Visual multimedia display system and method based on big data analysis

Abstract

The invention discloses a visual multimedia display system and method based on big data analysis, and the system comprises a display system which comprises a big data processing platform, an information display platform, a safety protection platform, and a big database platform. The big data processing platform collects, processes and transmits data needing to be displayed; the information display screen platform carries out diversified, real-time, visual and visual display on the processed data; the security protection platform performs information security protection on the transmitted content and the displayed content in real time; the large database platform stores all the data displayed by the display system, and through the arrangement of the security protection platform, the active discovery of scattered and heterogeneous data assets, the real-time construction of a data protection directory based on the holographic recognition technology of active and passive double engines, and the self-adaptive dynamic recognition of sensitive data types. The visual exhibition hall central control system is controlled in time, and the phenomena of system paralysis, control failure and hacker intrusion are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of visual multimedia display systems, and in particular to a visual multimedia display system and method based on big data analysis. Background Art

[0002] The visual multimedia display system converts complex data into intuitive forms such as charts, images, and maps, so that users can quickly capture key information without having to delve into tedious data tables. This presentation method greatly reduces the threshold for understanding data, and even people with non-professional backgrounds can quickly get started and gain insight into the stories behind the data. In a fast-paced business environment, the timeliness of data is crucial. The visual multimedia display system can integrate various data sources in real time and dynamically update key indicators to ensure that users are always up to date. For example, in the financial industry, the large screen displays stock market quotes, trading volumes, and market trends in real time, providing investors with valuable decision-making basis. The visual multimedia display system has strong interactivity, allowing users to further explore data details through operations such as clicking and dragging. This interactive data exploration method not only improves the flexibility of data analysis, but also stimulates user initiative. Users can customize display content as needed to achieve personalized data views to meet the needs of different roles, and display all important KPIs (key indicators) on one screen. Performance indicators), management can quickly assess business conditions, identify potential problems, and respond quickly. In supply chain management, large screens track inventory levels, transportation status, and order status in real time, helping companies avoid inventory shortages or backlogs and optimize inventory management strategies. In terms of corporate display and brand building, visual multimedia display systems can attract people's attention and enhance the overall image and brand value of the company. In places such as corporate exhibition halls, command centers, or monitoring rooms, the visual effects and dynamic displays of large screens can significantly enhance the user experience and impression. Visual multimedia display systems can adapt to various terminal devices, such as computers, mobile phones, tablets, etc., so that users can access them anytime, anywhere. This flexibility makes data display no longer limited to specific physical spaces or devices. In summary, visual multimedia display systems have significant advantages in data display, decision support, user experience, and corporate image enhancement, and are widely used in many fields such as corporate management, smart city management, and public safety.

[0003] However, the traditional visual multimedia display system has the following disadvantages:

[0004] The traditional visual multimedia display system controls the visual exhibition hall central control system through the traditional server. The traditional server has a low security factor and is prone to problems such as system paralysis, control failure and hacker intrusion. Summary of the invention

[0005] The purpose of the present invention is to provide a visualization multimedia display system and method based on big data analysis, so as to solve the problem that the traditional visualization multimedia display system proposed in the above background technology controls the visualization exhibition hall central control system through a traditional server, and the traditional server has a low safety factor and is prone to cause system paralysis, control failure and hacker intrusion.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a visual multimedia display system based on big data analysis, comprising a display system, wherein the display system comprises a big data processing platform, an information display platform, a security protection platform and a big database platform;

[0007] The big data processing platform collects, processes and transmits the data to be displayed;

[0008] The information display screen platform provides a diverse, real-time, and intuitive visual display of the processed data;

[0009] The security protection platform performs information security protection on the transmitted content and displayed content in real time;

[0010] The large database platform stores all data displayed by the display system to facilitate subsequent review and comparison.

