Automatic visual large screen display processing method based on control model and data driving

Abstract

The invention relates to the technical field of data processing, and discloses an automatic visual large-screen display processing method based on a control model and data driving, which comprises the following steps: optimizing large-screen information display, and switching parameters through layout density, visual attention and scenes; dynamically adjusting a data display strategy, and calculating data freshness, abnormal fluctuation and correlation parameters; user interaction experience is improved, and hot area response and multi-dimensional drilling parameters are optimized; enhancing information readability, and calculating graphic cognition and color conflict parameters; the system performance is stabilized, and the rendering frame rate and the data throughput are monitored; the business value, the attention decision response and the abnormity discovery timeliness are measured; dynamic weight, abnormal fusing, self-learning and multi-dimensional evaluation systems are adopted in intelligent optimization. According to the method, dynamic weight distribution, an abnormal fusing mechanism, self-learning optimization and a multi-dimensional evaluation system are combined, an intelligent, real-time and precise large-screen display system is achieved, and information display reasonability, interaction experience and system stability are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an automated visual large-screen display processing method based on a control model and data drive. Background Art

[0002] With the development of information technology, large-screen visualization display systems have been widely used in command and dispatch, smart cities, industrial monitoring, business decision-making and other fields, becoming an important tool for data display and decision support. Traditional large-screen display methods usually use preset layouts and fixed rules to present information, but they often show certain limitations when faced with complex environments such as dynamic data changes, user interaction needs and system operation status.

[0003] The existing large-screen display method lacks a flexible dynamic adjustment mechanism, which makes it difficult for information presentation to adapt to changes in data density. When the amount of data is large, information overload is prone to occur, key content is submerged, and decision-making efficiency is affected; when the amount of data is small, the information distribution is too sparse, reducing screen utilization. In addition, traditional display methods have weak timeliness management for data updates and cannot guarantee the real-time nature of content, especially in scenarios involving rapidly changing data, such as emergency monitoring and market analysis. Delayed display may lead to misjudgment.

[0004] On the other hand, the interactive experience of the current large-screen display system still has a lot of room for improvement. During use, users often find it difficult to intuitively obtain key information. The existing interactive methods fail to fully consider the user's attention allocation and visual cognition rules, resulting in high information search costs and affecting usage efficiency. At the same time, the support for multi-level data drilling and association analysis is limited, making it difficult for users to efficiently mine the deep value behind the data.

[0005] In addition, in a large-screen environment with high resolution and multiple data sources, the rendering performance of the system often fluctuates, resulting in screen freezes and information loading delays, affecting visual fluency and user experience. At the same time, the existing large-screen display system lacks quantitative standards for business value assessment, making it difficult to measure its actual improvement in business decision-making efficiency, and it is also impossible to effectively measure the degree of improvement in anomaly detection capabilities.

[0006] In response to the above problems, there is an urgent need for an automated visual large-screen display processing method based on control models and data-driven, which can perform intelligent optimization according to data characteristics, user interaction behavior and system operation status, improve the rationality of information display, data timeliness, interactive experience, system stability and business decision-making efficiency, and achieve more accurate, efficient and intelligent visualization. Summary of the invention

[0007] In view of this, the present invention proposes an automated visual large-screen display processing method based on a control model and data-driven, aiming to achieve dynamic adjustment and optimization of large-screen display content by constructing an intelligent control model and a data-driven mechanism.

[0008] The present invention proposes an automated visual large-screen display processing method based on a control model and data drive, comprising:

[0009] Set display logic control parameters to optimize large-screen information display based on layout density index, visual attention weight, and scene switching smoothness;

[0010] Calculating data-driven quantitative parameters, wherein the data-driven quantitative parameters include a data freshness index, an abnormal fluctuation detection parameter, and a correlation coupling degree, so as to dynamically adjust a data display strategy;

[0011] Set interaction performance parameters to optimize user interaction experience based on hotspot response performance and multi-dimensional drilling depth;

[0012] Calculating visualization effectiveness parameters, including graphic recognition efficiency and color conflict coefficient, to improve information readability and display effect;

[0013] Setting system performance parameters, including rendering frame rate stability and data pipeline throughput, to ensure stable operation of the large-screen display system;

[0014] Calculate business value parameters, including decision response improvement rate and anomaly discovery timeliness, to measure the business value of the large-screen display system;

[0015] Intelligent optimization is carried out using dynamic weight allocation, abnormal fuse mechanism, self-learning optimization and multi-dimensional evaluation system.

