A printing system, printing method and storage medium for regional printing

Abstract

The present invention discloses a printing system, a printing method and a storage medium for regional printing, wherein the printing method comprises the following steps: S1: opening a capture function in a printing software, and manually selecting a picture area to be captured; S2: calculating the position where the picture after capture starts to read the original picture data; S3: reading and copying data, and reading data line by line from the original image data with the starting position as a reference point; S4: printing the captured PRN picture; the present invention intelligently identifies image content through a deep learning algorithm, provides users with the best capture suggestions, allows users to easily select the most suitable capture area, improves the accuracy and integrity of the captured image, introduces a virtual reality interface and voice control functions, improves user experience, allows users to perform capture operations in a virtual environment, reduces manual operation requirements, and increases operational convenience.

Description

Technical Field

[0001] The present invention relates to the technical field of printers, and in particular to a printing system, a printing method and a storage medium for regional printing. Background Art

[0002] During the printing process, if you want to capture the image for printing, Figure 2 As shown, it is usually necessary to use special printing software such as RIP software or PS software to crop pictures. This method is cumbersome because it needs to be operated through external printing software every time; the area printing method provides the function of capturing pictures within the printing software, but if the user does not crop by entering coordinates and length and width, but chooses manual operation, some errors will occur in the capture process. This error may cause the captured picture to be inaccurate or not meet the requirements, affecting the final printing effect. Summary of the invention

[0003] The object of the present invention is to provide a printing system, a printing method and a storage medium for area printing, which solve the problems in the area printing process and provide a more efficient, accurate and user-friendly area printing method.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] A printing system for regional printing, characterized in that it comprises a module management unit, a printing unit and an image processing unit, and after a user imports an image into the image processing unit, the image is printed through the printing unit;

[0006] The image processing unit includes an image capture module, which manually selects the image area to be captured by dragging the selection area or drawing a capture frame;

[0007] The image processing unit further includes an image data calculation module, which calculates the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determines the starting position of the intercepted image in the original image data;

[0008] The image processing unit also includes an image data processing module, which reads data line by line from the original image data with the starting position as a reference point, and copies the valid data of each color channel line by line to a new memory block through a C language algorithm loop to obtain data of the intercepted area;

[0009] The printing unit prints the intercepted PRN picture, obtains the data of the intercepted area, converts the processed intercepted picture data into the PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN picture to the printing device for printing, thereby performing the function of regional printing.

[0010] The capture function of the image capture module allows users to manually capture or automatically select a capture area through intelligent recommendation. The deep learning algorithm is then used to perform content recognition and feature analysis on the image selected by the user, and analyze the image features. The image content and user habits are then combined to automatically generate the best capture suggestion, including the location, size and proportion of the capture area. The printing software provides users with intelligent recommendation options for the capture area based on the suggestions generated by the algorithm.

[0011] If the user chooses to use the smart recommendation option, the system automatically marks the best capture area, and the user can choose to accept the automatically generated capture area or make fine adjustments to meet personalized needs.

[0012] The intelligent recommendation option uses a deep learning algorithm to intelligently identify image content, establish a convolutional neural network model for image content recognition and feature analysis, extract image features and perform classification or regression tasks. The CNN model performs forward propagation on the image selected by the user and extracts features layer by layer.

[0013] The convolutional layer and pooling layer extract and abstract the features of the image, learn the low-level features such as edges and textures in the image, and map the extracted features to the final output layer through the fully connected layer to identify and classify the image content. Based on the CNN model's analysis and understanding of the image, the CNN model will output the prediction results of the image content to generate the best interception suggestions.

