Large-screen data display method and system and electronic equipment

Abstract

The invention provides a large-screen data display method and system and electronic equipment, and relates to the technical field of computers.The method comprises the steps that data collection is conducted from multiple different data sources according to multiple data collection modes, and an initial data set is obtained; performing data processing on the initial data set by adopting a plurality of data analysis models to obtain a plurality of target data sets; constructing at least one to-be-displayed data set according to the plurality of target data sets and the data sources and the target storage positions corresponding to the plurality of target data sets; determining at least one target display component template from the plurality of reference display component templates according to a data display demand, and dragging the at least one target display component template to a large visual screen; and mapping association is performed on the at least one to-be-displayed data set and the at least one target display component template in the large visual screen, so that the at least one to-be-displayed data set is displayed on the at least one target display component template in the large visual screen, and the timeliness of updating the contents of the large screen is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a large-screen data display method, system and electronic device. Background Art

[0002] Currently, large-screen display technology has been widely used in various industries, such as enterprise management, urban operations, traffic control, etc. By centrally displaying various data indicators and pipe information on the large screen, it helps decision makers quickly grasp business dynamics and improve work efficiency.

[0003] In the prior art, each step of data collection, data processing, and data visualization on a large screen is relatively independent, making it difficult to achieve real-time dynamic linkage among data collection, data processing, and data visualization on a large screen, making it difficult to reflect business changes in real time after the large-screen display content is updated.

[0004] With respect to the problem of delayed large-screen content update in the prior art, no effective technical solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a large-screen data display method, system and electronic device to at least solve the problem of delayed large-screen content update in the prior art.

[0006] According to one aspect of an embodiment of the present application, a large-screen data display method is provided, including: pre-setting multiple data collection methods, and collecting data from multiple different data sources according to the multiple data collection methods to obtain an initial data set; using multiple data analysis models to process the initial data set to obtain multiple target data sets, wherein each data analysis model corresponds to a target data set, and the multiple data analysis models include: a time series analysis model, a clustering analysis model, and an association analysis model; constructing at least one data set to be displayed according to the multiple target data sets and the data sources and target storage locations corresponding to the multiple target data sets; determining at least one target display component template from multiple reference display component templates according to data display requirements, and dragging the at least one target display component template to a visualization large screen; mapping and associating at least one data set to be displayed with at least one target display component template in the visualization large screen, so as to display at least one data set to be displayed on at least one target display component template in the visualization large screen.

[0007] According to another aspect of an embodiment of the present application, a large-screen data display system is also provided, including: a data acquisition unit, used to pre-set multiple data acquisition methods, and collect data from multiple different data sources according to the multiple data acquisition methods to obtain an initial data set; a data processing unit, used to use multiple data analysis models to process the initial data set to obtain multiple target data sets, wherein each data analysis model corresponds to a target data set; a data set construction unit, used to construct at least one data set to be displayed according to the multiple target data sets and the data sources and target storage locations corresponding to the multiple target data sets; a determination unit, used to determine at least one target display component template from multiple reference display component templates according to data display requirements, and drag the at least one target display component template to the visualization large screen; a mapping association unit, used to map and associate at least one data set to be displayed with at least one target display component template in the visualization large screen, so as to display at least one data set to be displayed on at least one target display component template in the visualization large screen.

[0008] Optionally, the above-mentioned data set construction unit includes a first determination subunit, which is used to determine the data update cycles corresponding to multiple target data sets according to multiple target data sets; an acquisition subunit, which is used to obtain the data volume corresponding to each target data set, and determine the target storage location of each target data set according to the multiple data volumes and the multiple data update cycles; a second determination subunit, which is used to determine the filtering method of at least one data set to be displayed according to the target account's selection operation on multiple data sources and multiple target storage locations on the construction page of the data set to be displayed, wherein the target account is the account for logging into the visualization large screen; and a construction subunit, which is used to construct at least one data set to be displayed according to the filtering method.

[0009] Optionally, the above-mentioned first determination subunit includes a first determination module, used to determine any target data set among multiple target data sets as the current data set; a second determination module, used to determine multiple data types corresponding to the current data set, and determine the update cycles corresponding to the multiple data types; a third determination module, used to determine the smallest update cycle among multiple update cycles as the data update cycle corresponding to the current data set; and a fourth determination module, used to determine any target data set among multiple target data sets except the current data set as the current data set.

[0010] Optionally, the acquisition subunit includes a storage submodule, which is used to store the target data set in a designated partition of the cloud database when the data volume corresponding to the target data set is greater than the preset data volume and the data update period corresponding to the target data set is less than the preset period; store the target data set in a local target storage location when the data volume corresponding to the target data set is greater than the preset data volume and the data update period corresponding to the target data set is greater than the preset period; store the target data set in a cache when the data volume corresponding to the target data set is less than the preset data volume and the data update period corresponding to the target data set is less than the preset period; store the target data set in a database when the data volume corresponding to the target data set is less than the preset data volume and the data update period corresponding to the target data set is greater than the preset period, wherein the database is a non-cloud database.

[0011] Optionally, the above-mentioned large-screen data display system also includes an extraction unit, which is used to extract the initial data set to the open source stream processing platform using a data extraction tool or a custom script; a stream processing unit, which is used for the open source stream processing platform to perform stream processing on the initial data set and output the stream processed data set; and a sending unit, which is used for the open source stream processing platform to send the stream processed data set to multiple data analysis models according to the model types corresponding to the multiple data analysis models.