[0016] Preferably, the layout density index is calculated by the following formula:

[0017] ;

[0018] Among them, LDI represents the layout density index; Ne represents the number of effective display units; As represents the total resolution area of ​​the screen.

[0019] Preferably, the visual attention weight is calculated by the following formula:

[0020] ;

[0021] Where VAW represents the visual attention weight; r e represents the element area ratio; Cc represents the color contrast; Df represents the dynamic effect factor, which is calculated by the eye tracking-based model.

[0022] Preferably, the data freshness index is calculated by the following formula:

[0023] ;

[0024] Among them, DFI represents the data freshness index; λ represents the data decay coefficient; Δt represents the data delay time; Id represents the data integrity coefficient.

[0025] Preferably, the abnormal fluctuation detection parameters include standard deviation threshold and month-on-month mutation rate;

[0026] The standard deviation threshold is:

[0027]

[0028] Among them, x represents the standard deviation threshold; μ represents the mean; σ represents the standard deviation; k represents the set anomaly detection factor;

[0029] The month-on-month mutation rate is:

[0030] ;

[0031] Where Rc represents the month-on-month mutation rate; x t Indicates the current value, x t−1 Indicates the value at the previous moment;

[0032] If Rc exceeds the set warning threshold, an abnormal alarm is triggered.

[0033] Preferably, the multi-dimensional drilling depth is calculated by the following formula:

[0034] ;

[0035] Where MDD stands for multidimensional drilling depth; d l Indicates the number of data levels; ρ d Indicates the dimension correlation, which is used to control the depth of information mining.

[0036] Preferably, the graphic recognition efficiency is calculated by the following formula:

[0037] ;

[0038] Among them, GCE represents graphic cognition efficiency; At represents the accuracy of information transmission; Tc represents the cognition time; and Tb represents the benchmark time.

[0039] Preferably, the color conflict coefficient is calculated by the following formula:

[0040] ;

[0041] Among them, CCC represents the color conflict coefficient; Ai represents the area where the color difference ΔE value of adjacent color blocks exceeds the set threshold; Ac represents the total display area, and the ΔE value is calculated based on the CIEDE2000 color difference formula.

[0042] Preferably, the rendering frame rate stability is calculated by the following formula:

[0043] ;

[0044] Among them, FRS represents the rendering frame rate stability; Tf represents the running time of reaching the standard frame rate; Tt represents the total running time; Vf represents the frame rate variance coefficient.

[0045] Preferably, the decision response improvement rate is calculated by the following formula:

[0046] ;

[0047] Wherein, DRR represents the decision response improvement rate; To represents the decision time of the traditional method; Ts represents the decision time of the large-screen system; and the timeliness of abnormal discovery satisfies:

[0048] ;

[0049] Among them, ADT represents the timeliness of anomaly discovery; Ta represents the time when the system discovers the anomaly, and Td represents the time when the anomaly is discovered manually. The target value should be ≤−30 minutes.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] Based on the combination of control model and data drive, this invention proposes an automated visual large-screen display processing method, which can effectively solve the deficiencies of the existing display system in terms of information layout, data timeliness, interactive experience, rendering performance, and business value evaluation. Its main beneficial effects include the following aspects:

[0052] Adaptive information layout optimization to improve display rationality: By introducing the layout density index (LDI), this method can dynamically adjust the information distribution density according to the ratio of the number of effective units of the displayed content to the screen resolution area, avoiding information overload or information omission, and ensuring the scientificity and rationality of data display. The dynamic adjustment mechanism of LDI can adapt to scenarios with different data densities, so that key information can be fully expressed and the efficiency of information transmission can be improved.