[0011] As a preferred technical solution of the present invention, the information display platform includes a front-end display module and a back-end display module. The front-end display module visualizes information and data on a visual screen, and the back-end display module facilitates information interaction between the display system and the user. The front-end display module includes a large-screen editing unit, a visualization component unit and an intelligent analysis unit. The large-screen editing unit is specially made for the large-screen plug-in FVS large-screen editing mode, which is based on the development mode of B / S end technology. It has dozens of built-in chart types and styles suitable for large screens. There is no need to prepare data in advance. Only a large-screen blueprint can be drawn by dragging components. After the template is designed, the data is directly applied. The visualization component unit provides a column chart component, a bar chart component, a pie chart component, a line chart component, a dashboard component and an indicator card component. The intelligent analysis unit collects and processes data, analyzes and applies data respectively, and helps enterprises make scientific and intelligent decisions. The back-end display module includes a security authentication unit and a user interaction unit. The security authentication unit performs security authentication on the user login method, such as fingerprint authentication, account password authentication and face authentication. The user interaction unit transmits the information display platform display content to the background for information interaction.

[0012] As a preferred technical solution of the present invention, the intelligent analysis unit includes analysis result application, data analysis and display, data modeling and data access. The analysis result application supports multi-terminal adaptive display of data on large screens, mobile terminals and smart TVs and performs self-service analysis, result application and monitoring and early warning. The self-service analysis specifically includes linkage, drilling, slicing and ranking screening. The result application specifically includes analysis, export, printing and sending to emails. The monitoring and early warning specifically includes email early warning, WeChat early warning, DingTalk early warning and API notification. The data analysis and display performs 3D scene + BI interactive analysis and provides online reports. The 3D scene + BI interactive analysis is equipped with 100+ charts, 20+ maps, online 3D scenes and 100+ visualization components. The online reports are specifically Chinese complex reports, Word reports and printed reports. The data modeling is equipped with cross-source modeling, SQL custom tables, data extraction, data isolation, real-time push and analysis expressions. The data access is a relational database, a domestic data source library, a JSON API, a file-type data source and time series data.

[0013] As a preferred technical solution of the present invention, the big data processing platform includes a big data acquisition module, a big data operation module, an algorithm model module and a data transmission module. The big data acquisition module collects data from various data sources, and the data sources are specifically sensor data, databases, API interfaces, log files, social media data and third-party data services. The big data operation module is specifically data preprocessing, data storage and data analysis. The data preprocessing cleans, integrates and converts the collected data through preprocessing tools to ensure the quality and consistency of the data. The preprocessing tools are specifically Hadoop and Spark. The data storage stores the collected preprocessed data through NoSQL databases and data warehouses. The data analysis performs statistical analysis, pattern recognition and advanced processing of the data. The statistical analysis is specifically statistical analysis, machine learning and data mining technology, such as time series analysis and clustering algorithms, to discover patterns and trends in the data. The data transmission module transmits the processed data to the information display screen platform for display.

[0014] As a preferred technical solution of the present invention, the calculation formula of the big data time series analysis is specifically: moving average,

[0015] SMAn=(x1+x2+......xn) / n or

[0016] The specific calculation formula for natural season analysis is:

[0017] $Yt$=$Tt$+$St$+$Rt$,

[0018] Where $Yt$ is the original time series, $Tt$ is the trend component, $St$ is the seasonal component, $Rt$ is the residual component, and the calculation formula of the non-seasonal time series is:

[0019] $$ / phi(B)(1-B)Λd / nabtaYt$$=$$ / theta(B) / epsilont$$,

[0020] Yt is the original time series, $$ / phi(B) and $$ / theta(B) are the parameters of regression and moving average, B is the regression term, d is the difference term, $$ / epsilont is white noise, and the clustering algorithm calculation formula is specifically Minkowski distance:

[0021] [\text{dist}(X,Y)=\left(\sum_{i=1}^n|x_i-y_i|^p\right)^{\frac{1}{p}}],

[0022] Among them, when (p=1) is Manhattan distance, when (p=2) is Euclidean distance, when (p=\infty) is Chebyshev distance, the cosine similarity is:

[0023] [\cos(\theta)=\frac{a\cdot b}{|a||b|}],

[0024] Where (a) and (b) are two vectors, and the KL distance is:

[0025] [D(P||Q)=\sum_x P(x)\log\left(\frac{P(x)}{Q(x)}\right)],

[0026] Used to measure the difference between two probability distributions (P) and (Q), Jaccard similarity coefficient

[0027] [J(A,B)=\frac{|A\cap B|}{|A\cup B|}],

[0028] Used to measure the similarity between two sets, Pearson correlation coefficient:

[0029] [\rho_{XY}=\frac{\text{Cov}(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}],

[0030] Used to measure the linear correlation between two variables (X) and (Y).

[0031] As a preferred technical solution of the present invention, the algorithm model module applies a machine learning or deep learning algorithm to the processed data to generate a model, and the machine learning is provided with a loss function, a regularization term and an optimization target. The mean square error calculation formula in the loss function is:

[0032]

[0033] The cross entropy loss calculation formula is:

[0034]

[0035] The regularization term, L1 regularization Ω(f) = λ||w||_1, where w is the model parameter, λ is the regularization coefficient, ||w||_1 represents the L1 norm of parameter w, L2 regularization Ω(f) = λ||w||^2_2, where ||w||^2_2 represents the L2 norm of parameter w, that is, the sum of squares, and the optimization target is min(L(y,f(x))+λΩ(f)), λ is a hyperparameter, balancing loss and complexity.

[0036] As a preferred technical solution of the present invention, the security protection platform includes a unified management console, a sensitive data directory, a data access controller, a data flow probe and an application scenario probe. The unified management console provides a unified collaborative workbench for multi-role personnel and constructs a unified data model to support fine-grained data security operations. The sensitive data directory presents the data structure and security classification and grading results of the protected data source in real time, providing the necessary basis for data protection measures. The data access controller provides access control / dynamic desensitization / authority management / transparent encryption / security audit protection capabilities with a unified policy model. The data flow probe collects data access flow in bypass mode and can be quickly launched for scenarios where only auditing and supervision of data access behavior is implemented. The application scenario probe captures contextual information related to data access on the application side, providing rich basis for the supervision and protection of data security risks.

[0037] The present invention provides a method for using a visual multimedia display system based on big data analysis, comprising the following steps:

[0038] Step 1: Data collection: The big data processing platform collects, processes and transmits the data to be displayed;

[0039] Step 2: Data display: The information display screen platform provides a diverse, real-time, and intuitive visual display of the processed data;

[0040] Step 3: Security protection: The security protection platform provides real-time information security protection for the transmitted and displayed content.

[0041] Compared with the prior art, the beneficial effects of the present invention are: by setting up a security protection platform, it actively discovers distributed and heterogeneous data assets, builds a data protection directory in real time based on the holographic recognition technology of the active and passive dual engines, adaptively and dynamically identifies sensitive data types, and timely controls the central control system of the visual exhibition hall to avoid system paralysis, control failure and hacker intrusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the architecture of the display system of the present invention;

[0043] Figure 2 This is a schematic diagram of the architecture of the information display platform of the present invention;

[0044] Figure 3 is a schematic diagram of the architecture of the intelligent analysis unit of the present invention;

[0045] Figure 4 This is a schematic diagram of the architecture of the big data processing platform of the present invention;

[0046] Figure 5 This is a schematic diagram of the architecture of the security protection platform of the present invention;

[0047] Figure 6 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0049] See also Figure 1-6 , the present invention provides a visual multimedia display system based on big data analysis, including a display system, the display system includes a big data processing platform, an information display platform, a security protection platform and a big database platform;

[0050] The big data processing platform collects, processes and transmits the data that needs to be displayed;

[0051] The information display screen platform provides a diverse, real-time, and intuitive visual display of processed data;

[0052] The security protection platform provides information security protection for the transmitted and displayed content in real time;

[0053] The big database platform stores all the data displayed by the display system to facilitate subsequent reference and comparison.