[0012] Optionally, the data collection unit includes: a third determination subunit, used to determine the data type of the data to be collected according to the collection requirements, and obtain multiple collection data types; a fourth determination subunit, used to determine the update frequency of each type of data to be collected and the data source corresponding to each type of data to be collected according to the multiple collection data types; a fifth determination subunit, used to determine the update frequency similarity and the highest update frequency corresponding to each data source according to multiple update frequencies; a collection subunit, used to collect data from multiple different data sources according to multiple data collection methods, multiple update frequency similarities and multiple highest update frequencies, and obtain an initial data set.

[0013] Optionally, the above-mentioned acquisition subunit includes a fifth determination module, which is used to determine the monitoring mechanism of multiple data sources based on multiple update frequency similarities and multiple highest update frequencies; a first acquisition module, which is used to, when the monitoring mechanism is a real-time monitoring mechanism, adopt a real-time acquisition method corresponding to the real-time monitoring mechanism to collect data from the corresponding data source, so as to obtain a first data subset in the initial data set, wherein the multiple data collection methods include the real-time collection method; a second acquisition module, which is used to, when the monitoring mechanism is periodic key monitoring, adopt a periodic collection method corresponding to the periodic key monitoring to collect data from the corresponding data source, so as to obtain a second data subset in the initial data set, wherein the multiple data collection methods include periodic key monitoring.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above large-screen data display method.

[0015] According to another aspect of the embodiments of the present application, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the large-screen data display method as described above.

[0016] Compared with the prior art, the technical solution provided by the embodiment of the present application may have the following beneficial effects:

[0017] The above-mentioned large-screen data display method solves the problem of delayed large-screen content update in the prior art and improves the timeliness of large-screen data display. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of an optional large-screen data display method according to an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of an optional large-screen data display method according to an embodiment of the present invention;

[0021] Figure 3 is a schematic structural diagram of an optional large-screen data display system according to an embodiment of the present invention;

[0022] Figure 4 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] Before introducing the technical solution of the present application, some terms involved in the present application are explained. The following related explanations can be used as optional solutions and can be combined with the technical solutions of the embodiments of the present application at will, and they all belong to the protection scope of the embodiments of the present application. The embodiments of the present application include at least part of the following contents.

[0025] WebGL: used to present graphics in the page, allowing graphics to be rendered directly in the browser and can be used in combination with other web technologies;

[0026] HTTP: a protocol used to transfer data between clients and servers;

[0027] SQL: A standard language for managing and manipulating relational databases.

[0028] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] Currently, large-screen display technology has been widely used in various industries, such as enterprise management, urban operations, traffic control, etc. By centrally displaying various data indicators and key information on the large screen, it helps decision makers quickly grasp business dynamics and improve work efficiency. Usually, this type of large-screen system consists of three modules: data collection, data analysis, and chart display.

[0031] In the data acquisition module, the system obtains raw business data from various information systems through HTTP interfaces or SQL queries. The data analysis module processes and correlates the collected raw data, extracts valuable information, and forms a visual data set. Finally, the chart display module converts the analyzed data set into intuitive chart forms such as line charts, bar charts, and heat maps, and presents them on the big screen.

[0032] This data-driven large-screen display technology helps users perceive complex business conditions more intuitively and discover potential problems and opportunities. However, the existing large-screen system also has some shortcomings: First, the data collection and processing processes are relatively independent, making it difficult to achieve dynamic linkage between data sources, analysis models, and chart displays, resulting in delayed updates of large-screen content and failure to reflect business changes in real time. Second, the chart style is single and the display form is fixed, making it difficult to meet the personalized needs of different users.

[0033] In general, the current large-screen system has problems such as delayed data updates, single display format, and limited usage. A more intelligent and flexible solution is urgently needed to meet actual application needs.

[0034] Specifically, the existing large-screen system has the following main problems:

[0035] 1. Data update lags: In existing systems, data collection, analysis, and chart display are usually relatively independent processes, lacking an effective linkage mechanism. Once the data source changes, the system needs to re-execute the entire data processing process to update the large-screen content, resulting in a gap between the display results and the actual business situation.

[0036] 2. Single display format: Existing large-screen systems usually provide limited chart types and styles, which makes it difficult to meet the personalized needs of different users for visualization effects. Users need to develop or configure complex chart components by themselves to achieve the desired display effect.

[0037] In order to solve the above problems, the embodiment of the present application provides a more intelligent and flexible large-screen data display method, which is applied to a Web-based intelligent large-screen system (i.e., a large-screen data display system). The system deeply integrates the three modules of data collection, analysis, and chart display, and realizes real-time linkage between data sources, analysis logic, and visualization effects, ensuring that the large-screen content can continuously and quickly reflect business changes. At the same time, the system supports users to customize chart styles and layouts, provides rich interactive functions, meets personalized needs, and enhances ease of use.

[0038] The technical architecture of the above-mentioned large-screen data display system includes four core parts: data processing layer, visualization display layer, data association layer, and data monitoring layer. The entire system follows the design principle of SOA (Service-Oriented Architecture). The layers are loosely coupled through APIs, which improves the scalability and maintainability of the system. This loosely coupled architectural design enables the system to have good scalability and maintainability. Each layer focuses on its own responsibilities and is independent of each other, which facilitates customized development for different needs. When it is necessary to add new data sources, optimize analysis algorithms, or adjust visualization requirements, it is only necessary to develop in the corresponding modules without affecting the stability of the entire system.