[0053] Based on visual attention modeling, optimize information priority: Using visual attention weight (VAW) modeling technology, this method combines element area ratio, color contrast and dynamic effect factors to build a visual attention calculation model to optimize the priority display of key information. This method is based on eye tracking models and cognitive psychology principles, enabling the system to automatically adjust the presentation of interface content and improve the accuracy and efficiency of users obtaining important information.

[0054] Enhance data timeliness and improve information real-time performance: Measure data effectiveness through the data freshness index (DFI), calculate data timeliness based on the exponential decay model, and optimize data display strategies in combination with the data integrity coefficient. This method can adapt to high-frequency data update scenarios, ensuring that the display of key data always has high timeliness, and is especially suitable for real-time data-driven application scenarios such as financial market analysis and industrial monitoring.

[0055] Enhanced anomaly detection capability and improved data quality control: This invention integrates the standard deviation threshold method and the month-on-month mutation rate detection to perform abnormal fluctuation analysis at the data level. By calculating the degree of data deviation from the mean and the month-on-month change rate, this method can quickly identify sudden data anomalies and trigger an early warning mechanism when it exceeds the preset threshold, thereby improving the intelligent monitoring capability of the large-screen display system and enhancing the perception and response capabilities to abnormal data.

[0056] Intelligent interaction optimization to improve user experience: Combining hot zone response efficiency (HRE) and multi-dimensional drilling depth (MDD), this method can optimize the distribution of interactive areas and provide progressive information drilling capabilities. The HRE calculation model optimizes the layout of the touch area, making the high-frequency interactive area respond faster and improving operational efficiency. At the same time, MDD enhances the ability to mine information in depth based on the calculation of data levels and dimensional correlation, allowing users to explore the internal relationship of data more intuitively and efficiently, and improve information analysis capabilities.

[0057] Improve visualization efficiency and enhance cognitive efficiency: This method optimizes the information cognitive efficiency of large-screen interfaces by using graphic cognitive efficiency (GCE) and color conflict coefficient (CCC). GCE determines the benchmark time based on user testing, and calculates the efficiency of visual information processing in combination with the accuracy of information transmission, so that the interface design conforms to the user's cognitive laws and improves the speed of information absorption. At the same time, CCC uses the CIEDE2000 color difference formula to calculate the contrast of adjacent color blocks, optimize color matching, reduce visual interference, and improve the intuitiveness and readability of data display.

[0058] Optimize rendering performance and ensure system stability: This method optimizes large-screen rendering performance and ensures the stability and smoothness of system operation by calculating rendering frame rate stability (FRS) and data pipeline throughput (DPT). FRS combines the frame rate compliance time and frame rate variance coefficient to ensure smoothness in high-resolution large-screen environments, especially in multi-data source rendering scenarios with resolutions of 4K and above, to ensure that the frame rate is stable above 30fps. The DPT calculation method optimizes the data transmission pipeline throughput so that the data loading rate matches the peak demand of the business and avoids display freezes caused by data transmission delays.

[0059] Quantify business value and improve decision support capabilities: This method achieves quantitative evaluation of business value by calculating the decision response improvement rate (DRR) and anomaly discovery timeliness (ADT). DRR evaluates the degree of improvement in business response efficiency of the visual large screen system by comparing the decision time of traditional decision-making methods with that of the method of the present invention, while ADT measures the comparison between the system's discovery time for anomalies and the manual discovery time, ensuring that the large screen display system can detect problems in advance in key business scenarios and improve the ability to handle anomalies.

