Front-end vector data real-time decryption and visualization system

Abstract

The present invention relates to the field of data visualization technology, specifically to a front-end vector data real-time declassification visualization system, which includes a data capture module, a real-time declassification processing module, a visual optimization module, and a visualization rendering module. In the present invention, by analyzing the actual operation behavior of the front-end user, the real-time and accuracy of data processing is achieved, and the data areas frequently accessed by the user are accurately identified, which not only improves the efficiency of data processing, but also enhances the personalization of data services, so that data decryption can be prioritized according to actual needs, combined with the display characteristics of the user's device to perform visual optimization, adjust color contrast and brightness, optimize the visual display of data on different devices, improve user experience, and dynamically adjust the rendering configuration so that smooth and high-quality visual effects can be maintained on devices with different performances, meeting the needs of rapid response and decision support.

Description

Technical Field

[0001] The present invention relates to the technical field of data visualization, and in particular to a front-end vector data real-time decryption visualization system. Background Art

[0002] The field of data visualization technology focuses on converting complex data sets into more understandable and intuitive graphical representations to help users quickly identify patterns, trends, and anomalies in the data. By using charts, graphs, and other visual summaries, information interpretation is made more intuitive. Data visualization is widely used in business intelligence, scientific research, data analysis, information technology, and other fields. Effective data visualization not only improves the speed and quality of decision-making, but also enables users with non-technical backgrounds to understand the insights of complex data. In addition, with the development of technology, data visualization has now expanded to real-time data processing, augmented reality, and virtual reality environments, providing a more immersive and interactive data experience.

[0003] Among them, the front-end vector data real-time decryption and visualization system is mainly used to process and convert encrypted vector data, decrypt and visualize it in real time on the front-end device, so that users can view and analyze data instantly. Key applications include geographic information systems, real-time monitoring, and graphical representation of dynamic data flows. By combining data visualization with real-time data processing, the system greatly improves the accessibility and practicality of data, especially in situations that require rapid response and decision support.

[0004] Existing data visualization technologies have obvious deficiencies in personalization and real-time data processing. Although complex data can be converted into visual formats, they often lack the ability to update in real time and respond to specific user operations. In large-scale or encrypted data processing, technical limitations make it impossible to provide data analysis and display in real time, affecting the speed and accuracy of decision-making. In addition, existing technologies lack optimization of multi-platform display characteristics, which often leads to inconsistent visual effects on different devices, thereby affecting the user experience across devices. This is particularly evident in situations that require fast dynamic responses, such as geographic information systems and real-time monitoring, which limits the wider application of data visualization technology. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a front-end vector data real-time decryption and visualization system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: the front-end vector data real-time decryption and visualization system comprises:

[0007] The data capture module captures the user's actual operation behavior on the front end, analyzes the demand for vector data behind the user's operation behavior, identifies the data area that the user is concerned about based on the analysis results, records the access frequency of the data area that the user is concerned about, and outputs the data of the point of interest;

[0008] The real-time decryption processing module divides the data based on the focus point data, identifies the vector data area that needs to be processed first, decrypts the area in real time according to the identification result, converts the ciphertext data into plaintext format, and generates a decrypted data set;

[0009] The visual optimization module receives the decrypted data set, adjusts the visual display parameters in combination with the display characteristics and environment settings of the user device, performs color contrast and brightness adjustment according to the adjustment results, optimizes the visualization effect of the data on the differentiated devices, and generates a visual optimization output result;

[0010] The visualization rendering module analyzes the processing capability of the current front-end device based on the visual optimization output result, dynamically adjusts the rendering speed and quality, optimizes the rendering process in real time, and generates a data dynamic visualization rendering configuration.

[0011] As a further solution of the present invention, the steps of analyzing the demand for vector data behind the user operation behavior are:

[0012] Collect user click, scroll and hover behavior data, using the formula,

[0013]

[0014] Generate an operation record set O, where Indicates Actual operation records, Indicates the total number of operations;

[0015] Based on the operation record set, operations interacting with vector data are extracted, using the formula:

[0016]

[0017] Generate a vector data element set V, where target express The target element of the operation, int type Indicates whether the operation type is interactive;

[0018] According to the vector data element set, element demand analysis is performed using the formula:

[0019]

[0020] Computational Elements Demand , generate a demand frequency list, where Representation elements The weight of express exist The number of occurrences in .