[0039] The above data processing layer: The system supports real-time collection of various business data through HTTP interface, database query, etc., and converts it into a standardized data set format. At the same time, the system has the ability to automatically monitor changes in data sources. Once a data source update is detected, the large-screen content can be updated immediately to ensure that the displayed data is always up to date. Based on the collected raw data, the system has built-in multiple analysis models, such as time series analysis, cluster analysis, association analysis, etc., which can mine the laws and trends contained in the data. At the same time, the system supports user-defined analysis logic and applies the analysis results to chart display in real time.

[0040] The back-end data processing layer is responsible for extracting raw data from the underlying database, and performing necessary data cleaning, aggregation, and analysis, and finally generating a data set suitable for visualization. Specifically, it includes the following functional modules: Data acquisition unit: responsible for connecting to the enterprise database, supporting mainstream relational databases such as MySQL, Oracle, and SQL Server. Through a flexible data source access mechanism, it can adapt to the heterogeneous data environment of the enterprise; Data processing unit: implements operations such as cleaning, conversion, and aggregation of raw data, removes noise data, and generates a data set that meets the visualization requirements. For example, smoothing time series data and aggregating geographic location data. At the same time, it supports user-defined data processing logic to improve the flexibility of the system; Determination unit and mapping association unit: According to the needs of the front-end display, define the visualization attributes of the data set, such as chart type, coordinate axis, legend, etc., and generate a data structure that can be directly used for chart rendering. This module can automatically identify the data type and give appropriate visualization configuration suggestions; The large-screen data display system also includes an interface service module: providing data services to the front-end display layer through RESTfulAPI, supporting real-time data query and customized report generation and other functions. The API design follows standard interface specifications to facilitate front-end application calls. At the same time, this module also provides data permission management function to ensure the security of data access

[0041] As an optional implementation, please refer to Figure 1 , which shows a flowchart of a large-screen data display method provided by an embodiment of the present application. The execution subject of each step of the method can be a device such as a terminal or a server. In the following method embodiment, for the sake of convenience of description, only the execution subject of each step is introduced as a "computer device". The method may include at least one of the following steps (S102 to S110):

[0042] S102, presetting a plurality of data collection methods, and collecting data from a plurality of different data sources according to the plurality of data collection methods to obtain an initial data set;

[0043] S104, using multiple data analysis models to process the initial data set to obtain multiple target data sets, wherein each data analysis model corresponds to one target data set;

[0044] S106, constructing at least one data set to be displayed according to the multiple target data sets and the data sources and target storage locations corresponding to the multiple target data sets;

[0045] S108, determining at least one target display component template from a plurality of reference display component templates according to data display requirements, and dragging the at least one target display component template to a visualization large screen;

[0046] S110, mapping and associating at least one data set to be displayed with at least one target display component template in the visualization large screen, so as to display at least one data set to be displayed on at least one target display component template in the visualization large screen.

[0047] It should be noted that the multiple data collection methods in the above S102 include real-time collection methods and periodic collection methods. In this application, corresponding monitoring mechanisms are set for different data sources. The monitoring mechanism is used to monitor the changes in data in the data source, and the data collection method can be understood, but not limited to, as a method of acquiring data based on the monitoring results of the monitoring mechanism (i.e., whether the data in the monitored data source has changed). The above multiple different data sources are used for the initial source of the initial data in the initial data set, that is, the initial data may come from the production platform, database (cloud database, mysql database, DAMO database, Jincang database, etc.), local disk, and other locations where the http interface is required to obtain data. That is, the above large-screen data display method can be understood, but not limited to, as a large-screen display method for data sets based on multi-type (data source) data access. The data collection operation in the above S102 includes both timely collection of real-time changing data and timed collection of periodically changing data. At the same time, it also includes the collection of data with different change periods (which can be understood as data with irregular change time). For the collection of irregularly changing data, a real-time monitoring monitoring mechanism can be set, so that when irregular data changes, the changed data can be collected in time.

[0048] The multiple data analysis models in S104 are used to perform different data processing on multiple pieces of initial data in the initial data set (which can be understood as but not limited to the processing and analysis process of the data), so as to obtain target data sets output by different data analysis models. It can be understood that the target data set includes not only the processing results of each data analysis model on the initial data, but also the multiple pieces of initial data analyzed by each data analysis model. The multiple data analysis models include: time series analysis model, cluster analysis model, and association analysis model.

[0049] The data set to be displayed in the above S106 can be understood, but not limited to, as a data set that can be directly visualized without any further data processing. In the process of constructing the data set to be displayed, the user can customize the name of the data set to be displayed or name the data set to be displayed by default according to the data set naming method. At the same time, the user can also set a custom note for the data set to be displayed, which is a brief description of the data in the data set to be displayed. When different visualizations are required for the same data set to be displayed in the future, the user can directly map and associate the same data set to be displayed with at least one other target display component template based on the name of the data set to be displayed and the custom note corresponding to the data set to be displayed, so as to achieve different display effects of the same data set to be displayed in the visualization large screen using different display component templates. The multiple reference display component templates in S108 include multiple templates in the visualization component template library, and also include custom templates of user history. The above-mentioned visualization component template library not only includes a variety of basic templates, such as text, hyperlinks, pictures, videos, etc.; it also includes a variety of chart templates, such as tables, bar charts, line charts, pie charts, radar charts, funnel charts, bar charts, line charts, word cloud charts, heat maps, dashboards, attribute charts, Nightingale rose charts, etc. Custom templates include custom chart templates, custom chart and text combination templates, etc. This application provides rich interactive functions to meet personalized needs by supporting users to customize chart styles (i.e., custom templates) and layouts (i.e., users can drag at least one target display component template to any position in the visualization large screen).