[0060] Intelligent optimization mechanism to improve system adaptability: The present invention constructs dynamic weight allocation, abnormal fuse mechanism, self-learning optimization and multi-dimensional evaluation system to form an intelligent parameter control method. Among them, the dynamic weight allocation uses the entropy weight method to calculate the real-time weight of each parameter, so that the system can automatically adjust the priority according to the actual operation situation; the abnormal fuse mechanism automatically triggers the downgrade plan when the key parameter exceeds the set threshold to ensure the stable operation of the system; the self-learning optimization model combines user feedback data and uses the reinforcement learning method to iteratively optimize the parameter configuration to improve the system adaptability; the multi-dimensional evaluation system integrates technical indicators, business value, user experience and other dimensions to form a scientific system evaluation framework to ensure the comprehensiveness and practicality of the method.

[0061] In summary, the present invention provides an efficient, intelligent and stable large-screen visualization display processing method, which can significantly improve the rationality of information display, data real-time, interactive experience, system stability and business decision-making ability, and provide powerful data visualization support for smart cities, industrial monitoring, financial analysis, emergency command and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0063] Figure 1It is a flow chart of the automatic visual large-screen display processing method based on control model and data drive of the present invention. DETAILED DESCRIPTION

[0064] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0065] See also Figure 1 This embodiment provides an automatic visual large-screen display processing method based on a control model and data-driven, including:

[0066] Set display logic control parameters to optimize large-screen information display based on layout density index, visual attention weight, and scene switching smoothness;

[0067] Calculating data-driven quantitative parameters, wherein the data-driven quantitative parameters include a data freshness index, an abnormal fluctuation detection parameter, and a correlation coupling degree, so as to dynamically adjust a data display strategy;

[0068] Set interaction performance parameters to optimize user interaction experience based on hotspot response performance and multi-dimensional drilling depth;

[0069] Calculating visualization effectiveness parameters, including graphic recognition efficiency and color conflict coefficient, to improve information readability and display effect;

[0070] Setting system performance parameters, including rendering frame rate stability and data pipeline throughput, to ensure stable operation of the large-screen display system;

[0071] Calculate business value parameters, including decision response improvement rate and anomaly discovery timeliness, to measure the business value of the large-screen display system;

[0072] Intelligent optimization is carried out using dynamic weight allocation, abnormal fuse mechanism, self-learning optimization and multi-dimensional evaluation system.

[0073] It can be seen that this embodiment proposes an innovative automatic large-screen visual display processing method, which combines the control model and data-driven principles to improve the efficiency of large-screen display and user experience. Specifically, the method includes the following key steps:

[0074] First, by setting display logic control parameters, this method can optimize the large-screen information display based on layout density index, visual attention weight, and scene switching smoothness, ensuring that the information display is both beautiful and efficient;

[0075] Secondly, data-driven quantitative parameters are calculated, including data freshness index, abnormal fluctuation detection parameters, and correlation coupling degree. These parameters can dynamically adjust the data display strategy to make the data display more in line with actual needs and user preferences;

[0076] In addition, interactive performance parameters are set to optimize the user interaction experience based on hot zone response performance and multi-dimensional drilling depth, allowing users to interact with the large screen more intuitively and conveniently;

[0077] At the same time, the visualization performance parameters are calculated, including graphic cognitive efficiency and color conflict coefficient. The calculation of these parameters helps to improve the readability and display effect of information, making the information transmission clearer and more accurate.

[0078] In order to ensure the stable operation of the large-screen display system, this method also sets system performance parameters, including rendering frame rate stability and data pipeline throughput. The optimization of these parameters ensures the stability and smoothness of the system under high load;

[0079] Furthermore, business value parameters are calculated, including decision response improvement rate and anomaly detection timeliness. The calculation of these parameters helps measure the business value of the large-screen display system and ensure that the system can provide strong support for decision-making;

[0080] Finally, this method uses dynamic weight allocation, abnormal fuse mechanism, self-learning optimization and multi-dimensional evaluation system for intelligent optimization. The comprehensive application of these technologies enables the large-screen display system to continuously improve itself and adapt to the ever-changing display needs.