[0021] As a further solution of the present invention, the step of outputting the focus point data is:

[0022] Based on the required frequency list, data elements are screened using the formula:

[0023]

[0024] Generate a collection of data areas of interest ,in, The demand exceeds the first threshold The data area, Indicates area The demand for Represents a list of demand frequencies;

[0025] According to the set of data regions of interest, the formula is adopted:

[0026]

[0027] Calculate the data area of ​​interest Frequency of visits , generate access frequency data , where count express The target is The number of occurrences of .

[0028] According to the access frequency data, the formula is used:

[0029]

[0030] Output exceeds the second threshold Point of interest data ,in, The access frequency exceeds the second threshold Area of ​​interest data.

[0031] As a further solution of the present invention, the step of identifying the vector data area that needs to be processed preferentially is:

[0032] Based on the focus point data, user interaction data is collected, the frequency of occurrence of each data point is extracted, and the formula is used:

[0033]

[0034] Calculate the frequency of each data point , generates data point frequency data, where is the target data point The number of occurrences of It is a data point The priority weight of is the total number of interactions;

[0035] Based on the data point frequency data, data screening is performed using the formula:

[0036]

[0037] Get the set of high-attention points after screening ,in, is the standard deviation, used to adjust the screening threshold, is the screening threshold, represents the filtered data points;

[0038] Based on the above-screened high-interest point set, data is grouped using the formula:

[0039]

[0040] Calculate the average attribute value of the data points , output priority processing area, where Represents the filtered data point The attribute value of |H| is the set The number of elements in , representing the average property value.

[0041] As a further solution of the present invention, the steps of generating the decrypted data set are:

[0042] Based on the priority processing area, the data points with ciphertext are screened and the formula is used:

[0043]

[0044] Generate a set of encrypted data points ,in, Represents the encryption status of data point k. A return value greater than 0 indicates encryption.

[0045] Decryption is performed on each data point in the encrypted data point set using the formula:

[0046]

[0047] Calculate the average decrypted value , output the initial decrypted data set ,in, is the key used for decryption, Representative data points The decrypted value of , |E| is the number of encrypted data points;

[0048] According to the preliminary decrypted data set, data cleaning and verification are performed, using the formula:

[0049]

[0050] Calculate the effective value of the initial decrypted data , generate a decrypted data set, where To check the data points Is it valid? If it is valid, output 1, otherwise output 0. Indicates the total number of data points after decryption.

[0051] As a further solution of the present invention, the steps of adjusting the visual display parameters are:

[0052] Receive the decrypted data set, measure the user device display characteristics and environment settings, and use the formula,

[0053]

[0054] Calculate preliminary equipment environment comprehensive parameters ,in, Represents the measured value of the device display characteristics, represents the environmental setting measurement value, and The weights of equipment and environment impact are used to balance the impact of equipment characteristics and environmental settings;

[0055] Based on the preliminary equipment environment comprehensive parameters, analyze the impact of equipment characteristics and environmental settings, and use the formula:

[0056]

[0057] Calculate the visual display parameters after preliminary adjustment ,in, is the balance adjustment factor, which indicates the relative adjustment ratio between device characteristics and environmental settings;

[0058] Based on the visual display parameters after preliminary adjustment, the formula is adopted:

[0059] Generate visual display parameter adjustment results ,in, To optimize the adjustment coefficient, it is used to refine the final adjustment effect.

[0060] As a further solution of the present invention, the steps of generating the visual optimization output result are:

[0061] According to the visual display parameter adjustment results, the color contrast requirements are analyzed and the formula is used.