[0050] The mapping association in S110 can be performed by selecting at least one data set to be displayed in the mapping association page that appears after the target account double-clicks or right-clicks at least one target display component template dragged to the visualization large screen, thereby realizing the above mapping association. After mapping and associating at least one data set to be displayed with at least one target display component template, at least one data set to be displayed can be displayed through at least one target display component template in the visualization large screen. The specific display effect is as follows: Figure 2 The line chart shown.

[0051] It should be noted that the above-mentioned data set to be displayed can be dynamic data (i.e. data that changes in real time) or dynamic data. In the process of the target account selecting at least one data set to be displayed to realize the above-mentioned mapping association, it is also necessary to select whether the data included in the selected data set to be displayed is static data or dynamic data.

[0052] The determination unit implementing S108 and the mapping association unit implementing S110 can be understood as a visualization layer as a whole, but not limited to, that is, the visualization layer is responsible for presenting the results of data analysis to the user in an intuitive and interactive manner. The visualization layer uses a Web-based interactive chart library (visual component template library), such as Echarts, D3.js, etc., to provide a rich variety of chart types, such as line charts, bar charts, pie charts, maps, etc., to meet the data display needs in different scenarios. At the same time, the visualization layer supports various interactive functions, such as zooming, filtering, drilling, etc., so that users can browse and analyze data in depth.

[0053] The main functions of the visualization layer include: visualization chart engine, that is, the large-screen data display system integrates the above-mentioned multiple reference display component templates (WebGL-based visualization chart engine), and supports a variety of chart types, such as line charts, bar charts, scatter plots, heat maps, etc. Users can freely define chart styles, layouts, etc. through simple configuration to achieve personalized display effects. In addition, the chart engine supports complex interactive functions, such as zooming, panning, mouse hover prompts, etc., to enhance the user experience. At the same time, the system also integrates a visualization component library to support users to quickly build personalized chart displays through dragging, configuration, etc.

[0054] That is, the above-mentioned large-screen data display method includes chart rendering (S104 to S110): according to the analysis results provided by the data analysis layer, various interactive data visualization charts are rendered. Common chart types such as line charts, bar charts, pie charts, as well as special charts such as combination charts and maps are supported to meet the display needs of different business scenarios; interactive functions: rich interactive functions such as zooming, filtering, and drilling are provided to enable users to browse and analyze data in depth. For example, the overall trend of the data can be viewed through the zoom operation, the key indicators can be highlighted through the filtering function, and the internal connection of the data can be explored in depth through the drilling function. That is, in the visualization display interface, users can perform operations such as zooming, filtering, and drilling on the displayed charts; interface customization (that is, the above-mentioned custom templates and drag operations (can be dragged to any position of the visualization large screen)): support for custom chart styles, layouts, colors and other attributes to meet the aesthetic preferences and brand image requirements of different users. At the same time, according to actual business needs, the chart combination and display method can be adjusted to achieve flexible interface customization.

[0055] The mapping association in the above S110 collaborates through API to achieve a loosely coupled architecture design. The specific collaboration process includes: the user proposes specific analysis requirements through the interface of the visualization layer, such as viewing the trend changes of a certain indicator in different dimensions; the visualization layer converts the user requirements into corresponding API calls and sends them to the data processing layer; the data processing layer provides the required data based on user requirements, and applies the corresponding analysis algorithm to generate analysis results; the visualization layer receives the analysis results and renders interactive data charts according to the visualization configuration and presents them to the user; the user can further drill down and analyze the data through the interactive function of the front-end interface without having to worry about the data processing and analysis details behind it.

[0056] Through the above implementation of the present application, data collection, data processing and data visualization on the large screen are dynamically linked in real time, solving the problem of delayed large screen content update in the prior art and improving the timeliness of large screen data display. At the same time, real-time linkage between data sources, data analysis models and visualization effects is achieved, ensuring that large screen content can quickly respond to business changes.

[0057] As an optional implementation, the above-mentioned constructing at least one data set to be displayed according to the multiple target data sets and the data sources and target storage locations corresponding to the multiple target data sets respectively includes:

[0058] S1, determining data update cycles corresponding to multiple target data sets according to multiple target data sets;

[0059] S2, obtaining the data volume corresponding to each target data set, and determining the target storage location of each target data set according to the multiple data volumes and the multiple data update cycles;

[0060] S3, determining a filtering method for at least one data set to be displayed according to a selection operation of a target account on a construction page of a data set to be displayed on multiple data sources and multiple target storage locations, wherein the target account is an account for logging into the visualization screen;

[0061] S4, constructing at least one data set to be displayed according to the screening method.

[0062] It should be noted that the data update cycle in S1 is used to indicate the update frequency of data in multiple target data sets, and the specific steps for determining the data update cycle in S1 include: S1-1, determining any target data set among the multiple target data sets as the current data set; S1-2, determining multiple data types corresponding to the current data set, and determining the update cycles corresponding to the multiple data types; S1-3, determining the smallest update cycle among the multiple update cycles as the data update cycle corresponding to the current data set; S1-4, determining any target data set among the multiple target data sets except the current data set as the current data set.

[0063] The above data types are used to indicate the application scenarios of the data. Each data type corresponds to an update cycle, and the update cycles corresponding to multiple data types may be the same or different. For example, financial transaction data in financial scenarios, real-time weather monitoring data in weather scenarios, and social media dynamic data in social media scenarios. Since the update frequency of the above three types of data is in seconds or milliseconds, the update cycle corresponding to the data in financial scenarios, weather scenarios, and social media scenarios is updated in real time. For example, the inventory level in the inventory scenario, the website visits in the website scenario, and the number of subscribed users in the subscription scenario, the update frequency of the above three types of data is hourly or daily. Therefore, the update cycle corresponding to the data in the inventory scenario, website scenario, and subscription scenario is updated periodically. Other data that are also updated periodically include sales reports, market research data, customer feedback data, etc.