[0081] It is understandable that the automated visual large-screen display processing method of this embodiment not only improves the efficiency of information display and user experience, but also provides a strong guarantee for the stable operation of the system and the realization of business value. By comprehensively considering multiple aspects such as display logic, data-driven, interactive efficiency, visualization efficiency, system performance, and business value, this method achieves comprehensive optimization of the large-screen display system. This innovative processing method not only meets the needs of the current large-screen display system, but also provides broad space for future system upgrades and optimizations. With the continuous development of technology, this method is expected to play a more important role in the fields of big data visualization and intelligent decision support.

[0082] In some embodiments of the present application, the layout density index is calculated by the following formula:

[0083] ;

[0084] Among them, LDI represents the layout density index; Ne represents the number of effective display units; As represents the total resolution area of ​​the screen.

[0085] It can be understood that the layout density index calculation method proposed in this embodiment can quantify the relationship between the number of effective display units on the large screen and the total resolution area of ​​the screen, providing a scientific basis for optimizing information display. By reasonably controlling the layout density, it can be ensured that the information on the large screen is neither too crowded nor too sparse, so as to achieve the best visual effect. In addition, the method also takes into account factors such as visual attention weight and scene switching smoothness, further improving the efficiency of information display and user experience. The proposal of these innovations not only enriches the theoretical system of automated visual large-screen display processing methods, but also provides strong technical support for practical applications.

[0086] In some embodiments of the present application, the visual attention weight is calculated by the following formula:

[0087] ;

[0088] Where VAW represents the visual attention weight; r e represents the element area ratio; Cc represents the color contrast; Df represents the dynamic effect factor, which is calculated by the eye tracking-based model.

[0089] It is understandable that the visual attention weight calculation method proposed in this embodiment comprehensively considers multiple factors such as element area ratio, color contrast and dynamic effect factor, and provides a scientific basis for evaluating the degree to which each element on the large screen attracts the user's visual attention. By reasonably setting the visual attention weight, the user's line of sight can be guided to focus more on key information, thereby improving the effect and efficiency of information communication. In addition, the method also combines parameters such as scene switching smoothness to further ensure the continuity of information display and the smoothness of user experience. The introduction of these innovations not only improves the practicality and flexibility of the automated visual large-screen display processing method, but also makes a positive contribution to promoting the development of fields such as big data visualization and intelligent decision support.

[0090] In some embodiments of the present application, the data freshness index is calculated by the following formula:

[0091] ;

[0092] Among them, DFI represents the data freshness index; λ represents the data decay coefficient; Δt represents the data delay time; Id represents the data integrity coefficient.

[0093] It can be understood that the data freshness index calculation method proposed in this embodiment comprehensively considers multiple factors such as data attenuation coefficient, data delay time and data integrity coefficient, and provides a scientific basis for evaluating the real-time and accuracy of data displayed on the large screen. By calculating the data freshness index, outdated or abnormal data can be discovered and processed in a timely manner to ensure that the data displayed on the large screen is always the latest and reliable. The introduction of this innovation not only improves the real-time and accuracy of the automated visual large-screen display processing method, but also provides users with more reliable data support when making decisions. In addition, the method also combines parameters such as abnormal fluctuation detection parameters and correlation coupling degree to further enhance the dynamic adjustment ability and adaptability of the data display strategy. The comprehensive application of these innovations makes the automated visual large-screen display processing method have a broader application prospect and practical value in the fields of big data visualization and intelligent decision support.

[0094] In some embodiments of the present application, the abnormal fluctuation detection parameters include a standard deviation threshold and a month-on-month mutation rate;

[0095] The standard deviation threshold is:

[0096]

[0097] Among them, x represents the standard deviation threshold; μ represents the mean; σ represents the standard deviation; k represents the set anomaly detection factor;

[0098] The month-on-month mutation rate is:

[0099] ;

[0100] Where Rc represents the month-on-month mutation rate; x t Indicates the current value, x t−1 Indicates the value at the previous moment;

[0101] If Rc exceeds the set warning threshold, an abnormal alarm is triggered.

