Individual share popularity transaction sorting display method and system based on increase and decrease amplitude and exchange rate

Abstract

The invention provides an individual share popularity transaction sorting display method and system based on a fluctuation range and a change rate, and relates to the technical field of individual share popularity transaction sorting display. An initial optical image containing fluctuation range and change rate data in a data display interface is collected in real time through an optical image collection device; and a final optical image is obtained after noise reduction. Performing Laplacian operator operation on the data to obtain a definition analysis result, and taking an area of which the definition exceeds a preset threshold value as an effective data area; converting the image signal of the area into an initial electric signal, and obtaining a target electric signal through adaptive filtering interference suppression and signal amplification; analyzing real-time individual stock data, matching the real-time individual stock data with a preset popularity evaluation data set to obtain scores, and sorting to obtain a preliminary result; and a final result is generated through stability verification and is displayed by using a dynamic rendering technology, so that accurate sorting and dynamic display of individual share popularity transactions can be realized through image acquisition processing, signal optimization, data matching scoring and sorting verification, and data processing reliability and display intuition are guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of ranking individual stocks based on their popularity fluctuations, and in particular to a method and system for ranking and displaying individual stocks based on their price changes and turnover rates. Background Technology

[0002] In the financial trading field, the fluctuations in individual stock popularity are among the core information that investors and analysts focus on. In particular, price changes and turnover rates are key indicators reflecting the trading activity of individual stocks, and their real-time dynamics and ranking display directly affect decision-making efficiency. In relevant scenarios, it is necessary to obtain the above indicators from various data display interfaces, process them, and present them in an intuitive way in a ranking of individual stock popularity fluctuations to meet the needs of real-time monitoring and rapid identification of key stocks.

[0003] Currently, existing technologies primarily acquire data on individual stock price fluctuations and turnover rates by connecting to financial data interfaces. After acquisition, a preset algorithm is used to calculate a popularity value, which is then sorted according to the popularity value. Finally, the sorted results are displayed on the terminal interface in a fixed table or list format. The entire process mainly revolves around data interface interaction, basic numerical calculations, and static display, forming a standardized data processing and presentation chain.

[0004] However, existing technologies have the following drawbacks: data acquisition is highly dependent on specific interfaces, making it difficult to effectively collect individual stock data from third-party display interfaces without interface permissions or in offline scenarios; at the same time, the processing of collected data is mostly limited to basic numerical calculations, lacking refined processing based on the data source, resulting in insufficient accuracy of sorting results and instability of display effects when data quality is interfered with. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for sorting and displaying individual stock popularity fluctuations based on price change and turnover rate, so as to solve the problem of unstable individual stock popularity fluctuation sorting in the prior art.

[0006] To address the aforementioned technical problems, firstly, this application provides a method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates, including:

[0007] An initial optical image containing price fluctuations and turnover rate data is acquired in real time through an optical image acquisition device, and the initial optical image is denoised to obtain the final optical image.

[0008] The final optical image is subjected to Laplacian operator operation to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the effective data region.

[0009] The image signal of the effective data area is converted into an initial electrical signal. An adaptive filtering algorithm is used to suppress interference in the initial electrical signal to obtain an intermediate electrical signal. The intermediate electrical signal is then amplified to obtain the target electrical signal.

[0010] The real-time stock data parsed from the target electrical signal is matched with a preset stock popularity assessment dataset to obtain a popularity fluctuation score for each stock. The popularity fluctuation scores of all stocks are then sorted to obtain a preliminary stock popularity fluctuation ranking result.

[0011] A stability verification algorithm is used to verify the preliminary stock popularity fluctuation ranking results. Based on the verification results, the final stock popularity fluctuation ranking results are generated. Dynamic rendering technology is used to display the final stock popularity fluctuation ranking results on the data display interface.

[0012] Optionally, the step of using a stability verification algorithm to verify the preliminary stock popularity fluctuation ranking result, generating a final stock popularity fluctuation ranking result based on the verification result, and using dynamic rendering technology to display the final stock popularity fluctuation ranking result on the data display interface includes:

[0013] The preliminary ranking results of individual stock popularity fluctuations are verified and prepared to obtain reference ranking data for each stock corresponding to a preset number of historical times;

[0014] A stability verification algorithm is used to compare the preliminary stock popularity fluctuation ranking results with the reference ranking data to obtain multiple position difference values ​​for each stock. All position difference values ​​of the same stock are integrated to obtain the ranking fluctuation value of each stock. The ranking fluctuation value is the verification result.

[0015] Stocks whose ranking fluctuation values ​​exceed the preset fluctuation judgment range are marked as stocks to be adjusted. The stocks to be adjusted are then subjected to position correction processing to obtain the final ranking result of stock popularity fluctuations.

[0016] The final stock popularity fluctuation ranking result is transformed to generate interface display parameters. Dynamic rendering technology is used to process the interface display parameters to obtain the interface display data.

[0017] The data displayed on the interface is transferred to the data display interface to present the final ranking results of individual stock popularity fluctuations on the data display interface.

[0018] Optionally, the step of performing position correction processing on the stocks to be adjusted to obtain the final ranking result of stock popularity fluctuations includes:

[0019] Information is collected and processed on the stocks to be adjusted to obtain basic information, which includes the heat fluctuation score of the stocks to be adjusted and their average position in the historical ranking results of a preset number of times.

[0020] Position analysis is performed on the basic information to obtain the position correction range of the stocks to be adjusted, and the non-adjustable stocks corresponding to the position correction range are selected from the preliminary stock popularity fluctuation ranking results.

[0021] Based on the aforementioned heat fluctuation score, the correction position of the individual stock to be adjusted is determined;

[0022] The corrected positions of all the stocks to be adjusted are sorted with the original positions of all the stocks not to be adjusted to obtain the final ranking result of stock popularity fluctuations.

[0023] Secondly, this application provides a system for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rate, including:

[0024] The acquisition module is used to acquire an initial optical image containing price fluctuations and turnover rate data in the data display interface in real time through an optical image acquisition device, and to reduce noise in the initial optical image to obtain a final optical image.

[0025] The calculation module is used to perform Laplacian operator operations on the final optical image to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the effective data region.

[0026] The processing module is used to convert the image signal of the effective data area into an initial electrical signal, perform interference suppression processing on the initial electrical signal using an adaptive filtering algorithm to obtain an intermediate electrical signal, and perform signal amplification processing on the intermediate electrical signal to obtain the target electrical signal;

[0027] The matching module is used to match the real-time individual stock data parsed from the target electrical signal with a preset individual stock popularity evaluation dataset to obtain a popularity fluctuation score for each individual stock, and to sort the popularity fluctuation scores of all individual stocks to obtain a preliminary individual stock popularity fluctuation ranking result.

[0028] The verification module is used to verify the preliminary stock popularity fluctuation ranking results using a stability verification algorithm, generate the final stock popularity fluctuation ranking results based on the verification results, and display the final stock popularity fluctuation ranking results on the data display interface using dynamic rendering technology.

[0029] Thirdly, this application provides an electronic device, comprising:

[0030] Memory, used to store computer programs;

[0031] A processor is configured to execute the computer program to implement the steps of the method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates as described in the first aspect above.

[0032] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates as described in the first aspect above.

[0033] The method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates provided in this application involves acquiring an initial optical image containing price change and turnover rate data in real time on the data display interface using an optical image acquisition device. The initial optical image is then denoised to obtain a final optical image. A Laplacian operator operation is performed on the final optical image to obtain a sharpness analysis result. Based on this result, regions in the final optical image with sharpness values ​​greater than a preset sharpness threshold are designated as valid data regions. The image signal of the valid data region is converted into an initial electrical signal, and an adaptive filtering algorithm is used to filter the initial electrical signal. Interference suppression processing is performed to obtain an intermediate electrical signal. The intermediate electrical signal is then amplified to obtain a target electrical signal. Real-time stock data parsed from the target electrical signal is matched with a preset stock popularity assessment dataset to obtain a popularity fluctuation score for each stock. The popularity fluctuation scores of all stocks are sorted to obtain a preliminary stock popularity fluctuation ranking result. A stability verification algorithm is used to verify the preliminary stock popularity fluctuation ranking result. Based on the verification result, a final stock popularity fluctuation ranking result is generated. Dynamic rendering technology is used to display the final stock popularity fluctuation ranking result on the data display interface.

[0034] The technical solution of this application has the following beneficial effects:

[0035] This application acquires core data on price fluctuations and turnover rates in real time from various data display interfaces. Simultaneously, it improves image data quality by removing noise and blur interference from images, providing a high-quality image foundation for subsequent clarity analysis and data extraction. By quantifying the clarity of each region of the image using the Laplacian operator, it accurately selects valid data regions that meet the clarity standards, eliminating interference from blurry and invalid regions and ensuring that data processing is performed only on high-quality regions, thus improving the accuracy of data extraction. Furthermore, it employs adaptive filtering to specifically suppress interference signals in the electrical signal, and then amplifies the signal to enhance the strength of the effective signal, ensuring the stability of the target electrical signal. The system ensures completeness and provides a reliable signal source for subsequent real-time stock data analysis. By matching real-time data with a preset evaluation dataset, and combining the price change and turnover rate as dual indicators, a heat fluctuation score is calculated to avoid the one-sidedness of a single indicator. After sorting, preliminary results are obtained, realizing the quantitative ranking of individual stock heat fluctuations and providing a basis for accurate display in the future. Stability verification is used to correct any short-term fluctuation anomalies that may exist in the preliminary ranking, improving the reliability and accuracy of the ranking results. Finally, the final results are displayed in a dynamic rendering manner, which presents the changes in individual stock heat fluctuations more intuitively than static display, making it easier for users to quickly capture key information and improve information acquisition efficiency.