[0062]

[0063] Calculate and generate color adjustment parameters ,in, For color contrast needs, Color adjustment weight is used to adjust the intensity of color adjustment;

[0064] Using the color adjustment parameters, combined with the brightness reference , using the formula,

[0065] Calculate and generate brightness adjustment parameters ,in, Adjust the parameters for color. Represents the brightness reference, is the brightness adjustment factor, used to adjust the sensitivity of brightness adjustment;

[0066] Applying the color adjustment parameter and the brightness adjustment parameter, using the formula,

[0067]

[0068] Make final visual optimization adjustments and generate visual optimization output results ,in, It is a comprehensive adjustment coefficient used to balance the overall effect of color and brightness adjustment.

[0069] As a further solution of the present invention, the steps of generating the data dynamic visualization rendering configuration are:

[0070] According to the visual optimization output results, the formula is used:

[0071]

[0072] Calculate the processing power factor of the device , generate preliminary equipment processing capacity assessment results, where It is the device performance sensing constant, which indicates the device's sensitivity to visual output;

[0073] According to the preliminary device processing capacity evaluation results, the rendering speed and quality are dynamically adjusted using the formula,

[0074]

[0075] Calculate the rendering quality estimate , generates dynamically adjusted rendering quality evaluation results, where , and They are rendering speed adjustment coefficient, basic adjustment threshold and exponential adjustment factor, which reflect the nonlinear relationship between rendering quality and device capabilities;

[0076] Using the dynamically adjusted rendering quality evaluation result, the formula is adopted:

[0077]

[0078] Calculate the visualization rendering configuration quantization value , generate data dynamic visualization rendering configuration, where, and It is the rendering configuration factor and the magnification factor.

[0079] Compared with the prior art, the advantages and positive effects of the present invention are:

[0080] In the present invention, by analyzing the actual operation behavior of the front-end users, the real-time and accuracy of data processing is achieved, and the data areas frequently visited by users are accurately identified, which not only improves the efficiency of data processing, but also enhances the personalization of data services, so that data decryption can be prioritized according to actual needs, and visual optimization is performed in combination with the display characteristics of the user's device, adjusting the color contrast and brightness, optimizing the visual display of data on different devices, and improving the user experience. The dynamically adjusted rendering configuration can maintain smooth and high-quality visual effects on devices with different performances, meeting the needs of rapid response and decision support. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 is a system flow chart of the present invention;

[0082] Figure 2 The flowchart of the analysis of the demand for vector data behind the user operation behavior of the present invention;

[0083] Figure 3 This is a flow chart for outputting the focus data of the present invention;

[0084] Figure 4 A flow chart for identifying a vector data region that needs to be processed preferentially in the present invention;

[0085] Figure 5 A flow chart for generating a decrypted data set of the present invention;

[0086] Figure 6 is a flow chart of adjusting visual display parameters of the present invention;

[0087] Figure 7A flow chart for generating the visual optimization output result of the present invention;

[0088] Figure 8 A flow chart for generating a configuration for dynamic visualization rendering of data according to the present invention. DETAILED DESCRIPTION

[0089] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0090] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0091] See also Figure 1 , the front-end vector data real-time decryption and visualization system includes:

[0092] The data capture module captures the user's actual operation behavior on the front end, analyzes the demand for vector data behind the user's operation behavior, identifies the data area that the user is concerned about based on the analysis results, records the access frequency of the data area that the user is concerned about, and outputs the data of the point of interest;

[0093] The real-time decryption processing module divides the data based on the focus point data, identifies the vector data area that needs to be processed first, decrypts the area in real time according to the identification results, converts the ciphertext data into plaintext format, and generates a decrypted data set;

[0094] The visual optimization module receives the decrypted data set, adjusts the visual display parameters based on the display characteristics and environment settings of the user's device, performs color contrast and brightness adjustment based on the adjustment results, optimizes the visualization effect of the data on differentiated devices, and generates visual optimization output results;

[0095] The visualization rendering module is based on the visual optimization output results, analyzes the processing capabilities of the current front-end device, dynamically adjusts the rendering speed and quality, optimizes the rendering process in real time, and generates data dynamic visualization rendering configuration.