[0014] The image data calculation module calculates the position of the intercepted image in the original image data, and calculates the precise position and range of the intercepted image in the original image data according to the length, width and starting point coordinates of the intercepted area selected by the user, that is, the position and size parameters of the intercepted rectangle;

[0015] First determine the starting position of the cropped image in the original image data. The starting point coordinates are the position of the upper left corner of the cropped image in the original image data. Then, calculate the range of the cropped image in the original image data based on the length and width parameters of the cropped area. The length and width parameters determine the width and height of the cropped image. The specific expressions are as follows:

[0016] Set the starting point coordinates of the intercepted area to ((x_0, y_0)), the width of the intercepted area to (w), and the height of the intercepted area to (h); then the position and range of the intercepted image in the original image data are calculated using the following formula:

[0017] The coordinates of the upper left corner are: ((x_0, y_0)), and the coordinates of the lower right corner are ((x_1, y_1)), where (x_1 = x_0 + w) and (y_1 = y_0 + h).

[0018] The image data calculation module quickly extracts and processes the intercepted image data, divides the intercepted image data extraction task into multiple subtasks, each subtask processes a different part of the data block, divides the entire image data into multiple small blocks, each small block is used as a subtask, and each subtask is assigned to the GPU processor for parallel processing, using multiple cores of the GPU to process multiple data blocks at the same time. Each GPU core is responsible for processing one subtask and processing multiple data blocks at the same time.

[0019] The image data processing module reads the data of the intercepted area, extracts the features of the data by analyzing the read data, intelligently selects the optimal compression algorithm according to the data features and compression requirements, automatically selects the most suitable compression method according to the data characteristics and the selected algorithm, and then processes the data using the selected compression algorithm, encodes and compresses the data according to the algorithm rules, and after the compression processing is completed, stores or outputs the compressed data to reduce storage space occupancy.

[0020] The module management unit has a virtual reality interface, provides an intuitive operation interface, allows users to perform interception operations in a virtual environment, integrates an interception function in the virtual reality interface, allows users to select an interception area and perform an interception operation, and performs an interactive process of the interception function;

[0021] Then, by integrating voice recognition technology, voice control function is implemented, allowing users to quickly complete interception operations through voice commands and set voice command sets, including starting interception, confirming interception, and canceling operation commands.

[0022] The image capture module converts the processed captured image data into a PRN format for printing, ensuring the quality and accuracy of the PRN image;

[0023] Identify the connected printing device through the printing software, obtain the device information, detect the paper type loaded in the printing device, and determine the best printing parameter settings. Automatically adjust the printing resolution according to the highest resolution and paper type supported by the printing device;

[0024] Integrated cloud printing service allows users to print captured images at any time through cloud service. Users upload captured images to the cloud, select printing devices and parameters, and perform remote printing. Users select the cloud printing option through the printing software interface and upload captured images to the cloud printing service. In the cloud printing service, users set printing parameters, select printing devices, and confirm printing tasks.

[0025] Manage the print task queue in the cloud printing service, print in the order set by the user, provide print status monitoring and notification functions, inform users of the progress and results of printing tasks, and encrypt image data uploaded by users to prevent unauthorized users from accessing and protect user data privacy.

[0026] A method for area printing, comprising the following steps:

[0027] S1. Open the capture function in the printing software and manually select the image area to be captured by dragging the selection area or drawing a capture box;

[0028] S2, calculating the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determining the starting position of the intercepted image in the original image data;

[0029] S3, data reading and copying, reading data line by line from the original image data with the starting position as the reference point, and copying the valid data of each color channel line by line to the new memory block through the loop of the C language algorithm to obtain the data of the intercepted area;

[0030] S4, print the intercepted PRN image, obtain the data of the intercepted area, convert the processed intercepted image data into PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN image to the printing device for printing, and performs the function of regional printing.

[0031] A storage medium stores computer program instructions, which implement the above-mentioned printing method when the computer program instructions are executed by a processor.