[0102] It can be understood that the abnormal fluctuation detection parameters proposed in this embodiment, including the standard deviation threshold and the month-on-month mutation rate, provide an effective means for identifying and processing abnormal fluctuations in the data displayed on the large screen. The standard deviation threshold can accurately define the normal range of data fluctuations by comprehensively considering the mean, standard deviation and set abnormal detection factor of the data. Once the data fluctuation exceeds this range, it can be regarded as an abnormal fluctuation. The month-on-month mutation rate can timely detect the sharp changes in data by comparing the current value with the value of the previous moment. Once the month-on-month mutation rate exceeds the set early warning threshold, an abnormal alarm can be triggered to remind users to pay attention to and deal with potential problems. The proposal of these innovative points not only enhances the abnormal detection capability of the automated visual large-screen display processing method, but also provides strong technical support for ensuring the accuracy and reliability of the data displayed on the large screen. By combining parameters such as data freshness index, abnormal fluctuation detection parameters and correlation coupling degree, this method can dynamically adjust the data display strategy so that the data display is more in line with actual needs and user preferences, further improving the efficiency of information display and user experience.

[0103] In some embodiments of the present application, the multi-dimensional drilling depth is calculated by the following formula:

[0104] ;

[0105] Where MDD stands for multidimensional drilling depth; d l Indicates the number of data levels; ρ d Indicates the dimension correlation, which is used to control the depth of information mining.

[0106] It is understandable that the multi-dimensional drilling depth calculation method proposed in this embodiment comprehensively considers multiple factors such as the number of data levels and the correlation between dimensions, and provides a scientific basis for evaluating the depth of information mining by users on the large screen. By reasonably setting the multi-dimensional drilling depth, the needs of different users for information details can be met, so that users can deeply explore the data information on the large screen according to their interests and concerns. The proposal of this innovation not only improves the interactivity and personalization level of the automated visual large-screen display processing method, but also provides users with a more convenient and efficient tool when exploring and analyzing data. In addition, the method also combines parameters such as hot zone response efficiency to further optimize the user interaction experience, allowing users to interact with the large screen more smoothly, and improving the overall user satisfaction and the practicality of the system. The comprehensive application of these innovations makes the automated visual large-screen display processing method have more prominent advantages and broad application prospects in the fields of big data visualization and intelligent decision support.

[0107] In some embodiments of the present application, the graphic recognition efficiency is calculated by the following formula:

[0108] ;

[0109] Among them, GCE represents graphic cognition efficiency; At represents the accuracy of information transmission; Tc represents the cognition time; and Tb represents the benchmark time.

[0110] It can be understood that the graphic cognitive efficiency calculation method proposed in this embodiment comprehensively considers multiple factors such as information transmission accuracy and cognitive time, and provides a scientific basis for evaluating the cognitive efficiency of graphic information on the large screen. By calculating the graphic cognitive efficiency, the user's understanding and acceptance of the graphic information on the large screen can be quantified, thereby guiding us to optimize the graphic design and improve the information transmission effect. Specifically, the accuracy of information transmission reflects the degree to which the graphic information is correctly understood and accepted by the user, while the cognitive time reflects the time required for the user to understand and accept this information. By comparing the cognitive time with the benchmark time, we can evaluate the pros and cons of the graphic design and then optimize it. The proposal of this innovative point not only improves the scientificity and effectiveness of the automated visual large-screen display processing method, but also provides strong technical support for optimizing the graphic design on the large screen. By combining parameters such as the color conflict coefficient, this method can further improve the information readability and display effect, so that the large-screen display system is more in line with the user's visual habits and information acceptance methods, thereby providing users with better information display services.

[0111] In some embodiments of the present application, the color conflict coefficient is calculated by the following formula:

[0112] ;

[0113] Among them, CCC represents the color conflict coefficient; Ai represents the area where the color difference ΔE value of adjacent color blocks exceeds the set threshold; Ac represents the total display area, and the ΔE value is calculated based on the CIEDE2000 color difference formula.

