanning and imaging.

[0038] The controller is model RK3399 and has multiple pins. The controller is connected to the command input device through pin 4, the controller is connected to the wireless transceiver through pins 20 and 21, and the controller is connected to the driver through pin 38.

[0039] The command input device is model TLP290, with four pins. Pin 1 is the control command input pin. A ninth resistor, a tenth resistor, and a fourth capacitor are connected in parallel between pins 1 and 2. A fifth capacitor and an eighth resistor are connected in parallel between pins 3 and 4. Pin 3 is connected to pin 4 of the controller. The wireless transceiver is model ESP8266, with eight pins. Pin 4 is connected to pin 20 of the controller, and pin 8 is connected to pin 21 of the controller. The driver is model ULN2003, with 16 pins. Pin 1 is connected to pin 38 of the controller. A first relay is connected to pin 16 of the driver. A first resistor and a first diode are connected in parallel on the first relay. The first relay has a device interface for connecting a camera.

[0040] This invention employs the aforementioned structure, using a detection and analysis system to capture images of the shrimp to be detected through geometric scanning, obtaining vector graphics composed of lines and trajectories. The obtained vector graphics are then filtered and verified to remove duplicates, those with high overlap, or those with significant deviations. The basic dataset is digitally translated, and the shrimp's characteristics are evaluated and determined based on the digitized data and the shrimp's specific color elements. The evaluation results are transmitted to the user's host computer, enabling remote detection and analysis of shrimp characteristics. This method offers advantages such as accuracy, practicality, speed, and convenience. Attached image description:

[0041] Figure 1 This is a schematic diagram of the process of the present invention.

[0042] Figure 2 This is a schematic diagram of the structure of the scanning and imaging area of ​​the present invention.

[0043] Figure 3 This is a schematic diagram illustrating the use of the scanning and imaging area of ​​the present invention.

[0044] Figure 4 This is a schematic diagram of the detection and analysis system of the present invention.

[0045] Figure 5 This is the electrical schematic diagram of the controller of the present invention.

[0046] Figure 6 This is the electrical schematic diagram of the instruction input device of the present invention.

[0047] Figure 7 This is the electrical schematic diagram of the wireless transceiver of the present invention.

[0048] Figure 8 This is the electrical schematic diagram of the driver of the present invention. Detailed implementation method:

[0049] To clearly illustrate the technical features of this solution, the invention will be described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0050] like Figure 1-8 As shown in the figure, the method for detecting and analyzing the traits of shrimp based on vector graphics digitization includes the following steps:

[0051] S1, the detection and analysis system uses geometric scanning to photograph the shrimp to be detected, and obtains a vector graphic composed of lines and trajectories;

[0052] S2, filter and verify the obtained vector graphics, and remove vector graphics that are duplicated, have a high degree of overlap or have large deviations;

[0053] S3, the remaining vector graphics are summarized and stored to form the basic dataset;

[0054] S4, digitizes the basic dataset, and evaluates and judges the characteristics of the shrimp based on the digitized data and the specific color elements of the shrimp.

[0055] S5 transmits the evaluation results of shrimp traits to the user's host computer, enabling remote detection and analysis of shrimp traits.

[0056] The detection and analysis system uses geometric scanning to photograph the shrimp to be detected, obtaining a vector graphic composed of lines and trajectories. The steps include:

[0057] S1.1, Set the scanning area, divide the scanning area into equal parts, and obtain multiple equally divided discrimination partitions;

[0058] S1.2, each discrimination partition is marked and numbered, and multiple standard coordinate points are set on the discrimination partition at the center position;

[0059] S1.3, the geometric trajectory of the shrimp to be detected is placed in the scanning and imaging area close to the standard coordinate point, thereby obtaining the standard vector image of the shrimp.

[0060] The process of filtering and verifying the obtained vector graphics, removing duplicates, those with high overlap, or those with large deviations, includes the following steps:

[0061] S2.1, set the standard area percentage of the standard vector diagram corresponding to the superior traits of the shrimp in each discrimination partition;

[0062] S2.2, compare the actual area ratio of the standard shrimp vector map to the standard area ratio in each discrimination partition;

[0063] S2.3, set correction parameters, establish a linear regression equation based on the relative difference between the actual area ratio and the standard area ratio of all shrimp products, and quickly remove vector graphics with duplication, high overlap or large deviation based on the linear regression trend.

