Automatic visual screen configuration method

Abstract

The invention provides an automatic visual screen configuration method, and belongs to the technical field of data processing, and the method comprises the steps: obtaining initial data from a plurality of data sources, carrying out the analysis processing of the types of the data sources through a dynamic protocol adaptation layer, and carrying out the data cleaning and standardization of the analyzed data, and obtaining a standard data set; extracting feature information based on the standard data set, and matching the feature information through a pre-configured rule base to obtain a visual display component; and obtaining a target screen physical parameter, determining the priority of the visual display component, and obtaining a component layout coordinate scheme through an adaptive layout algorithm, the target screen physical parameter and the priority. A visual component template is automatically matched by analyzing a data source type, and an optimal layout scheme is generated by utilizing an algorithm, so that full-process automation from data access to visual display is realized, and the problems of low efficiency and high cost of traditional manual configuration are avoided.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to an automated visualization screen configuration method. Background Art

[0002] In today's digital age, data visualization screens, as an efficient data display method, are widely used in many fields, including enterprise operation monitoring, smart city management, traffic command and dispatch, and financial risk warning. They can present complex data in intuitive and vivid graphics and charts, helping decision makers quickly obtain key information and make accurate decisions.

[0003] However, existing visualization screen configuration requires manual work throughout the entire process, including data processing, component design, and layout adjustment. The entire process, from data access to final screen display, requires significant manual intervention, and the construction cycle for a single screen can take days or even weeks. For example, when connecting to a new data source, technicians must manually write SQL statements to extract data, then manually select chart types using visual editing tools, and adjust component styles, sizes, and layouts one by one. Furthermore, existing layouts often use fixed templates or manual dragging, making them difficult to adapt to screens of varying sizes or changes in data volume. For example, when the screen size changes from 16:9 to 4:3, component positions and sizes must be manually adjusted, which is inefficient and prone to layout imbalances.

[0004] Therefore, there is an urgent need to develop an automated visualization screen configuration method that integrates data processing, component generation, and layout planning to solve the problems of deployment efficiency and ease of use of visualization screens. Summary of the Invention

[0005] In order to solve at least one technical problem existing in the background technology, the present application provides an automated visualization screen configuration method, which automatically matches the visualization component template by parsing the data source type, and uses an algorithm to generate the optimal layout solution, thereby realizing full process automation from data access to visualization display.

[0006] The technical solutions adopted in this application are:

[0007] The first embodiment of the present application provides an automated visualization screen configuration method, comprising:

[0008] Obtain initial data from multiple data sources, parse and process the data source type through the dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set;

[0009] Extracting feature information based on a standard data set and matching the feature information with a pre-configured rule base to obtain a visual display component;

[0010] The target screen physical parameters are obtained, the priorities of the visual display components are determined, and a component layout coordinate solution is obtained through an adaptive layout algorithm, the target screen physical parameters and the priorities.

[0011] According to one embodiment of the present application, the parsing process based on the data source type by the dynamic protocol adaptation layer is specifically as follows:

[0012] The multiple data sources include at least one of relational databases, non-relational databases, real-time data streams, and API interfaces;

[0013] If the data source is a relational database, use the JDBC or ODBC protocol to execute the DESCRIBE statement to obtain table structure information and parse field types and constraints;

[0014] If the data source is a non-relational database, the JSON Schema is inferred by querying a single document and the nested structure is parsed based on a recursive algorithm to obtain the field type.

[0015] If the data source is a real-time data stream, the initial data is parsed based on Kafka's Topic metadata or message schema, and the data type is dynamically modified;

[0016] If the data source is an API interface, the response content is determined based on the Content-Type field, and JSON data is parsed using the Jackson / Gson library, or XML data is parsed using DOM.

[0017] According to one embodiment of the present application, before obtaining initial data from multiple data sources, the method further includes:

[0018] Set up a dynamic connection pool, adjust the number of data source connections based on data source load information, and achieve load balancing among multiple data sources through a weighted round-robin algorithm.

[0019] According to one embodiment of the present application, the visual display component is obtained by matching the feature information through a rule base, specifically:

[0020] Determine the visual display component corresponding to the feature information in the rule base through a matching model, where the matching model includes at least one of an exact matching model, a fuzzy matching model, a data proportion dynamic adjustment model, and a personalized matching model;

[0021] The feature information includes at least any one of a data dimension, a value type, a data feature, and a matching template identification number.

[0022] According to one embodiment of the present application, the target screen physical parameters are obtained, the priority of the visual display components is determined based on the data importance and business requirements, and the component layout coordinate scheme is obtained based on the target screen physical parameter component priority through an adaptive layout algorithm, specifically:

[0023] The adaptive layout algorithm includes at least one of a grid layout algorithm, a flow layout algorithm, and a free layout algorithm;

[0024] allocating positions and / or areas of the visual display components based on their priorities;

[0025] The visual display component includes at least a first component and a second component, determining the correlation between the first component and the second component, and when the correlation is higher than a preset threshold, determining the relative position relationship between the first component and the second component;

[0026] The physical parameters include at least any one of screen size, resolution, aspect ratio and display area division.

