Mathematical element interaction control method

Abstract

The invention discloses a mathematical element interaction control method, which allows a user to upload a picture, identifies objects in the picture through a CNN (Convolutional Neural Network) and Face + + API (Application Program Interface), classifies and counts, and maps a result to a preset calculation area in a mathematical element form. A user can learn by operating the mathematical elements. Besides, the uploaded picture graph can be segmented, a U-Net model is adopted to segment the graphs such as fruits in the uploaded picture into a plurality of parts, and the user can splice the segmented blocks to form a new graph and name the new graph. The color and the shape of the new graph can be automatically generated according to the characteristics of the original segmentation blocks. The function not only improves interestingness and interactivity of mathematics learning, but also helps a user to better understand mathematical concepts such as proportion and graphic combination through actual operation. The design of the APP realizes seamless connection between mathematics learning and the real world, so that the learning process is more visual, interesting and challenging.

Description

Technical Field

[0001] The present invention relates to the technical field of online teaching, and in particular to a mathematical element interactive control method. Background Art

[0002] With the rapid development of mobile Internet technology and the increasing penetration rate of smart phones, mobile applications have become an indispensable part of people's daily lives. As an important branch of artificial intelligence, image recognition technology has been widely used in mobile applications in recent years. Through image recognition technology, mobile applications can automatically analyze and process the content of pictures uploaded by users, greatly improving user experience and application value.

[0003] However, in the current mobile application market, although there are a large number of applications that integrate image recognition functions, most of these applications only stay at the level of simple object recognition or classification, and fail to deeply combine the recognition results with specific application scenarios. Especially in the field of education, how to effectively use image recognition technology to assist mathematics teaching and improve students' learning interest and efficiency is still an urgent problem to be solved.

[0004] On the other hand, the process of mathematics teaching often involves the display and interaction of various mathematical elements. Traditional mobile applications often use static charts or text to display mathematical elements, which lack interactivity and fun, and are difficult to stimulate students' interest in learning. Therefore, developing a mobile application that can dynamically display mathematical elements and allow users to interact is of great significance to improving the effectiveness of mathematics teaching. Summary of the invention

[0005] This application provides a method for interactive control of mathematical elements, and in response to the above problems, provides a mobile application that combines image recognition technology and interactive display of mathematical elements. By integrating convolutional neural networks (CNN) and Face++ image recognition API, the present invention can realize automatic recognition, classification and counting of user-uploaded pictures, and dynamically display the recognition results in the form of mathematical elements in a preset calculation area. At the same time, the present invention also provides a rich interactive function, allowing users to select and move displayed mathematical elements, split self-uploaded graphics, and recombine them into user-favorite patterns, helping users better understand mathematical operations, thereby improving the interactivity and fun of the application.

[0006] The present application provides a mathematical element interactive control method, comprising:

[0007] S101, adding a picture upload button and a recognition result display area on the APP interface;

[0008] S102, integrating image recognition technology into the APP;

[0009] S103, when entering the interactive page, initializing the preset calculation area;

[0010] S104, dividing the preset calculation area into grids;

[0011] S105, mapping the identified object types and quantities to a preset calculation area;

[0012] S106, detecting an input operation in a preset calculation area, selecting a target mathematical element according to the input and displaying its coordinate position in real time;

[0013] S107, judging whether there is overlap according to the real-time coordinates of the first mathematical element and the second mathematical element;

[0014] S108: If there is any area overlap, the final result is displayed in the recognition result display area.

[0015] Preferably, the step S102, integrating image recognition technology into the APP, includes:

[0016] Select CNN convolutional neural network as the image recognition technology, and integrate Face++ image recognition API.

[0017] Preferably, the S102 includes:

[0018] S201, using a graphic segmentation algorithm to segment the graphic on the uploaded image into multiple parts;

[0019] S202, integrating the graph segmentation algorithm into the APP;

[0020] S203, adding a graphic segmentation button and a segmentation result display area in the APP;

[0021] S204, mapping each segmented block into mathematical elements, and allowing the user to perform mathematical operations on these elements.