[0096] The focus data includes area identification and access count, the decrypted data set includes data block number and data plaintext, the visual optimization output results include adjustment parameter records and color configuration results, and the data dynamic visualization rendering configuration includes processing capability adaptation results and rendering parameter adjustment results.

[0097] See also Figure 2 ,The analysis steps for the demand for vector data behind user operation behavior are:

[0098] Collect user click, scroll and hover behavior data, using the formula,

[0099]

[0100] Generate an operation record set O, where Indicates Actual operation records, Indicates the total number of operations;

[0101] Based on the set of operation records, operations that interact with vector data are extracted using the formula,

[0102]

[0103] Generate a vector data element set V, where target express The target element of the operation, int type Indicates whether the operation type is interactive;

[0104] According to the vector data element set, element demand analysis is performed using the formula:

[0105]

[0106] Computational Elements Demand , generate a demand frequency list, where Representation Elements The weight of express exist The number of occurrences in .

[0107] Assume that within a specific time period, the following three operations are monitored:

[0108] : The user clicked a button on the page.

[0109] : The user scrolled the page.

[0110] : The user hovers over a specific element.

[0111] gather It consists of these three operations:

[0112]

[0113] The collection directly represents the user's behavior pattern during the monitoring period. Each operation May contain details such as action type, target element, and timestamp to further analyze user interaction with the interface.

[0114] Assume that among the operational data collected previously, only and Is related to vector data elements and Interaction:

[0115] target , int type

[0116] target , int_type

[0117] 1 indicates the interaction type.

[0118] According to the formula, we get:

[0119]

[0120] This collection It shows which vector data elements the user interacted with during the observation period, which helps to understand the user's specific interests.

[0121] Assumptions: Operated twice, weight , Operated once, weight , total number of operations .

[0122] Substitute into the formula and calculate and Demand for:

[0123]

[0124]

[0125] mean The demand degree is 1.0, indicating that it is a data element that users are very concerned about; The demand degree of is 0.167, indicating that users pay less attention to it. In this way, the vector data elements that users are most interested in can be determined for subsequent in-depth analysis or optimized design.

[0126] See also Figure 3 , the output steps of the focus data are:

[0127] Based on the required frequency list, data elements are screened using the formula:

[0128]

[0129] Generate a collection of data areas of interest ,in, The demand exceeds the first threshold The data area, Indicates area The demand for Represents a list of demand frequencies;

[0130] Based on the set of data areas of interest, the formula is used:

[0131]

[0132] Calculate the data area of ​​interest Frequency of visits , generate access frequency data , where count express The target is The number of occurrences of .

[0133] According to the access frequency data, the formula is used:

[0134]

[0135] Output exceeds the second threshold Point of interest data ,in, The access frequency exceeds the second threshold Area of ​​interest data.

[0136] Combined with the above calculation results, assuming that the demand frequency list as follows:

[0137]

[0138] Set the first threshold , apply the formula to filter:

[0139]

[0140]

[0141] gather show This is the data area that users pay special attention to because its demand exceeds the first threshold , which helps identify areas of the data that should be focused on for optimization or further analysis.

[0142] Assuming the total number of operations , Area of ​​Interest , in all operations Visited 2 times, count (for , two of the three operations involve .

[0143] Substitute into the formula and calculate Frequency of visits:

[0144]

[0145] The result 0.667 indicates The frequency of occurrence in all user operations, that is, the user's This frequency can help us further understand the user's usage habits and interests.

[0146] Assume access frequency data as follows,

[0147]

[0148] Set the second threshold , apply the formula to output:

[0149]

[0150]

[0151] gather show As the data of interest, because the access frequency exceeds the second threshold , which means It is very important in the user's operation and should be considered as a key goal to optimize or enhance the interactive experience.

[0152] See also Figure 4 , the steps for identifying the vector data areas that need to be processed first are:

[0153] Based on the focus data, collect user interaction data, extract the frequency of occurrence of each data point, and use the formula,

[0154]

[0155] Calculate the frequency of each data point , generates data point frequency data, where is the target data point The number of occurrences of It is a data point The priority weight of is the total number of interactions;

[0156] Based on the data point frequency data, data screening is performed using the formula:

[0157]

[0158] Get the set of high-attention points after screening ,in, is the standard deviation, used to adjust the screening threshold, is the screening threshold, represents the filtered data points;

[0159] Based on the set of high-interest points after screening, data is grouped and the formula is used.

