Data Processing Method, System and Electronic Device Based on an Electronic Drawing Board

Abstract

The present invention relates to the technical field of drawing board data processing, and in particular to a data processing method, system and electronic device based on an electronic drawing board. The method includes the following steps: obtaining electronic drawing board drawing data and electronic drawing board area data; performing area gridification on the electronic drawing board drawing data to generate electronic drawing board area grid data; performing area drawing positioning association on the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data; performing drawing pattern segmentation on the electronic drawing board drawing behavior data to generate electronic drawing board drawing pattern segmentation data; performing drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data. The present invention improves the drawing accuracy and sensitivity by obtaining the electronic drawing board drawing data and performing abnormal area division and influence prediction on the electronic drawing board.

Description

Technical Field

[0001] The present invention relates to the technical field of drawing board data processing, and in particular to a data processing method, system and electronic device based on an electronic drawing board. Background Art

[0002] With the improvement of computer hardware performance and the enhancement of graphics processing capabilities, electronic drawing boards have begun to become important tools for professional graphic design and digital art creation. At the same time, the development of touch technology has also brought innovation to the interaction mode of electronic drawing boards. With the popularization of flat panel displays and the improvement of touch technology, electronic drawing boards have gradually entered the consumer market and become common tools for designers, educators and ordinary users. With the rise of digital office, electronic drawing boards have been widely used in enterprises and become important auxiliary tools for meetings, training and presentations. In recent years, with the development of artificial intelligence and machine learning technologies, electronic drawing boards have also begun to integrate more intelligent functions, such as handwriting recognition, intelligent handwriting correction, etc., further improving the user experience and efficiency. However, currently in liquid crystal drawing boards / liquid crystal writing boards, there is a situation of drawing delay, which will lead to a decline in the user experience. At the same time, the existing technology fails to fully consider the drawing pressure data, resulting in insufficiently fine division of the drawing sensing area, and thus lower drawing accuracy and sensitivity. Summary of the Invention

[0003] Based on this, it is necessary to provide a data processing method, system and electronic device based on an electronic drawing board to solve at least one of the above technical problems.

[0004] To achieve the above object, a data processing method based on an electronic drawing board, the method includes the following steps:

[0005] Step S1: Obtain electronic drawing board drawing data and electronic drawing board area data; perform area gridification on the electronic drawing board drawing data to generate electronic drawing board area grid data; perform area drawing positioning association on the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data;

[0006] Step S2: Perform drawing pattern segmentation on the electronic drawing board drawing behavior data to generate electronic drawing board drawing pattern segmentation data; perform drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data; perform high-delay area division on the electronic drawing board area data according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing area data;

[0007] Step S3: Obtain the drawing pressure data; perform drawing induction area division on the grid data of the electronic drawing board area through the drawing pressure data to generate the electronic drawing board induction area data; calculate the induction uniformity based on the electronic drawing board drawing behavior data and the electronic drawing board induction area data to obtain the electronic drawing board induction uniformity data; perform weak induction area division on the electronic drawing board induction area data based on the electronic drawing board induction uniformity data to generate the electronic drawing board weak response drawing area data;

[0008] Step S4: Perform grid marking on the grid data of the electronic drawing board area according to the high-latency drawing area data and the weak response drawing area data of the electronic drawing board to generate the high-abnormality drawing board grid area data and the low-abnormality drawing board grid area data; construct an area repair strategy through the high-abnormality drawing board grid area data and the low-abnormality drawing board grid area data to execute the electronic drawing board drawing operation.

[0009] The present invention obtains the electronic drawing board drawing data and the electronic drawing board area data and performs area gridification on them. This helps to associate the drawing data with specific drawing areas to generate the electronic drawing board drawing behavior data. This area drawing positioning association helps to accurately capture the drawing behavior and provide more accurate data and information. The electronic drawing board drawing behavior data undergoes drawing pattern segmentation and delay detection to generate the electronic drawing board drawing pattern segmentation data and the drawing delay detection data. These data can be used to analyze and understand the characteristics and delay conditions of the drawing patterns. Through high-latency area division, the areas with drawing delays on the electronic drawing board can be determined, and corresponding measures can be taken for optimization. The drawing pressure data is used for induction area division and induction uniformity calculation. Through drawing induction area division, the grid data of the electronic drawing board area can be divided into different induction areas to better capture the characteristics of the drawing. The calculation of the induction uniformity can evaluate the response performance of the electronic drawing board and help to understand the induction situation during the drawing process. The division of the weak response drawing area can help to identify the areas with response problems on the electronic drawing board. According to the high-latency drawing area data and the weak response drawing area data, grid marking is performed on the grid data of the electronic drawing board area. This helps to identify the high-abnormality and low-abnormality drawing board grid areas. Through the construction of an area repair strategy, corresponding repair measures can be taken for these abnormal areas to improve the quality and efficiency of the electronic drawing board drawing operation. Therefore, the present invention improves the drawing accuracy and sensitivity by obtaining the electronic drawing board drawing data and performing abnormal area division and impact prediction on the electronic drawing board.

[0010] In this specification, a data processing system based on an electronic drawing board is provided for executing the above-mentioned data processing method based on an electronic drawing board. The data processing system based on an electronic drawing board includes:

[0011] A drawing behavior analysis module, configured to obtain the drawing data of an electronic drawing board and the area data of the electronic drawing board; perform area gridification on the drawing data of the electronic drawing board to generate area grid data of the electronic drawing board; perform area drawing positioning association on the drawing data of the electronic drawing board and the area grid data of the electronic drawing board to generate drawing behavior data of the electronic drawing board.

[0012] A delay area analysis module, configured to perform drawing pattern segmentation on the drawing behavior data of the electronic drawing board to generate drawing pattern segmentation data of the electronic drawing board; perform drawing delay detection on the drawing pattern segmentation data of the electronic drawing board to generate drawing delay detection data of the electronic drawing board; perform high-delay area division on the area data of the electronic drawing board according to the drawing delay detection data of the electronic drawing board to generate high-delay drawing area data of the electronic drawing board.

[0013] An induction area analysis module, configured to obtain drawing pressure data; perform drawing induction area division on the area grid data of the electronic drawing board through the drawing pressure data to generate induction area data of the electronic drawing board; perform induction uniformity calculation according to the drawing behavior data of the electronic drawing board and the induction area data of the electronic drawing board to obtain induction uniformity data of the electronic drawing board; perform weak induction area division on the induction area data of the electronic drawing board based on the induction uniformity data of the electronic drawing board to generate weak response drawing area data of the electronic drawing board.

[0014] An abnormal area impact analysis module, configured to perform grid marking on the area grid data of the electronic drawing board according to the high-delay drawing area data of the electronic drawing board and the weak response drawing area data of the electronic drawing board to generate high-abnormal drawing board grid area data and low-abnormal drawing board grid area data; construct an area repair strategy through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to execute the drawing operation of the electronic drawing board.

[0015] The present invention further provides an electronic device, where the electronic device includes:

[0016] At least one processor;

[0017] A memory communicatively connected to the at least one processor;

[0018] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the data processing method based on the electronic drawing board as described in any one of the above.