[0032] The beneficial effects of the present invention are:

[0033] Through deep learning algorithms, it can intelligently identify image content and provide users with the best interception suggestions, allowing users to easily select the most suitable interception area, improve the accuracy and completeness of the intercepted image, introduce virtual reality interface and voice control functions to enhance user experience, allowing users to perform interception operations in a virtual environment, reduce manual operation requirements, and increase operational convenience;

[0034] Through GPU parallel computing, the captured image data processing task is decomposed into multiple subtasks and parallel processing is achieved, which speeds up data extraction, improves data processing efficiency, and ensures the accuracy and completeness of data extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0036] Figure 1 A schematic diagram of a cascade topology structure of a method for area printing provided in Example 1 of the present application;

[0037] Figure 2 This is a schematic diagram of the structure of the prior art of this application;

[0038] Figure 3 A schematic diagram of an intelligent data processing and optimization algorithm code for a method for area printing provided in Example 1 of the present application;

[0039] Figure 4 This is a schematic diagram of the algorithm code for data reading and copying in Example 3 of the present application. DETAILED DESCRIPTION

[0040] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0041] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0042] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. Example

[0043] This embodiment provides a printing system for regional printing, including a module management unit, a printing unit and an image processing unit. After a user imports an image into the image processing unit, the image is printed by the printing unit.

[0044] The image processing unit includes an image capture module, which manually selects the image area to be captured by dragging the selection area or drawing a capture frame;

[0045] The image processing unit further includes an image data calculation module, which calculates the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determines the starting position of the intercepted image in the original image data;

[0046] The image processing unit also includes an image data processing module, which reads data line by line from the original image data with the starting position as a reference point, and copies the valid data of each color channel line by line to a new memory block through a C language algorithm loop to obtain data of the intercepted area;

[0047] The printing unit prints the intercepted PRN picture, obtains the data of the intercepted area, converts the processed intercepted picture data into the PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN picture to the printing device for printing, thereby performing the function of regional printing.

[0048] The user manually selects the area to be captured in the printing software, and the capture position is calculated based on the area selected by the user, the original image data is read and the valid data is copied, and finally a PRN image is generated and the regional printing effect is achieved, providing users with convenient and accurate capture and printing functions.

[0049] In this embodiment, the capture function of the image capture module allows the user to manually capture and select a capture area, and then the deep learning algorithm is used to perform content recognition and feature analysis on the image selected by the user, identify key information such as objects, scenes, boundaries, etc. in the image, and analyze image features, and then combine the image content and user habits to generate the best capture suggestion, including the position, size and proportion of the capture area. The printing software provides the user with recommended options for the capture area based on the suggestions generated by the algorithm;

[0050] The algorithm automatically marks the optimal interception area. Users can choose to accept the automatically generated interception area or make fine adjustments to meet personalized needs.

[0051] Based on the manual capture of image areas using deep learning algorithms, the printing software analyzes image content and user needs through algorithms and automatically generates optimal capture suggestions, allowing users to easily select the most suitable capture area, thereby ensuring the accuracy and completeness of the captured image.

[0052] In this embodiment, the intelligent recommendation option uses a deep learning algorithm to intelligently identify image content and establish a convolutional neural network (CNN) model for image content recognition and feature analysis. CNN is a deep learning algorithm suitable for image processing. Through structures such as convolutional layers, pooling layers, and fully connected layers, it can effectively extract image features and perform classification or regression tasks. The CNN model will forward propagate the image selected by the user and extract features layer by layer.

[0053] In the convolution layer and pooling layer, the image features are extracted and abstracted, and low-level features such as edges and textures in the image are learned. The extracted features are mapped to the final output layer through the fully connected layer to realize image content recognition and classification. Based on the analysis and understanding of the image by the CNN model, the best interception suggestion is generated, including determining the parameters such as the position, size and proportion of the interception area.

[0054] Assume that the input image is (X), the specific formula of the forward propagation process of the CNN model is as follows:

[0055] [ Z^[l] = W^[l] * A^[l-1] + b^[l] ];

[0056] [ A^[l] = g^[l](Z^[l]) ];

[0057] Among them, (Z^[l]) represents the linear transformation output of the (l)th layer, (W^[l]) is the weight matrix of the (l)th layer, (A^[l-1]) is the activation output of the previous layer, (b^[l]) is the bias term of the (l)th layer, and (g^[l]) is the activation function. Finally, the CNN model will output the prediction results of the image content to generate the best cropping suggestions.