[0027] According to one embodiment of the present application, the method further includes:

[0028] Dynamically configure the visual configuration interface through interactive actions;

[0029] Wherein, the visual configuration interface includes at least any one of a component library area, a screen canvas area and a property editing area;

[0030] The interaction action includes at least one of component adjustment, style modification, data binding and interaction setting.

[0031] According to one embodiment of the present application, the method further includes:

[0032] Establish a data subscription mechanism to monitor data sources and obtain real-time updated data;

[0033] The real-time update data is refreshed through the component refresh strategy to obtain the refresh component and the new layout coordinate solution of the refresh component.

[0034] A second embodiment of the present application provides an apparatus for automated visual screen configuration, comprising:

[0035] The data preprocessing module is suitable for obtaining initial data from various data sources, parsing and processing the data based on the data source type through the dynamic protocol adaptation layer, and cleaning and standardizing the parsed data to obtain a standard data set;

[0036] The component automatic generation module is suitable for extracting feature information based on standard data sets, including data type, data dimension, data distribution, etc., and matching the feature information based on the rule base to obtain visual display components;

[0037] The intelligent layout planning module obtains the physical parameters of the target screen and is suitable for determining component priorities based on data importance and business needs. It uses an adaptive layout algorithm to obtain component layout coordinate solutions based on the target screen physical parameters and component priorities.

[0038] An embodiment of the third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the automated visual screen configuration method described in any embodiment of the first aspect is implemented.

[0039] The present application also provides a non-volatile computer storage medium having computer executable instructions stored thereon, which, when executed by a processor, can implement the automated visualization screen configuration method in any embodiment of the first aspect as described above.

[0040] Beneficial effects:

[0041] The automated visualization screen configuration method provided in this application automatically matches the visualization component template by parsing the data source type, and uses an algorithm to generate the optimal layout solution to achieve full process automation from data access to visualization display, avoiding the low efficiency and high cost problems of traditional manual configuration. The method first uses a plug-in architecture and a dynamic protocol adaptation layer to achieve flexible access to multi-source heterogeneous data, and significantly improves data quality and system compatibility through rule-driven data cleaning and standardization processes; then, based on data feature extraction and rule base matching mechanism, it automatically selects the optimal visualization component, effectively reducing the configuration threshold and the degree of manual intervention; based on screen size, component priority and data characteristics, a dynamic layout algorithm is used to automatically generate the optimal display solution, solving the problems of poor flexibility and low adaptability of traditional fixed templates or manual layout methods. This automated visualization screen configuration method not only improves the efficiency and intelligence level of data visualization configuration, but also significantly optimizes the user experience, has good scalability and practical value, and is suitable for the rapid construction and dynamic operation and maintenance needs of data screens in various industry scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0043] Figure 1A flowchart of an automated visual screen configuration method provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the structure of an automated visual screen configuration device provided in an embodiment of the present application;

[0045] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0046] Reference numerals:

[0047] 110. Data preprocessing module; 120. Component automatic generation module; 130. Layout intelligent planning module;

[0048] 810 , processor; 820 , communication interface; 830 , memory; 840 , communication bus. DETAILED DESCRIPTION

[0049] In order to more clearly illustrate the overall concept of the present application, a detailed description is given below in an illustrative manner in conjunction with the accompanying drawings.

[0050] The following description sets forth many specific details to facilitate a thorough understanding of the present application. However, the present application may also be implemented in other ways than those described herein, and therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below. It should be noted that the embodiments of the present application and the features of each embodiment may be combined with each other unless there is a conflict.

[0051] In this application, unless otherwise expressly specified and limited, a first feature "above" or "below" a second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0052] like Figure 1 As shown, the first embodiment of the present application provides an automated visualization screen configuration method, comprising:

[0053] Step 100: Acquire initial data from multiple data sources, parse and process the data source type through a dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set.

[0054] Step 200: Extract feature information based on a standard data set, and match the feature information through a rule base to obtain a visual display component.

[0055] Step 300: Obtain the physical parameters of the target screen, determine the priorities of the components to be displayed, and obtain a component layout coordinate solution through an adaptive layout algorithm, the physical parameters of the target screen, and the component priorities.

[0056] In step 100, the system supports access to multiple data sources, including relational databases (such as MySQL and Oracle), non-relational databases (such as MongoDB), real-time data streams, and API interfaces. It uses a plug-in architecture to achieve multi-source heterogeneous data access and automatically identifies and processes different data sources through a dynamic protocol adaptation layer, ensuring the flexibility and scalability of the data acquisition process.