[0019] The beneficial effects of the present invention are as follows: By analyzing and processing the drawing behavior data of the electronic drawing board, the drawing intention of the user can be captured more accurately, thereby improving the accuracy and efficiency of drawing. By detecting and analyzing the drawing delay and induction area, the drawing experience of the electronic drawing board can be optimized, reducing the drawing delay and enhancing the induction of drawing, making the user feel smoother and more natural when using the electronic drawing board. By repairing abnormal drawing board areas and optimizing the drawing experience, the user's satisfaction and usage experience with the electronic drawing board can be improved, enhancing the user's favorability towards the product, which is beneficial to the promotion of the product and its market competitiveness. Optimizing the drawing experience and enhancing user satisfaction helps the product gain more competitive advantages in the market, attract more users to choose and use it, and thus enhance the product's competitiveness in the industry. Therefore, the present invention improves the accuracy and sensitivity of drawing by obtaining the drawing data of the electronic drawing board and dividing the abnormal areas of the electronic drawing board and predicting the impacts. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic flow chart of the steps of a data processing method based on an electronic drawing board;

[0021] Figure 2 is Figure 1 a detailed implementation step flow chart of step S2 in

[0022] Figure 3 is Figure 2 a detailed implementation step flow chart of step S23 in

[0023] Figure 4 is Figure 1 a detailed implementation step flow chart of step S3 in

[0024] The realization, functional characteristics, and advantages of the object of the present invention will be further described in conjunction with the embodiments with reference to the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The technical method of the present invention for the patent will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0026] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0027] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0028] To achieve the above object, please refer to Figures 1 to 4 , the present invention provides a data processing method based on an electronic drawing board, and the method includes the following steps:

[0029] Step S1: Obtain the electronic drawing board drawing data and the electronic drawing board area data; perform area gridification on the electronic drawing board drawing data to generate electronic drawing board area grid data; perform area drawing positioning association on the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data;

[0030] Step S2: Perform drawing pattern segmentation on the electronic drawing board drawing behavior data to generate electronic drawing board drawing pattern segmentation data; perform drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data; perform high-delay area division on the electronic drawing board area data according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing area data;

[0031] Step S3: Obtain the drawing pressure data; perform drawing sensing area division on the electronic drawing board area grid data through the drawing pressure data to generate electronic drawing board sensing area data; perform sensing uniformity calculation according to the electronic drawing board drawing behavior data and the electronic drawing board sensing area data to obtain electronic drawing board sensing uniformity data; perform weak sensing area division on the electronic drawing board sensing area data based on the electronic drawing board sensing uniformity data to generate electronic drawing board weak response drawing area data;

[0032] Step S4: Perform grid marking on the electronic drawing board area grid data according to the high-latency drawing area data and weak-response drawing area data of the electronic drawing board to generate high-abnormality drawing board grid area data and low-abnormality drawing board grid area data; construct an area repair strategy through the high-abnormality drawing board grid area data and low-abnormality drawing board grid area data to execute the electronic drawing board drawing operation.

[0033] The present invention obtains the electronic drawing board drawing data and the electronic drawing board area data and performs area gridification on them. This helps to associate the drawing data with specific drawing areas and generate electronic drawing board drawing behavior data. This area drawing positioning association helps to accurately capture the drawing behavior and provide more accurate data and information. The electronic drawing board drawing behavior data undergoes drawing pattern segmentation and delay detection to generate electronic drawing board drawing pattern segmentation data and drawing delay detection data. These data can be used to analyze and understand the characteristics and delay conditions of the drawing patterns. Through high-latency area division, the areas with drawing delays on the electronic drawing board can be determined, and corresponding measures can be taken for optimization. The drawing pressure data is used for induction area division and induction uniformity calculation. Through drawing induction area division, the electronic drawing board area grid data can be divided into different induction areas to better capture the characteristics of the drawing. The calculation of induction uniformity can evaluate the response performance of the electronic drawing board and help to understand the induction situation during the drawing process. The division of the weak-response drawing area can help to identify the areas with response problems on the electronic drawing board. According to the high-latency drawing area data and weak-response drawing area data, grid marking is performed on the electronic drawing board area grid data. This helps to identify the high-abnormality and low-abnormality drawing board grid areas. Through the construction of an area repair strategy, corresponding repair measures can be taken for these abnormal areas to improve the quality and efficiency of the electronic drawing board drawing operation. Therefore, the present invention improves the drawing accuracy and sensitivity by obtaining the electronic drawing board drawing data and performing abnormal area division and impact prediction on the electronic drawing board.

[0034] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of a data processing method based on an electronic drawing board according to the present invention. In this example, the data processing method based on an electronic drawing board includes the following steps:

[0035] Step S1: Obtain the electronic drawing board drawing data and the electronic drawing board area data; perform area gridification on the electronic drawing board drawing data to generate electronic drawing board area grid data; perform area drawing positioning association on the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data;

[0036] In the embodiments of the present invention, drawing data of the electronic drawing board is obtained by using appropriate sensors or interfaces, including information such as drawing coordinates, timestamps, and drawing pressures. Area data of the electronic drawing board is obtained, that is, information such as the size and resolution of the drawing board. The drawing area of the electronic drawing board is divided into grids, and methods such as regular grid division or adaptive grid division can be used. The purpose of gridification is to correspond the drawing data with the drawing board area and facilitate subsequent data processing and analysis. The drawing data of the electronic drawing board is associated with the area grid data to determine the grid position where each drawing action is located. According to information such as the position and time of the drawing action in the grid, drawing behavior data of the electronic drawing board is generated, including information such as drawing trajectories, speeds, and accelerations.

[0037] Step S2: Perform drawing pattern segmentation on the drawing behavior data of the electronic drawing board to generate drawing pattern segmentation data of the electronic drawing board; perform drawing delay detection on the drawing pattern segmentation data of the electronic drawing board to generate drawing delay detection data of the electronic drawing board; divide the high-delay areas of the electronic drawing board area data according to the drawing delay detection data of the electronic drawing board to generate high-delay drawing area data of the electronic drawing board;

[0038] In the embodiments of the present invention, according to the drawing trajectory information in the drawing behavior data, some image processing or machine learning methods, such as edge detection and clustering analysis, can be used to segment the drawing trajectory and segment different drawing patterns. The purpose of segmentation is to divide continuous drawing actions into different patterns for subsequent delay detection and analysis. After different drawing patterns are segmented, the drawing time of each drawing pattern can be calculated according to the time information of the drawing trajectory. By comparing the difference between the drawing time and the actual operation time, it can be detected whether there is a drawing delay. If a high drawing delay is detected in a certain area, the drawing board area can be divided into a high-delay area and a low-delay area according to the delay detection data. For the high-delay area, further analysis and optimization can be carried out to improve the drawing experience.

[0039] Step S3: Obtain drawing pressure data; perform drawing sensing area division on the area grid data of the electronic drawing board through the drawing pressure data to generate sensing area data of the electronic drawing board; calculate the sensing uniformity according to the drawing behavior data of the electronic drawing board and the sensing area data of the electronic drawing board to obtain sensing uniformity data of the electronic drawing board; perform weak sensing area division on the sensing area data of the electronic drawing board based on the sensing uniformity data of the electronic drawing board to generate weak response drawing area data of the electronic drawing board;

[0040] In the embodiments of the present invention, by using the pressure sensor of the electronic drawing board or other related devices, the pressure data exerted by the user on the surface of the drawing board during the drawing process is obtained. These data may include information such as the magnitude of the pressure for each drawing action and the position where the pressure is applied. Based on the drawing pressure data, the drawing induction degree of each regional grid can be determined. By setting appropriate thresholds or using machine learning algorithms, the drawing board area is divided into different induction areas to represent different degrees of pressure induction. These induction area data can reflect the response of the electronic drawing board to the drawing pressure at different positions. Combining the drawing behavior data and the induction area data, the drawing uniformity of each area can be calculated. The drawing uniformity can be defined according to different metrics, such as the degree of change in drawing pressure and the uniformity of drawing speed. Through the calculated induction uniformity data, the drawing response of the electronic drawing board in different areas can be evaluated. According to the induction uniformity data, the areas with weak response can be identified, that is, the areas with insufficient or uneven drawing response. For these weak response areas, corresponding measures can be taken for optimization, such as adjusting the sensor sensitivity and improving the hardware structure. The generated weak response drawing area data can be used as the basis for subsequent optimization.

[0041] Step S4: Perform grid marking on the electronic drawing board area grid data according to the high-latency drawing area data and the weak-response drawing area data of the electronic drawing board to generate high-abnormal drawing board grid area data and low-abnormal drawing board grid area data; construct an area repair strategy through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to execute the electronic drawing board drawing operation.