[0058] Through the forward propagation process of the CNN model, features are extracted layer by layer and linearly transformed and output, and finally the prediction result of the image content is output, thereby improving the accuracy and completeness of the cropped area and ensuring that the user selects the most suitable cropped area, thus affecting the final printing effect.

[0059] The image data calculation module calculates the position of the intercepted image in the original image data, and calculates the precise position and range of the intercepted image in the original image data according to the length, width and starting point coordinates of the intercepted area selected by the user, that is, the position and size parameters of the intercepted rectangle;

[0060] First determine the starting position of the cropped image in the original image data. The starting point coordinates are the position of the upper left corner of the cropped image in the original image data. Then, calculate the range of the cropped image in the original image data based on the length and width parameters of the cropped area. The length and width parameters determine the width and height of the cropped image. The specific expressions are as follows:

[0061] Set the starting point coordinates of the intercepted area to ((x_0, y_0)), the width of the intercepted area to (w), and the height of the intercepted area to (h); then the position and range of the intercepted image in the original image data are calculated using the following formula:

[0062] The coordinates of the upper left corner are: ((x_0, y_0)), and the coordinates of the lower right corner are ((x_1, y_1)), where (x_1 = x_0 + w) and (y_1 = y_0 + h).

[0063] Through precise position and range calculation, the accuracy of the captured image in the original image data can be improved, ensuring that the captured area selected by the user can be accurately extracted, thereby affecting the final printing effect.

[0064] The image data calculation module quickly extracts and processes the intercepted image data, divides the intercepted image data extraction task into multiple subtasks, each subtask processes a different part of the data block, divides the entire image data into multiple small blocks, each small block is used as a subtask, and each subtask is assigned to the GPU processor for parallel processing. Multiple cores of the GPU are used to process multiple data blocks at the same time. Each GPU core is responsible for processing one subtask and processes multiple data blocks at the same time to achieve parallel computing.

[0065] By utilizing the parallel computing power of GPU to divide the task of extracting the captured image data into multiple subtasks and implement parallel processing, large amounts of data can be processed efficiently, speeding up data extraction, improving data processing efficiency, and ensuring the accuracy and completeness of data extraction.

[0066] The image data processing module reads the data of the intercepted area, and extracts the features of the data by analyzing the read data, including text data, image data, audio data, video data, etc., or an array, linked list, tree, etc. that describes the organizational relationship between the data;

[0067] Intelligently select the best compression algorithm, such as Lempel-Ziv-Welch, Huffman, etc., based on data characteristics and compression requirements. Automatically select the most suitable compression method, such as lossless compression or lossy compression, based on data characteristics and the selected algorithm.

[0068] The data is then processed using the selected compression algorithm, and the data is encoded and compressed according to the algorithm rules.

[0069] Then evaluate the compressed data to check the compression effect, compression ratio and data quality. According to the evaluation results, dynamically adjust the parameters of the compression algorithm to achieve better compression effect and data quality.

[0070] After the compression process is completed, the compressed data is stored or output to reduce the storage space occupied. The algorithm is as follows Figure 3 shown.

[0071] For example:

[0072] Assume that three different types of data need to be processed: text data, image data, and audio data. First, by analyzing these data, their characteristics, such as text, image, and audio, are determined. According to the compression requirements, the goal is to achieve lossless compression. Based on the data characteristics and compression requirements, the optimal compression algorithm is intelligently selected;

[0073] For text data, the gzip compression algorithm was selected to compress the text data. For image data, the jpeg compression algorithm was selected to compress the image through the OpenCV library. As for audio data, a virtual audio compression method was used.

[0074] After the compression is completed, the text data is evaluated, the compression ratio is calculated and the compression quality is evaluated. Finally, the selected compression algorithm and the evaluation results are output to achieve the purpose of reducing the storage space occupied.