[0064] The basic dataset undergoes digital translation processing. The evaluation and judgment of shrimp traits based on the digitized data and the specific color elements of the shrimp include the following steps:

[0065] S4.1 Define vector graphics files within the basic dataset, and convert the vector graphics files into plaintext format data based on the translation function. The plaintext format data includes punctuation files, trajectory files, and region files.

[0066] S4.2, combine plaintext data with different positions in the vector graphic file, and extract the plaintext data according to certain subdivision criteria;

[0067] S4.3 uses a correction function to quantitatively describe and analyze the uncertainty of corrected plaintext format data in order to achieve digital translation processing of vector graphics.

[0068] Define the vector graphics files within the base dataset, and convert the vector graphics into plaintext data based on the translation function, including the following steps:

[0069] S4.1.1, Establish a polar coordinate system associated with the trajectory of the vector graphic file;

[0070] S4.1.2, generalize the trajectory and curvature changes of the vector graphic file, and read the corresponding change coordinate parameters;

[0071] S4.1.3 converts vector graphics files into plaintext format.

[0072] Combining plaintext data with different locations in a vector graphic file and extracting the plaintext data according to certain subdivision criteria includes the following steps:

[0073] S4.2.1, Set the subdivision parameters for two intersecting directions;

[0074] S4.2.2, set the angle between the two intersecting directions and the polar coordinate system accordingly;

[0075] S4.2.3, the vector graphic file is meshed by discrimination partitioning, and plaintext data is extracted by combining subdivision parameters and standard coordinate points.

[0076] Using correction functions to quantitatively describe and analyze the uncertainty of corrected plaintext format data includes the following steps:

[0077] S4.3.1, group plaintext data into multiple arrays;

[0078] S4.3.2, Calculate the corresponding probability interval to determine the uncertainty of the array;

[0079] S4.3.3 outputs relevant uncertain data in a digital format.

[0080] The detection and analysis system includes a controller, on which an instruction input device, a wireless transceiver, and a driver are connected. The instruction input device is used to transmit control instructions to the controller for shrimp morphology detection. The wireless transceiver is used to establish remote communication between the controller and the user's host computer. The driver is used to drive the camera to perform geometric scanning and imaging.

[0081] The controller is model RK3399 and has multiple pins. The controller is connected to the command input device through pin 4, the controller is connected to the wireless transceiver through pins 20 and 21, and the controller is connected to the driver through pin 38.

[0082] The command input device is model TLP290, with four pins. Pin 1 is the control command input pin. A ninth resistor, a tenth resistor, and a fourth capacitor are connected in parallel between pins 1 and 2. A fifth capacitor and an eighth resistor are connected in parallel between pins 3 and 4. Pin 3 is connected to pin 4 of the controller. The wireless transceiver is model ESP8266, with eight pins. Pin 4 is connected to pin 20 of the controller, and pin 8 is connected to pin 21 of the controller. The driver is model ULN2003, with 16 pins. Pin 1 is connected to pin 38 of the controller. A first relay is connected to pin 16 of the driver. A first resistor and a first diode are connected in parallel on the first relay. The first relay has a device interface for connecting a camera.

[0083] The working principle of the shrimp trait detection and analysis method based on vector graphic digitization in this invention embodiment is as follows: Based on the combined action of multiple vector graphic calculation methods, the obtained shrimp vector graphic is digitally translated according to predetermined calculation steps and information transmission direction, ensuring that the collected image information can be accurately and effectively identified; simultaneously, a unified evaluation standard is set, eliminating reliance on subjective judgment by staff, and accurately identifying and judging each type of shrimp product. This allows for the selection of shrimp products with superior traits from a large number of shrimp, while screening out unqualified shrimp products, for different categories and forms of shrimp products.

[0084] Vector graphics, also known as object-oriented images or drawing images, are mathematically defined as a series of lines connected by points. The graphic elements in a vector file are called objects. Each object is a self-contained entity with attributes such as color, shape, outline, size, and screen position. Vector graphics are drawn based on geometric properties; a vector can be a point or a line. This type of image file contains independent, separate images that can be freely and unlimitedly recombined. Its characteristic is that the image does not lose quality when enlarged, making it well-suited for detecting the shape and contour trajectory of shrimp products.