[0114] It can be understood that the color conflict coefficient calculation method proposed in this embodiment comprehensively considers multiple factors such as the color difference of adjacent color blocks and the display area, and provides a scientific basis for evaluating the rationality of color matching on the large screen. By calculating the color conflict coefficient, we can quantify the degree of visual conflict that may be caused by color matching on the large screen, thereby guiding us to optimize the color design and improve the overall visual effect. Specifically, the color difference ΔE value of adjacent color blocks reflects the degree of difference between colors. When the color difference exceeds the set threshold, it may cause visual conflict. The ratio of the area to the total display area reflects the proportion of color conflict in the overall display. By calculating the color conflict coefficient, we can clearly identify which areas need to adjust the color matching, and then optimize the color design, so that the large-screen display system is more harmonious and beautiful. The proposal of this innovation not only enriches the theoretical system of automated visual large-screen display processing methods, but also provides strong technical support for optimizing color design on large screens. By combining parameters such as graphic cognitive efficiency, this method can further improve information readability and display effects, and provide users with better information display services.

[0115] In some embodiments of the present application, the rendering frame rate stability is calculated by the following formula:

[0116] ;

[0117] Among them, FRS represents the rendering frame rate stability; Tf represents the running time of reaching the standard frame rate; Tt represents the total running time; Vf represents the frame rate variance coefficient.

[0118] It can be understood that the rendering frame rate stability calculation method proposed in this embodiment comprehensively considers multiple factors such as the standard frame rate running time, the total running time, and the frame rate variance coefficient, and provides a scientific basis for evaluating the stability of the large-screen rendering effect. By calculating the rendering frame rate stability, we can quantify the fluctuation of the frame rate of the large screen during the display process, so as to judge the stability and fluency of the rendering effect. Specifically, the standard frame rate running time reflects the length of time that the frame rate of the large screen remains within the ideal range during the display process, while the total running time reflects the total length of the entire display process. By comparing the two, we can evaluate the stability of the large-screen rendering effect. In addition, the frame rate variance coefficient reflects the degree of frame rate fluctuation. When the frame rate variance coefficient is small, it means that the frame rate fluctuation is small and the rendering effect is more stable. The proposal of this innovative point not only improves the stability and fluency of the automated visual large-screen display processing method, but also provides strong technical support for optimizing the large-screen rendering effect. By combining other parameters such as data freshness index, abnormal fluctuation detection parameters, etc., this method can further improve the overall performance and user experience of the large-screen display system, so that the large-screen display is more in line with user needs and expectations.

[0119] In some embodiments of the present application, the decision response improvement rate is calculated by the following formula:

[0120] ;

[0121] Wherein, DRR represents the decision response improvement rate; To represents the decision time of the traditional method; Ts represents the decision time of the large-screen system; and the timeliness of abnormal discovery satisfies:

[0122] ;

[0123] Among them, ADT represents the timeliness of anomaly discovery; Ta represents the time when the system discovers the anomaly, and Td represents the time when the anomaly is discovered manually. The target value should be ≤−30 minutes.

[0124] It can be understood that the decision response improvement rate calculation method proposed in this embodiment comprehensively considers the difference in decision time between the traditional method and the large-screen system, and provides a scientific basis for evaluating the effect of the large-screen system in improving decision efficiency. By calculating the decision response improvement rate, we can quantify the advantages of the large-screen system in shortening decision time and improving decision efficiency. Specifically, the decision time of the traditional method reflects the time required for the decision process without the assistance of the large-screen system, while the decision time of the large-screen system reflects the time required for the decision process with the assistance of the large-screen system. By comparing the two, we can evaluate the actual effect of the large-screen system in improving decision efficiency. In addition, the timeliness of abnormal discovery, as another important indicator, further reflects the ability of the large-screen system to discover and handle abnormalities in a timely manner. When the time when the system discovers the abnormality is earlier than the time when the abnormality is discovered manually, and the difference meets the target value (that is, the time when the system discovers the abnormality is at least 30 minutes earlier than the time when the abnormality is discovered manually), it means that the large-screen system has significant advantages in abnormal monitoring and early warning, and can provide decision makers with more timely and accurate information support. This innovative point not only enhances the decision support capability of the automated visual large-screen display processing method, but also provides strong technical support for improving decision efficiency and optimizing the decision process. By combining multiple parameters such as layout density index and visual attention weight, this method can comprehensively evaluate the performance and effect of the large-screen display system and provide users with better quality and more efficient decision support services.