[0022] Preferably, the step S201 includes:

[0023] Choose U-Net, an image segmentation algorithm based on deep learning;

[0024] U-Net uses CNN convolutional neural network to extract features and classify images;

[0025] The input image size is set to 512x512 pixels, and the U-Net model is trained until the IoU ratio reaches a segmentation accuracy of more than 0.8;

[0026] According to user requirements, different graphics or areas in the image are defined as different parts.

[0027] Preferably, the S203 includes:

[0028] In the UI design of the app, add a button to trigger the segmentation of the graph and create an area to display the segmentation results.

[0029] When the user clicks the split button, the APP calls the graphic segmentation module for processing and displays the result in the specified area.

[0030] Preferably, the S204 includes:

[0031] From uploading the image to completing the image segmentation, the segmentation process is required to be completed within 2 seconds.

[0032] Preferably, the S204 further includes:

[0033] Perform contour extraction and shape recognition on each segmented part;

[0034] Each segmented block after segmentation can be regarded as a different mathematical element, and the number of segments can be directly used for addition and subtraction operations.

[0035] Preferably, the S204 further includes:

[0036] S301, splicing different fruit segments and processing the spliced ​​fruit features;

[0037] S302, adding a naming input box in the APP to allow the user to enter a custom name for the new combination of fruits;

[0038] S303, calculating the average value of RGB parameters of two or more fruit colors as the color of the new combined fruit;

[0039] S304, generating the shape of the newly combined fruit according to the proportion of each fruit segment to the original fruit and the shape characteristics of the original fruit.

[0040] Preferably, the S303 includes:

[0041] Extract RGB color parameters from each fruit segment;

[0042] The RGB is a color model, where R stands for red, G stands for green, and B stands for blue, and the value range of each color is 0 to 255;

[0043] Calculate the average of RGB parameters of two or more fruit colors.

[0044] Preferably, the S304 includes:

[0045] Calculate the proportion of each fruit segment to the original fruit;

[0046] Extract the shape features of the original fruit;

[0047] The shape of the new combined fruit is generated according to the proportion of each fruit segment and the shape characteristics of the original fruit.

[0048] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0049] 1. By adding a picture upload button, users can easily upload real-life images to the APP, thus achieving a seamless connection between math learning and the real world. This function makes math learning closer to reality and improves the practicality of the software. After the user uploads the picture, the APP can immediately recognize and display the results. This timely feedback mechanism greatly enhances the interactivity of the software. Users can adjust the learning content instantly according to the recognition results to achieve a more personalized learning experience.

[0050] 2. Using convolutional neural networks (CNN) and Face++ image recognition API, the app can accurately identify objects in pictures and classify and count them. This advanced technology makes math learning more interesting and challenging, attracting users' attention. The types and quantities of identified objects are mapped to preset calculation areas and presented to users in the form of mathematical elements. This innovative presentation method makes math learning no longer boring, but full of fun and exploration.

[0051] 3. This solution uses the U-Net image segmentation algorithm based on deep learning, and uses a convolutional neural network (CNN) to extract and classify images to achieve efficient and accurate image segmentation. The input image size is set to 512x512 pixels, and the U-Net model is trained to make its IoU intersection-over-union ratio reach above 0.8 to ensure the accuracy of the segmentation results. At the same time, the uploaded images are preprocessed by resizing and normalizing to meet the input requirements of the model, further improving the accuracy of the segmentation. This technical point enables the entire process from uploading the image to completing the segmentation to be completed within 2 seconds, greatly improving the segmentation efficiency and providing users with a fast and accurate segmentation experience.

[0052] 4. In the UI design of the APP, a button for triggering graphic segmentation and an area for displaying segmentation results are added. Users can implement graphic segmentation and view results by simply clicking. At the same time, contour extraction and shape recognition are performed on each segmented part, and they are mapped to different mathematical elements, so that users can perform mathematical operations on these elements. By allowing users to drag the segmented block graphics to the preset calculation area and complete the calculation by splicing the graphics, this technical point not only improves user participation, but also makes the learning process more intuitive and interesting, helping users to better understand and master mathematical knowledge.