[0075] The module management unit has a virtual reality interface, provides an intuitive operation interface, allows users to perform interception operations in a virtual environment, integrates an interception function in the virtual reality interface, allows users to select an interception area and perform an interception operation, and implements an interactive process of the interception function, including operations such as dragging the selected area and confirming the interception;

[0076] By integrating voice recognition technology, the voice control function is realized, allowing users to quickly complete the interception operation through voice commands and set the voice command set, including starting interception, confirming interception, canceling operations and other commands.

[0077] The integrated voice recognition technology allows users to quickly complete the interception operation through voice commands. A series of voice command sets are set, including start interception, confirm interception, cancel operation and other commands, which improves the convenience of operation and user experience. Users can achieve more intelligent and convenient interception operations through voice control, reducing the need for manual operation.

[0078] The image capture module converts the processed captured image data into a PRN format for printing, ensuring the quality and accuracy of the PRN image to obtain the best printing effect;

[0079] Identify the connected printing device through the printing software and obtain device-related information, such as supported resolution, color mode, and printing speed. Then detect the paper type loaded in the printing device, such as A4, letter paper, etc., to determine the best printing parameter settings. According to the highest resolution and paper type supported by the printing device, the printing resolution is automatically adjusted to ensure clear and delicate printing effects.

[0080] Integrated cloud printing service allows users to print captured images at any time through cloud service. Users upload captured images to the cloud, select printing devices and parameters, and realize remote printing function. Users select cloud printing option through the printing software interface and upload captured images to cloud printing service. In cloud printing service, users set printing parameters, select printing devices, and confirm printing tasks.

[0081] The cloud printing service manages the print task queue, prints in the order set by the user, provides print status monitoring and notification functions, informs users of the progress and results of printing tasks, and encrypts the image data uploaded by users to ensure that the cloud printing function has user authority management and data security, prevents unauthorized users from accessing and protects user data privacy, and ensures the security of data transmission and storage, thereby improving user experience and operational convenience.

[0082] The present invention combines intelligent technology and user experience optimization, effectively improving the accuracy of regional printing, data processing efficiency and user operation convenience, promoting the development and application of regional printing technology, and then ensuring the accuracy and completeness of interception through deep learning algorithms and intelligent data processing. At the same time, it provides a user-friendly interaction method and supports cloud printing, providing users with a more intelligent and convenient regional printing experience. Example

[0083] like Figure 1 As shown, this embodiment provides a regional printing method based on the printing system in Embodiment 1, comprising the following steps:

[0084] S1. Open the capture function in the printing software and manually select the image area to be captured by dragging the selection area or drawing a capture box;

[0085] S2, calculating the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determining the starting position of the intercepted image in the original image data;

[0086] S3, data reading and copying, reading data line by line from the original image data with the starting position as the reference point, and copying the valid data of each color channel line by line to the new memory block through the loop of the C language algorithm to obtain the data of the intercepted area;

[0087] S4, print the intercepted PRN image, obtain the data of the intercepted area, convert the processed intercepted image data into PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN image to the printing device for printing, and performs the function of regional printing.

[0088] This embodiment further provides a storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the above-mentioned printing method is implemented.

[0089] In this embodiment, the YOLO target detection algorithm is used, and the user can automatically select the best interception area through the intelligent recommendation option, thereby improving the accuracy and efficiency of interception, while retaining the flexibility of manual intervention by the user, improving the user experience and the convenience of operation. The YOLO algorithm is an efficient real-time target detection algorithm, which divides the image into multiple grids through a single neural network, and predicts the bounding box and category probability of each grid to achieve fast and accurate target detection.

[0090] Object detection: Pass the input image to the trained YOLO model, and the model will identify the object in the image and provide the location and category of the bounding box;

[0091] Intelligent recommendation: based on the targets detected by the YOLO algorithm, the system intelligently recommends the interception area and provides the best interception suggestions based on the target location and importance;

[0092] User selection: Users can choose to accept the cropping area recommended by the system, or they can make manual adjustments to meet their individual needs. Example

[0093] This embodiment is a method for regional printing that combines intelligent technology and user experience optimization, improves interception accuracy, data processing efficiency, and user operation convenience, and promotes the development and application of regional printing technology.