[0036] Furthermore, this application prepares for verification by checking the preliminary stock popularity fluctuation ranking results, obtaining reference ranking data for each stock corresponding to a preset number of historical times; then, a stability verification algorithm is used to compare the preliminary ranking results with the reference ranking data to obtain multiple position difference values ​​for each stock, which are then integrated to generate a ranking fluctuation value for each stock; next, stocks whose ranking fluctuation values ​​exceed a preset range are marked as stocks to be adjusted and their positions are corrected to obtain the final stock popularity fluctuation ranking results; subsequently, the final ranking results are converted into interface display parameters, processed by dynamic rendering technology to obtain interface display data; finally, the interface display data is transmitted to the data display interface to realize the presentation of the final ranking results.

[0037] This application accurately identifies and corrects individual stocks with abnormal ranking fluctuations by comparing and verifying historical reference ranking data with preliminary results. This effectively avoids ranking deviations caused by short-term data fluctuations and significantly improves the stability and accuracy of the final ranking results for individual stock popularity anomalies. At the same time, the final ranking results are converted into display parameters adapted to the interface and dynamically rendered to ensure that the ranking results are presented in a way that is more in line with users' reading habits, intuitively conveying information on individual stock popularity anomalies, and further improving the efficiency of users in obtaining and understanding data. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 A flowchart illustrating a method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rate, provided in an embodiment of this application;

[0040] Figure 2 A schematic diagram illustrating a specific implementation of a method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rate, provided in this application embodiment;

[0041] Figure 3 A schematic diagram of a stock popularity ranking and display system based on price change and turnover rate provided in this application embodiment;

[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0043] In financial trading scenarios, investors and analysts need to obtain real-time data on stock price fluctuations and turnover rates, and intuitively understand stock price movements through sorting. However, existing technologies struggle to meet the reliable data processing and display needs across diverse scenarios. Firstly, current methods heavily rely on specific financial data interfaces. If a third-party display interface lacks access permissions, the required price fluctuations and turnover rates cannot be effectively collected, significantly limiting the data collection scenarios. Secondly, even if data can be acquired, current technologies handle it crudely, lacking refined optimization for image-based data sources. Issues such as image noise, blurriness, or interference during signal transmission can easily lead to inaccurate sorting results. Furthermore, current sorting results are often displayed in static tables, lacking verification of sorting stability. Short-term data fluctuations can cause deviations in the sorting results, affecting users' accurate judgment of stock price movements.

[0044] To address the aforementioned issues, this application proposes a method for ranking and displaying individual stock popularity fluctuations based on price changes and turnover rates. This method first uses an optical image acquisition device to capture real-time images of the interface displaying price changes and turnover rate data. After obtaining the initial image, noise and blurring interference are removed, and then clear, valid data areas are selected from the image. Next, the image signal of the valid area is converted into an electrical signal, interference is removed, and signal strength is enhanced to extract real-time individual stock data. Subsequently, combined with a preset popularity evaluation standard, a popularity fluctuation score is calculated for each stock and a preliminary ranking is established. Then, ranking deviations caused by short-term fluctuations are eliminated through verification. Finally, the final ranking results are dynamically displayed on the interface.

[0045] This solution replaces "data interface dependency" with "optical image acquisition," solving the data acquisition problem in scenarios without interface permissions or offline. By "image noise reduction, effective area filtering, and electrical signal optimization," it improves data quality and avoids inaccurate sorting caused by coarse processing. "Stability verification" reduces the impact of short-term fluctuations on sorting, and combined with "dynamic display" makes the results more intuitive. It comprehensively overcomes the limitations of existing technologies and provides users with a more reliable and flexible stock popularity fluctuation sorting service.

[0046] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0047] The core of this application is to provide a method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rate. A flowchart of one specific implementation method is shown below. Figure 1 As shown, the method includes:

[0048] S101. The initial optical image containing the price fluctuation and turnover rate data in the data display interface is acquired in real time through the optical image acquisition device, and the initial optical image is denoised to obtain the final optical image.

[0049] Optionally, step S101, denoising the initial optical image to obtain the final optical image, includes:

[0050] Step 1011: Calculate the difference between the color value of each pixel in the initial optical image and the color value of the corresponding surrounding preset number of pixels. Mark the pixels whose difference value exceeds the preset difference range as noise points. Based on the positions of all the noise points in the initial optical image, obtain a noise distribution image.

[0051] Step 1012: Based on a preset continuous distribution range, divide the noise distribution image to obtain discrete noise and patch noise. Calculate the average color value of the surrounding preset number of normal pixels of the discrete noise to obtain the color replacement value corresponding to each discrete noise.

[0052] Step 1013: Analyze the surrounding areas adjacent to the area where the noise patch is located to obtain the pixel feature template corresponding to each noise patch, and select the normal area in the initial optical image that is similar to the pixel feature template.

[0053] Step 1014: Based on the size of the noise patch, the corresponding normal area is cropped to obtain multiple pixel blocks. The pixel blocks are then overlaid onto the corresponding noise patch area to obtain a coverage result.

[0054] Step 1015: Based on the color replacement value and coverage result, the noise distribution image is corrected to obtain a preliminary denoised image. The preliminary denoised image is then subjected to residual noise detection processing to obtain the final optical image.

[0055] In the above scheme, the initial optical image refers to the original image obtained after real-time acquisition of the data display interface by the optical image acquisition device, containing visual information of the price fluctuation and turnover rate data in the interface; the pixel color value refers to the color data of each pixel in the initial optical image, including red, green, and blue channel values, used to characterize the pixel color features; the surrounding preset number of pixels refers to a fixed number of adjacent pixels selected around the target pixel, used for comparison with the target pixel color value; the preset difference range refers to a pre-set color difference range for determining whether a pixel is noise, and pixels exceeding this range are considered abnormal; noise points refer to pixels whose difference values ​​exceed the preset difference range, which will interfere with the clear display of data in the image; the noise distribution image refers to an image generated after marking all noise points according to their positions in the initial optical image, which can intuitively present the noise distribution; the preset continuous distribution range refers to a pre-set threshold for the number of continuous noise points used to distinguish between discrete noise and patch noise; discrete noise refers to noise points that exist individually or in fewer consecutive numbers than the preset continuous distribution range in the noise distribution image; patch noise refers to noise points in the noise distribution image that have more than or equal to the preset continuous distribution range in consecutive numbers; the color replacement value is... The first part of the optical image describes a set of color data obtained by averaging the color values ​​of a predetermined number of normal pixels surrounding discrete noise, used to replace the color of discrete noise points. The second part describes a set of pixel features extracted from the surrounding areas of a region containing noise patches, including pixel color distribution patterns and color intensity trends, serving as a reference for finding replaceable regions. The third part describes similar normal regions in the initial optical image that have pixel features consistent with the pixel feature template and are free of noise, used to extract pixel blocks to cover the noise patches. The fourth part describes the size of the noise patch in the initial optical image, including both horizontal and vertical pixel counts. The fifth part describes a pixel block, extracted from similar normal regions, with the same size as the noise patch and containing pixel features from the normal region. The sixth part describes the preliminary denoising image, generated after replacing discrete noise points with color replacement values ​​and correcting the noise distribution image based on the coverage results. The seventh part describes residual noise detection, which uses the same difference value calculation and comparison method to check for unprocessed noise points point by point in the preliminary denoising image. The final optical image is the image obtained after residual noise detection confirms the absence of residual noise points and can be used for subsequent sharpness analysis.

[0056] In this application example, firstly, through step 1011, for the initial optical image, the difference between the color value of each pixel and the color value of a preset number of neighboring pixels is calculated point by point. Then, the calculated difference value of each pixel is compared with a preset difference range, and pixels whose difference value exceeds the preset difference range are marked as noise points. Finally, a noise distribution image is generated based on the position of all noise points in the initial optical image.

[0057] Next, in step 1012, based on a preset continuous distribution range, the noise points in the obtained noise distribution image are divided to distinguish between discrete noise and patch noise. Then, for each discrete noise, a preset number of normal pixels around it are selected, and the average value of the color values ​​of these normal pixels is calculated to obtain the color replacement value corresponding to each discrete noise.

[0058] Then, in step 1013, pixel feature analysis is performed on the surrounding areas adjacent to each area of ​​noise, and the pixel features of the area are extracted to generate a pixel feature template corresponding to each area of ​​noise. Subsequently, in the obtained initial optical image, areas with pixel features similar to the template and without noise points are found and identified as similar normal areas.

[0059] Next, in step 1014, the size of each piece of noise is first determined. Then, based on the size, the corresponding pixel block is cropped from the determined similar normal area. Finally, the cropped pixel block is completely covered to the corresponding piece of noise area to obtain the coverage result.