[0053] 5. The precise splicing and feature processing of fruit segments greatly improves the user experience and visual effects. By accurately segmenting the uploaded images through the U-Net model, users can easily obtain segments of different fruits. On the APP interface, users can freely combine these segments into new fruit shapes by dragging or clicking to select. After the splicing is completed, the APP will automatically smooth the edges and perform color transition processing on the newly combined fruits to ensure that the spliced ​​fruits look natural and beautiful without obvious splicing marks. This precise splicing and feature processing technology not only meets the user's personalized needs for fruit shape innovation, but also enhances the visual beauty of the newly combined fruits, allowing users to have a better experience during the operation process.

[0054] 6. The custom naming, color and shape generation functions of the newly combined fruits further enhance the user's sense of participation and creativity. After the splicing is completed, the user can enter a custom name for the newly combined fruit through the naming input box on the APP interface. This name will be saved by the APP and associated with the image data of the newly combined fruit, which is convenient for users to retrieve or call later. At the same time, the APP will also generate the shape of the newly combined fruit based on the proportion of each fruit segment to the original fruit and the shape characteristics of the original fruit. This custom naming and shape generation function enables users to create a unique fruit image, fully meeting the user's creativity and personalized needs. In addition, the APP will also calculate the average value of the RGB parameters of two or more fruit colors as the color of the newly combined fruit. This color fusion technology allows the color of the newly combined fruit to retain the characteristics of the original fruit color and have a certain degree of innovation, further enhancing the user's visual experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 The figure is a flow chart of a mathematical element interactive control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0056] To facilitate the understanding of the present invention, the present application will be described more comprehensively below with reference to the relevant drawings; the drawings show preferred embodiments of the present invention, but the present invention can be implemented in many different forms and is not limited to the embodiments described herein; on the contrary, the purpose of providing these embodiments is to enable a more thorough and comprehensive understanding of the disclosed content of the present invention.

[0057] It should be noted that the terms “vertical”, “horizontal”, “up”, “down”, “left”, “right” and similar expressions used in this document are only for illustrative purposes and do not represent the only implementation method.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which the present invention belongs; the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention; the term "and / or" used herein includes any and all combinations of one or more related listed items.

[0059] Figure 1 It is a flowchart of a mathematical element interactive control method according to an embodiment of the present invention.

[0060] like Figure 1 As shown, a mathematical element interactive control method comprises the following steps:

[0061] S101, add a picture upload button and a recognition result display area on the APP interface.

[0062] S102, integrate image recognition technology into the APP, and implement image uploading, recognition, classification and counting functions through the API interface.

[0063] Specifically, we selected convolutional neural network (CNN) as the image recognition technology and integrated Face++ image recognition API. CNN can automatically and accurately extract image features from raw pixels. Through training, CNN can learn to recognize specific objects.

[0064] In the app, users can upload images by clicking a button or selecting a file. When uploading an image, it is sent to the API interface via an HTTP POST request. When the interface receives the image, it returns a confirmation response indicating that the image has been successfully received. After the API interface receives the image, it processes the image using the CNN image recognition algorithm. The recognition process includes steps such as feature extraction, object detection, and classification. After the recognition is completed, the API returns a response containing the recognition results, usually in JSON format or other structured data format. Based on the recognition results, the API can classify the objects in the image. The classification can be based on predefined categories such as animals, plants, buildings, etc., or it can be a custom category. The classification results are returned to the app together with the recognition results. If the image contains multiple identical objects, the API can also count them. The counting function is usually based on the detection and classification results of the recognition algorithm. The counting results are also returned to the app in the form of structured data.

[0065] S103, when entering the interactive page, initializing the preset calculation area.

[0066] Specifically, when a page containing a preset calculation region is opened, the region is initialized immediately and all mathematical elements in the current region are cleared.

[0067] S104, gridding the preset calculation area.

[0068] Specifically, before or after displaying the mathematical elements (depending on specific needs), the preset calculation area is divided into a plurality of rectangular grids by horizontal and vertical grid lines.

[0069] S105, mapping the identified object types and quantities to a preset calculation area.

[0070] Specifically, according to the image recognition result, the corresponding mathematical elements are displayed in the preset calculation area.