[0042] In the embodiments of the present invention, according to the high-latency and weak-response drawing area data obtained in step S3, the corresponding grids are marked for further construction of the repair strategy. The grids in the high-latency area and the weak-response area can use different marks or mark different attributes. According to the marked high-latency and weak-response areas, the area grid data of the electronic drawing board can be divided into two categories: high-abnormal and low-abnormal. The high-abnormal drawing board grid area data contains the high-latency areas, and the low-abnormal drawing board grid area data contains the weak-response areas. According to the high-abnormal and low-abnormal grid area data, corresponding repair strategies are designed to improve the drawing performance of the electronic drawing board. For the high-abnormal areas, it is necessary to adjust the drawing algorithm, optimize the data transmission method, etc. to reduce the drawing latency. For the low-abnormal areas, it is necessary to adjust the sensor sensitivity, increase the compensation mechanism, etc. to improve the drawing responsiveness.

[0043] Preferably, step S1 includes the following steps:

[0044] Step S11: Obtain the electronic drawing board drawing data and the electronic drawing board area data;

[0045] Step S12: Perform data preprocessing on the drawing data of the electronic drawing board to generate standard electronic drawing board drawing data, where the data preprocessing includes data cleaning, filling of missing data values, and data standardization;

[0046] Step S13: Perform regional gridification on the electronic drawing board area data to generate electronic drawing board area grid data;

[0047] Step S14: Perform regional drawing positioning association on the standard electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data.

[0048] The present invention obtains the drawing data of the electronic drawing board and the electronic drawing board area data. By obtaining these data, subsequent data processing and analysis can be carried out, so as to better understand and utilize the drawing information of the electronic drawing board. Data preprocessing is a process of cleaning, filling missing values, and standardizing the drawing data of the electronic drawing board. Data cleaning can remove outliers and noise, improving the quality of the data. Filling missing data values can supplement the missing data, making the data set more complete. Data standardization can convert the data into a unified standard format, facilitating subsequent processing and analysis. These preprocessing steps help improve the reliability and consistency of the data. Perform regional gridification on the electronic drawing board area data to generate electronic drawing board area grid data. By converting the area data into a grid form, it is more convenient to locate and analyze different areas, laying a foundation for the generation of subsequent drawing behavior data. Perform regional drawing positioning association on the standard electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data. This step associates the drawing data with specific areas, enabling accurate recording and analysis of drawing behaviors in different areas. This helps to deeply understand the patterns and characteristics in the drawing process, providing a basis for subsequent analysis and optimization.

[0049] In the embodiment of the present invention, by ensuring that the electronic drawing board is connected and working properly. Obtain the drawing data and area data on the electronic drawing board through appropriate interfaces or software, which involves using APIs, serial communication, or other data transmission methods. Clean the obtained drawing data to remove outliers or incomplete data. Fill the missing data values in the data, using appropriate filling algorithms such as mean filling, interpolation filling, etc. Perform standardization processing on the drawing data to ensure that the data is within a certain range, which is beneficial for subsequent processing and analysis. Divide the drawing board area into grids, which can be regular square grids or grids with adjusted shapes and sizes according to the actual situation. Map the drawing board area data to the corresponding grids to form electronic drawing board area grid data. Analyze the standardized drawing data and area grid data to determine the location where the drawing action occurs. Associate the drawing data with the corresponding drawing board area grid to determine which area the drawing behavior occurs in. Generate the final electronic drawing board drawing behavior data, including the drawing content and location information.

[0050] Preferably, step S2 includes the following steps:

[0051] Step S21: Analyze the drawing content of the electronic drawing board drawing behavior data to generate electronic drawing board drawing content data; segment the electronic drawing board drawing behavior data according to the electronic drawing board drawing content data to generate electronic drawing board drawing pattern segmentation data;

[0052] Step S22: Analyze the drawing timing of the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing timing data;

[0053] Step S23: Detect delays in the electronic drawing board drawing behavior data based on the electronic drawing board drawing timing data to generate electronic drawing board drawing delay detection data;

[0054] Step S24: Compare the electronic drawing board drawing delay detection data with a preset drawing delay threshold. When the electronic drawing board drawing delay detection data is greater than or equal to the preset drawing delay threshold, divide the electronic drawing board area data into high-delay areas according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing area data.

[0055] In the present invention, by analyzing the drawing content of the electronic drawing board drawing behavior data, electronic drawing board drawing content data is generated. By analyzing the drawing behavior data, specific drawing content information can be extracted, such as the shapes, patterns, or text drawn. This helps to better understand the specific content of the drawing and provides richer data for subsequent analysis and processing. Analyze the drawing timing of the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing timing data. By analyzing the timing of the drawn patterns, timing characteristics such as the drawing sequence, speed, and duration can be understood. This helps to reveal the dynamic process of the drawing behavior, provides more detailed timing information, and serves as a basis for subsequent delay detection and analysis. Detect delays in the electronic drawing board drawing behavior data based on the electronic drawing board drawing timing data to generate electronic drawing board drawing delay detection data. By analyzing the drawing timing data, existing drawing delay situations can be detected. This helps to identify delay problems in the drawing process and provides a basis for subsequent processing and optimization. Compare the electronic drawing board drawing delay detection data with a preset drawing delay threshold, and divide the electronic drawing board area data into high-delay areas according to the detection results to generate electronic drawing board high-delay drawing area data. This step can help determine the areas with high delays and mark them as high-delay drawing areas. This helps to locate and process the specific areas of the drawing delay problem so as to take corresponding measures for optimization and improvement.

[0056] As an example of the present invention, refer to Figure 2As shown, in this example, step S2 includes:

[0057] Step S21: Analyze the drawing content of the electronic drawing board drawing behavior data to generate electronic drawing board drawing content data; segment the electronic drawing board drawing pattern based on the electronic drawing board drawing content data to generate electronic drawing board drawing pattern segmentation data;

[0058] In the embodiment of the present invention, by analyzing the drawing behavior data of the electronic drawing board, the drawn content information is extracted. Technologies such as image processing, pattern recognition, or deep learning are used to identify and classify the drawn content, such as graphics, text, etc. The analyzed drawn content information is sorted and stored to form an electronic drawing board drawing content data set. The drawing content data can be stored in a structured manner for subsequent analysis and application. Based on the drawing content data, the electronic drawing board drawing behavior data is segmented into patterns, and the drawn patterns are segmented into different parts or layers. Image processing algorithms or deep learning models can be used to identify and segment different pattern parts. The information of the segmented pattern parts is stored to form electronic drawing board drawing pattern segmentation data. These data can describe the position, shape, color, and other attributes of each pattern part.

[0059] Step S22: Analyze the drawing time sequence of the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing time sequence data;

[0060] In the embodiment of the present invention, by performing time sequence analysis on the electronic drawing board drawing pattern segmentation data, that is, analyzing the drawing order and time information of each pattern part. Determine the drawing start time, end time, and duration of each pattern part, etc. According to the results of the time sequence analysis, the time sequence data of the electronic drawing board drawing is sorted and stored. The time sequence data can be sorted according to the drawing order to form a time series of each pattern part.

[0061] Step S23: Detect delays in the electronic drawing board drawing behavior data based on the electronic drawing board drawing time sequence data to generate electronic drawing board drawing delay detection data;

[0062] In the embodiment of the present invention, by performing delay detection based on the time sequence data of the electronic drawing board drawing, that is, detecting the time delay situation of the drawing action. The delay situation can be determined by comparing the timestamps in the drawing behavior data with the time information in the actual drawing time sequence data. According to the results of the delay detection, the delay detection data is sorted and stored to describe the delay situation of each drawing action. Information such as the length of the delay and the location where the delay occurs can be recorded.

[0063] Step S24: Compare the drawing delay detection data of the electronic drawing board with a preset drawing delay threshold. When the drawing delay detection data of the electronic drawing board is greater than or equal to the preset drawing delay threshold, divide the electronic drawing board area data according to the drawing delay detection data to generate electronic drawing board high-delay drawing area data.