[0094] S1. Intelligent capture and optimization. The capture function is turned on in the printing software. Users can capture manually or automatically select the capture area through intelligent recommendation. The deep learning algorithm is used to intelligently identify the image content and provide the best capture suggestions. The image content is identified through intelligent recommendation and deep learning algorithm, and the best capture suggestions are provided. This can ensure that users select the most suitable capture area, thereby avoiding errors that may be caused by manual operation and improving the accuracy and completeness of the captured image.

[0095] S2, data processing and calculation, according to the length and width of the intercepted area and the coordinates of the starting point, calculate the position of the intercepted image in the original image data, use GPU acceleration processing to quickly extract and process the intercepted image data;

[0096] S3, intelligent data processing and optimization, by extracting and processing the intercepted image data, reading the data line by line at the beginning, and intelligently compressing the data to reduce the storage space occupied, using the intelligent data compression algorithm to automatically select the optimal compression method, generate the final intercepted PRN image, and improve data processing efficiency;

[0097] S4, user interaction and experience, provides a virtual reality interface, allowing users to perform interception operations in a virtual environment to enhance the user experience. At the same time, a voice control function is added, allowing users to quickly complete interception operations through voice commands, improving operational convenience.

[0098] S5, intelligent printing and cloud support, the printing software achieves regional printing effect through the generated intercepted PRN picture, automatically optimizes the printing parameters to obtain the best printing effect, and supports cloud printing function. Users can print the intercepted pictures at any time through cloud service, which increases flexibility and convenience.

[0099] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A printing system for regional printing, characterized in that: It includes a module management unit, a printing unit and an image processing unit. After the user imports the image into the image processing unit, the image is printed through the printing unit; The image processing unit includes an image capture module, which manually selects the image area to be captured by dragging the selection area or drawing a capture frame; The image processing unit further includes an image data calculation module, which calculates the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determines the starting position of the intercepted image in the original image data; The image processing unit also includes an image data processing module, which reads data line by line from the original image data with the starting position as a reference point, and copies the valid data of each color channel line by line to a new memory block through a C language algorithm loop to obtain data of the intercepted area; The printing unit prints the intercepted PRN picture, obtains the data of the intercepted area, converts the processed intercepted picture data into the PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN picture to the printing device for printing, thereby performing the function of regional printing.

2. A printing system for regional printing according to claim 1, characterized in that: The capture function of the image capture module allows users to manually capture or automatically select a capture area through intelligent recommendation. The deep learning algorithm is then used to perform content recognition and feature analysis on the image selected by the user, and analyze the image features. The image content and user habits are then combined to automatically generate the best capture suggestion, including the location, size and proportion of the capture area. The printing software provides users with intelligent recommendation options for the capture area based on the suggestions generated by the algorithm. After the user chooses to use the smart recommendation option, the system automatically marks the best interception area. After the user chooses to accept the automatically generated interception area, it can be fine-tuned as needed to meet personalized needs.

3. A printing system for regional printing according to claim 2, characterized in that: The intelligent recommendation option uses a deep learning algorithm to intelligently identify image content, establish a convolutional neural network model for image content recognition and feature analysis, extract image features and perform classification or regression tasks. The CNN model performs forward propagation on the image selected by the user and extracts features layer by layer. The image features are extracted and abstracted in the convolutional layer and pooling layer, and the low-level features of edges and textures in the image are learned. The extracted features are mapped to the final output layer through the fully connected layer to identify and classify the image content. Based on the CNN model's analysis and understanding of the image, the CNN model will output the prediction results of the image content to generate the best interception suggestions.