[0071] S106, detecting an input operation in a preset calculation area, selecting a target mathematical element according to the input and displaying its coordinate position in real time.

[0072] The target mathematical element is the first mathematical element or the second mathematical element. Both the first mathematical element and the second mathematical element can be digital or graphic materials.

[0073] S107, judging whether there is overlap according to the real-time coordinates of the first mathematical element and the second mathematical element.

[0074] S108: If there is any area overlap, the final result is displayed in the recognition result display area.

[0075] Specifically, if there is an overlapped area, the digital information and the graphic information in the overlapped first mathematical element and the second mathematical element are obtained, and the two mathematical elements are converted into a graphic folder at the overlapped area. The graphic folder has icons with the same number as the digital information and the same graphics as the graphic information.

[0076] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0077] 1. By adding a picture upload button, users can easily upload real-life images to the APP, thus achieving a seamless connection between math learning and the real world. This function makes math learning closer to reality and improves the practicality of the software. After the user uploads the picture, the APP can immediately recognize and display the results. This timely feedback mechanism greatly enhances the interactivity of the software. Users can adjust the learning content instantly according to the recognition results to achieve a more personalized learning experience.

[0078] 2. Using convolutional neural networks (CNN) and Face++ image recognition API, the app can accurately identify objects in pictures and classify and count them. This advanced technology makes math learning more interesting and challenging, attracting users' attention. The types and quantities of identified objects are mapped to preset calculation areas and presented to users in the form of mathematical elements. This innovative presentation method makes math learning no longer boring, but full of fun and exploration.

[0079] Example 2: By adding a picture upload button, using convolutional neural network (CNN) and Face++ image recognition API, the above example 1 APP can accurately identify objects in pictures and classify and count them, making math learning more interesting and challenging. In order to further make abstract mathematical concepts more intuitive and easy to understand, the example 1 is further improved by adding a graphic segmentation function, such as segmenting an apple into multiple pieces, to assist users in understanding and interacting with addition and subtraction operations. Further improvements are made based on step S102 of the example 1, specifically:

[0080] S201, using a graphic segmentation algorithm, segmenting the graphic on the uploaded image into multiple parts.

[0081] Specifically, we selected the U-Net image segmentation algorithm based on deep learning. U-Net uses a convolutional neural network (CNN) to extract and classify images. The input image size is set to 512x512 pixels, and the U-Net model is trained until the IoU (intersection over union) reaches a segmentation accuracy of more than 0.8. Different graphics or areas in the image are defined as different parts according to user needs.

[0082] For example, in an image containing multiple geometric shapes, each shape is considered as an independent part. After resizing and normalizing the uploaded image, the preprocessed image is input into the U-Net model for segmentation. The model outputs a segmentation mask with the same size as the input image, where the value of each pixel corresponds to the part it belongs to. According to the segmentation mask, the original image is segmented into multiple parts.

[0083] S202, integrating the graph segmentation algorithm into the APP.

[0084] S203, adding a graphic segmentation button and a segmentation result display area in the APP.

[0085] Specifically, in the UI design of the APP, add a button for triggering graphic segmentation and create an area for displaying the segmentation results (which can be an image view or canvas). When the user clicks the segmentation button, the APP calls the graphic segmentation module for processing and displays the results in the specified area.

[0086] S204, mapping each segmented block into mathematical elements, and allowing the user to perform mathematical operations on these elements.

[0087] Specifically, contour extraction and shape recognition are performed on each part of the segmented image. Each segmented block can be regarded as a different mathematical element, and the number of segments can be directly used for addition and subtraction operations. For example, an apple is divided into 3 pieces and 2 pieces, and then the addition operation is performed to get the total number.

[0088] It should be noted that, in the contour extraction and shape recognition of each part of the segmented image, the cv::findContours() function in OpenCV is used to extract the contour, the cv::contourArea() function is used to calculate the area of ​​the contour, and the cv::arcLength() function is used to calculate the perimeter of the contour. The above functions are all existing technologies and will not be described in detail in this article.

[0089] Among them, it is stipulated that the segmentation process of the uploaded image shall be completed within 2 seconds from uploading the image to completing the image segmentation; for the image to be segmented, the image with a complete recognizable outline and shape shall be the preferred segmentation image.