[0060] Finally, in step 1015, based on the obtained color replacement values ​​and coverage results, the generated noise distribution image is corrected, discrete noise points are replaced and large noise areas are covered to generate a preliminary denoising image. Then, the preliminary denoising image is subjected to residual noise detection processing. If residual noise points are detected, the targeted processing steps of steps 1012-1014 are repeated until there are no residual noise points, and the final optical image is finally obtained.

[0061] The above-described S101 overall solution, by calculating pixel color differences and comparing them with a preset range, can accurately identify noise points in the initial optical image. Simultaneously, the generated noise distribution image can intuitively present the specific location of the noise, avoiding omissions or misjudgments of noise in subsequent noise reduction processing. By using a preset continuous distribution range, it achieves accurate classification of noise types, distinguishing between scattered discrete noise and concentrated area noise, avoiding poor noise reduction results caused by using a uniform processing method. It also calculates the color replacement value for discrete noise, preparing for subsequent replacement of discrete noise points and elimination of scattered interference. By analyzing the surrounding area of ​​area noise to generate pixel feature templates, it ensures that the selected normal area is consistent with the surrounding environment characteristics of area noise, avoiding the appearance of overlapping area noise by cropped pixel blocks. The system addresses issues such as abrupt color changes and inconsistent textures. It accurately locates similar normal areas, providing a reliable source for selecting appropriate pixel blocks and ensuring the naturalness of the image after denoising noisy areas. By selecting pixel blocks of the same size as the noisy areas and covering them, the interference of noisy areas on the image can be completely eliminated. Since the pixel blocks come from normal areas with similar characteristics to the surrounding noise, there are no issues of image fragmentation or color mismatch after covering, ensuring the continuity and integrity of the image after denoising noisy areas and further improving image quality. By correcting and generating a preliminary denoised image and checking for residual noise, all noise points in the initial optical image can be completely eliminated, avoiding the impact of residual noise on the subsequent extraction and analysis of price fluctuations and turnover rate data.

[0062] S102. Perform Laplacian operator operation on the final optical image to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the valid data region.

[0063] Optionally, step S102, performing Laplacian operator operations on the final optical image to obtain sharpness analysis results, includes:

[0064] Step 1021: Perform region segmentation processing on the final optical image to obtain the key computational region containing the target to be analyzed.

[0065] Step 1022: Perform Laplace operator operations on the key computational regions to obtain the operator operation results for each key computational region.

[0066] Step 1023: Perform numerical statistical processing on the operator operation results to obtain the numerical concentration range and numerical average value of each key operation region. Based on the numerical concentration range and numerical average value, calculate the clear quantified value of each key operation region.

[0067] Step 1024: Integrate all the aforementioned sharpness quantifications to obtain the sharpness analysis results.

[0068] In the above scheme, region segmentation refers to the operation of dividing the image into multiple independent regions based on the distribution location of the content to be analyzed in the image; the target to be analyzed refers to the core content in the final optical image whose sharpness needs to be judged, namely the display area of ​​individual stock price fluctuations and turnover rate; the key operation region refers to the independent region containing the target to be analyzed, which is selected through region segmentation, and subsequent sharpness-related operations are only performed on this region; the Laplacian operator operation refers to the operation of calculating the sum of the second derivatives of each pixel in the horizontal and vertical directions in the key operation region, which is used to quantify the intensity of color changes around the pixel; the operator operation result refers to the value obtained after each pixel is processed by the Laplacian operator, and the value corresponds to the intensity of color changes around the pixel; numerical statistical processing refers to the operation of calculating the maximum and minimum values ​​and the arithmetic mean of the operator operation results; the numerical concentration range refers to the interval between the maximum and minimum values ​​in the operator operation results; the sharpness quantification value refers to the value that intuitively represents the sharpness of the region, which is calculated by combining the numerical concentration range and the numerical mean; the sharpness analysis result refers to the integrated dataset containing each key operation region and its corresponding sharpness quantification value, which is used to determine the effective data region in the subsequent process.

[0069] In this application example, firstly, through step 1021, the final optical image is divided into regions. The division is based on the actual distribution location of the target to be analyzed, ensuring that each divided region contains only the complete data display content of a single stock, avoiding confusion between data regions of different stocks, and thus obtaining the key calculation region containing all the targets to be analyzed.

[0070] Next, in step 1022, the Laplacian operator is performed on each key computation region. During the operation, the second derivative of the horizontal direction and the second derivative of the vertical direction of each pixel in the region are calculated point by point, and then the two are added together to obtain the operator operation result of the pixel. After all the pixels in the key computation regions have completed the operation, the set of operator operation results corresponding to each key computation region is obtained.

[0071] Then, through step 1023, the operator operation results of each key operation area are subjected to numerical statistical processing. First, the maximum and minimum values ​​in the result set of each area are found to determine the range of numerical concentration, and then the arithmetic mean of all results is calculated. Subsequently, the range of numerical concentration and the numerical mean are substituted into the clear quantification value formula for calculation to obtain the clear quantification value of each key operation area.

[0072] Finally, in step 1024, the clarity quantification values ​​of all key computational regions are integrated. During integration, the correlation between each quantification value and the corresponding key computational region is preserved to avoid misalignment between quantification values ​​and regions. After the quantification values ​​of all regions have been correlated and organized, a structured dataset is formed. This dataset is the clarity analysis result, which provides a basis for subsequent screening of effective data regions based on the clarity analysis results.

[0073] In practical applications, the final optical image is a financial data display interface image acquired by optical equipment. The image resolution is set to 400×300 pixels. The interface clearly displays the price changes and turnover rates of stocks A, B, and C. Based on the actual display position of each stock's data within the image, the regions are divided. The price changes and turnover rate data of stock A are concentrated in the upper left half of the image. This region is defined as the range of 20 to 80 pixels on the x-axis and 50 to 100 pixels on the y-axis, designated as the key calculation region 1. The data of stock B is displayed in the middle left half of the image, defined as the range of 20 to 80 pixels on the x-axis and 120 to 170 pixels on the y-axis, designated as the key calculation region 2. The data of stock C is displayed in the lower left half of the image, defined as the range of 20 to 80 pixels on the x-axis and 190 to 240 pixels on the y-axis, designated as the key calculation region 3.

[0074] Next, the Laplace operator was performed on the three key calculation areas: key calculation area 1, key calculation area 2, and key calculation area 3. To ensure complete coverage of the price fluctuations and turnover rate display content within each area, 100 pixels were selected for calculation in each key calculation area. The selected pixels were evenly distributed in the data text and number display areas. During the calculation, the horizontal and vertical second derivatives of each pixel were calculated point by point, and then the second derivatives in the two directions were summed to obtain the operator calculation result for each pixel. The result was then calculated using the formula... ,in The result of the operator operation is given, where f is the color value of the pixel, x is the horizontal coordinate of the pixel, and y is the vertical coordinate of the pixel. The second derivative in the horizontal direction. For the second derivative in the vertical direction, the minimum value of the 100 operator operation results in the key operation region 1 is -80 and the maximum value is -30; the minimum value of the 100 operator operation results in the key operation region 2 is -90 and the maximum value is -40; and the minimum value of the 100 operator operation results in the key operation region 3 is -70 and the maximum value is -25.

[0075] Then, the operator operation results for the three key operation areas were statistically processed. For key operation area 1: the minimum value of the operator operation result was -80, the maximum value was -30, and the sum of 100 results was -5500. The average value was calculated by dividing the sum by the number of pixels, i.e., -5500 divided by 100 equals -55. The minimum, maximum, and average values ​​were then substituted into the sharpness quantification formula. ,in -55, -80, The result is -30, and the calculated clear quantized value is 50. For the key operation area 2: the minimum value of the operator operation result is -90, the maximum value is -40, the sum of 100 results is -6500, the average value is -6500 divided by 100 equals -65, and the calculated clear quantized value is 50. For the key operation area 3: the minimum value of the operator operation result is -70, the maximum value is -25, the sum of 100 results is -4500, the average value is -4500 divided by 100 equals -45, and the calculated clear quantized value is approximately 55.6.

[0076] Finally, the clarity metrics obtained for each key operation area are integrated in a structured form of "area identifier - corresponding stock - clarity metrics". The results are: key operation area 1 corresponds to stock A with a clarity metrics of 50, key operation area 2 corresponds to stock B with a clarity metrics of 50, and key operation area 3 corresponds to stock C with a clarity metrics of approximately 55.6. The structured dataset formed after integration is the clarity analysis result.

[0077] The above-described S102 overall scheme focuses on key computational regions containing the target to be analyzed through region segmentation, avoiding computation on meaningless background areas in the image, reducing unnecessary computation, and ensuring that subsequent sharpness analysis only targets core data regions, improving computational efficiency and analytical focus. The Laplacian operator is used to convert the sharpness features of pixels within the key computational regions into specific numerical values, providing basic data for subsequent quantitative analysis of sharpness. Simultaneously, point-by-point computation ensures coverage of all core pixels within the region, avoiding sharpness judgment bias due to missing local data and guaranteeing the comprehensiveness of the analysis. Numerical statistical processing transforms discrete operator operations... The calculation results are converted into a set range and average value, making it easy to intuitively grasp the clarity distribution characteristics within the region. Then, a standardized clarity quantification value is calculated through a formula to eliminate the influence of numerical differences in calculation results from different regions, allowing for direct comparison of clarity in each region and providing a clear numerical basis for subsequent screening of effective data regions. By integrating these results into a structured clarity analysis, the clarity level of each key calculation region is clearly presented, avoiding confusion in subsequent use caused by the dispersion of quantification values. At the same time, the region is linked to individual stock information, enabling direct location of the corresponding stock's data region when screening effective data regions, improving the operational efficiency and accuracy of subsequent steps.