[0054] The information display platform includes a front-end display module and a back-end display module. The front-end display module visualizes information and data on a visual screen, and the back-end display module facilitates information interaction between the display system and users. The front-end display module includes a large-screen editing unit, a visualization component unit, and an intelligent analysis unit. The large-screen editing unit is specially designed for large-screen plug-ins FVS large-screen editing mode, which is based on the development model of B / S-end technology. It has dozens of built-in chart types and styles suitable for large screens. There is no need to prepare data in advance. You can draw a large-screen blueprint by just dragging components. After designing the template, you can directly apply the data. The visualization component unit provides column chart components, bar chart components, pie chart components, line chart components, dashboard components, and indicator card components. The intelligent analysis unit collects and processes data, analyzes and applies data, and helps enterprises make scientific and intelligent decisions. The back-end display module includes a security authentication unit and a user interaction unit. The security authentication unit performs security authentication on the user's login method, such as fingerprint verification, account password verification, and face verification. The user interaction unit transmits the display content of the information display platform to the background for information interaction.

[0055] The intelligent analysis unit includes analysis result application, data analysis and display, data modeling and data access. The analysis result application supports multi-terminal adaptive display of data on large screens, mobile terminals and smart TVs, and performs self-service analysis, result application, monitoring and early warning. Self-service analysis includes linkage, drilling, slicing and ranking screening. Result application includes analysis, export, printing and sending to emails. Monitoring and early warning include email alerts, WeChat alerts, DingTalk alerts and API notifications. Data analysis and display provide 3D scenes + BI interactive analysis and online reports. The 3D scene + BI interactive analysis has 100+ charts, 20+ maps, online 3D scenes and 100+ visualization components. Online reports include Chinese complex reports, Word reports and printed reports. Data modeling includes cross-source modeling, SQL custom tables, data extraction, data isolation, real-time push and analysis expressions. Data access includes relational databases, domestic data source libraries, JSON APIs, file-type data sources and time series data.

[0056] The big data processing platform includes a big data acquisition module, a big data operation module, an algorithm model module and a data transmission module. The big data acquisition module collects data from various data sources. The data sources are specifically sensor data, databases, API interfaces, log files, social media data and third-party data services. The big data operation module is specifically data preprocessing, data storage and data analysis. Data preprocessing uses preprocessing tools to clean, integrate and convert the collected data to ensure the quality and consistency of the data. The preprocessing tools are specifically Hadoop and Spark. Data storage uses NoSQL databases and data warehouses to store the collected preprocessed data. Data analysis performs statistical analysis, pattern recognition and advanced processing of the data. Statistical analysis is specifically statistical analysis, machine learning and data mining techniques, such as time series analysis and clustering algorithms, to discover patterns and trends in the data. The data transmission module transmits the processed data to the information display screen platform for display.

[0057] The calculation formula for big data time series analysis is as follows: moving average,

[0058] SMAn=(x1+x2+......xn) / n or

[0059] The specific calculation formula for natural season analysis is:

[0060] $Yt$=$Tt$+$St$+$Rt$,

[0061] Where $Yt$ is the original time series, $Tt$ is the trend component, $St$ is the seasonal component, $Rt$ is the residual component, and the calculation formula for the non-seasonal time series is:

[0062] $$ / phi(B)(1-B)Λd / nabtaYt$$=$$ / theta(B) / epsilont$$,

[0063] Yt is the original time series, $$ / phi(B) and $$ / theta(B) are the parameters of regression and moving average, B is the regression term, d is the difference term, $$ / epsilont is white noise, and the clustering algorithm calculation formula is Minkowski distance:

[0064] [\text{dist}(X,Y)=\left(\sum_{i=1}^n|x_i-y_i|^p\right)^{\frac{1}{p}}],

[0065] Among them, when (p=1) is Manhattan distance, when (p=2) is Euclidean distance, when (p=\infty) is Chebyshev distance, cosine similarity:

[0066] [\cos(\theta)=\frac{a\cdot b}{|a||b|}],

[0067] Among them, (a) and (b) are two vectors, KL distance:

[0068] [D(P||Q)=\sum_x P(x)\log\left(\frac{P(x)}{Q(x)}\right)],

[0069] Used to measure the difference between two probability distributions (P) and (Q), Jaccard similarity coefficient

[0070] [J(A,B)=\frac{|A\cap B|}{|A\cup B|}],

[0071] Used to measure the similarity between two sets, Pearson correlation coefficient:

[0072] [\rho_{XY}=\frac{\text{Cov}(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}],

[0073] Used to measure the linear correlation between two variables (X) and (Y).

[0074] The algorithm model module applies machine learning or deep learning algorithms to the processed data to generate a model. Machine learning has a loss function, regularization term, and optimization target. The mean square error calculation formula in the loss function is:

[0075]

[0076] The cross entropy loss calculation formula is:

[0077]

[0078] Regularization term, L1 regularization Ω(f)=λ||w||_1, where w is the model parameter, λ is the regularization coefficient, ||w||_1 represents the L1 norm of parameter w, L2 regularization Ω(f)=λ||w||^2_2, where ||w||^2_2 represents the L2 norm of parameter w, i.e., the sum of squares, and the optimization target min(L(y,f(x))+λΩ(f)), λ is a hyperparameter, balancing loss and complexity. The security protection platform includes a unified management console, a sensitive data directory, a data access controller, a data flow probe, and an application scenario probe. The unified management console provides a unified coordination service for multi-role personnel. A workbench is created to construct a unified data model to support fine-grained data security operations. The sensitive data catalog presents the data structure and security classification and grading results of protected data sources in real time, providing the necessary basis for data protection measures. The data access controller provides access control / dynamic desensitization / authority management / transparent encryption / security audit protection capabilities with a unified policy model. The data traffic probe collects data access traffic in bypass mode and can be quickly launched for scenarios where only auditing and supervising data access behaviors are implemented. The application scenario probe captures contextual information related to data access on the application side, providing a rich basis for the supervision and protection of data security risks.

[0079] The present invention provides a method for using a visual multimedia display system based on big data analysis, comprising the following steps:

[0080] Step 1: Data collection: The big data processing platform collects, processes and transmits the data to be displayed;

[0081] Step 2: Data display: The information display screen platform provides a diverse, real-time, and intuitive visual display of the processed data;

[0082] Step 3: Security protection: The security protection platform provides real-time information security protection for the transmitted and displayed content.

[0083] In the present invention, the big data processing platform collects, processes and transmits the data to be displayed; the big data collection module collects data from various data sources, and the data sources are specifically sensor data, databases, API interfaces, log files, social media data and third-party data services. The big data operation module is specifically data preprocessing, data storage and data analysis. The data preprocessing uses preprocessing tools to clean, integrate and convert the collected data to ensure the quality and consistency of the data. The preprocessing tools are specifically Hadoop and Spark. The data storage uses NoSQL databases and data warehouses to store the collected preprocessed data. The data analysis performs statistical analysis, pattern recognition and advanced processing on the data. The statistical analysis is specifically statistical analysis, machine learning and data mining technology, such as time series analysis and clustering algorithms, to discover patterns and trends in the data. The data transmission module transmits the processed data to the information display screen platform for Display, the information display screen platform performs diverse, real-time, and intuitive visual displays of processed data; the large-screen editing unit specially produces the large-screen plug-in FVS large-screen editing mode, which is based on the development model of B / S-end technology and has dozens of built-in chart types and styles suitable for large screens. There is no need to prepare data in advance, and you can draw a large-screen blueprint by just dragging components. After designing the template, you can directly apply the data. The visualization component unit provides column chart components, bar chart components, pie chart components, line chart components, dashboard components, and indicator card components. The intelligent analysis unit collects and processes data, analyzes and applies data to help enterprises make scientific and intelligent decisions. The security authentication unit performs security authentication on the user login method, such as fingerprint authentication, account password authentication, and face authentication. The user interaction unit transmits the content displayed by the information display platform to the background for information interaction. The security protection platform performs information security protection on the transmitted content and displayed content in real time.