[0090] For example, upload a picture containing apples, oranges and watermelons. The APP automatically preprocesses the picture and calls the U-Net model for segmentation. The segmentation result display area of ​​the APP displays three independent shapes: apple, orange, and watermelon, and these independent graphics have been divided into multiple small graphics - that is, the apple is divided into 3 segmented small graphics, the orange is divided into 2 segmented graphics, and the watermelon is divided into 5 segmented graphics. Drag the segmented block graphics to the preset calculation area, so that the segmented block graphics are assembled into a complete graphic, and the final calculation number can be displayed in the result display area. For example, drag the apple with 3 segmented small graphics to the preset calculation area one by one. After dragging, the number 3 is displayed in the result display area.

[0091] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0092] 1. This solution uses the U-Net image segmentation algorithm based on deep learning, and uses a convolutional neural network (CNN) to extract and classify images to achieve efficient and accurate image segmentation. The input image size is set to 512x512 pixels, and the U-Net model is trained to make its IoU intersection-over-union ratio reach above 0.8 to ensure the accuracy of the segmentation results. At the same time, the uploaded images are preprocessed by resizing and normalizing to meet the input requirements of the model, further improving the accuracy of the segmentation. This technical point enables the entire process from uploading the image to completing the segmentation to be completed within 2 seconds, greatly improving the segmentation efficiency and providing users with a fast and accurate segmentation experience.

[0093] 2. In the UI design of the APP, a button for triggering graphic segmentation and an area for displaying segmentation results are added. Users can implement graphic segmentation and view results by simply clicking. At the same time, contour extraction and shape recognition are performed on each segmented part, and they are mapped to different mathematical elements, so that users can perform mathematical operations on these elements. By allowing users to drag the segmented block graphics to the preset calculation area and complete the calculation by splicing the graphics, this technical point not only improves user participation, but also makes the learning process more intuitive and interesting, helping users to better understand and master mathematical knowledge.

[0094] Embodiment three: the above-mentioned embodiment two increases the function of graphic segmentation, such as apple segmentation into multiple pieces, to assist the understanding and interaction of addition and subtraction operations. In order to enhance the understanding of mathematical operations, proportions and graphic combinations, the splicing and naming functions of segmented fruits can be increased, and the user can combine the fruit segments of different types to form new "fruits", and output the characteristics such as their colors and shapes, further improving the interactivity and interest of the mathematics teaching software, so that the user can understand mathematical concepts such as proportions and graphic combinations through actual operations. Further improvements are made on the basis of step S204 of embodiment two, specifically as follows:

[0095] S301, splicing different fruit segments and processing the spliced ​​fruit features.

[0096] Specifically, first, the uploaded image is segmented through the U-Net model mentioned earlier to obtain blocks of different fruits; then, the user can select the fruit blocks they want to splice on the APP interface, and combine these blocks into a new fruit shape by dragging or clicking; finally, after the splicing is completed, the APP will perform feature processing on the newly combined fruit, which may include edge smoothing, color transition, etc., to ensure that the spliced ​​fruit looks natural and beautiful.

[0097] S302, adding a naming input box in the APP to allow the user to enter a custom name for the new combination of fruits.

[0098] Specifically, a naming input box should be added to the UI design of the APP, which is usually located below or next to the display area of ​​the new combination of fruits. The user can enter a custom name for the new combination of fruits through keyboard or handwriting input. The name entered by the user will be saved by the APP and associated with the image or data of the new combination of fruits, so that the user can later retrieve or call the new combination of fruits by name.

[0099] S303, calculating the average value of the RGB parameters of two or more fruit colors as the color of the new combined fruit.