[0125] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0129] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An automated visual large-screen display processing method based on a control model and data-driven, characterized in that: include: Set display logic control parameters to optimize large-screen information display based on layout density index, visual attention weight, and scene switching smoothness; Calculating data-driven quantitative parameters, wherein the data-driven quantitative parameters include a data freshness index, an abnormal fluctuation detection parameter, and a correlation coupling degree; Set interactive performance parameters to optimize user interactive experience based on hot zone response performance and multi-dimensional drilling depth; calculate visualization performance parameters, including graphic recognition efficiency and color conflict coefficient, to improve information readability and display effect; Setting system performance parameters, wherein the system performance parameters include rendering frame rate stability and data pipeline throughput; Calculate business value parameters, including decision response improvement rate and anomaly detection timeliness, to measure the business value of the large-screen display system; use dynamic weight allocation, anomaly fuse mechanism, self-learning optimization and multi-dimensional evaluation system for intelligent optimization.

2. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The layout density index is calculated by the following formula: ； Among them, LDI represents the layout density index; Ne represents the number of effective display units; As represents the total resolution area of ​​the screen.

3. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The visual attention weight is calculated by the following formula: ； Where VAW represents the visual attention weight; r e represents the element area ratio; Cc represents the color contrast; Df represents the dynamic effect factor, which is calculated by the eye tracking-based model.

4. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The data freshness index is calculated by the following formula: ； Among them, DFI represents the data freshness index; λ represents the data decay coefficient; Δt represents the data delay time; Id represents the data integrity coefficient.

5. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The abnormal fluctuation detection parameters include standard deviation threshold and month-on-month mutation rate; The standard deviation threshold is: Among them, x represents the standard deviation threshold; μ represents the mean; σ represents the standard deviation; k represents the set anomaly detection factor; The month-on-month mutation rate is: ； Where Rc represents the month-on-month mutation rate; x t Indicates the current value, x t−1 Indicates the value at the previous moment; If Rc exceeds the set warning threshold, an abnormal alarm is triggered.

6. The automatic large-screen visual display processing method based on control model and data drive according to claim 1 is characterized in that: The multi-dimensional drill depth is calculated by the following formula: ； Where MDD stands for multidimensional drilling depth; d l Indicates the number of data levels; ρ d Indicates the dimension correlation, which is used to control the depth of information mining.

7. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The graphic recognition efficiency is calculated by the following formula: ； Among them, GCE represents graphic cognition efficiency; At represents the accuracy of information transmission; Tc represents the cognition time; and Tb represents the benchmark time.

8. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The color conflict coefficient is calculated by the following formula: ； Among them, CCC represents the color conflict coefficient; Ai represents the area where the color difference ΔE value of adjacent color blocks exceeds the set threshold; Ac represents the total display area, and the ΔE value is calculated based on the CIEDE2000 color difference formula.

9. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The rendering frame rate stability is calculated by the following formula: ； Among them, FRS represents the rendering frame rate stability; Tf represents the running time of reaching the standard frame rate; Tt represents the total running time; Vf represents the frame rate variance coefficient.

10. The automatic visual large-screen display processing method based on control model and data drive according to claim 1 is characterized in that: The decision response improvement rate is calculated by the following formula: ； Wherein, DRR represents the decision response improvement rate; To represents the decision time of the traditional method; Ts represents the decision time of the large-screen system; and the abnormality discovery timeliness satisfies: ； Among them, ADT represents the timeliness of anomaly discovery; Ta represents the time when the system discovers the anomaly, and Td represents the time when the anomaly is manually discovered. The target value should be ≤−30 minutes.

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