[0085] Due to the characteristics of shrimp products, the evaluation results of shrimp products can be obtained by detecting their morphological outlines and color elements, thereby selecting shrimp products with excellent traits.

[0086] This application uses vector graphics to detect the characteristics of shrimp products. Compared with existing imaging detection technology, it can clearly obtain the required trajectory and color elements, and can effectively solve the problems of overlap or ghosting, making the obtained images accurate and effective and reducing errors.

[0087] The overall solution mainly includes the following steps: The detection and analysis system uses geometric scanning to photograph the shrimp to be detected, obtaining vector graphics composed of lines and trajectories; the obtained vector graphics are screened and verified to remove duplicates, those with high overlap, or those with large deviations; the remaining vector graphics are summarized and stored to form a basic dataset; the basic dataset is digitally translated, and the shrimp traits are evaluated and judged based on the digital data and the specific color elements of the shrimp; the evaluation and judgment results of the shrimp traits are transmitted to the user's host computer to realize remote detection and analysis of shrimp traits.

[0088] The detection and analysis system includes a controller, on which an instruction input device, a wireless transceiver, and a driver are connected. The instruction input device is used to transmit control instructions to the controller for shrimp morphology detection. The wireless transceiver is used to establish remote communication between the controller and a user's host computer. The driver is used to drive a camera to perform geometric scanning and imaging.

[0089] Preferably, the controller is model RK3399, which has multiple pins. The controller is connected to the command input device through pin 4, the controller is connected to the wireless transceiver through pins 20 and 21, and the controller is connected to the driver through pin 38. This constitutes the overall hardware circuit, and the data interaction and transmission and related parameter determination are performed by the above-mentioned overall hardware circuit to ensure accurate and efficient data processing.

[0090] Furthermore, the instruction input device is model TLP290, with four pins. Pin 1 of the instruction input device is the control instruction input pin. A ninth resistor, a tenth resistor, and a fourth capacitor are connected in parallel between pins 1 and 2. A fifth capacitor and an eighth resistor are connected in parallel between pins 3 and 4. Pin 3 of the instruction input device is connected to pin 4 of the controller. The wireless transceiver is model ESP8266, with eight pins. Pin 4 of the wireless transceiver is connected to pin 20 of the controller, and pin 8 of the wireless transceiver is connected to pin 21 of the controller. The driver is model ULN2003, with 16 pins. The driver is connected to pin 38 of the controller via pin 1. A first relay is connected to pin 16 of the driver. A first resistor and a first diode are connected in parallel on the first relay. The first relay has a device interface for connecting a camera.

[0091] The detection and analysis system uses geometric scanning to photograph the shrimp to be detected, resulting in a vector graphic composed of lines and trajectories. This mainly involves setting up a scanning area, dividing the scanning area into multiple equally divided discrimination zones, marking and numbering each discrimination zone, and setting multiple standard coordinate points on the central discrimination zone. The geometric trajectory of the shrimp to be detected is placed in the scanning area close to the standard coordinate points to obtain a standard vector graphic of the shrimp.

[0092] Generally, the scanning area is a nine-square grid, and each part is labeled in a predetermined order. In the actual detection process, the geometric trajectory of the shrimp is placed in the middle of the standard coordinate points as much as possible to facilitate subsequent calculations.

[0093] After obtaining multiple standard vector graphics of shrimp, it is necessary to filter and verify the vector graphics to remove duplicates, vector graphics with high overlap or large deviations, thereby reducing the actual amount of computation, speeding up the overall detection and analysis process, and avoiding unnecessary additional calculations.

[0094] Preferably, the method mainly includes the following steps: setting the standard area proportion of the standard vector map corresponding to the superior shrimp in each discrimination partition; comparing the actual area proportion of the standard vector map of the shrimp in each discrimination partition with the standard area proportion; setting correction parameters, establishing a linear regression equation based on the relative difference between the actual area proportion and the standard area proportion of all shrimp products, and quickly removing vector maps with duplication, high overlap or large deviation based on the linear regression trend.