[0064] It is understandable that the above update cycle is not only related to the data type, but also to the data itself. That is, when determining the update cycle, it is necessary to comprehensively determine it based on the data type and the specific data corresponding to the data type. For example, when the data types corresponding to the real-time weather monitoring data and date data are both date scenes, the update cycle of the real-time weather monitoring data is shorter than that of the date data. Different data may correspond to one or more data types, so the data types corresponding to different data need to be determined based on the data itself.

[0065] The data volume in S2 is used to indicate the number of target data included in the target data set. The operation in S2 can be understood, but not limited to, as determining the target storage location of each target data set based on the data volume and the data update frequency. The operations in S3 to S4 can be understood, but not limited to, as, when determining the data set to be displayed, there are multiple situations. One situation is that the target data set can be directly determined as the data set to be displayed, that is, the data in the target data set is displayed as a whole. The other is to filter the required data in the target data set as needed, but the target storage locations corresponding to different target data sets may be the same or different. For the data stored in different target storage locations, the filtering method is also different. The filtering method includes SQL query statements, interface addresses, etc. For example, for data stored in the database, it is necessary to write SQL statements to filter from the target data set, while for data stored in the browser, it can be obtained through the interface.

[0066] It should be noted that, since there are multiple target data sets, there are also multiple target storage locations, and the target data sets stored in the same target storage location may be one or more. Therefore, when creating a data set to be displayed, it is necessary to select at least one target storage location from the multiple target storage locations, and select (or the user manually enters) the name of the target data set in the at least one target storage location. When the user determines the name of the target data set by selection, the application will also display the custom remarks corresponding to each target data set in the preset position of the selection box to facilitate the user's accurate selection; at the same time, the data set to be created and to be displayed is named using a custom or default data set naming method, and the user can customize the remarks information for the data set to be displayed for subsequent use.

[0067] The above-mentioned large-screen data display method also includes: data set feature extraction and data set recommendation based on content recommendation model: data set feature extraction is specifically: after obtaining the above-mentioned at least one data set to be displayed, it also includes natural language processing of metadata (such as file name, description information, tags, etc.) corresponding to each data to be displayed included in at least one data set to be displayed, and extracting semantic features such as keywords and themes. Perform statistical analysis on the content structure of the data to be displayed in at least one data set to be displayed (such as data type, field information, number of rows and columns, etc.) to obtain the physical characteristics of at least one data set to be displayed. Combine the source information of at least one data set to be displayed (that is, the relevant information of the above data source, such as the publishing platform, upload time, etc.), extract the characteristics of at least one data set to be displayed. Combine the above multi-dimensional features into a feature vector of at least one data set to be displayed, and prepare for subsequent classification and recommendation tasks.

[0068] The content-based recommendation model is specifically used to recommend data sets as follows: a collaborative filtering algorithm is used to calculate the similarity between at least one data set to be displayed based on at least one data set to be displayed including the user's historical browsing, downloading and other behavioral data; deep learning text matching technology is used to perform semantic understanding of the description information of at least one data set to be displayed to improve the accuracy of similarity calculation; the user's (also referred to as the target user, i.e., the user corresponding to the above target account) interest number is modeled into a user portrait, which is matched with the feature vector of at least one data set to be displayed to recommend a personalized data set to the user.

[0069] Through the above implementation of the present application, at least one data set to be displayed can be created. The data set to be displayed created in this way can not only be visualized at the moment, but also can be displayed with different effects as needed in the future.

[0070] As an optional implementation manner, the above-mentioned determining the target storage location of each target data set according to multiple data amounts and multiple data update cycles includes:

[0071] S1, when the data volume corresponding to the target data set is greater than the preset data volume and the data update cycle corresponding to the target data set is less than the preset cycle, the target data set is stored in a designated partition of the cloud database;

[0072] S2, when the data volume corresponding to the target data set is greater than the preset data volume and the data update period corresponding to the target data set is greater than the preset period, storing the target data set to the local target storage location;

[0073] S3, when the data volume corresponding to the target data set is less than the preset data volume and the data update period corresponding to the target data set is less than the preset period, storing the target data set in the cache;

[0074] S4, when the data volume corresponding to the target data set is less than the preset data volume and the data update period corresponding to the target data set is greater than the preset period, the target data set is stored in a database, wherein the database is a non-cloud database.

[0075] The local target storage location in S2 can be understood as browser storage, but is not limited to it. Browser storage methods include localStorage and sessionStorage. The cache in S3 includes the local storage location, as well as IndexedDB, Service Worker, etc. The databases include MySQL, DAMO, Jincang, etc. The types of databases include relational databases, non-relational databases, distributed databases, document databases, key-value databases, column storage databases, and graph databases, etc.

[0076] Through the above implementation of the present application, the target data set (that is, the multiple target data included in the target data set) can be stored in a targeted manner according to the data volume and data update cycle corresponding to the target data set, so as to ensure the accuracy of the data while ensuring the timely display of the data, as well as the long-term maintenance of the large-screen data display system (avoiding repeated operations of the large-screen data display system caused by storing them in the same location). At the same time, it can effectively solve the problems existing in the existing large-screen system, and provide Russian and Japanese companies and organizations with a more intelligent and efficient large-screen display solution.