[0100] Specifically, first, extract the RGB color parameters from each fruit segment. RGB is a color model, where R represents red, G represents green, and B represents blue, and the value range of each color is 0 to 255. Then, calculate the average value of the RGB parameters of two or more fruit colors. Assuming that there are two fruits A and B, whose RGB parameters are (Ra, Ga, Ba) and (Rb, Gb, Bb) respectively, the RGB parameters of the newly combined fruit can be calculated by the following formula: R_new = (Ra + Rb) / 2, G_new = (Ga + Gb) / 2, B_new = (Ba + Bb) / 2. Here, Ra, Ga, Ba, etc. represent the RGB parameter values ​​of fruit A, Rb, Gb, Bb, etc. represent the RGB parameter values ​​of fruit B, and R_new, G_new, B_new represent the RGB parameter values ​​of the newly combined fruit. RGB parameters are specific values ​​of color and are the basis for calculating the color of the newly combined fruit. By calculating the average value, the colors of two or more fruits can be merged together to obtain a new color. This color not only retains the characteristics of the original fruit color, but also has a certain degree of innovation.

[0101] S304, generating the shape of the newly combined fruit according to the proportion of each fruit segment to the original fruit and the shape characteristics of the original fruit.

[0102] Specifically, the proportion of each fruit segment to the original fruit is first calculated, and then the shape features of the original fruit are extracted. Finally, the shape of the new combined fruit is generated based on the proportion of each fruit segment and the shape features of the original fruit.

[0103] For example, first use the upload function of the APP to upload pictures of apples, bananas, and oranges to the system. The APP uses the U-Net model to accurately segment the three pictures and obtain the segmented blocks of apples, bananas, and oranges.

[0104] On the APP interface, users can combine the upper part of the apple, the middle part of the banana and the lower part of the orange to form a new fruit shape by dragging and clicking.

[0105] The APP automatically smooths the edges and performs color transition processing on the newly combined fruits to ensure that the combined fruits look natural and beautiful. After the combination is completed, a naming input box appears on the APP interface, located below the display area of ​​the newly combined fruits. The user enters "Fruit World Hero" as the name of the newly combined fruit in the input box. The APP saves this name and associates it with the image data of the newly combined fruit.

[0106] The APP extracts the RGB color parameters of each apple, banana and orange from the divided blocks (assuming that the RGB parameters of apples are (255,0,0), the RGB parameters of bananas are (255,255,0), and the RGB parameters of oranges are (255,128,0)), and calculates the RGB parameters of the new combined fruit according to the formula (R_new=(255+255+255) / 3≈255, G_new=(0+255+128) / 3≈128, B_new=(0+0+0) / 3=0).

[0107] Therefore, the color of the new combination fruit "Fruit World Hero" is (255,128,0), which is a new color between red, yellow and orange. The APP also calculates the proportion of each fruit segment to the original fruit (the upper half of the apple accounts for 30%, the middle part of the banana accounts for 40%, and the lower half of the orange accounts for 30%), and extracts the shape features of the original fruit (such as the round shape of the apple, the curved shape of the banana and the spherical shape of the orange).

[0108] Finally, based on these proportions and shape features, the APP generated the shape of a new combination of fruit "Fruit Hero": the upper part is the round shape of an apple, the middle part is the curved shape of a banana, and the lower part is the spherical shape of an orange, but the overall look is more harmonious and unified.

[0109] By adding the functions of splicing and naming the divided fruits, the interactivity and fun of the mathematics teaching software are further improved, allowing users to understand mathematical concepts such as proportions and graphic combinations through practical operations.

[0110] The technical solutions in the above embodiments of the present application have at least the following technical effects or advantages:

[0111] 1. The precise splicing and feature processing of fruit segments greatly improves the user experience and visual effects. By accurately segmenting the uploaded images through the U-Net model, users can easily obtain segments of different fruits. On the APP interface, users can freely combine these segments into new fruit shapes by dragging or clicking to select. After the splicing is completed, the APP will automatically smooth the edges and perform color transition processing on the newly combined fruits to ensure that the spliced ​​fruits look natural and beautiful without obvious splicing marks. This precise splicing and feature processing technology not only meets the user's personalized needs for fruit shape innovation, but also enhances the visual beauty of the newly combined fruits, allowing users to have a better experience during the operation process.