[0160]

[0161] Calculate the average attribute value of the data points , output the priority processing area, where Represents the filtered data point The attribute value of |H| is the set The number of elements in , representing the average attribute value.

[0162] Suppose in a set of data, a data point Appeared 50 times , total number of interactions , and the priority weight of the data point .

[0163] Using the formula, calculate Frequency of occurrence :

[0164]

[0165] The resulting value 0.00316 represents the data point The importance and frequency of data points in user interactions relative to other data points. This frequency is used in subsequent steps to decide whether to classify the data point into the set of high-attention points.

[0166] Combined with the above calculation results, the screening threshold Set to 0.005, standard deviation is 0.1, then:

[0167]

[0168] because:

[0169]

[0170] So the data point Will be classified into the high-interest data point set .

[0171] Hypothesis set Contains three data points, whose attribute values ​​are 10, 20, and 30 respectively.

[0172] Clustering results Calculated as:

[0173]

[0174] The result 20 represents the average attribute value of the data points in the priority processing area for further decision making and resource allocation.

[0175] See also Figure 5 , the steps to generate the decrypted data set are:

[0176] Based on the priority processing area, filter the data points with ciphertext and use the formula:

[0177]

[0178] Generate a set of encrypted data points ,in, Represents the encryption status of data point k. A return value greater than 0 indicates encryption.

[0179] Decrypt each data point in the encrypted data point set using the formula:

[0180]

[0181] Calculate the average decrypted value , output the initial decrypted data set ,in, is the key used for decryption, Representative data points The decrypted value of , |E| is the number of encrypted data points;

[0182] According to the preliminary decrypted data set, data cleaning and verification are carried out, using the formula,

[0183]

[0184] Calculate the effective value of the initial decrypted data , generate a decrypted data set, where To check the data points Is it valid? If it is valid, output 1, otherwise output 0. Indicates the total number of data points after decryption.

[0185] Assume that in the priority processing area, there are 5 data points, 3 of which are encrypted.

[0186] Assume that three data points , and The values ​​are 1, 1, and 0 respectively, then the filtered encrypted data point set The first two data points will be included, namely:

[0187]

[0188] Assumptions Includes two encrypted data points, the key The value is 2.

[0189] If the decrypted values ​​of these two data points and The values ​​are 10 and 20 respectively.

[0190] Substituting into the formula, we get:

[0191]

[0192] The result 30 represents the average decryption value of the decrypted data set, which is used for further data processing and analysis.

[0193] Assume preliminary decryption data set Contains three data points with validity values ​​of 1, 0, and 1.

[0194] The effective value is calculated as follows:

[0195]

[0196] This result of 0.667 indicates that approximately 67% of the data in the preliminary decryption dataset is valid and can be used for further data analysis or decision support.

[0197] See also Figure 6 , the adjustment steps of visual display parameters are:

[0198] Receive decrypted data sets, measure user device display characteristics and environment settings, and use the formula,

[0199]

[0200] Calculate preliminary equipment environment comprehensive parameters ,in, Represents the measured value of the device display characteristics, represents the environmental setting measurement value, and The weights of equipment and environment impact are used to balance the impact of equipment characteristics and environmental settings;

[0201] Based on the preliminary equipment environment comprehensive parameters, analyze the impact of equipment characteristics and environmental settings, and use the formula,

[0202]

[0203] Calculate the visual display parameters after preliminary adjustment ,in, is the balance adjustment factor, which indicates the relative adjustment ratio between device characteristics and environmental settings;

[0204] After the preliminary adjustment of the visual display parameters, the formula is adopted.

[0205]

[0206] Generate visual display parameter adjustment results ,in, To optimize the adjustment coefficient, it is used to refine the final adjustment effect.