[0064] In the embodiment of the present invention, by obtaining the drawing delay detection data of the electronic drawing board and a preset drawing delay threshold. Compare the delay detection data with the preset threshold to determine whether there is a situation where the delay exceeds the threshold. When the delay detection data is greater than or equal to the preset drawing delay threshold, it indicates that there is a high-delay drawing action. According to the delay detection data, divide the electronic drawing board area data, and mark the area with a higher delay as the high-delay drawing area. Save or record the divided high-delay drawing area data to form the electronic drawing board high-delay drawing area data. The position information, delay degree, etc. of the high-delay area can be stored in the dataset.

[0065] Preferably, step S23 includes the following steps:

[0066] Step S231: Based on the drawing timing data of the electronic drawing board, use the liquid crystal drawing pen to confirm the drawing touch time points to obtain drawing touch time data, where the drawing touch time data includes the touch start time point and the touch end time point; perform touch trajectory analysis according to the touch start time point and the touch end time point to generate touch trajectory data;

[0067] Step S232: Confirm the display time points of the electronic drawing board for the electronic drawing board drawing behavior data to generate electronic drawing board display time data, where the electronic drawing board display time data includes the display start time point and the display end time point; perform display trajectory analysis according to the display start time point and the display end time point to generate display trajectory data;

[0068] Step S233: Based on a preset time stamp, truncate the touch trajectory data and the display trajectory data to obtain a touch truncated trajectory and a display truncated trajectory;

[0069] Step S234: Perform trajectory truncation difference analysis on the touch truncated trajectory and the display truncated trajectory to generate drawing trajectory difference data; calculate the drawing delay for the electronic drawing board drawing behavior data based on the drawing trajectory difference data to generate the electronic drawing board drawing delay detection data.

[0070] The present invention obtains drawing touch time data by using a liquid crystal drawing pen to confirm the drawing touch time points of the drawing timing data of an electronic drawing board. By confirming the touch start time point and the touch end time point, the touch operation moment of the liquid crystal drawing pen during the drawing process can be determined. This helps to accurately record the drawing touch events and provides a basis for subsequent trajectory analysis and delay calculation. The present invention confirms the display time points of the electronic drawing board for the drawing behavior data of the electronic drawing board to generate the electronic drawing board display time data. By confirming the display start time point and the display end time point, the display moment of the drawing content on the electronic drawing board can be determined. This helps to capture the appearance and disappearance process of the drawing content on the electronic drawing board and provides relevant data on the display trajectory. Based on a preset time stamp, the touch trajectory data and the display trajectory data are truncated to obtain a touch truncated trajectory and a display truncated trajectory. By truncating the touch trajectory and the display trajectory according to the preset time stamp, the trajectory data of the touch and the display can be corresponding to the drawing behavior data. This helps to align the drawing behavior with the timing information of the touch and the display and provides accurate data for subsequent difference analysis and delay calculation. The present invention performs a trajectory truncation difference analysis on the touch truncated trajectory and the display truncated trajectory to generate drawing trajectory difference data. By analyzing the difference between the touch and the display truncated trajectories, the delay situation between the touch and the display during the drawing process can be evaluated. This helps to reveal the specific characteristics and differences of the drawing delay and provides a basis for subsequent delay detection and optimization.

[0071] As an example of the present invention, refer to Figure 3 shown, in this example, the step S23 includes:

[0072] Step S231: Based on the drawing timing data of the electronic drawing board, use a liquid crystal drawing pen to confirm the drawing touch time points to obtain the drawing touch time data, where the drawing touch time data includes the touch start time point and the touch end time point; perform touch trajectory analysis according to the touch start time point and the touch end time point to generate touch trajectory data;

[0073] In the embodiment of the present invention, by using a liquid crystal drawing pen to draw on the electronic drawing board, the touch time points of each drawing are recorded. When the drawing starts, the touch start time point is recorded; when the drawing ends, the touch end time point is recorded. The recorded touch start time point and end time point are sorted and stored to form the drawing touch time data. Each drawing action corresponds to a set of touch time data, including the start time and the end time. According to the touch start time point and the end time point, the touch trajectory during the drawing process is analyzed. Information such as the length, direction, and speed of the touch trajectory can be calculated, as well as the curve characteristics of the touch trajectory. According to the results of the touch analysis, the touch trajectory data is sorted and stored to describe the touch trajectory characteristics during the drawing process. The touch trajectory data may include information such as the coordinates of the touch points and the curve parameters of the trajectory.

[0074] Step S232: Confirm the display time points of the electronic drawing board for the drawing behavior data of the electronic drawing board, generate the display time data of the electronic drawing board, where the display time data of the electronic drawing board includes the display start time point and the display end time point; perform display trajectory analysis based on the display start time point and the display end time point to generate display trajectory data;

[0075] In the embodiment of the present invention, by using corresponding methods or tools, the display state of the electronic drawing board is monitored, and the start time point and the end time point of each display are recorded. When the electronic drawing board starts to be displayed, the start time point of the display is recorded; when the display ends, the end time point of the display is recorded. The recorded start time points and end time points of the display are sorted out to form the display time data of the electronic drawing board. Each display corresponds to a set of display time data, including the start time and the end time. Based on the display start time point and the display end time point, the trajectory during the display of the electronic drawing board is analyzed. Features such as changes in the display content and the persistence of the display time can be analyzed. According to the results of the display analysis, the display trajectory data is sorted out and stored to describe the trajectory characteristics during the display. The display trajectory data may include changes in the display content, position information of the display area, etc.

[0076] Step S233: Perform trajectory truncation on the touch trajectory data and the display trajectory data based on a preset time stamp to obtain a touch truncated trajectory and a display truncated trajectory;

[0077] In the embodiment of the present invention, by using corresponding methods or tools, the display state of the electronic drawing board is monitored, and the start time point and the end time point of each display are recorded. When the electronic drawing board starts to be displayed, the start time point of the display is recorded; when the display ends, the end time point of the display is recorded. The recorded start time points and end time points of the display are sorted out to form the display time data of the electronic drawing board. Each display corresponds to a set of display time data, including the start time and the end time. Based on the display start time point and the display end time point, the trajectory during the display of the electronic drawing board is analyzed. Features such as changes in the display content and the persistence of the display time can be analyzed. According to the results of the display analysis, the display trajectory data is sorted out and stored to describe the trajectory characteristics during the display. The display trajectory data may include changes in the display content, position information of the display area, etc.

[0078] Step S234: Perform trajectory truncation difference analysis on the touch truncated trajectory and the display truncated trajectory to generate drawing trajectory difference data; perform drawing delay calculation on the drawing behavior data of the electronic drawing board based on the drawing trajectory difference data to generate electronic drawing board drawing delay detection data.

[0079] In the embodiments of the present invention, by obtaining the touch trajectory data and display trajectory data generated in the previous steps, align the touch trajectory and the display trajectory according to the time stamps to ensure that the two sets of data can correspond in time. Calculate the difference between the touch trajectory and the display trajectory at each corresponding time point. The difference can be a position difference, a time difference, etc. Organize and store the calculated trajectory difference information to form drawing trajectory difference data. According to the drawing trajectory difference data, calculate the delay of each touch action. The delay can be determined by calculating the time difference between the touch trajectory and the display trajectory. Statistically analyze the delay situations of all drawing actions to generate delay detection data, including average delay, maximum delay, delay distribution, etc. Organize the calculated delay data and store it in a suitable data structure to form electronic drawing board drawing delay detection data. The delay detection data can include the touch start time, touch end time, display start time, display end time, and the corresponding delay time of each drawing action, etc.