[0078] S103. The image signal of the effective data area is converted into an initial electrical signal. An adaptive filtering algorithm is used to suppress interference in the initial electrical signal to obtain an intermediate electrical signal. The intermediate electrical signal is then amplified to obtain the target electrical signal.

[0079] In the above scheme, the image signal refers to the light signal carrying visual information within the effective data area, manifested as light fluctuations of different intensities and frequencies; the initial electrical signal refers to the electrical signal obtained after converting the image signal through photoelectric conversion technology, usually manifested as voltage or current changes, used for subsequent electronic equipment processing; the adaptive filtering algorithm refers to a processing method that can dynamically adjust the filtering parameters according to the real-time characteristics of interference in the electrical signal, used to specifically suppress interference; interference suppression processing refers to the operation of removing irrelevant interference signals from the initial electrical signal through the adaptive filtering algorithm; the intermediate electrical signal refers to the electrical signal that, after interference suppression processing, has removed most of the interference but may have a weak signal amplitude; the signal amplification processing refers to the operation of enhancing the amplitude of the intermediate electrical signal through an amplification module to achieve the strength required for subsequent data analysis; the target electrical signal refers to the electrical signal that, after signal amplification processing, has no obvious interference and has an appropriate amplitude, which can be directly used for analysis and extraction of real-time individual stock data.

[0080] In this application example, the image signal of the effective data area is first converted into an initial electrical signal by a photoelectric conversion module. This process uses photoelectric elements to convert the light intensity changes in the image signal into corresponding voltage or current changes, thereby realizing the conversion of visual information into an electronically processable signal format.

[0081] Next, after the initial electrical signal conversion is completed, an adaptive filtering algorithm is used to suppress interference in the initial electrical signal. The algorithm monitors the interference characteristics in the initial electrical signal in real time and dynamically adjusts the filtering parameters to filter out irrelevant interference, making the electrical signal fluctuation more stable and obtaining the intermediate electrical signal.

[0082] Finally, after obtaining the intermediate electrical signal, it is amplified by the signal amplification module. According to the signal amplitude required by the subsequent data analysis module, the amplitude of the intermediate electrical signal is amplified by a preset amplification ratio to finally obtain the target electrical signal, which is ready for subsequent analysis and extraction of real-time individual stock data.

[0083] In practical applications, for the effective data areas corresponding to stocks A, B, and C, a photoelectric conversion module is used to convert the image signals of each area into initial electrical signals. After conversion, the initial electrical signal voltage range of the effective area of ​​stock A is 0.3 to 4.8 volts, that of stock B is 0.2 to 4.5 volts, and that of stock C is 0.4 to 5.0 volts. These initial electrical signals contain voltage spikes of up to 12 volts caused by electromagnetic interference from the equipment.

[0084] Next, the adaptive filtering module is activated. Its adaptive filtering algorithm detects that the interference characteristics of the voltage spike are 100 Hz in frequency and 10 to 12 volts in amplitude. It automatically adjusts the filter cutoff frequency to 80 Hz, suppressing the spike interference to below 0.3 volts. After processing, the intermediate electrical signal voltage range of stock A is 0.3 to 4.7 volts, stock B is 0.2 to 4.4 volts, and stock C is 0.4 to 4.9 volts.

[0085] Finally, the signal amplification ratio is set to K, and its amplification formula is: ,in The target electrical signal voltage, Here, K represents the intermediate electrical signal voltage, and K is the amplification ratio, which is set to 3. The target electrical signals for each stock are calculated using the formula: For stock A, the intermediate electrical signal of 0.3 volts is amplified to 0.9 volts (0.3 x 3), and the intermediate signal of 4.7 volts is amplified to 14.1 volts (4.7 x 3), resulting in target electrical signals ranging from 0.9 to 14.1 volts; for stock B, the intermediate electrical signal of 0.2 volts is amplified to 0.6 volts, and the intermediate signal of 4.4 volts is amplified to 13.2 volts, resulting in target electrical signals ranging from 0.6 to 13.2 volts; for stock C, the intermediate electrical signal of 0.4 volts is amplified to 1.2 volts, and the intermediate signal of 4.9 volts is amplified to 14.7 volts, resulting in target electrical signals ranging from 1.2 to 14.7 volts.

[0086] The above-mentioned S103 overall scheme achieves format adaptation of visual information into electronically processable signals by converting image signals into initial electrical signals, providing a foundation for subsequent data analysis. The interference suppression processing of the adaptive filtering algorithm specifically removes irrelevant interference in the electrical signals, ensuring signal purity. The signal amplification processing enhances the signal amplitude, meeting the signal strength requirements of the subsequent analysis module. The synergistic effect of these three processes ensures that the target electrical signal has the characteristics of no obvious interference and appropriate amplitude, providing a reliable signal guarantee for accurately analyzing and extracting price fluctuation and turnover rate data.

[0087] S104. The real-time individual stock data parsed from the target electrical signal is matched with a preset individual stock popularity evaluation dataset to obtain a popularity fluctuation score for each individual stock. The popularity fluctuation scores of all individual stocks are sorted to obtain a preliminary individual stock popularity fluctuation ranking result.

[0088] Optionally, step S104, which involves matching the real-time stock data parsed from the target electrical signal with a preset stock popularity assessment dataset to obtain a popularity fluctuation score for each stock, and sorting all the popularity fluctuation scores to obtain a preliminary stock popularity fluctuation ranking result, includes:

[0089] Step 1041: Analyze the target electrical signal to obtain real-time stock data, which includes the stock's identification information, the magnitude of price fluctuations, and the percentage of transactions per unit time.

[0090] Step 1042: Based on the identification information, match the real-time individual stock data with the historical variation range and average trading activity level corresponding to the preset individual stock popularity assessment dataset to obtain the corresponding matching data.

[0091] Step 1043: Calculate the deviation between the transaction price fluctuation range and the historical fluctuation range in the matching data, and calculate the difference between the percentage of transactions per unit time and the average transaction activity in the matching data.

[0092] Step 1044: Based on the preset scoring rules, the deviation value and the difference value are comprehensively calculated to obtain the popularity fluctuation score of each stock.

[0093] Step 1045: Perform a comprehensive sorting process on all the aforementioned heat fluctuation scores to obtain a preliminary individual stock heat fluctuation sorting result.

[0094] In the above scheme, real-time stock data refers to data parsed from the target electrical signal that reflects the current trading status of a stock, including the stock's identification information, price fluctuation range, and the percentage of trading volume per unit time. Identification information refers to information used to uniquely distinguish a stock, such as its code or name, ensuring accurate association between the data and the corresponding stock. Price fluctuation range refers to the percentage change in the current trading price of a stock relative to the benchmark price, corresponding to the price change index. The percentage of trading volume per unit time refers to the ratio of the stock's trading volume within a set time period to the total tradable volume within that time period, corresponding to the turnover rate index. The preset stock popularity assessment dataset refers to a pre-stored dataset containing historical trading characteristics of each stock, used as a reference for assessing current popularity. Historical fluctuation range refers to the normal fluctuation range of the stock's price fluctuation range within the preset dataset over a past set period. Average trading activity refers to the average percentage of trading volume per unit time for the corresponding stock within the preset dataset over a past set period. Matching data refers to data obtained by associating with the preset data through identification information. The system is based on a combination of historical price fluctuation ranges and average trading activity data extracted from individual stocks. The deviation value is the difference between the price fluctuation range in real-time stock data and the median of the historical price fluctuation range in the matching data, used to quantify the degree of deviation between the current price fluctuation and historical norms. The difference value is the difference between the percentage of trading volume per unit time in real-time stock data and the average trading activity in the matching data, used to quantify the degree of difference between the current turnover rate and historical norms. The preset scoring rules are pre-defined rules for calculating the heat anomaly score by combining the deviation value and the difference value, including the weight allocation of the two indicators. The heat anomaly score is a value calculated using the preset scoring rules, reflecting the degree of anomaly in the current trading status of an individual stock; a higher value indicates a more significant anomaly. The comprehensive sorting process involves sorting all stocks according to their heat anomaly scores in descending order. The preliminary stock heat anomaly sorting result is a stock ranking obtained through comprehensive sorting without stability verification, reflecting the preliminary priority of current stock heat anomalies.

[0095] In this application example, firstly, through step 1041, the signal parsing module parses and processes the target electrical signal, identifies the semantic information corresponding to the voltage or current changes in the electrical signal, and converts it into readable real-time stock data, ensuring that the data contains identification information, the magnitude of price changes, and the proportion of trading volume per unit time. After obtaining the real-time stock data, it is used as the basic data for matching with the preset dataset in the subsequent step 1042, realizing the conversion and connection from electronic signals to business data.

[0096] Next, in step 1042, the identification information in the obtained real-time individual stock data is used as an index to search and match in the preset individual stock popularity assessment dataset to determine the dataset entry corresponding to each identification information; then, the historical change range and average trading activity of the corresponding individual stock are extracted from the matching entries to form matching data.