[0084] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A visual multimedia display system based on big data analysis, comprising a display system, characterized in that: The display system includes a big data processing platform, an information display platform, a security protection platform and a big database platform; The big data processing platform collects, processes and transmits the data to be displayed; The information display screen platform provides a diverse, real-time, and intuitive visual display of the processed data; The security protection platform performs information security protection on the transmitted content and displayed content in real time; The large database platform stores all data displayed by the display system to facilitate subsequent review and comparison.

2. A visual multimedia display system based on big data analysis according to claim 1, characterized in that: The information display platform includes a front-end display module and a back-end display module. The front-end display module visualizes information and data on a visual screen, and the back-end display module facilitates information interaction between the display system and the user. The front-end display module includes a large-screen editing unit, a visualization component unit and an intelligent analysis unit. The large-screen editing unit is specially made for the large-screen plug-in FVS large-screen editing mode, which is based on the development mode of B / S terminal technology. It has dozens of chart types and styles suitable for large screens. There is no need to prepare data in advance. You can draw a large-screen blueprint by just dragging components. After designing the template, you can directly apply the data. The visualization component unit provides column chart components, bar chart components, pie chart components, line chart components, dashboard components and indicator card components. The intelligent analysis unit collects and processes data, analyzes and applies data respectively, and helps enterprises make scientific and intelligent decisions. The back-end display module includes a security authentication unit and a user interaction unit. The security authentication unit performs security authentication on the user login method, such as fingerprint authentication, account password authentication and face authentication. The user interaction unit transmits the information display platform display content to the background for information interaction.

3. A visual multimedia display system based on big data analysis according to claim 2, characterized in that: The intelligent analysis unit includes analysis result application, data analysis and display, data modeling and data access. The analysis result application supports multi-terminal adaptive display of data on large screens, mobile terminals and smart TVs, and performs self-service analysis, result application, monitoring and early warning. The self-service analysis specifically includes linkage, drilling, slicing and ranking screening. The result application specifically includes analysis, export, printing and sending to emails. The monitoring and early warning specifically includes email early warning, WeChat early warning, DingTalk early warning and API notification. The data analysis and display performs 3D scene + BI interactive analysis and provides online reports. The 3D scene + BI interactive analysis is equipped with 100+ charts, 20+ maps, online 3D scenes and 100+ visualization components. The online reports are specifically Chinese complex reports, Word reports and printed reports. The data modeling is equipped with cross-source modeling, SQL custom tables, data extraction, data isolation, real-time push and analysis expressions. The data access is relational database, domestic data source library, JSON API, file-type data source and time series data.

4. The visual multimedia display system based on big data analysis according to claim 1, characterized in that: The big data processing platform includes a big data acquisition module, a big data operation module, an algorithm model module and a data transmission module. The big data acquisition module collects data from various data sources, and the data sources are specifically sensor data, databases, API interfaces, log files, social media data and third-party data services. The big data operation module is specifically data preprocessing, data storage and data analysis. The data preprocessing cleans, integrates and converts the collected data through preprocessing tools to ensure the quality and consistency of the data. The preprocessing tools are specifically Hadoop and Spark. The data storage stores the collected preprocessed data through NoSQL databases and data warehouses. The data analysis performs statistical analysis, pattern recognition and advanced processing of the data. The statistical analysis is specifically statistical analysis, machine learning and data mining technology, such as time series analysis and clustering algorithms, to discover patterns and trends in the data. The data transmission module transmits the processed data to the information display screen platform for display.