[0207] : A measurement representing a device's display characteristics (e.g., screen brightness, in nits), assuming Nit represents the brightness of a general indoor display.

[0208] : represents the measurement value of the environment setting (such as ambient light intensity, unit: lux), assuming Lux represents the light intensity of indoor lighting.

[0209] Setting weights , ,reflecting that the environmental settings have a greater impact on visual display parameters than the device display characteristics.

[0210] Substituting into the formula, we get:

[0211]

[0212]

[0213]

[0214] result It indicates the preliminary visual display parameters after combining the device display characteristics and environmental settings. This value is large, indicating that the impact of ambient light on the display effect is more significant and further adjustment is needed to optimize the visual effect.

[0215] Setting the balance factor , indicating that the relative adjustment ratio of device characteristics and environmental settings is not high.

[0216] Substituting into the formula, we get:

[0217]

[0218]

[0219]

[0220] result It represents the adjusted visual display parameters after considering the influence of equipment and environment. It is much smaller, indicating that after the balance factor is adjusted, the impact of the equipment and the environment is effectively balanced.

[0221] Setting the adjustment factor , indicating the degree of fine-tuning of the final adjustment result.

[0222] Substituting into the formula, we get:

[0223]

[0224]

[0225]

[0226]

[0227] This result Indicates the final adjustment result of visual display parameters. A slight decrease indicates that after fine-tuning, the device display is more adapted to environmental conditions and provides a more comfortable visual effect.

[0228] See also Figure 7 , the steps to generate the visual optimization output are:

[0229] According to the visual display parameter adjustment results, analyze the color contrast requirements, and use the formula,

[0230]

[0231] Calculate and generate color adjustment parameters ,in, For color contrast needs, Color adjustment weight is used to adjust the intensity of color adjustment;

[0232] Use color adjustment parameters combined with brightness benchmarks , using the formula,

[0233]

[0234] Calculate and generate brightness adjustment parameters ,in, Adjust the parameters for color. Represents the brightness reference, is the brightness adjustment factor, used to adjust the sensitivity of brightness adjustment;

[0235] Apply the color adjustment parameter and the brightness adjustment parameter, using the formula,

[0236]

[0237] Make final visual optimization adjustments and generate visual optimization output results ,in, It is a comprehensive adjustment coefficient used to balance the overall effect of color and brightness adjustment.

[0238] Assumptions , set color contrast standards Nits, reflecting common screen color benchmarks, set color adjustment coefficients , indicating the sensitivity of color adjustment.

[0239] Substituting into the formula, we get:

[0240]

[0241]

[0242]

[0243] This result Represents the color adjustment parameter, which is used to further refine the color contrast adjustment. A smaller value means that the adjustment is not large, maintaining a natural color transition.

[0244] Setting brightness benchmark Lux, suitable for most indoor environments, sets the brightness adjustment factor , indicating the responsiveness of brightness adjustment.

[0245] Substituting into the formula, we get:

[0246]

[0247]

[0248] result Indicates the brightness adjustment parameter, which almost maintains the original brightness level, indicating that the effect of color adjustment on brightness is very small, which is conducive to maintaining the stability of brightness.

[0249] Setting comprehensive adjustment coefficient , enhancing the overall effect of the adjustment.

[0250] Substituting into the formula, we get:

[0251]

[0252]

[0253]

[0254]

[0255] This result Represents the final visual optimization output result. The value represents the visual display parameters after slight adjustments to color and brightness, slightly higher than the original , ensuring the optimal display effect in different environments and improving the user's visual experience.

[0256] See also Figure 8 , the steps to generate the data dynamic visualization rendering configuration are:

[0257] According to the visual optimization output results, the formula is used.

[0258]

[0259] Calculate the processing power factor of the device , generate preliminary equipment processing capacity assessment results, where It is the device performance sensing constant, which indicates the device's sensitivity to visual output;

[0260] According to the preliminary equipment processing capacity assessment results, the rendering speed and quality are dynamically adjusted using the formula,

[0261]

[0262] Calculate the rendering quality estimate , generates dynamically adjusted rendering quality evaluation results, where , and They are rendering speed adjustment coefficient, basic adjustment threshold and exponential adjustment factor, which reflect the nonlinear relationship between rendering quality and device capabilities;

[0263] Using the dynamically adjusted rendering quality evaluation results, the formula is adopted,

[0264]

[0265] Calculate the visualization rendering configuration quantization value , generate data dynamic visualization rendering configuration, where, and It is the rendering configuration factor and the magnification factor.