[0097] Then, in step 1043, the deviation value is calculated. First, the median value of the historical range of change in the matching data is determined, which is the sum of the maximum and minimum values ​​of the historical range of change divided by 2. Then, the median value is subtracted from the trading price change range in the real-time individual stock data. Next, the difference value is calculated by directly subtracting the average trading activity level in the matching data from the percentage of trading volume per unit time in the real-time individual stock data.

[0098] Next, in step 1044, the preset scoring rules are determined, and the weights of deviation and difference values ​​are set, such as a deviation value weight of 0.6 and a difference value weight of 0.4. The scoring method is clarified as follows: the heat fluctuation score is equal to the deviation value multiplied by the deviation value weight plus the difference value multiplied by the difference value weight. Then, the deviation value and difference value of each stock are substituted into the formula to calculate the heat fluctuation score of each stock.

[0099] Finally, through step 1045, all the stock popularity fluctuation scores were collected to ensure that the score of each stock corresponds one-to-one with the identification information; then, these scores were sorted in descending order so that stocks with higher scores were ranked higher; finally, the preliminary stock popularity fluctuation ranking results were obtained.

[0100] The aforementioned S104 overall solution transforms target electrical signals into intuitive real-time stock data through signal analysis, realizing the conversion from electronic signals to business data. This ensures that subsequent processing can be directly based on the core indicators of individual stocks. Simultaneously, the extraction of identification information provides a unique basis for accurate matching of the preset dataset, avoiding data confusion. The identification information enables accurate matching of real-time stock data with the preset dataset, ensuring that the evaluation of each stock is based on its own historical trading characteristics, avoiding evaluation bias caused by using general standards. This provides targeted reference data for subsequent quantitative analysis of the differences between the current trading status and historical norms. Furthermore, the quantitative calculation of deviation values... The difference value transforms the current stock price change and turnover rate differences from historical norms into specific numerical values, avoiding the ambiguity of subjective judgment and providing an objective and quantifiable basis for subsequent popularity anomaly scoring. It also ensures that the score accurately reflects the degree of anomaly in the current trading status of the stock. By weighting and combining the deviation and difference values ​​through preset scoring rules, the anomaly characteristics of the two dimensions are integrated into a single popularity anomaly score. This takes into account the impact of anomalies in price change and turnover rate, while highlighting the role of key indicators through weight allocation. The scoring results comprehensively and objectively reflect the overall popularity anomaly situation of the stock, providing a unified and comparable quantitative standard for subsequent ranking.

[0101] S105. The initial stock popularity fluctuation ranking result is verified using a stability verification algorithm. The final stock popularity fluctuation ranking result is generated based on the verification result. The final stock popularity fluctuation ranking result is displayed on the data display interface using dynamic rendering technology.

[0102] Optionally, step S105, which involves verifying the preliminary stock popularity fluctuation ranking result using a stability verification algorithm, generating the final stock popularity fluctuation ranking result based on the verification result, and displaying the final stock popularity fluctuation ranking result on the data display interface using dynamic rendering technology, includes:

[0103] Step 1051: Prepare for verification of the preliminary ranking results of individual stock popularity fluctuations, and obtain reference ranking data for each stock corresponding to the preset historical number of times.

[0104] Step 1052: The preliminary stock popularity fluctuation ranking result is compared with the reference ranking data using a stability verification algorithm to obtain multiple position difference values ​​for each stock. All position difference values ​​of the same stock are integrated to obtain the ranking fluctuation value of each stock. The ranking fluctuation value is the verification result.

[0105] Step 1053: Mark the stocks whose ranking fluctuation values ​​exceed the preset fluctuation judgment range as stocks to be adjusted, perform position correction processing on the stocks to be adjusted, and obtain the final ranking result of stock popularity fluctuations.

[0106] Step 1053 may specifically include the following steps: collecting and processing information on the stocks to be adjusted to obtain basic information, which includes the stock's popularity fluctuation score and its average position in the historical ranking results of a preset number of times; performing position analysis on the basic information to obtain the position correction range of the stock to be adjusted; selecting non-adjustable stocks corresponding to the position correction range from the preliminary stock popularity fluctuation ranking results; determining the correction position of the stock to be adjusted based on the popularity fluctuation score; and sorting the correction positions of all the stocks to be adjusted with the original positions of all the non-adjustable stocks to obtain the final stock popularity fluctuation ranking result.

[0107] Step 1054: The final stock popularity fluctuation ranking result is converted to generate interface display parameters. Dynamic rendering technology is used to process the interface display parameters to obtain interface display data.

[0108] Step 1054 may specifically include the following steps: Extracting information from the final stock popularity ranking results to obtain the identifier information, current ranking information, and changes from the previous ranking information for each stock; based on the mapping rules between the current ranking information and row / column positions, obtaining the row / column position of each stock in the data display interface; based on the mapping rules between the changes and display identifiers, obtaining the display color and additional identifiers for each stock; integrating the display color and the additional identifiers to obtain interface display parameters; processing the interface display parameters using dynamic rendering technology based on the preset pixel coordinate system of the data display interface and the row / column positions to obtain the coordinate parameters, visual attributes, transition methods, and transition durations corresponding to each stock; integrating the coordinate parameters, visual attributes, transition methods, and transition durations to obtain an interface element attribute set; associating the interface element attribute set with the corresponding stock display elements to obtain interface display data.

[0109] Step 1055: Transfer the interface display data to the data display interface to present the final ranking results of individual stock popularity fluctuations on the data display interface.

[0110] In the above scheme, verification preparation refers to the basic data retrieval and organization operations carried out for stability verification, the core of which is to extract historical sorting records from the database; preset historical number of times refers to the pre-set number of historical sorting results used for reference, ensuring that the reference data has statistical representativeness; stability verification algorithm refers to the algorithm used to compare the current sorting with historical sorting and quantify the degree of sorting fluctuation, the core operation of which is to calculate and integrate position differences; position difference value refers to the absolute value of the difference between the current preliminary sorting ranking of an individual stock and a certain historical reference sorting ranking, reflecting the magnitude of a single ranking fluctuation; integration processing refers to the operation of calculating the arithmetic mean of multiple position difference values ​​of the same stock; sorting fluctuation value refers to the value obtained through integration processing that reflects the historical average fluctuation degree of the individual stock ranking, the smaller the value, the more stable the sorting, and this value is the verification result; preset fluctuation judgment range refers to the pre-set range of sorting fluctuation values ​​for judging whether the individual stock ranking needs to be adjusted, within which the sorting is stable and no adjustment is needed; individual stocks to be adjusted refer to those with sorting fluctuations. Stocks whose values ​​exceed the preset fluctuation judgment range; Basic information refers to the average position of the stock's heat fluctuation score and the historical ranking results of the preset number of times, with the average position being the arithmetic mean of the historical rankings; Position correction interval refers to the reasonable ranking range of the stock to be adjusted based on the basic information analysis; Interface display parameters refer to the set of parameters that control the presentation of the stock on the data display interface, including row and column positions, display colors, and additional labels; Dynamic rendering technology refers to the image rendering technology that makes interface elements present dynamic effects; Coordinate parameters refer to the specific position of the stock display element in the interface pixel coordinate system; Visual attributes refer to the appearance characteristics of the stock display element, including display color and font size; Transition mode refers to the type of dynamic effect when the interface element appears or changes; Interface element attribute set refers to the structured data set that integrates coordinate parameters, visual attributes, transition mode, and transition duration; Data transmission refers to the operation of sending the interface display data from the processing module to the data display interface, using an adapted communication protocol.

[0111] In this application example, firstly, through step 1051, a verification preparation operation is performed. Based on the individual stock identification information, the heat fluctuation ranking results corresponding to a preset number of historical times for each individual stock are retrieved from the database storing historical ranking data. Then, the retrieved historical ranking results are organized in chronological order to form exclusive reference ranking data for each individual stock, ensuring that the data accurately corresponds to the individual stock identification.

[0112] Next, in step 1052, a stability verification algorithm is used to compare the ranking of each stock in the preliminary stock popularity fluctuation ranking results with each ranking in the reference ranking data of the same stock, and calculate the position difference value for each comparison; then, all position difference values ​​of the same stock are integrated and the arithmetic mean is calculated to obtain the ranking fluctuation value of each stock.

[0113] Then, in step 1053, the obtained stock ranking fluctuation value is compared with the preset fluctuation judgment range to mark the stocks to be adjusted; then, information is collected and processed for each stock to be adjusted to obtain its heat fluctuation score and historical average ranking position to form basic information; then, based on the basic information, the position correction range of the stocks to be adjusted is obtained, and the non-adjustable stocks within the position correction range are screened from the preliminary ranking results. The heat fluctuation scores of the stocks to be adjusted and these non-adjustable stocks are compared, and finally, the correction position of the stocks to be adjusted is determined according to the score. The correction positions of all the stocks to be adjusted and the original positions of the non-adjustable stocks are re-sorted to obtain the final stock heat fluctuation ranking result.