[0095] Linear regression is a statistical analysis method that uses regression analysis in mathematical statistics to determine the quantitative relationship of interdependence between two or more variables. It is widely used. It includes only one independent variable and one dependent variable, and the relationship between the two can be approximated by a straight line. In this application, unwanted vector graphics are quickly removed based on the changing trend of relative difference values, thereby obtaining a skewed and reliable vector graphics, which constitutes the basic dataset.

[0096] The process of digitizing and translating a basic dataset to evaluate and determine the characteristics of shrimp based on the digitized data and specific color elements includes the following steps: defining vector graphics files within the basic dataset; converting the vector graphics into plaintext data using a translation function, wherein the plaintext data includes punctuation files, trajectory files, and region files; combining the plaintext data with different positions in the vector graphics files; extracting the plaintext data according to certain subdivision criteria; and using a correction function to quantitatively describe and analyze the uncertainty of the corrected plaintext data to achieve the digitization and translation of the vector graphics.

[0097] In the polar coordinate system, the trajectory of the vector graphic file is integrated and generalized, the corresponding changing coordinate parameters are read, and then the vector graphic file is converted into plain text format.

[0098] The polar coordinate system refers to a coordinate system in a plane consisting of a pole, a polar axis, and a polar radius. It is more suitable for vector graphics of shrimp products, whose trajectories are relatively curved. Since the trajectory and curvature changes on the vector graphics of shrimp products are quite diverse, it is necessary to extract the plaintext data according to certain subdivision standards. The detailed steps are as follows: set the subdivision parameters for two intersecting directions; set the angles between the two intersecting directions and the polar coordinate system; grid the vector graphics file through discrimination partitioning; and extract the plaintext data by combining the subdivision parameters and standard coordinate points.

[0099] The larger the subdivision parameter, the closer the two intersecting directions are to the clamps of the polar coordinate system, resulting in more authentic and reliable plaintext data, which is easier to extract and process.

[0100] Furthermore, using correction functions to quantitatively describe and analyze the uncertainty of corrected plaintext format data includes the following steps: forming multiple arrays from the plaintext format data; calculating the corresponding probability intervals to determine the uncertainty of the arrays; and outputting the relevant uncertain data in a digital format.

[0101] It should be noted that due to the special nature of shrimp products, multiple sampling and testing are required to ensure accurate and reliable test results. In the actual testing and analysis process, the color elements of shrimp products must be effectively combined with the characteristics of shrimp to ensure that the shrimp products meet the actual requirements.

[0102] In summary, the shrimp trait detection and analysis method based on vector graphic digitization in this embodiment of the invention, through the combined action of multiple vector graphic operation methods, performs digital translation processing on the acquired shrimp vector graphics according to predetermined operation steps and information transmission direction, ensuring that the collected image information can be accurately and effectively identified; at the same time, it sets a unified evaluation standard, without relying on subjective judgment by staff, and can accurately identify and judge each type of shrimp product, so as to select shrimp products with excellent traits from a large number of shrimp and screen out unqualified shrimp products, for different categories and different forms of shrimp products.

[0103] The above specific embodiments should not be construed as limiting the scope of protection of the present invention. For those skilled in the art, any alternative improvements or modifications made to the embodiments of the present invention shall fall within the scope of protection of the present invention.

[0104] Any aspects of this invention not described in detail are well-known to those skilled in the art.