[0080] Preferably, step S3 includes the following steps:

[0081] Step S31: Obtain drawing pressure data according to the pressure sensor in the liquid crystal drawing pen;

[0082] Step S32: Perform pressure change analysis on the drawing pressure data to generate drawing pressure change data; divide the drawing induction area of the electronic drawing board area grid data through the drawing pressure change data to generate electronic drawing board induction area data;

[0083] Step S33: Perform drawing induction association on the electronic drawing board drawing behavior data and the electronic drawing board induction area data to generate electronic drawing board drawing induction association data; perform induction intensity analysis on the electronic drawing board drawing induction association data to generate electronic drawing board drawing induction intensity data;

[0084] Step S34: Calculate the induction uniformity of the electronic drawing board drawing induction intensity data to obtain electronic drawing board induction uniformity data; divide the weak induction area of the electronic drawing board induction area data based on the electronic drawing board induction uniformity data to generate electronic drawing board weak response drawing area data.

[0085] The present invention obtains drawing pressure data according to the pressure sensor in the liquid crystal drawing pen. By obtaining the drawing pressure data, the pressure change applied to the drawing tool during the drawing process can be understood. This helps to identify the pressure characteristics of different drawing operations and provides a basis for subsequent analysis and optimization. The pressure change analysis is performed on the drawing pressure data to generate drawing pressure change data. By analyzing the change of the drawing pressure, the pressure change pattern and characteristics during the drawing process can be revealed. According to the drawing pressure change data, the electronic drawing board area can be divided into drawing induction areas to generate electronic drawing board induction area data. This helps to understand the sensitivity of different areas to the drawing pressure and provides a basis for subsequent drawing induction association. The electronic drawing board drawing behavior data and the electronic drawing board induction area data are subjected to drawing induction association to generate electronic drawing board drawing induction association data. By associating the drawing behavior data with the induction area data, it can be determined in which induction areas the drawing behavior occurs. The induction intensity analysis is performed on the electronic drawing board drawing induction association data to generate electronic drawing board drawing induction intensity data. This helps to understand the intensity and sensitivity of the drawing behavior in different induction areas. The induction uniformity calculation is performed on the electronic drawing board drawing induction intensity data to obtain the electronic drawing board induction uniformity data. By calculating the uniformity of the induction intensity, the uniformity and consistency of the induction area can be evaluated. Based on the electronic drawing board induction uniformity data, the weak induction areas of the electronic drawing board induction area data are divided to generate electronic drawing board weak response drawing area data. This helps to identify the areas with weak response and provides guidance for subsequent optimization and improvement.

[0086] As an example of the present invention, refer to Figure 4 shown, in this example, step S3 includes:

[0087] Step S31: Obtain drawing pressure data according to the pressure sensor in the liquid crystal drawing pen;

[0088] In the embodiment of the present invention, by installing the pressure sensor at the tip of the drawing pen or inside the pen body, the pressure generated when the tip contacts the screen can be detected. Preliminary calibration is performed to ensure that the sensor can accurately sense different pressure values and output corresponding electrical signals, involving multiple calibration points (such as light press, medium pressure, heavy press) to establish the relationship between pressure and signal. During the drawing process, the pressure sensor continuously collects the pressure applied to the tip and converts it into an electrical signal. The electrical signal is converted into a digital signal through a signal processing unit (such as an analog-to-digital converter), and these digital signals represent specific pressure values. The digital pressure data is transmitted to the liquid crystal screen control unit or the drawing application program by wired or wireless means. The control unit or the application program adjusts parameters such as the thickness of the drawing stroke and the color concentration in real time according to these pressure data to achieve the pressure sensing drawing effect.

[0089] Step S32: Analyze the pressure change of the drawing pressure data to generate drawing pressure change data; divide the drawing induction area of the electronic drawing board area grid data through the drawing pressure change data to generate electronic drawing board induction area data;

[0090] In the embodiment of the present invention, the original pressure data is smoothed by using a filtering algorithm (such as moving average or Gaussian filtering) to reduce noise interference. The outlier detection and correction algorithm is applied to remove outliers and ensure data stability. Calculate the pressure change rate for each sampling point, that is, the difference between the pressure value at each moment and the pressure value at the previous moment. Set a threshold, and when the pressure change rate exceeds the threshold, it is marked as a change point. Record the timestamp and pressure value of each change point. Analyze the distribution of the change points to determine the pressure change trend (such as the stroke becoming heavier or lighter) during the drawing process. Divide the electronic drawing board into uniform grid cells. For example, it can be divided into 5x5 pixels for each cell. Select an appropriate grid resolution according to the size and requirements of the drawing board. Map the pressure value of each sampling point to the corresponding grid cell and record all the pressure values within each grid cell. Statistically analyze the pressure values within each grid cell, calculate the average pressure value or the pressure change value. Identify the high-pressure area (the grid cell with a larger pressure value) and the low-pressure area (the grid cell with a smaller pressure value) according to the average pressure value or the pressure change value. Use an image processing algorithm (such as Canny edge detection) to identify the boundaries between the high-pressure area and the low-pressure area. Assign a unique label or ID to each induction area, and mark its boundaries and attributes. Record the geometric attributes (such as position, size) and pressure attributes (such as average pressure value, change trend) of each induction area. Update the induction area data in real time during the drawing process to ensure instant feedback of the drawing effect.

[0091] Step S33: Perform drawing induction association on the electronic drawing board drawing behavior data and the electronic drawing board induction area data to generate electronic drawing board drawing induction association data; perform induction intensity analysis on the electronic drawing board drawing induction association data to generate electronic drawing board drawing induction intensity data;

[0092] In the embodiments of the present invention, each drawing point in the drawing behavior data (such as the specific coordinates of the pen tip) is position-matched with the corresponding sensing area data to determine the grid cell where each drawing point is located. Ensure the temporal synchronization of the drawing behavior data and the sensing area data, and perform interpolation or alignment operations when necessary. Establish the association rules between the drawing behavior data and the sensing area data. For example, according to the pressure value and the pressure change value within the grid cell, determine the sensing intensity of the drawing point. Merge the matched drawing behavior data with the corresponding sensing area data to form a comprehensive data set. The sensing intensity can be defined as a comprehensive measure of the drawing point pressure and the pressure change value of the sensing area. For example, it can be calculated using a weighted average of the pressure values or other statistical methods. For each drawing point, calculate the sensing intensity using the following formula: Sensing intensity = f(drawing point pressure, sensing area pressure change); where f can be a simple linear combination, a non-linear function, or a machine learning model. Statistically analyze the distribution of the sensing intensity in different sensing areas to generate a spatial distribution map of the sensing intensity. Perform aggregation analysis on the overall drawing behavior and calculate global sensing intensity metrics, such as the average sensing intensity and the maximum sensing intensity. Design an appropriate data structure (such as a two-dimensional matrix, a database table) to store the sensing intensity data to ensure its retrievability and efficiency.

[0093] Step S34: Calculate the sensing uniformity of the drawing sensing intensity data of the electronic drawing board to obtain the electronic drawing board sensing uniformity data; based on the electronic drawing board sensing uniformity data, divide the weak sensing areas of the electronic drawing board sensing area data to generate the electronic drawing board weak response drawing area data.

[0094] In the embodiments of the present invention, the sensed intensity data of the electronic drawing board that has been calculated is obtained from the previous step. The sensing uniformity can be defined as a reflection of the degree of dispersion of the intensity values in each sensing area. Common indicators include variance, standard deviation, coefficient of variation, etc. Uniformity = 1 - σ / μ; where σ is the standard deviation of the sensed intensity and μ is the average value of the sensed intensity. The closer the uniformity value is to 1, the more uniform the sensing is. The drawing board is divided into smaller sub-areas, and the sensing uniformity of each sub-area is calculated separately. The drawing board is divided into several sub-grids, for example, each sub-grid is a 10x10 pixel block. The uniformity of the sensed intensity data within each sub-grid is calculated. A sensed intensity threshold is set, and the area below this threshold is determined as a weak sensing area. For example, according to the statistical distribution of the sensed intensity, an area below a certain percentage of the average value can be set as a weak sensing area. The sensed intensity values of the grid cells are checked one by one, and the grid cells below the threshold are marked. The adjacent low-intensity grid cells are merged into a continuous weak sensing area. Image processing techniques (such as connected component analysis) are used to determine the boundaries of each weak sensing area. A unique label or ID is assigned to each weak sensing area for subsequent processing and analysis. An appropriate data structure is designed to store the data of the weak response drawing area, such as a two-dimensional array or a list. The data of each weak sensing area should include attributes such as its position, size, and boundary point coordinates.