[0077] As an optional implementation, before using multiple data analysis models to process the initial data set to obtain multiple target data sets, the method further includes:

[0078] S1, extract the initial data set to the open source stream processing platform using data extraction tools or custom scripts;

[0079] S2, the open source stream processing platform processes the initial data set and outputs the stream processed data set;

[0080] S3, an open source stream processing platform, sends stream processing data sets to multiple data analysis models according to their corresponding model types.

[0081] The data extraction tools in S1 above include various tools, such as ETL (abbreviation of Extract, Transform, Load) tools (such as Apache NiFi, Talend), Sqoop (SQL-to-Hadoop) tools, etc. The above open source stream processing platform can be a stream processing platform determined from a variety of stream processing platforms based on the amount of data and specific data in the initial data set. The various stream processing platforms include Apache Kafka, Apache Flink, Apache Storm, Apache Samza, etc. The above stream processing specifically includes filtering, transforming, aggregating, and calculating data (it can be understood that the calculation here is only a simple calculation, which is suitable for the situation where the original data is not required for subsequent multiple data analysis models).

[0082] Through the above-mentioned implementation methods of the present application, timely initial processing of a large amount of data can be achieved without affecting the timely processing of subsequent data analysis models and the timely display of data.

[0083] As an optional implementation, the above-mentioned data collection from a variety of different data sources according to a variety of data collection methods to obtain an initial data set includes:

[0084] S1, determining the data type of the data to be collected according to the collection requirements, and obtaining multiple collection data types;

[0085] S2, determining the update frequency of each type of data to be collected and the data source corresponding to each type of data to be collected according to the multiple types of collected data;

[0086] S3, determining the update frequency similarity and the highest update frequency corresponding to each data source according to the multiple update frequencies;

[0087] S4, collecting data from a variety of different data sources according to a variety of data collection methods, a plurality of update frequency similarities, and a plurality of maximum update frequencies to obtain an initial data set.

[0088] The data type in S1 is also used to indicate the application scenario of the data. For details, please refer to the explanation and examples of the data type above, which will not be repeated here. When determining the update frequency in S2, it is necessary to comprehensively determine it based on the data type of the data to be collected and the data to be collected itself. The update frequency similarity in S3 is used to indicate the similarity between multiple update frequencies corresponding to each data source. After determining the update similarity and the highest update frequency corresponding to each data source, when the update similarity corresponding to the data source is greater than or equal to the preset similarity threshold and the highest update frequency is higher than the preset frequency, the data source can be monitored using a real-time monitoring mechanism; when the update similarity corresponding to the data source is greater than the preset similarity and the highest update frequency is lower than the preset frequency, the data source is monitored using a periodic key monitoring mechanism; when the update similarity corresponding to the data source is less than the preset similarity, the monitoring mechanism of the data source is determined according to the highest update frequency. The operation in S4 can be understood, but not limited to, as collecting data from a variety of different data sources using a data collection method corresponding to the monitoring mechanism corresponding to the data source determined above.

[0089] Specifically, the operations in the above S4 include: S4-1, multiple update frequency similarities and multiple maximum update frequencies determine the monitoring mechanism of multiple data sources; S4-2, when the monitoring mechanism is a real-time monitoring mechanism, the real-time collection method corresponding to the real-time monitoring mechanism is used to collect data from the corresponding data source to obtain a first data subset in the initial data set, wherein the multiple data collection methods include the real-time collection method; S4-3, when the monitoring mechanism is periodic key monitoring, the periodic collection method corresponding to the periodic key monitoring is used to collect data from the corresponding data source to obtain a second data subset in the initial data set, wherein the multiple data collection methods include periodic key monitoring.

[0090] The above-mentioned periodic key monitoring can be understood, but is not limited to, as pre-setting the interval monitoring period (ie, every 15 days) and the monitoring duration (3 hours), that is, for example: monitoring for 3 hours is performed every 15 days.

[0091] The above-mentioned real-time monitoring mechanism and periodic key monitoring use adaptive monitoring thresholds, that is, the large-screen data display system (hereinafter referred to as the system) can automatically adjust the monitoring threshold according to the changes in real-time data. Through machine learning algorithms, the system will analyze historical data and the current environment, identify the normal fluctuation range of the data, and automatically update the threshold to ensure the effectiveness and accuracy of monitoring. At the same time, users can customize monitoring parameters according to their own needs, and the system will make intelligent adjustments based on the user's customized parameters and historical data. This flexibility enables the system to adapt to different scenarios and needs and improve the pertinence of monitoring. Through adaptive thresholds, the system can more effectively identify abnormal situations, reduce false alarms and missed alarms, and improve the reliability of monitoring.

[0092] The above monitoring mechanism also includes predictive monitoring mechanisms, including: Trend analysis: The system uses historical data to perform trend analysis and predict possible data changes in the future. This predictive capability allows users to take measures in advance to avoid potential problems. Early warning mechanism: Based on the prediction results, the system can generate early warning information. When the data trend reaches a certain critical point, the system will proactively notify the user, thereby facilitating timely decision-making and response. Intelligent decision support: By analyzing the pattern of data changes, the system can provide users with decision support suggestions to help them better understand the trends and potential risks behind the data.

[0093] The system adopts an event-driven data monitoring mechanism. By regularly polling the data source, the system can detect data change events and immediately trigger the data update process. At the same time, the system also supports the active push data update mode. When the external data source actively notifies the data change, the system can quickly respond and update the large screen content.