[0112] 2. The custom naming, color and shape generation functions of the newly combined fruits further enhance the user's sense of participation and creativity. After the splicing is completed, the user can enter a custom name for the newly combined fruit through the naming input box on the APP interface. This name will be saved by the APP and associated with the image data of the newly combined fruit, which is convenient for users to retrieve or call later. At the same time, the APP will also generate the shape of the newly combined fruit based on the proportion of each fruit segment to the original fruit and the shape characteristics of the original fruit. This custom naming and shape generation function enables users to create a unique fruit image, fully meeting the user's creativity and personalized needs. In addition, the APP will also calculate the average value of the RGB parameters of two or more fruit colors as the color of the newly combined fruit. This color fusion technology allows the color of the newly combined fruit to retain the characteristics of the original fruit color and have a certain degree of innovation, further enhancing the user's visual experience.

[0113] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mathematical element interactive control method, characterized in that: include: S101, adding a picture upload button and a recognition result display area on the APP interface; S102, integrating image recognition technology into the APP; S103, when entering the interactive page, initializing the preset calculation area; S104, dividing the preset calculation area into grids; S105, mapping the identified object types and quantities to a preset calculation area; S106, detecting an input operation in a preset calculation area, selecting a target mathematical element according to the input and displaying its coordinate position in real time; S107, judging whether there is overlap according to the real-time coordinates of the first mathematical element and the second mathematical element; S108: If there is any area overlap, the final result is displayed in the recognition result display area.

2. A mathematical element interactive control method as claimed in claim 1, characterized in that: The step S102, integrating the image recognition technology into the APP, includes: Select CNN convolutional neural network as the image recognition technology, and integrate Face++ image recognition API.

3. A mathematical element interactive control method as claimed in claim 1, characterized in that: The S102 includes: S201, using a graphic segmentation algorithm to segment the graphic on the uploaded image into multiple parts; S202, integrating the graph segmentation algorithm into the APP; S203, adding a graphic segmentation button and a segmentation result display area in the APP; S204, mapping each segmented block into mathematical elements, and allowing the user to perform mathematical operations on these elements.

4. A mathematical element interactive control method as claimed in claim 3, characterized in that: The S201 includes: Choose U-Net, an image segmentation algorithm based on deep learning; U-Net uses CNN convolutional neural network to extract features and classify images; The input image size is set to 512x512 pixels, and the U-Net model is trained until the IoU ratio reaches a segmentation accuracy of more than 0.8; According to user requirements, different graphics or areas in the image are defined as different parts.

5. A mathematical element interactive control method as claimed in claim 3, characterized in that: The S203 includes: adding a button for triggering graphic segmentation in the UI design of the APP, and creating an area for displaying the segmentation result; When the user clicks the split button, the APP calls the graphic segmentation module for processing and displays the result in the specified area.

6. A mathematical element interactive control method as claimed in claim 3, characterized in that: The S204 includes: From uploading the image to completing the image segmentation, the segmentation process is required to be completed within 2 seconds.

7. A mathematical element interactive control method as claimed in claim 3, characterized in that: The step S204 further includes: performing contour extraction and shape recognition on each segmented part; Each segmented block is regarded as a different mathematical element, and the number of segments can be directly used for addition and subtraction operations.

8. A mathematical element interactive control method as claimed in claim 3, characterized in that: The S204 further includes: S301, splicing different fruit segments and processing the spliced ​​fruit features; S302, adding a naming input box in the APP to allow the user to enter a custom name for the new combination of fruits; S303, calculating the average value of RGB parameters of two or more fruit colors as the color of the new combined fruit; S304, generating the shape of the newly combined fruit according to the proportion of each fruit segment to the original fruit and the shape characteristics of the original fruit.

9. A mathematical element interactive control method as claimed in claim 8, characterized in that: The S303 includes: Extract RGB color parameters from each fruit segment; The RGB is a color model, where R stands for red, G stands for green, and B stands for blue, and the value range of each color is 0 to 255; Calculate the average of RGB parameters of two or more fruit colors.

10. A mathematical element interactive control method as claimed in claim 8, characterized in that: The S304 includes: Calculate the proportion of each fruit segment to the original fruit; Extract the shape features of the original fruit; The shape of the new combined fruit is generated according to the proportion of each fruit segment and the shape characteristics of the original fruit.

Images