[0095] Preferably, the analysis of the sensed intensity of the sensed intensity correlation data of the electronic drawing board includes:

[0096] Performing sensing area positioning according to the sensed intensity correlation data of the electronic drawing board to obtain the sensed area positioning data of the electronic drawing board;

[0097] Discretizing the sensed area data of the electronic drawing board based on the sensed area positioning data of the electronic drawing board to generate a discretized sensed area of the electronic drawing board;

[0098] Performing drawing action correlation matching on the discretized sensed area of the electronic drawing board to generate a matched sensed area of the electronic drawing board;

[0099] Calculating the sensed intensity of the sensed intensity correlation data of the electronic drawing board through the matched sensed area of the electronic drawing board and the sensed area positioning data of the electronic drawing board to obtain the sensed intensity data of the electronic drawing board.

[0100] The present invention locates the induction area by drawing induction correlation data according to an electronic drawing board, and obtains the positioning data of the induction area for drawing on the electronic drawing board. By analyzing the drawing induction correlation data, the position and range of the drawing behavior in the induction area can be determined. This helps to locate the induction area and provides accurate area positioning data for subsequent induction intensity calculation. Based on the positioning data of the induction area for drawing on the electronic drawing board, the induction area data of the electronic drawing board is discretized to generate a discrete induction area of the electronic drawing board. By discretizing the induction area data, the continuous induction area can be divided into discrete area units. This helps to perform refined analysis and processing on the induction area and provides more accurate data for subsequent matching and intensity calculation. The drawing action is associated and matched with the discrete induction area of the electronic drawing board to generate a matching induction area of the electronic drawing board. By matching the discrete induction area, the drawing action can be associated with the corresponding induction area. This helps to determine the induction area involved in each drawing action and provides association data for subsequent intensity calculation. The induction intensity of the drawing induction correlation data of the electronic drawing board is calculated through the matching induction area of the electronic drawing board and the positioning data of the induction area for drawing on the electronic drawing board, and the drawing induction intensity data of the electronic drawing board is obtained. By comprehensively considering the matching induction area, the positioning data of the induction area for drawing, and the drawing induction correlation data, the intensity of the drawing behavior in different induction areas can be calculated. This helps to understand the difference in the induction intensity of the drawing behavior in different areas and provides a basis for subsequent analysis and optimization.

[0101] In the embodiments of the present invention, by obtaining the induction correlation data of the electronic drawing board from the previous steps, including drawing behavior data and induction area data. Each drawing point is position-matched with the induction area data to determine the induction area where it is located. According to the position of the drawing point, the corresponding induction area is determined to obtain the positioning data of the induction area for the electronic drawing board. The induction area information of each drawing point is stored, including area ID, position coordinates, etc. The electronic drawing board is divided into smaller discrete grid cells, such as a 5x5 pixel grid. The induction area positioning data is mapped into the discrete grid, and each grid records its induction intensity data. The induction intensity and position attributes of each discrete grid are recorded to form the discrete induction area of the electronic drawing board. The discrete induction area data is stored in a two-dimensional array or other data structures for subsequent processing. The drawing action data is collected, including information such as the pressure, speed, and direction of the pen. The drawing action data is analyzed to identify different drawing modes (such as light touch, heavy press, fast movement, etc.). Using a pattern matching algorithm, the discrete induction area is associated and matched with the drawing action data. According to the drawing action characteristics (such as pressure and speed), the matching situation of each discrete area is determined to generate the matching induction area of the electronic drawing board. According to the drawing action and discrete induction area data, an induction intensity calculation formula is defined. For example: induction intensity = α·pressure + β·speed; where α and β are weight parameters. For each drawing point, the induction intensity is calculated using the formula and recorded in the induction intensity data of the electronic drawing board. The calculated intensity data is merged according to the induction area, and indicators such as the average intensity and maximum intensity of each area are statistically calculated.

[0102] Preferably, step S4 includes the following steps:

[0103] Step S41: Grid-mark the grid data of the electronic drawing board according to the high-latency drawing area data and weak-response drawing area data of the electronic drawing board, and mark the grid areas of the electronic drawing board under the high-latency drawing area data and weak-response drawing area data of the electronic drawing board as high-abnormal drawing board grid area data, to obtain high-abnormal drawing board grid area data;

[0104] Step S42: Mark the grid areas of the electronic drawing board under the high-latency drawing area data or weak-response drawing area data of the electronic drawing board as low-abnormal drawing board grid area data, to generate low-abnormal drawing board grid area data;

[0105] Step S43: Perform prediction on the influence of abnormal drawing of the electronic drawing board through the high-abnormal drawing board grid area data and low-abnormal drawing board grid area data, to generate electronic drawing board abnormal influence prediction data;

[0106] Step S44: Construct a regional repair strategy for the electronic drawing board through the electronic drawing board abnormal influence prediction data to execute the drawing operation of the electronic drawing board.

[0107] In the present invention, grid marking is performed on the electronic drawing board area grid data according to the high-latency drawing area data and the weak-response drawing area data of the electronic drawing board. The electronic drawing board area grids in the high-latency drawing area and the weak-response drawing area are marked as high-abnormal drawing board grid areas to obtain high-abnormal drawing board grid area data. By marking the high-abnormal drawing board grid areas, areas with high latency or weak response in the electronic drawing board can be identified. This helps to locate and mark potential abnormal areas, providing a basis for subsequent abnormal impact prediction and repair. The electronic drawing board area grids under the high-latency drawing area data or the weak-response drawing area data are marked as low-abnormal drawing board grid areas to generate low-abnormal drawing board grid area data. By marking the low-abnormal drawing board grid areas, areas with low response but not high latency in the electronic drawing board can be identified. This helps to identify other existing abnormal situations, providing more comprehensive data for subsequent abnormal impact prediction and repair. The abnormal drawing impact of the electronic drawing board is predicted through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to generate electronic drawing board abnormal impact prediction data. By analyzing the abnormal grid area data, potential abnormal drawing impacts can be predicted. This helps to understand the impact degree of the abnormal areas on the drawing operation, providing information for subsequent repair strategies. The area repair strategy of the electronic drawing board is constructed based on the electronic drawing board abnormal impact prediction data to execute the drawing operation of the electronic drawing board. According to the abnormal impact prediction data, corresponding area repair strategies can be formulated to reduce or eliminate the impact of abnormal drawing. This helps to improve the drawing quality and efficiency and enhance the user experience.

[0108] In the embodiments of the present invention, data of high-latency drawing areas and weak-response drawing areas of an electronic drawing board is collected. The regional grid data of the electronic drawing board is obtained, and the drawing board is divided into small grid units. Conditions for marking high-abnormality areas are determined, that is, grid units that simultaneously meet high latency and weak response. The specific marking process is as follows: Each grid unit is checked one by one to determine whether it is simultaneously in a high-latency and weak-response area, and grid units that meet the conditions are marked as high-abnormality drawing board grid areas. The marked high-abnormality grid area data is stored in an appropriate data structure (such as a two-dimensional array, list). Conditions for marking low-abnormality areas are determined, that is, any area in high latency or weak response. The specific marking process is as follows: Each grid unit is checked one by one to determine whether it is in a high-latency or weak-response area, and grid units that meet the conditions are marked as low-abnormality drawing board grid areas. The high-abnormality and low-abnormality drawing board grid area data is combined. An abnormal drawing impact prediction model is established, and methods such as statistical analysis and machine learning can be used. The specific prediction process is as follows: The main factors affecting the drawing effect (such as grid latency, response intensity) are determined. A prediction algorithm is applied to estimate the impact of each abnormal area on the overall drawing effect. An abnormal impact score is generated for each abnormal area to represent its potential impact on the drawing effect. The abnormal impact scores of all areas are integrated to generate abnormal impact prediction data for the electronic drawing board. A repair strategy is formulated with the goal of reducing or eliminating the negative impact of abnormal areas on the drawing effect, such as adjusting the sensor sensitivity, increasing the data acquisition frequency, etc. The repair strategy is applied to the electronic drawing board system, relevant parameters and configurations are adjusted, and the repair effect is monitored in real time to ensure that abnormal areas are effectively processed.