4. A printing system for regional printing according to claim 1, characterized in that: The image data calculation module calculates the position of the intercepted image in the original image data according to the length, width and starting point coordinates of the intercepted area selected by the user, that is, the position and size parameters of the intercepted rectangle, and calculates the precise position and range of the intercepted image in the original image data; First determine the starting position of the cropped image in the original image data. The starting point coordinates are the position of the upper left corner of the cropped image in the original image data. Then, calculate the range of the cropped image in the original image data based on the length and width parameters of the cropped area. The length and width parameters determine the width and height of the cropped image. The specific expressions are as follows: Set the starting point coordinates of the intercepted area to (x_0, y_0), the width of the intercepted area to w, and the height of the intercepted area to h; then the position and range of the intercepted image in the original image data are calculated using the following formula: The coordinates of the upper left corner are: (x_0, y_0), and the coordinates of the lower right corner are (x_1, y_1), where x_1 = x_0 +w, y_1 = y_0 + h.

5. A printing system for regional printing according to claim 1, characterized in that: The image data calculation module quickly extracts and processes the intercepted image data, divides the intercepted image data extraction task into multiple subtasks, each subtask processes a different part of the data block, divides the entire image data into multiple small blocks, each small block is used as a subtask, and each subtask is assigned to the GPU processor for parallel processing, using multiple cores of the GPU to process multiple data blocks at the same time. Each GPU core is responsible for processing one subtask and processing multiple data blocks at the same time.

6. A printing system for regional printing according to claim 1, characterized in that: The image data processing module reads the data of the captured area, extracts the features of the data by analyzing the read data, intelligently selects the optimal compression algorithm according to the data features and compression requirements, automatically selects the most suitable compression method according to the data characteristics and the selected algorithm, and then processes the data using the selected compression algorithm, encodes and compresses the data according to the algorithm rules, and after the compression process is completed, stores or outputs the compressed data to reduce storage space occupancy.

7. A printing system for regional printing according to claim 1, characterized in that: The module management unit has a virtual reality interface, provides an intuitive operation interface, allows users to perform interception operations in a virtual environment, integrates an interception function in the virtual reality interface, allows users to select an interception area and perform an interception operation, and performs an interactive process of the interception function; Then, by integrating voice recognition technology, voice control function is implemented, allowing users to quickly complete interception operations through voice commands and set voice command sets, including starting interception, confirming interception, and canceling operation commands.

8. A printing system for regional printing according to claim 5, characterized in that: The image capture module converts the processed captured image data into a PRN format for printing, thereby ensuring the quality and accuracy of the PRN image; Identify the connected printing device through the printing software, obtain the device information, detect the paper type loaded in the printing device, and determine the best printing parameter settings. Automatically adjust the printing resolution according to the highest resolution and paper type supported by the printing device; Integrate cloud printing service to allow users to print captured images at any time through cloud service. Users upload captured images to the cloud, select printing devices and parameters, and perform remote printing. Users select cloud printing option through the printing software interface and upload captured images to cloud printing service. In cloud printing service, users set printing parameters, select printing devices, and confirm printing tasks. Manage the print task queue in the cloud printing service, print in the order set by the user, provide print status monitoring and notification functions, inform users of the progress and results of printing tasks, and encrypt image data uploaded by users to prevent unauthorized users from accessing and protect user data privacy.

9. A printing method based on the printing system according to any one of claims 1 to 8, characterized in that: The steps include: S1. Open the capture function in the printing software and manually select the image area to be captured by dragging the selection area or drawing a capture box; S2, calculating the precise position and range of the intercepted image in the original image data according to the length and width manually intercepted by the user and the coordinates of the starting point, and determining the starting position of the intercepted image in the original image data; S3, data reading and copying, reading data line by line from the original image data with the starting position as the reference point, and copying the valid data of each color channel line by line to the new memory block through the loop of the C language algorithm to obtain the data of the intercepted area; S4, print the intercepted PRN picture, obtain the data of the intercepted area, convert the processed intercepted picture data into PRN format, and the user selects the printing area through the printing software, sets the printing parameters, and then sends the generated PRN picture to the printing device for printing, and performs the function of regional printing.

10. A storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the printing method described in claim 9 is implemented.

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