[0094] The above-mentioned large-screen data display method can be simply understood as: creating a data set to be displayed; determining the target storage location of the data set to be displayed (i.e., a database or a data source that requires an http interface to obtain data). Determine the above-mentioned screening method according to the target storage location (for example, when the target storage location is a database, fill in the sql statement in the screening method, and when the target storage location is a location that requires an http interface to obtain data, fill in the http interface address in the screening method), add query parameters (i.e., the name of the data set to be displayed in the above-mentioned target storage location, etc.), and then you can query the specific data to be displayed, and then create a display plan (i.e., the above-mentioned S108), and then associate the components to achieve display. At the same time, the refresh time of the data set can also be set in this application.

[0095] It should be noted that in the above description of this application, only the database and the need for an http interface to obtain data are mentioned. At the same time, this application is also applicable to ElasticSearch.

[0096] Through the above implementation of the present application, not only can the changed data be monitored immediately, but also the energy consumption of the system and meaningless monitoring can be saved, thereby saving the implementation cost of the large-screen data display solution. At the same time, the above data monitoring mechanism monitors data changes and predicts and warns the data through adaptive monitoring thresholds and predictive monitoring, thereby improving the fault tolerance of the system. The large-screen data display method in the present application can realize the real-time linkage of data sources, analysis models and visualization effects, meet personalized needs, improve ease of use, and provide enterprises and organizations with more intelligent and efficient large-screen display solutions. The present application realizes the seamless integration of multiple data sources, obtains different types of data through a unified interface; provides an automated data processing pipeline, reduces manual intervention, and improves efficiency; supports real-time visualization of large-scale data, and ensures smooth interaction of charts through optimized rendering algorithms. Use caching mechanisms and incremental update technologies to improve the efficiency of data acquisition and processing. Multi-dimensional data analysis view, users can customize chart layouts by dragging and dropping.

[0097] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0098] According to another aspect of the embodiment of the present invention, a large-screen data display system for implementing the large-screen data display method is also provided. Figure 3 , Figure 3 Schematic diagram of the structure of the large-screen data display system of the present invention. Figure 3 As shown, the large-screen data display system includes:

[0099] The data collection unit 302 is used to pre-set a plurality of data collection methods, and collect data from a plurality of different data sources according to the plurality of data collection methods to obtain an initial data set;

[0100] A data processing unit 304 is used to process the initial data set using multiple data analysis models to obtain multiple target data sets, wherein each data analysis model corresponds to one target data set;

[0101] A data set construction unit 306, configured to construct at least one data set to be displayed according to the multiple target data sets and the data sources and target storage locations corresponding to the multiple target data sets;

[0102] A determining unit 308, configured to determine at least one target display component template from a plurality of reference component templates according to data display requirements, and drag the at least one target display component template to the visualization interface;

[0103] The mapping and associating unit 310 is used to map and associate at least one to-be-displayed data set with at least one target display component template in the visualization screen, so as to display at least one to-be-displayed data set on at least one target display component template in the visualization screen.

[0104] According to another aspect of the embodiment of the present invention, an electronic device for implementing the above-mentioned large-screen data display method is also provided, and the electronic device may be a terminal device or a server. This embodiment is described by taking the electronic device as a terminal device as an example. Figure 4 As shown, the electronic device includes: at least one processor 404; and a memory 402 that is communicatively connected to the at least one processor 404; wherein the memory 402 stores a computer program that can be executed by the at least one processor 404, and the computer program is executed by the at least one processor 404 so that the at least one processor 404 performs the steps in any one of the above method embodiments.

[0105] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0106] Optionally, in this embodiment, the processor may be configured to execute the large-screen data display method through a computer program.

[0107] Alternatively, those skilled in the art will appreciate that Figure 4 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 4 The structure shown does not limit the structure of the electronic device. Figure 4 More or fewer components (such as network interfaces, etc.), or with Figure 4 Different configurations.

[0108] Among them, the memory 402 can be used to store software programs and modules, such as the program instructions / modules corresponding to the large-screen data display method and system in the embodiment of the present invention. The processor 404 executes various functional applications and data processing by running the software programs and modules stored in the memory 402, that is, realizing the above-mentioned large-screen data display method. The memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 402 may further include a memory remotely arranged relative to the processor 404, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. Among them, the memory 402 may be specifically used, but not limited to, for storing various data involved in the above-mentioned large-screen data display method. As an example, the above-mentioned memory 402 may include, but is not limited to, various units in the above-mentioned large-screen data display system. In addition, it may also include, but is not limited to, other module units in the above-mentioned large-screen data display system, which will not be repeated in this example.

[0109] Optionally, the transmission device 406 is used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 406 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0110] In addition, the electronic device mentioned above further includes: a display 408 and a connection bus 410, which is used to connect various module components in the electronic device mentioned above.

[0111] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. Among them, the nodes may form a peer-to-peer (P2P, PeerTo Peer) network, and any form of computing device, such as a server, terminal or other electronic device, may become a node in the blockchain system by joining the peer-to-peer network.

[0112] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0113] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0114] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned large-screen data display method.

[0115] Optionally, in this embodiment, the computer-readable storage medium may be configured to store a computer program for executing the large-screen data presentation method.

[0116] Those skilled in the art can understand that the implementation of all or part of the processes in the above method embodiments can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above method embodiments. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory (FM), a hard disk drive (HDD), or a solid-state drive (SSD), etc.; the storage medium can also include a combination of the above types of memory.

[0117] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to perform all or part of the steps of the above methods of various embodiments of the present invention.