[0109] Preferably, step S43 includes the following steps:

[0110] Step S431: Extract abnormal features from the high-abnormality drawing board grid area data and the low-abnormality drawing board grid area data to obtain abnormal drawing board grid area feature data;

[0111] Step S432: Divide the abnormal drawing board grid area feature data into a data set to generate a model training set and a model test set; Use the support vector machine algorithm to train the model training set to generate an abnormal drawing area severity training model;

[0112] Step S433: Iteratively test the abnormal drawing area severity training model through the model test set to generate an abnormal drawing area severity prediction model; Import the abnormal drawing board grid area feature data into the abnormal drawing area severity prediction model for abnormal severity evaluation to generate abnormal impact prediction data for the electronic drawing board.

[0113] The present invention extracts abnormal feature data from the data of the high-abnormal drawing board grid area and the low-abnormal drawing board grid area to obtain the feature data of the abnormal drawing board grid area. By extracting the features of the abnormal grid area, the key attributes and indicators of the abnormal situation can be captured. This helps to convert the abnormal situation into quantifiable feature data, providing a basis for subsequent abnormal severity assessment and prediction modeling. The feature data of the abnormal drawing board grid area is divided into a data set to generate a model training set and a model test set. By dividing the data set, the data can be divided into two parts for model training and model test. This helps to build a severity training model for the abnormal drawing area on the training set, providing a basis for the subsequent prediction model. The severity training model for the abnormal drawing area is iteratively tested through the model test set to generate a severity prediction model for the abnormal drawing area. By iteratively testing the model, the performance and accuracy of the model can be optimized, enabling it to more accurately predict the severity of the abnormal drawing area. This helps to improve the accuracy and reliability of the prediction model. The feature data of the abnormal drawing board grid area is imported into the severity prediction model for the abnormal drawing area to evaluate the abnormal severity, generating prediction data on the impact of the electronic drawing board abnormality. By inputting the grid area feature data into the prediction model, the severity of the abnormal drawing area can be evaluated. This helps to determine the impact of the abnormal drawing area on the drawing operation and generate relevant prediction data on the abnormal impact.

[0114] In the embodiments of the present invention, by collecting data of high-abnormality drawing board grid regions and data of low-abnormality drawing board grid regions, the main features affecting abnormalities are determined, such as: location features: coordinates of the grid region, adjacent region information; sensing features: sensing intensity, delay time, pressure value, response time; drawing features: pen speed, direction, acceleration, etc. The above feature values are calculated from the high- and low-abnormality region data to form feature vectors. The extracted feature data is stored in an appropriate data structure, such as a two-dimensional array or a data table. The feature data of the abnormal drawing board grid region is divided into a model training set and a model test set. Common methods include: random division: randomly select data samples for division, such as 80% for training and 20% for testing; cross-validation: use the cross-validation method to enhance the generalization ability of the model. A support vector machine (SVM) is selected as the training model. Hyperparameters of the SVM model are set, such as the type of kernel function (linear, RBF, etc.), penalty parameter C, etc. The SVM model is trained using the training set data to generate a severity training model for the abnormal drawing region. The trained SVM model is tested using the divided test set data. The metrics for evaluating the model performance include accuracy, precision, recall, F1-score, etc. The model is optimized according to the test results, adjusting the parameters of the SVM or feature selection, and iteratively training and testing the model until the performance reaches a satisfactory standard. A final severity prediction model for the abnormal drawing region is generated. The feature data of the new abnormal drawing board grid region is imported into the trained prediction model. The severity of each grid region is predicted by evaluating the imported feature data using the severity prediction model for the abnormal drawing region. A severity score is generated for each grid region to indicate its potential impact on the drawing effect. The severity scores of all grid regions are integrated to generate prediction data on the impact of the electronic drawing board abnormality.

[0115] In this specification, a data processing system based on an electronic drawing board is provided for performing the above-mentioned data processing method based on the electronic drawing board. The data processing system based on the electronic drawing board includes:

[0116] A drawing behavior analysis module, configured to obtain electronic drawing board drawing data and electronic drawing board region data; perform regional gridification on the electronic drawing board drawing data to generate electronic drawing board region grid data; perform regional drawing positioning association on the electronic drawing board drawing data and the electronic drawing board region grid data to generate electronic drawing board drawing behavior data;

[0117] A delay region analysis module, configured to perform drawing pattern segmentation on the electronic drawing board drawing behavior data to generate electronic drawing board drawing pattern segmentation data; perform drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data; perform high-delay region division on the electronic drawing board region data according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing region data;

[0118] An induction area analysis module, configured to obtain drawing pressure data; perform drawing induction area division on the area grid data of the electronic drawing board through the drawing pressure data to generate electronic drawing board induction area data; calculate the induction uniformity according to the electronic drawing board drawing behavior data and the electronic drawing board induction area data to obtain electronic drawing board induction uniformity data; perform weak induction area division on the electronic drawing board induction area data based on the electronic drawing board induction uniformity data to generate electronic drawing board weak response drawing area data;

[0119] An abnormal area impact analysis module, configured to perform grid marking on the area grid data of the electronic drawing board according to the electronic drawing board high-latency drawing area data and the electronic drawing board weak response drawing area data to generate high-abnormal drawing board grid area data and low-abnormal drawing board grid area data; construct an area repair strategy through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to execute the electronic drawing board drawing operation.

[0120] The present invention also provides an electronic device, which includes:

[0121] At least one processor;

[0122] A memory communicatively connected to the at least one processor;

[0123] Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the data processing method based on the electronic drawing board as described in any one of the above.

[0124] The beneficial effects of the present invention are as follows: By analyzing and processing the electronic drawing board drawing behavior data, the user's drawing intention can be captured more accurately, thereby improving the accuracy and efficiency of drawing. Through the detection and analysis of drawing latency and induction area, the drawing experience of the electronic drawing board can be optimized, reducing drawing latency and enhancing the induction of drawing, making the user feel smoother and more natural when using the electronic drawing board. By repairing abnormal drawing board areas and optimizing the drawing experience, the user's satisfaction and usage experience with the electronic drawing board can be improved, enhancing the user's favorability towards the product, which is beneficial to the promotion of the product and its market competitiveness. Optimizing the drawing experience and enhancing user satisfaction helps the product gain more competitive advantages in the market, attract more users to choose and use it, and thus enhance the product's competitiveness in the industry. Therefore, the present invention improves the drawing accuracy and sensitivity by obtaining the electronic drawing board drawing data and performing abnormal area division and impact prediction on the electronic drawing board.