[0114] Next, in step 1054, information extraction processing is performed on the final stock popularity fluctuation ranking results to obtain the identification information, current ranking information, and change information from the previous ranking for each stock. Then, based on the preset current ranking information and row and column position mapping rules, the row and column position of each stock is determined, and the display color and additional labels are determined based on the change information and display label mapping rules. The two are then integrated to form interface display parameters. Next, based on the preset interface pixel coordinate system, dynamic rendering technology is used to process the interface display parameters, converting the row and column positions into coordinate parameters. Combined with visual attributes, preset transition methods, and transition durations, an interface element attribute set is formed. Finally, the interface element attribute set is associated with the corresponding stock display elements to obtain the interface display data.

[0115] Finally, in step 1055, the obtained interface display data is transmitted from the data processing module to the data display interface using an adapted communication protocol. After receiving the data, the data display interface parses the coordinate parameters, visual attributes, transition methods, and other information in the interface display data. Based on the parsing results, it renders the display elements of each stock on the interface. Finally, the data display interface fully presents the final ranking results of individual stock popularity fluctuations, completing the entire process.

[0116] In practical applications, a preset historical count of 3 times is set, and verification preparation is carried out based on the preliminary individual stock popularity fluctuation ranking results obtained: From the data storage database, according to the identification information of stock A code 0001, stock B code 0002, and stock C code 0003, their most recent 3 popularity fluctuation ranking results are retrieved respectively, that is, the 3 historical rankings of stock A are 2, 3, 2, the 3 historical rankings of stock B are 1, 2, 1, and the 3 historical rankings of stock C are 3, 1, 3; these historical ranking results are then organized into reference ranking data for stock A, stock B, and stock C respectively.

[0117] Next, a stability check algorithm based on arithmetic mean integration is used, and the formula for calculating the sorting fluctuation value is as follows: Where F is the sorting fluctuation value, This is the current preliminary ranking of individual stocks. This is the ranking of a stock based on its i-th historical reference, where n is the preset number of historical references. The single position difference value is used to calculate the ranking fluctuation value of each stock based on the reference ranking data: Stock A is initially ranked 1, and its historical ranking is 2, 3, 2. The single position difference values ​​are |1-2|=1, |1-3|=2, and |1-2|=1, respectively. The sum of the three difference values ​​is 1+2+1=4. Substituting into the formula, we get F=4÷3≈1.33; Stock B is initially ranked 2, and its historical ranking is 1, 2, 1, respectively. The single position difference values ​​are |2-1|=1, |2-2|=0, and |2-1|=1, respectively. The sum is 2. Substituting into the formula, we get F=2÷3≈0.67; Stock C is initially ranked 3, and its historical ranking is 3, 1, 3, respectively. The single position difference values ​​are |3-3|=0, |3-1|=2, and |3-3|=0, respectively. The sum is 2. Substituting into the formula, we get F=2÷3≈0.67.

[0118] Then, the preset fluctuation judgment range is set as a ranking fluctuation value ≤ 1. The ranking fluctuation value above is compared with 1. Stock A's fluctuation value of 1.33 exceeds the range and is marked as a stock to be adjusted. Stocks B and C both have fluctuation values ​​≈ 0.67, and are not stocks to be adjusted. Basic information for stock A is collected: a popularity fluctuation score of 5.12%, and its historical rankings of 2nd, 3rd, and 2nd three times. The average position is calculated using the following formula: Where P is the average position. Let n be the number of historical rankings for the i-th time, and n be the number of historical rankings. Substituting the data, we get P = (2 + 3 + 2) ÷ 3 ≈ 2.33. After rounding, we determine the position adjustment range as 2-3. Then, from the preliminary ranking results, we select stocks that are not to be adjusted within the position adjustment range of 2-3. Since the popularity fluctuation score of stock B (ranked 2nd, 2.26%) and stock C (ranked 3rd, 1.5%) are within the adjustment range, we determine that stock B (ranked 2nd) and stock C (ranked 3rd) are not to be adjusted. Then, we compare the popularity fluctuation scores of stock A with these two stocks that are not to be adjusted: 5.12% > 2.26% > 1.5%. Therefore, we determine that the adjustment position of stock A is 2, the original ranking of stock B is adjusted to 3, and the original ranking of stock C remains unchanged. Finally, we form the final ranking result of stock popularity fluctuations: 1. Stock A, 2. Stock B, 3. Stock C.

[0119] Next, information was extracted from the final ranking results of individual stock popularity fluctuations. Stock A was identified as stock code 0001, currently ranked 1st, previously ranked 2nd, with a change of 1 place increase; Stock B was identified as stock code 0002, currently ranked 2nd, previously ranked 1st, with a change of 1 place decrease; Stock C was identified as stock code 0003, currently ranked 3rd, previously ranked 3rd, with no change. Based on the preset mapping rule that the Nth position corresponds to the Nth row and 1st column, an increase corresponds to red and an additional indicator ↑1, a decrease corresponds to green and an additional indicator ↓1, and no change corresponds to black and no additional indicator, the above information extraction results were analyzed to obtain the following interface display parameters: Stock A is positioned in row 1, column 1, displayed in red, with an additional indicator ↑1; Stock B is positioned in row 2 ... The display color is green with an additional ↓1 marker; stock C is positioned in row 3, column 1, displayed in black with no additional marker; the pixel coordinate system of the data display interface is preset with the top left corner as the origin, the top y-coordinate of the first row is 50 pixels, the height of each row is 50 pixels, and the x-coordinate is uniformly 20 pixels. The coordinate parameters are converted and calculated to obtain stock Ax=20, y=50, stock Bx=20, y=100, and stock Cx=20, y=150; then, dynamic rendering technology is used to set the transition method: rising stocks slide upwards, falling stocks slide downwards, and unchanged stocks remain static, with a uniform transition duration of 0.5 seconds. The coordinate parameters, visual attributes, transition methods, and transition duration are integrated to form an interface element attribute set; then, the interface element attribute set of each stock is associated with its corresponding display element to obtain the interface display data.

[0120] Finally, using the TCP / IP communication protocol, the obtained interface display data is transmitted from the data processing module to the financial data display interface. After receiving the data, the financial data display interface first parses the interface display data for stock A: coordinate parameter 2050, visual attribute red 14-point font, transition method upward slide of 0.5 seconds, display elements including stock A name code 0001, popularity rating 5.12% (ranked 1st), and additional identifier ↑1. At interface coordinate 2050, the information for stock A is rendered according to the parsed results. Next, the interface display data for stock B is parsed: coordinate parameter 20100, visual attribute green 14-point font, transition method downward slide of 0.5 seconds, display elements including stock B name code 0001, popularity rating 5.12% (ranked 1st), and additional identifier ↑1. Stock code 0002, with a popularity score of 2.26%, ranks 2nd and has an additional identifier ↓1. Information about stock B is rendered at coordinates 20100. Finally, the interface display data for stock C is analyzed: coordinate parameter 20150, visual attribute black 14-point font, static transition method, and display elements including the name of stock C. Stock code 0003, with a popularity score of 1.5%, ranks 3rd. Information about stock C is rendered at coordinates 20150. The final stock popularity ranking results are then fully presented on the financial data display interface, allowing users to intuitively view the popularity rankings and changes of each stock.

[0121] The aforementioned S105 overall scheme obtains reference ranking data for each stock through verification preparation, providing a historical comparison benchmark for subsequent stability verification and avoiding the bias caused by judging solely based on the current single ranking result; the stability verification algorithm transforms the historical fluctuations of individual stock rankings into specific ranking fluctuation values, realizing a quantitative assessment of ranking stability and avoiding the bias of subjective judgment on whether the ranking is reasonable; at the same time, it integrates and processes multiple position difference values ​​to ensure that the fluctuation values ​​can reflect the overall historical fluctuation situation; by marking stocks to be adjusted and performing position correction based on basic information, it effectively corrects the ranking deviation caused by short-term fluctuations in the initial ranking; and by screening non-adjustable stocks for comparison, it avoids ranking conflicts after correction, ensuring the rationality and accuracy of the final ranking result; By transforming and generating interface display parameters and using dynamic rendering technology, the abstract sorting results are converted into interface element attributes with specific positions, appearances, and dynamic effects. These elements are then linked to form the interface display data, allowing the sorting results to be presented in an intuitive and dynamic way. The addition of dynamic effects makes ranking changes more noticeable, improving users' understanding of individual stock popularity fluctuations. Data transmission pushes the interface display data to the data display interface, realizing the transformation of sorting results from data processing to terminal presentation, allowing users to intuitively obtain individual stock popularity fluctuation ranking information. Furthermore, the dynamic effects and clear labels in the interface presentation further help users quickly capture ranking changes and core data, improving information acquisition efficiency and ensuring the practical value of the entire method.

[0122] The following is a complete example for steps S101 to S105, such as Figure 2 As shown, firstly, an optical image acquisition device is used to acquire the initial optical image of the financial data display interface in real time. This interface displays the price changes and turnover rate data of stocks A, B, and C. Then, when denoising the initial optical image, the RGB color value difference between each pixel and its three adjacent pixels is calculated point by point. The preset difference range is that the difference in each RGB channel is ≤20. Taking a pixel next to the "+5.2%" price change of stock A as an example, its RGB value is 255, 255, 255, and the RGB values ​​of its three surrounding pixels are respectively... 240, 240, 240, 238, 238, 238, 242, 242, 242, the average difference value of a single channel is calculated to be (15+17+13)÷3=15, which is within the preset range; the RGB value of a certain pixel to the right of the stock B turnover rate "12%" is 100, 100, 100, and the RGB values ​​of the surrounding pixels are all 0, 0, 0, with an average difference value of 100 per channel, which exceeds the preset range, so this pixel is marked as a noise point; then all noise points in the initial optical image are removed to obtain the final optical image.