5. A visual multimedia display system based on big data analysis according to claim 4, characterized in that: The calculation formula for the big data time series analysis is specifically: moving average, SMAn=(x1+x2+......xn) / n or The specific calculation formula for natural season analysis is: $Yt$=$Tt$+$St$+$Rt$, Where $Yt$ is the original time series, $Tt$ is the trend component, $St$ is the seasonal component, $Rt$ is the residual component, and the calculation formula of the non-seasonal time series is: $$ / phi(B)(1-B)Λd / nabtaYt$$=$$ / theta(B) / epsilont$$, Yt is the original time series, $$ / phi(B) and $$ / theta(B) are the parameters of regression and moving average, B is the regression term, d is the difference term, $$ / epsilont is white noise, and the clustering algorithm calculation formula is specifically Minkowski distance: [\text{dist}(X,Y)=\left(\sum_{i=1}^n|x_i-y_i|^p\right)^{\frac{1}{p}}], Among them, when (p=1) is Manhattan distance, when (p=2) is Euclidean distance, when (p=\infty) is Chebyshev distance, the cosine similarity is: [\cos(\theta)=\frac{a\cdot b}{|a||b|}], Where (a) and (b) are two vectors, and the KL distance is: [D(P||Q)=\sum_x P(x)\log\left(\frac{P(x)}{Q(x)}\right)], Used to measure the difference between two probability distributions (P) and (Q), Jaccard similarity coefficient [J(A,B)=\frac{|A\cap B|}{|A\cup B|}], Used to measure the similarity between two sets, Pearson correlation coefficient: [\rho_{XY}=\frac{\text{Cov}(X,Y)}{\sqrt{D(X)}\sqrt{D(Y)}}], Used to measure the linear correlation between two variables (X) and (Y).

6. The visual multimedia display system based on big data analysis according to claim 4 is characterized in that: The algorithm model module applies machine learning or deep learning algorithm to the processed data to generate a model. The machine learning is provided with a loss function, a regularization term and an optimization target. The mean square error calculation formula in the loss function is: The cross entropy loss calculation formula is: The regularization term, L1 regularization Ω(f) = λ||w||_1, where w is the model parameter, λ is the regularization coefficient, ||w||_1 represents the L1 norm of parameter w, L2 regularization Ω(f) = λ||w||^2_2, where ||w||^2_2 represents the L2 norm of parameter w, that is, the sum of squares, and the optimization target is min(L(y,f(x))+λΩ(f)), λ is a hyperparameter, balancing loss and complexity.

7. The visual multimedia display system based on big data analysis according to claim 1, characterized in that: The security protection platform includes a unified management console, a sensitive data directory, a data access controller, a data flow probe and an application scenario probe. The unified management console provides a unified collaborative workbench for multi-role personnel and constructs a unified data model to support fine-grained data security operations. The sensitive data directory presents the data structure and security classification and grading results of the protected data source in real time, providing the necessary basis for data protection measures. The data access controller provides access control / dynamic desensitization / authority management / transparent encryption / security audit protection capabilities with a unified policy model. The data flow probe collects data access flow in bypass mode and can be quickly launched for scenarios where only auditing and supervision of data access behavior is implemented. The application scenario probe captures contextual information related to data access on the application side, providing a rich basis for the supervision and protection of data security risks.

8. A method for using a visualization multimedia display system based on big data analysis according to any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Data collection: The big data processing platform collects, processes and transmits the data to be displayed; Step 2: Data display: The information display screen platform provides a diverse, real-time, and intuitive visual display of the processed data; Step 3: Security protection: The security protection platform provides real-time information security protection for the transmitted and displayed content.

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