Claims

1. A method for detecting and analyzing the traits of marine shrimp based on vector graphic digitization, characterized in that, The detection and analysis method includes the following steps: S1, the detection and analysis system uses geometric scanning to photograph the shrimp to be detected, and obtains a vector graphic composed of lines and trajectories; S2, filter and verify the obtained vector graphics, and remove vector graphics that are duplicated, have a high degree of overlap or have large deviations; S3, the remaining vector graphics are summarized and stored to form the basic dataset; S4, digitizes the basic dataset, and evaluates and judges the characteristics of the shrimp based on the digitized data and the specific color elements of the shrimp. S5 transmits the evaluation results of shrimp traits to the user's host computer, enabling remote detection and analysis of shrimp traits. The basic dataset undergoes digital translation processing. The evaluation and judgment of shrimp traits based on the digitized data and the specific color elements of the shrimp include the following steps: S4.1 Define vector graphics files within the basic dataset, and convert the vector graphics files into plaintext format data based on the translation function. The plaintext format data includes punctuation files, trajectory files, and region files. S4.2, combine plaintext data with different positions in the vector graphic file, and extract the plaintext data according to certain subdivision criteria; S4.3 uses a correction function to quantitatively describe and analyze the uncertainty of corrected plaintext format data in order to realize the digital translation processing of vector graphics; Define the vector graphics files within the base dataset, and convert the vector graphics into plaintext data based on the translation function, including the following steps: S4.1.1, Establish a polar coordinate system associated with the trajectory of the vector graphic file; S4.1.2, generalize the trajectory and curvature changes of the vector graphic file, and read the corresponding change coordinate parameters; S4.1.3, convert vector graphics files into plain text format; Combining plaintext data with different locations in a vector graphic file and extracting the plaintext data according to certain subdivision criteria includes the following steps: S4.2.1, Set the subdivision parameters for two intersecting directions; S4.2.2, set the angle between the two intersecting directions and the polar coordinate system accordingly; S4.2.3, the vector graphic file is meshed by discrimination partitioning, and plaintext data is extracted by combining subdivision parameters and standard coordinate points; Using correction functions to quantitatively describe and analyze the uncertainty of corrected plaintext format data includes the following steps: S4.3.1, group plaintext data into multiple arrays; S4.3.2, Calculate the corresponding probability interval to determine the uncertainty of the array; S4.3.3 outputs relevant uncertain data in a digital format.

2. The method for detecting and analyzing the traits of shrimp based on vector graphic digitization according to claim 1, characterized in that, The detection and analysis system uses geometric scanning to photograph the shrimp to be detected, obtaining a vector graphic composed of lines and trajectories. The steps include: S1.1, Set the scanning area, divide the scanning area into equal parts, and obtain multiple equally divided discrimination partitions; S1.2, each discrimination partition is marked and numbered, and multiple standard coordinate points are set on the discrimination partition at the center position; S1.3, the geometric trajectory of the shrimp to be detected is placed in the scanning and imaging area close to the standard coordinate point, thereby obtaining the standard vector image of the shrimp.

3. The method for detecting and analyzing the traits of shrimp based on vector graphic digitization according to claim 1, characterized in that, The process of filtering and verifying the obtained vector graphics, removing duplicates, those with high overlap, or those with large deviations, includes the following steps: S2.1, set the standard area percentage of the standard vector diagram corresponding to the superior traits of the shrimp in each discrimination partition; S2.2, compare the actual area ratio of the standard shrimp vector map to the standard area ratio in each discrimination partition; S2.3, set correction parameters, establish a linear regression equation based on the relative difference between the actual area ratio and the standard area ratio of all shrimp products, and quickly remove vector graphics with duplication, high overlap or large deviation based on the linear regression trend.

4. The method for detecting and analyzing the traits of shrimp based on vector graphic digitization according to claim 1, characterized in that: The detection and analysis system includes a controller, on which an instruction input device, a wireless transceiver, and a driver are connected. The instruction input device is used to transmit control instructions to the controller for shrimp morphology detection. The wireless transceiver is used to establish remote communication between the controller and the user's host computer. The driver is used to drive the camera to perform geometric scanning and imaging.

5. The method for detecting and analyzing the traits of shrimp based on vector graphic digitization according to claim 4, characterized in that: The controller is model RK3399 and has multiple pins. The controller is connected to the command input device through pin 4, the controller is connected to the wireless transceiver through pins 20 and 21, and the controller is connected to the driver through pin 38.

6. The method for detecting and analyzing the traits of shrimp based on vector graphic digitization according to claim 5, characterized in that: The command input device is model TLP290, with four pins. Pin 1 is the control command input pin. A ninth resistor, a tenth resistor, and a fourth capacitor are connected in parallel between pins 1 and 2. A fifth capacitor and an eighth resistor are connected in parallel between pins 3 and 4. Pin 3 is connected to pin 4 of the controller. The wireless transceiver is model ESP8266, with eight pins. Pin 4 is connected to pin 20 of the controller, and pin 8 is connected to pin 21 of the controller. The driver is model ULN2003, with 16 pins. Pin 1 is connected to pin 38 of the controller. A first relay is connected to pin 16 of the driver. A first resistor and a first diode are connected in parallel on the first relay. The first relay has a device interface for connecting a camera.

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