[0123] Next, after obtaining the final optical image, it is divided into key processing regions. The stock A data region is set to 20-80 pixels on the x-axis and 50-100 pixels on the y-axis; the stock B data region is set to 20-80 pixels on the x-axis and 120-170 pixels on the y-axis; and the stock C data region is set to 20-80 pixels on the x-axis and 190-240 pixels on the y-axis. Then, the Laplacian operator is performed on each key processing region. The operator formula is as follows: ,in Let f be the pixel color value, x be the horizontal coordinate of the pixel, and y be the vertical coordinate of the pixel. Taking a pixel in the stock A data region as an example, its second derivative in the horizontal direction is -30, and its second derivative in the vertical direction is also -30. The result of the operator operation is -30 + (-30) = -60. Then, the numerical results of the operator operation in each region are statistically analyzed. The numerical concentration range in the stock A data region is from -80 to -30, and the total sum of the values ​​is -5500. Therefore, the average value is -5500 ÷ 100 = -55. Finally, a clear quantization formula is used. Calculate the clarity metric, where Q is the clarity metric. The average value is the numerical average. It represents the minimum value within the range of the numerical set. As the maximum value of the numerical concentration range, substituting the data, the quantified value of the clarity measure of the stock A data area is (-55-(-80)) ÷ (-30-(-80)) × 100 = 50; the preset clarity threshold is 40. Since 50 > 40, the stock A data area is determined as a valid data area. Similarly, the stock B and stock C data areas are also determined as valid data areas.

[0124] Then, after determining the effective data area, the image signals of each area are converted into initial electrical signals. The initial electrical signal voltage range for the effective area of ​​stock A is 0.3 to 4.8V, for stock B it is 0.2 to 4.5V, and for stock C it is 0.4 to 5.0V. However, these initial electrical signals contain electromagnetic interference with a frequency of 100Hz and an amplitude of 10 to 12V. Therefore, an adaptive filtering algorithm is used to suppress the interference in the initial electrical signals. The filter cutoff frequency is adjusted to 80Hz, and the interference amplitude is suppressed to below 0.3V, resulting in an intermediate electrical signal for stock A. The electrical signal voltage range is 0.3 to 4.7V for stock B, 0.2 to 4.4V for stock C, and 0.4 to 4.9V for stock C. The intermediate electrical signals are then amplified, with an amplification ratio of 3. The amplification calculation method is to multiply the intermediate electrical signal voltage value by 3. For stock A, the 0.3V intermediate electrical signal is amplified to 0.3 × 3 = 0.9V, and the 4.7V intermediate signal is amplified to 4.7 × 3 = 14.1V. Finally, the target electrical signal voltage range is obtained as follows: 0.9 to 14.1V for stock A, 0.6 to 13.2V for stock B, and 1.2 to 14.7V for stock C.

[0125] After obtaining the target electrical signal, it is analyzed and processed to obtain real-time stock data. Stock A's real-time data is: +5.2% change, 18% turnover rate; Stock B's change is: -2.1% change, 12% turnover rate; and Stock C's change is: +3.5% change, 15% turnover rate. Based on the stock identification information, the real-time stock data is matched with a preset stock popularity assessment dataset. In this dataset, Stock A's historical price fluctuation range is 1% to 3%, with an average trading activity of 10%; Stock B's historical price fluctuation range is -3% to 1%, with an average trading activity of 8%; and Stock C's historical price fluctuation range is 2% to 4%, with an average trading activity of... The degree is 12%; then calculate the deviation and difference values. The deviation value is the difference between the real-time price change and the median of the historical range. The median of the historical range for stock A is (1%+3%)÷2=2%, so the deviation value is 5.2%-2%=3.2%. The difference value is the difference between the real-time turnover rate and the average trading activity. So the difference value for stock A is 18%-10%=8%. Similarly, the deviation value for stock B is -2.1%-[(-3%+1%)÷2]=-1.1%, and the difference value is 12%-8%=4%. The deviation value for stock C is 3.5%-[(2%+4%)÷2]=0.5%, and the difference value is 15%-12%=3%. The heat fluctuation score is calculated based on the preset scoring rules, and the scoring formula is as follows: Where S is the popularity fluctuation score, D1 is the deviation value (absolute value), and D2 is the difference value, with 0.6 and 0.4 being the weights of the two respectively, the calculated scores for stock A are 3.2%×0.6+8%×0.4=1.92%+3.2%=5.12%, for stock B it is 1.1%×0.6+4%×0.4=0.66%+1.6%=2.26%, and for stock C it is 0.5%×0.6+3%×0.4=0.3%+1.2%=1.5%. The popularity fluctuation scores are sorted in descending order, resulting in a preliminary ranking of individual stocks as: 1. Stock A, 2. Stock B, 3. Stock C.

[0126] Subsequently, after obtaining the preliminary ranking results, a stability verification algorithm was used to verify them. The preset historical ranking was 3 times. Stock A's last 3 historical rankings were 2, 3, 2; Stock B's were 1, 2, 1; and Stock C's were 3, 1, 3. Then, the ranking fluctuation value was calculated, and its formula was as follows: Where F is the sorting fluctuation value and Rc is the initial sorting rank. For the i-th historical ranking, the volatility value of stock A is (|1-2|+|1-3|+|1-2|)÷3=(1+2+1)÷3≈1.33, the volatility value of stock B is (|2-1|+|2-2|+|2-1|)÷3=(1+0+1)÷3≈0.67, and the volatility value of stock C is (|3-3|+|3-1|+|3-3|)÷3=(0+2+0)÷3≈0.67. Furthermore, the volatility judgment range is preset as the ranking volatility value ≤ 1. It is found that the volatility value of stock A exceeds this range. The stocks were marked as needing adjustment. Then, basic information about stock A was collected, resulting in a popularity fluctuation score of 5.12% and an average historical ranking position of (2+3+2)÷3≈2.33. The position correction range was determined to be 2 to 3. Based on the correction range, stocks not needing adjustment were selected as stock B ranked 2nd and stock C ranked 3rd. Since stock A's score was higher than both, the correction position was determined to be 2nd. However, considering its highest score, the initial ranking result was still maintained as 1st stock A, 2nd stock B, and 3rd stock C, and the final stock popularity fluctuation ranking result was generated.

[0127] Finally, the final ranking results are transformed to generate interface display parameters. Based on the ranking and row / column position mapping rules, the 1st place corresponds to 1 row and 1 column, the 2nd place to 2 rows and 1 column, and the 3rd place to 3 rows and 1 column. Based on the ranking change and display label mapping rules, stock A rises from 2nd to 1st in the previous ranking, displaying red with an additional label ↑1; stock B falls from 1st to 2nd in the previous ranking, displaying green with an additional label ↓1; stock C remains unchanged in ranking, displaying black with no additional label. The preset data display interface pixel coordinate system has the top left corner as the origin, the top y-coordinate of the first row is 50 pixels, the height of each row is 50 pixels, and the x-coordinate is uniformly 20 pixels. The coordinate parameters for each stock are calculated: stock A has x=20 pixels and y=50 pixels. Stock B has x=20 pixels and y=100 pixels, while stock C has x=20 pixels and y=150 pixels. Dynamic rendering technology is then used to process the interface display parameters. The transition method for rising stocks is set to slide upwards, for falling stocks to slide downwards, and for unchanged stocks to remain static. The transition duration is uniformly set to 0.5 seconds. The coordinate parameters, visual attributes, transition methods, and transition durations are integrated to obtain the interface element attribute set. This attribute set is then associated with the corresponding stock display elements to obtain the interface display data. The TCP / IP communication protocol is then used to transmit the interface display data to the Type B financial data display interface. After receiving the data, the interface renders the stock information according to the parsing results, and finally, the final stock popularity ranking results are dynamically presented on the data display interface.

[0128] Figure 3 This is a schematic diagram illustrating a specific implementation of a stock popularity ranking and display system based on price changes and turnover rate, as provided in this application. (Refer to...) Figure 3 The system may include:

[0129] The acquisition module 31 is used to acquire an initial optical image containing price fluctuation and turnover rate data in the data display interface in real time through an optical image acquisition device, and to reduce noise in the initial optical image to obtain a final optical image.

[0130] The calculation module 32 is used to perform Laplacian operator operation on the final optical image to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the valid data region.

[0131] Processing module 33 is used to convert the image signal of the effective data area into an initial electrical signal, perform interference suppression processing on the initial electrical signal using an adaptive filtering algorithm to obtain an intermediate electrical signal, and perform signal amplification processing on the intermediate electrical signal to obtain the target electrical signal.

[0132] The matching module 34 is used to match the real-time individual stock data parsed from the target electrical signal with a preset individual stock popularity evaluation dataset to obtain a popularity fluctuation score for each individual stock, and to sort the popularity fluctuation scores of all individual stocks to obtain a preliminary individual stock popularity fluctuation ranking result.