[0266] Assumptions = 1000 (the unit is usually some visual metric, such as nits of brightness), is 0.01, and we substitute it into the formula to get:

[0267]

[0268]

[0269] result , close to 1000, indicating that the device has very high processing power and can process visual output almost losslessly. It means that the device has excellent performance and strong processing ability for high-quality visual content.

[0270] : Rendering speed adjustment factor, set to 5 (unitless, affects the magnification of rendering speed).

[0271] : Base adjustment threshold, set to 2 (unitless, affects the baseline of adjustment).

[0272] : Exponential adjustment factor, set to 0.5 (unitless, describing nonlinear effects).

[0273] Substituting into the formula, we get:

[0274]

[0275]

[0276] result Indicates the rendering quality after dynamic adjustment. A higher value means that the rendering process is optimized to support faster rendering speed and higher quality. The value reflects the efficiency of rendering adjustment.

[0277] : Rendering configuration factor, set to 0.8 (unitless, affects the strength of the final configuration).

[0278] : Amplification factor, set to 0.1 (unitless, enhances of the regulatory impact).

[0279] Substituting into the formula, we get:

[0280]

[0281]

[0282]

[0283]

[0284] result Represents the final data dynamic visualization rendering configuration. The value indicates that the system's rendering configuration is highly optimized and can handle complex dynamic data visualization requirements and support high-quality real-time rendering output. This configuration ensures a smooth end-user experience and optimized visual performance.

[0285] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. Front-end vector data real-time decryption and visualization system, characterized by: The system comprises: The data capture module captures the user's actual operation behavior on the front end, analyzes the demand for vector data behind the user's operation behavior, identifies the data area that the user is concerned about based on the analysis results, records the access frequency of the data area that the user is concerned about, and outputs the data of the point of interest; The real-time decryption processing module divides the data based on the focus point data, identifies the vector data area that needs to be processed first, decrypts the area in real time according to the identification result, converts the ciphertext data into plaintext format, and generates a decrypted data set; The visual optimization module receives the decrypted data set, adjusts the visual display parameters in combination with the display characteristics and environment settings of the user device, performs color contrast and brightness adjustment according to the adjustment results, optimizes the visualization effect of the data on the differentiated devices, and generates a visual optimization output result; The visualization rendering module analyzes the processing capability of the current front-end device based on the visual optimization output result, dynamically adjusts the rendering speed and quality, optimizes the rendering process in real time, and generates a data dynamic visualization rendering configuration; The steps for analyzing the demand for vector data behind the user operation behavior are as follows: Collect user click, scroll and hover behavior data, using the formula, Generate an operation record set O, where Indicates Actual operation records, Indicates the total number of operations; Based on the operation record set, operations interacting with vector data are extracted, using the formula: Generate a vector data element set V, where target express The target element of the operation, int type Indicates whether the operation type is interactive; According to the vector data element set, element demand analysis is performed using the formula: Computational Elements Demand , generate a demand frequency list, where Representation Elements The weight of express exist The number of occurrences in ; The steps for generating the decrypted data set are: Based on the priority processing area, the data points with ciphertext are screened and the formula is used: Generate a set of encrypted data points ,in, Represents the encryption status of data point k. A return value greater than 0 indicates encryption. Decryption is performed on each data point in the encrypted data point set using the formula: Calculate the average decrypted value , output the initial decrypted data set ,in, is the key used for decryption, Representative data points The decrypted value of , |E| is the number of encrypted data points; According to the preliminary decrypted data set, data cleaning and verification are performed, using the formula: Calculate the effective value of the initial decrypted data , generate a decrypted data set, where To check the data points Is it valid? If it is valid, output 1, otherwise output 0. Indicates the total number of data points after decryption; The steps for generating the visual optimization output result are: According to the visual display parameter adjustment results, the color contrast requirements are analyzed and the formula is used. Calculate and generate color adjustment parameters ,in, For color contrast needs, Color adjustment weight is used to adjust the intensity of color adjustment; Using the color adjustment parameters, combined with the brightness reference , using the formula, Calculate and generate brightness adjustment parameters ,in, Adjust the parameters for color. Represents the brightness reference, is the brightness adjustment factor, used to adjust the sensitivity of brightness adjustment; Applying the color adjustment parameter and the brightness adjustment parameter, using the formula, Make final visual optimization adjustments and generate visual optimization output results ,in, It is a comprehensive adjustment coefficient used to balance the overall effect of color and brightness adjustment.