[0118] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0119] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0120] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0121] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0122] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A large-screen data display method, applied to a large-screen data display system, characterized in that: include: Presetting a plurality of data collection methods, and collecting data from a plurality of different data sources according to the plurality of data collection methods to obtain an initial data set; Using multiple data analysis models to process the initial data set to obtain multiple target data sets, wherein each data analysis model corresponds to one target data set; Constructing at least one data set to be displayed according to the plurality of target data sets and the data sources and target storage locations respectively corresponding to the plurality of target data sets; Determine at least one target display component template from a plurality of reference display component templates according to data display requirements, and drag at least one of the target display component templates to the visualization large screen; At least one of the to-be-displayed data sets is mapped and associated with at least one of the target display component templates in the visualization screen, so as to display at least one of the to-be-displayed data sets on at least one of the target display component templates in the visualization screen.

2. The method according to claim 1, characterized in that Constructing at least one data set to be displayed according to the plurality of target data sets and the data sources and target storage locations respectively corresponding to the plurality of target data sets, comprising: Determining data update cycles corresponding to the plurality of target data sets according to the plurality of target data sets; Acquire the data volume corresponding to each of the target data sets, and determine the target storage location of each of the target data sets according to the multiple data volumes and the multiple data update cycles; Determining a filtering method for at least one of the data sets to be displayed according to a selection operation of a target account on a construction page of the data set to be displayed on the plurality of data sources and the plurality of target storage locations, wherein the target account is an account for logging into the visualization screen; At least one data set to be displayed is constructed according to the screening method.

3. The method according to claim 2, characterized in that Determining data update periods corresponding to the plurality of target data sets according to the plurality of target data sets includes: Determine any target data set among the multiple target data sets as a current data set; Determine multiple data types corresponding to the current data set, and determine update cycles corresponding to each of the multiple data types; Determine the smallest update period among the multiple update periods as the data update period corresponding to the current data set; Any target data set other than the current data set among the plurality of target data sets is determined as the current data set.

4. The method according to claim 2, characterized in that: Determining a target storage location of each target data set according to the plurality of data amounts and the plurality of data update cycles includes: When the data volume corresponding to the target data set is greater than the preset data volume, and the data update period corresponding to the target data set is less than the preset period, the target data set is stored in a designated partition of the cloud database; When the data volume corresponding to the target data set is greater than the preset data volume, and the data update period corresponding to the target data set is greater than the preset period, storing the target data set in a local target storage location; When the data volume corresponding to the target data set is smaller than a preset data volume, and the data update period corresponding to the target data set is smaller than a preset period, storing the target data set in a cache; When the data volume corresponding to the target data set is smaller than a preset data volume and the data update period corresponding to the target data set is larger than a preset period, the target data set is stored in a database, wherein the database is a non-cloud database.

5. The method according to claim 1, characterized in that Various data analysis models include: time series analysis model, cluster analysis model, and association analysis model.

6. The method according to claim 1, characterized in that Before using multiple data analysis models to process the initial data set to obtain multiple target data sets, the method further includes: Extracting the initial data set into an open source stream processing platform using a data extraction tool or a custom script; The open source stream processing platform performs stream processing on the initial data set and outputs a stream processed data set; The open source stream processing platform sends the stream processing data set to the multiple data analysis models according to the model types corresponding to the multiple data analysis models.

7. The method according to claim 1, characterized in that Data is collected from a variety of different data sources according to a variety of data collection methods to obtain an initial data set, including: Determine the data type of the data to be collected according to the collection requirements, and obtain multiple collection data types; Determine the update frequency of each type of data to be collected and the data source corresponding to each type of data to be collected according to the multiple types of data to be collected; Determine the update frequency similarity and the highest update frequency corresponding to each of the data sources according to the multiple update frequencies; Data is collected from a plurality of different data sources according to the plurality of data collection methods, the plurality of update frequency similarities and the plurality of maximum update frequencies to obtain an initial data set.

8. The method according to claim 7, characterized in that According to the plurality of data collection methods, the plurality of update frequency similarities and the plurality of maximum update frequencies, data is collected from a plurality of different data sources to obtain an initial data set, including: A plurality of the update frequency similarities and a plurality of the highest update frequencies determine a monitoring mechanism for a plurality of the data sources; In the case where the monitoring mechanism is a real-time monitoring mechanism, data is collected from the corresponding data source using a real-time collection method corresponding to the real-time monitoring mechanism to obtain a first data subset in the initial data set, wherein the multiple data collection methods include the real-time collection method; In the case where the monitoring mechanism is periodic key monitoring, a periodic collection method corresponding to the periodic key monitoring is used to collect data from the corresponding data source to obtain a second data subset in the initial data set, wherein the multiple data collection methods include periodic key monitoring.

9. A large-screen data display system, characterized in that: include: A data collection unit, used to pre-set a plurality of data collection methods, and collect data from a plurality of different data sources according to the plurality of data collection methods to obtain an initial data set; A data processing unit, configured to process the initial data set using a plurality of data analysis models to obtain a plurality of target data sets, wherein each data analysis model corresponds to a target data set; A data set construction unit, configured to construct at least one data set to be displayed according to the plurality of target data sets and the data sources and target storage locations respectively corresponding to the plurality of target data sets; A determination unit, configured to determine at least one target display component template from a plurality of reference display component templates according to data display requirements, and drag at least one of the target display component templates to a visualization large screen; A mapping association unit is used to map and associate at least one of the to-be-displayed data sets with at least one of the target display component templates in the visualization screen, so as to display at least one of the to-be-displayed data sets on at least one of the target display component templates in the visualization screen.

10. An electronic device, characterized in that: The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor executes the large-screen data display method described in any one of claims 1-8.

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