[0133] The verification module 35 is used to verify the preliminary stock popularity fluctuation ranking result using a stability verification algorithm, generate the final stock popularity fluctuation ranking result based on the verification result, and display the final stock popularity fluctuation ranking result on the data display interface using dynamic rendering technology.

[0134] The stock popularity fluctuation sorting and display system based on price change and turnover rate in this application embodiment is used to implement the aforementioned stock popularity fluctuation sorting and display method based on price change and turnover rate. Therefore, the specific implementation of the stock popularity fluctuation sorting and display system based on price change and turnover rate can be found in the embodiment section of the stock popularity fluctuation sorting and display method based on price change and turnover rate above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0135] like Figure 4 As shown, this application also provides an electronic device, including: a memory 41 for storing a computer program; and a processor 42 for executing the computer program to implement the steps of the above-described method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates.

[0136] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates.

[0137] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0138] Embodiments of the present invention also provide a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps in any of the above embodiments of the method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates.

[0139] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0140] The above provides a detailed description of the method and system for ranking and displaying individual stock popularity fluctuations based on price changes and turnover rate, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for sorting and displaying individual stock popularity fluctuations based on price change and turnover rate, characterized in that, include: An initial optical image containing price fluctuations and turnover rate data is acquired in real time through an optical image acquisition device, and the initial optical image is denoised to obtain the final optical image. The final optical image is subjected to Laplacian operator operation to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the effective data region. The image signal of the effective data area is converted into an initial electrical signal. An adaptive filtering algorithm is used to suppress interference in the initial electrical signal to obtain an intermediate electrical signal. The intermediate electrical signal is then amplified to obtain the target electrical signal. The real-time stock data parsed from the target electrical signal is matched with a preset stock popularity assessment dataset to obtain a popularity fluctuation score for each stock. The popularity fluctuation scores of all stocks are then sorted to obtain a preliminary stock popularity fluctuation ranking result. A stability verification algorithm is used to verify the preliminary stock popularity fluctuation ranking results. Based on the verification results, the final stock popularity fluctuation ranking results are generated. Dynamic rendering technology is used to display the final stock popularity fluctuation ranking results on the data display interface.

2. The method according to claim 1, characterized in that, The process involves using a stability verification algorithm to validate the preliminary stock popularity fluctuation ranking results, generating a final stock popularity fluctuation ranking result based on the validation results, and displaying the final stock popularity fluctuation ranking result on the data display interface using dynamic rendering technology. This includes: The preliminary ranking results of individual stock popularity fluctuations are verified and prepared to obtain reference ranking data for each stock corresponding to a preset number of historical times; A stability verification algorithm is used to compare the preliminary stock popularity fluctuation ranking results with the reference ranking data to obtain multiple position difference values ​​for each stock. All position difference values ​​of the same stock are integrated to obtain the ranking fluctuation value of each stock. The ranking fluctuation value is the verification result. Stocks whose ranking fluctuation values ​​exceed the preset fluctuation judgment range are marked as stocks to be adjusted. The stocks to be adjusted are then subjected to position correction processing to obtain the final ranking result of stock popularity fluctuations. The final stock popularity fluctuation ranking result is transformed to generate interface display parameters. Dynamic rendering technology is used to process the interface display parameters to obtain the interface display data. The data displayed on the interface is transferred to the data display interface to present the final ranking results of individual stock popularity fluctuations on the data display interface.

3. The method according to claim 2, characterized in that, The step of performing position correction processing on the stocks to be adjusted to obtain the final ranking result of stock popularity fluctuations includes: Information is collected and processed on the stocks to be adjusted to obtain basic information, which includes the heat fluctuation score of the stocks to be adjusted and their average position in the historical ranking results of a preset number of times. Position analysis is performed on the basic information to obtain the position correction range of the stocks to be adjusted, and the non-adjustable stocks corresponding to the position correction range are selected from the preliminary stock popularity fluctuation ranking results. Based on the aforementioned heat fluctuation score, the correction position of the individual stock to be adjusted is determined; The corrected positions of all the stocks to be adjusted are sorted with the original positions of all the stocks not to be adjusted to obtain the final ranking result of stock popularity fluctuations.

4. The method according to claim 2, characterized in that, The final stock popularity fluctuation ranking result is transformed to generate interface display parameters. Dynamic rendering technology is then used to process these parameters to obtain the interface display data, including: The final ranking results of individual stock popularity fluctuations are processed to extract information, obtain the identification information, current ranking information, and change information from the previous ranking information for each stock; Based on the mapping rules between the current ranking information and the row and column positions, the row and column positions of each stock in the data display interface are obtained. Based on the mapping rules between the change information and the display identifier, the display color and additional identifier of each stock are obtained. The display color and the additional identifier are integrated to obtain the interface display parameters. Based on the pixel coordinate system of the preset data display interface and the row and column positions, dynamic rendering technology is used to process the interface display parameters to obtain the coordinate parameters, visual attributes, transition methods and transition durations corresponding to each stock. The coordinate parameters, visual attributes, transition methods and transition durations are integrated to obtain the interface element attribute set. The interface element attribute set is associated with the corresponding individual stock display element to obtain the interface display data.

5. The method according to claim 1, characterized in that, The Laplacian operator operation on the final optical image to obtain the sharpness analysis results includes: The final optical image is divided into regions to obtain a key computational region containing the target to be analyzed; The Laplace operator is performed on the key computational regions to obtain the operator operation results for each key computational region; The results of the operator operation are subjected to numerical statistical processing to obtain the numerical concentration range and numerical average value of each key operation region. Based on the numerical concentration range and numerical average value, the clear quantification value of each key operation region is calculated. All the aforementioned sharpness metrics are integrated to obtain the sharpness analysis results.

6. The method according to claim 1, characterized in that, The process of matching the real-time stock data parsed from the target electrical signal with a preset stock popularity assessment dataset to obtain a popularity fluctuation score for each stock, and then sorting the popularity fluctuation scores of all stocks to obtain a preliminary stock popularity fluctuation ranking result, includes: The target electrical signal is parsed and processed to obtain real-time individual stock data, which includes the stock's identification information, the magnitude of price fluctuations, and the percentage of trading volume per unit time. Based on the identification information, the real-time individual stock data is matched with the corresponding historical variation range and average trading activity in the preset individual stock popularity assessment dataset to obtain the corresponding matching data. Calculate the deviation between the transaction price fluctuation range and the historical fluctuation range in the matching data, and calculate the difference between the percentage of transactions per unit time and the average transaction activity in the matching data; Based on the preset scoring rules, the deviation value and the difference value are comprehensively calculated to obtain the popularity fluctuation score of each stock. All the aforementioned popularity fluctuation scores are comprehensively sorted to obtain preliminary individual stock popularity fluctuation ranking results.

7. The method according to claim 1, characterized in that, The step of denoising the initial optical image to obtain the final optical image includes: Calculate the difference between the color value of each pixel in the initial optical image and the color value of a preset number of surrounding pixels. Mark pixels whose difference values ​​exceed a preset difference range as noise points. Based on the positions of all the noise points in the initial optical image, obtain a noise distribution image. Based on a preset continuous distribution range, the noise distribution image is divided to obtain discrete noise and patch noise. The average value of the color values ​​corresponding to a preset number of normal pixels surrounding the discrete noise is calculated to obtain the color replacement value corresponding to each discrete noise. The surrounding areas adjacent to the area where the noise patch is located are analyzed to obtain the pixel feature template corresponding to each noise patch, and the normal area in the initial optical image that is similar to the pixel feature template is selected. Based on the size of the noise patch, the corresponding normal region is cropped to obtain multiple pixel blocks. The pixel blocks are then overlaid onto the corresponding noise patch to obtain a coverage result. Based on the color replacement value and coverage result, the noise distribution image is corrected to obtain a preliminary denoised image. The preliminary denoised image is then subjected to residual noise detection processing to obtain the final optical image.

8. A system for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rate, characterized in that, include: The acquisition module is used to acquire an initial optical image containing price fluctuations and turnover rate data in the data display interface in real time through an optical image acquisition device, and to reduce noise in the initial optical image to obtain a final optical image. The calculation module is used to perform Laplacian operator operations on the final optical image to obtain sharpness analysis results. Based on the sharpness analysis results, the region in the final optical image with a sharpness value greater than a preset sharpness threshold is taken as the effective data region. The processing module is used to convert the image signal of the effective data area into an initial electrical signal, perform interference suppression processing on the initial electrical signal using an adaptive filtering algorithm to obtain an intermediate electrical signal, and perform signal amplification processing on the intermediate electrical signal to obtain the target electrical signal; The matching module is used to match the real-time individual stock data parsed from the target electrical signal with a preset individual stock popularity evaluation dataset to obtain a popularity fluctuation score for each individual stock, and to sort the popularity fluctuation scores of all individual stocks to obtain a preliminary individual stock popularity fluctuation ranking result. The verification module is used to verify the preliminary stock popularity fluctuation ranking results using a stability verification algorithm, generate the final stock popularity fluctuation ranking results based on the verification results, and display the final stock popularity fluctuation ranking results on the data display interface using dynamic rendering technology.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, enables the method for sorting and displaying individual stock popularity fluctuations based on price changes and turnover rates as described in any one of claims 1 to 7.

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