[0125] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0126] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A data processing method based on an electronic drawing board, characterized in that: Acting on the LCD drawing board and the LCD drawing pen, including the following steps: Step S1: obtaining electronic drawing board drawing data and electronic drawing board area data; performing regional gridding on the electronic drawing board drawing data to generate electronic drawing board area grid data; performing regional drawing positioning association on the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data; Step S2: segmenting the electronic drawing board drawing behavior data into drawing patterns to generate electronic drawing board drawing pattern segmentation data; performing drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data; dividing the electronic drawing board area data into high-delay areas according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing area data; Step S2 includes the following steps: Step S21: performing drawing content analysis on the electronic drawing board drawing behavior data to generate electronic drawing board drawing content data; performing drawing pattern segmentation on the electronic drawing board drawing behavior data according to the electronic drawing board drawing content data to generate electronic drawing board drawing pattern segmentation data; Step S22: performing drawing timing analysis on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing timing data; Step S23: performing delay detection on the electronic drawing board drawing behavior data based on the electronic drawing board drawing timing data to generate electronic drawing board drawing delay detection data; Step S23 includes the following steps: Step S231: confirming the drawing touch time point using a liquid crystal drawing pen based on the drawing timing data of the electronic drawing board to obtain drawing touch time data, wherein the drawing touch time data includes a touch start time point and a touch end time point; performing touch trajectory analysis according to the touch start time point and the touch end time point to generate touch trajectory data; Step S232: Confirm the electronic drawing board display time point of the electronic drawing board drawing behavior data to generate the electronic drawing board display time data, wherein the electronic drawing board display time data includes a display start time point and a display end time point; perform display trajectory analysis according to the display start time point and the display end time point to generate display trajectory data; Step S233: performing trajectory truncation on the touch trajectory data and the display trajectory data based on a preset timestamp to obtain a touch truncation trajectory and a display truncation trajectory; Step S234: performing trajectory truncation difference analysis on the touch truncation trajectory and the display truncation trajectory to generate drawing trajectory difference data; performing drawing delay calculation on the electronic drawing board drawing behavior data based on the drawing trajectory difference data to generate electronic drawing board drawing delay detection data; Step S24: comparing the electronic drawing board drawing delay detection data with a preset drawing delay threshold, and when the electronic drawing board drawing delay detection data is greater than or equal to the preset drawing delay threshold, dividing the electronic drawing board area data into high-delay areas according to the electronic drawing board drawing delay detection data, and generating electronic drawing board high-delay drawing area data; Step S3: obtaining drawing pressure data; dividing the electronic drawing board area grid data into drawing sensing areas according to the drawing pressure data to generate electronic drawing board sensing area data; calculating sensing uniformity according to the electronic drawing board drawing behavior data and the electronic drawing board sensing area data to obtain the electronic drawing board sensing uniformity data; dividing the electronic drawing board sensing area data into weak sensing areas based on the electronic drawing board sensing uniformity data to generate electronic drawing board weak response drawing area data; Step S4: Grid-mark the electronic drawing board area grid data according to the electronic drawing board high-delay drawing area data and the electronic drawing board weak-response drawing area data to generate high-abnormal drawing board grid area data and low-abnormal drawing board grid area data; construct a regional repair strategy through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to execute the electronic drawing board drawing operation.

2. The data processing method based on the electronic drawing board according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: Acquire electronic drawing board drawing data and electronic drawing board area data; Step S12: preprocessing the electronic drawing board drawing data to generate standard electronic drawing board drawing data, wherein the data preprocessing includes data cleaning, data missing value filling and data standardization; Step S13: performing regional gridding on the electronic drawing board area data to generate electronic drawing board area grid data; Step S14: Associating the standard electronic drawing board drawing data with the electronic drawing board area grid data in terms of area drawing positioning to generate electronic drawing board drawing behavior data.

3. The data processing method based on the electronic drawing board according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: obtaining drawing pressure data according to the pressure sensor in the liquid crystal drawing pen; Step S32: performing pressure change analysis on the drawing pressure data to generate drawing pressure change data; dividing the electronic drawing board area grid data into drawing sensing areas according to the drawing pressure change data to generate electronic drawing board sensing area data; Step S33: performing drawing induction association on the electronic drawing board drawing behavior data and the electronic drawing board sensing area data to generate electronic drawing board drawing induction association data; performing induction intensity analysis on the electronic drawing board drawing induction association data to generate electronic drawing board drawing induction intensity data; Step S34: Calculate the sensing uniformity of the electronic drawing board drawing sensing intensity data to obtain the sensing uniformity data of the electronic drawing board; divide the sensing area data of the electronic drawing board into weak sensing areas based on the sensing uniformity data of the electronic drawing board to generate the weak response drawing area data of the electronic drawing board.

4. The data processing method based on the electronic drawing board according to claim 3 is characterized in that: The induction intensity analysis of the electronic drawing board drawing induction associated data includes: Positioning the sensing area according to the electronic drawing board drawing sensing associated data to obtain the electronic drawing board drawing sensing area positioning data; Discretize the sensing area data of the electronic drawing board based on the positioning data of the sensing area drawn by the electronic drawing board to generate a discrete sensing area of ​​the electronic drawing board; Performing drawing action association matching on discrete sensing areas of the electronic drawing board to generate matching sensing areas of the electronic drawing board; The electronic drawing board drawing sensing associated data is subjected to sensing intensity calculation through the electronic drawing board matching sensing area and the electronic drawing board drawing sensing area positioning data to obtain the electronic drawing board drawing sensing intensity data.

5. The data processing method based on the electronic drawing board according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: marking the electronic drawing board area grid data according to the electronic drawing board high-delay drawing area data and the electronic drawing board weak response drawing area data, marking the electronic drawing board area grid under the electronic drawing board high-delay drawing area data and the electronic drawing board weak response drawing area data as a high-abnormal drawing board grid area, and obtaining the high-abnormal drawing board grid area data; Step S42: marking the electronic drawing board area grid under the electronic drawing board high-delay drawing area data or the electronic drawing board weak-response drawing area data as a low-abnormal drawing board grid area to generate low-abnormal drawing board grid area data; Step S43: predicting the impact of abnormal drawing on the electronic drawing board through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data, and generating electronic drawing board abnormal impact prediction data; Step S44: constructing a regional repair strategy for the electronic drawing board based on the electronic drawing board abnormal impact prediction data to execute the electronic drawing board drawing operation.

6. The data processing method based on the electronic drawing board according to claim 5, characterized in that: Step S43 includes the following steps: Step S431: extracting abnormal features from the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to obtain abnormal drawing board grid area feature data; Step S432: dividing the abnormal drawing board grid area feature data into a data set to generate a model training set and a model test set; using a support vector machine algorithm to perform model training on the model training set to generate an abnormal drawing area severity training model; Step S433: Iterate the model test of the abnormal drawing area severity training model through the model test set to generate an abnormal drawing area severity prediction model; import the abnormal drawing board grid area feature data into the abnormal drawing area severity prediction model to perform abnormal severity assessment and generate electronic drawing board abnormal impact prediction data.

7. A data processing system based on an electronic drawing board, characterized in that: Used to execute the data processing method based on the electronic drawing board as claimed in claim 1, the data processing system based on the electronic drawing board comprises: A drawing behavior analysis module is used to obtain electronic drawing board drawing data and electronic drawing board area data; regional gridding the electronic drawing board drawing data to generate electronic drawing board area grid data; regional drawing positioning association of the electronic drawing board drawing data and the electronic drawing board area grid data to generate electronic drawing board drawing behavior data; The delay area analysis module is used to segment the drawing behavior data of the electronic drawing board into drawing patterns to generate electronic drawing board drawing pattern segmentation data; perform drawing delay detection on the electronic drawing board drawing pattern segmentation data to generate electronic drawing board drawing delay detection data; divide the electronic drawing board area data into high-delay areas according to the electronic drawing board drawing delay detection data to generate electronic drawing board high-delay drawing area data; The sensing area analysis module is used to obtain drawing pressure data; divide the electronic drawing board area grid data into drawing sensing areas according to the drawing pressure data to generate the electronic drawing board sensing area data; calculate the sensing uniformity according to the electronic drawing board drawing behavior data and the electronic drawing board sensing area data to obtain the electronic drawing board sensing uniformity data; divide the electronic drawing board sensing area data into weak sensing areas based on the electronic drawing board sensing uniformity data to generate the electronic drawing board weak response drawing area data; The abnormal area impact analysis module is used to grid-mark the electronic drawing board area grid data according to the electronic drawing board high-latency drawing board area data and the electronic drawing board weak response drawing board area data, generate high-abnormal drawing board grid area data and low-abnormal drawing board grid area data; construct a regional repair strategy through the high-abnormal drawing board grid area data and the low-abnormal drawing board grid area data to execute the electronic drawing board drawing operation.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the data processing method based on the electronic drawing board as described in any one of claims 1-6.

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