2. The front-end vector data real-time decryption and visualization system according to claim 1, characterized in that: The output steps of the focus point data are: Based on the required frequency list, data elements are screened using the formula: Generate a collection of data areas of interest ,in, The demand exceeds the first threshold The data area, Indicates area The demand for Represents a list of demand frequencies; According to the set of data regions of interest, the formula is adopted: Calculate the data area of ​​interest Frequency of visits , generate access frequency data , where count express The target is The number of occurrences of According to the access frequency data, the formula is used: Output exceeds the second threshold Point of interest data ,in, The access frequency exceeds the second threshold Area of ​​interest data.

3. The front-end vector data real-time decryption and visualization system according to claim 2, characterized in that: The steps for identifying the vector data area that needs to be processed first are: Based on the focus point data, user interaction data is collected, the frequency of occurrence of each data point is extracted, and the formula is used: Calculate the frequency of each data point , generates data point frequency data, where is the target data point The number of occurrences of It is a data point The priority weight of is the total number of interactions; Based on the data point frequency data, data screening is performed using the formula: Get the set of high-attention points after screening ,in, is the standard deviation, used to adjust the screening threshold, is the screening threshold, represents the filtered data points; Based on the above-screened high-interest point set, data is grouped using the formula: Calculate the average attribute value of the data points , output priority processing area, where Represents the filtered data point The attribute value of |H| is the set The number of elements in , representing the average property value.

4. The front-end vector data real-time decryption and visualization system according to claim 1, characterized in that: The adjustment steps of the visual display parameters are: Receive the decrypted data set, measure the user device display characteristics and environment settings, and use the formula, Calculate preliminary equipment environment comprehensive parameters ,in, Represents the measured value of the device display characteristics, represents the environmental setting measurement value, and The weights of equipment and environment impact are used to balance the impact of equipment characteristics and environmental settings; Based on the preliminary equipment environment comprehensive parameters, analyze the impact of equipment characteristics and environmental settings, and use the formula: Calculate the visual display parameters after preliminary adjustment ,in, is the balance adjustment factor, which indicates the relative adjustment ratio between device characteristics and environmental settings; Based on the visual display parameters after preliminary adjustment, the formula is adopted: Generate visual display parameter adjustment results ,in, To optimize the adjustment coefficient, it is used to refine the final adjustment effect.

5. The front-end vector data real-time decryption and visualization system according to claim 1, characterized in that: The steps for generating the data dynamic visualization rendering configuration are as follows: According to the visual optimization output results, the formula is used: Calculate the processing power factor of the device , generate preliminary equipment processing capacity assessment results, where It is the device performance sensing constant, which indicates the device's sensitivity to visual output; According to the preliminary device processing capacity evaluation results, the rendering speed and quality are dynamically adjusted using the formula, Calculate the rendering quality estimate , generates dynamically adjusted rendering quality evaluation results, where , and They are rendering speed adjustment coefficient, basic adjustment threshold and exponential adjustment factor, which reflect the nonlinear relationship between rendering quality and device capabilities; Using the dynamically adjusted rendering quality evaluation result, the formula is adopted: Calculate the visualization rendering configuration quantization value , generate data dynamic visualization rendering configuration, where, and It is the rendering configuration factor and the magnification factor.

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