[0057] The parsed data enters the data cleaning and data format standardization stage, and the system automatically processes the initial data based on the rule engine. During the data cleaning stage, the system processes missing values, duplicate values, and outliers according to preset rules. Specifically, for data missing value exceptions, such as numeric fields are filled with mean, median, or mode, and text fields are completed or marked as "unknown" through associated field logic. For fields with missing rates exceeding a threshold (such as 30%), field deletion or overall record discarding strategies are automatically triggered. For duplicate value processing, unique identification fields are combined for rapid deduplication, or the Levenshtein distance algorithm is used to perform fuzzy matching deduplication on data without clear identification.

[0058] During the data format standardization phase, the system converts data of different formats and types into the system's internal standard format. For example, it formats date and time according to the ISO 8601 standard, converts numeric data into floating-point numbers of specified precision, standardizes numeric precision, and eliminates meaningless characters (such as removing redundant expressions in the gender field).

[0059] This step automatically identifies and processes different data sources through a dynamic protocol adaptation layer, supporting access to multiple types of data sources. Furthermore, the system's data cleansing mechanism can effectively reduce errors and inconsistencies in the data, improving overall data quality. A unified data format standardization process enables data from different sources to be compared and analyzed on the same platform, greatly enhancing data compatibility and availability. Cleaned and standardized data can be used directly for the generation and layout planning of visualization components without the need for additional data preprocessing, greatly simplifying subsequent operational processes and improving work efficiency.

[0060] In step 200, the system automatically extracts characteristic information that can be used to generate visualization components from a cleaned and standardized standard dataset. It then matches this characteristic information against a predefined rule base to select the most appropriate data presentation format. The ultimate goal is to automate the conversion from data to visualization, improve the efficiency of data visualization configuration, and lower the technical barriers to entry, allowing even non-technical personnel to easily customize screen configuration.

[0061] Specifically, the system first analyzes the standard dataset and extracts key feature information, including but not limited to data type (such as numeric, text, time series, etc.), data dimension, data distribution, and other characteristics. For example, the system identifies a dataset containing timestamps and numeric values ​​as time series data; and geographic coordinate data as spatial distribution features.

[0062] The system has a built-in rule base, which contains pre-defined visualization component templates and their applicable conditions based on data characteristics. The rule base is managed based on factors such as data dimension, value type, and data characteristics. During the matching process, the system will automatically compare the extracted feature information with the entries in the rule base to find the most suitable visualization component template. For example, single-dimensional numerical data may be matched to a bar chart, while data containing time and geographic location may be recommended to be displayed using a map component. Once the best match is found, the system will automatically generate the corresponding visualization display component instance and configure the basic parameters. For example, for a line chart component, the system will automatically set the horizontal axis to the time dimension, the vertical axis to the value dimension, and set the appropriate scale range based on the data range.

[0063] This step automates the entire process from raw data to visualization component generation by automatically extracting data features and intelligently matching them with the rule base, significantly shortening configuration time and improving system response speed and overall work efficiency. At the same time, the rule-driven mechanism and feature recognition logic introduced enable the system to autonomously complete complex data understanding and display decision-making processes. The design of the rule base supports flexible expansion of multiple data dimensions, types, and distribution features, enabling the system to adapt to data display needs in different industries and business scenarios. Whether it is time series data, spatial data, or multidimensional classified data, the system can accurately identify and recommend appropriate visualization forms. The matching strategy based on data features ensures the scientific and rational selection of visualization components, helping to improve the accuracy of data expression and the effectiveness of information transmission.

[0064] In step 300, an adaptive layout algorithm intelligently generates the optimal component layout coordinates based on the target screen's physical parameters (such as resolution and aspect ratio) and the importance and priority of the components to be displayed. This improves the flexibility and aesthetics of the visualization screen layout, ensuring that key information is displayed in the most prominent position while adapting to screen sizes of various devices.

[0065] Specifically, the system automatically obtains the physical parameters of the target screen, including screen size (resolution, aspect ratio), display area division and other information. For example, for a 16:9 widescreen display, the system will automatically identify its aspect ratio characteristics. Based on the importance of the data and business needs, the system prioritizes the generated visualization components. For example, core indicator components (such as total sales) are usually given higher priority to ensure that they are in a more prominent position; while auxiliary analysis components (such as regional sales share) may be arranged in a secondary position. Combining the physical parameters of the screen and the priority of the components, the system uses an improved adaptive layout algorithm to generate the optimal layout solution. The algorithm supports multiple layout modes, such as grid layout, flow layout, free layout, etc. Specifically, for high-priority components, the system will allocate a larger display area and place it in the visual focus position; for components with strong correlation, they will be arranged as close as possible to enhance information coherence.

[0066] This step utilizes an adaptive layout algorithm that dynamically adjusts component position and size based on actual physical parameters, ensuring the layout remains optimal regardless of screen size. Furthermore, by setting a priority mechanism, the system can place the most critical information where it's most likely to be noticed, helping users quickly capture the most important data points and make more accurate decisions. This enables efficient, flexible, and aesthetically pleasing data visualization layouts, providing users with a powerful tool for managing and displaying complex data sets. This not only addresses the poor layout adaptability and lack of flexibility found in existing technologies, but also significantly improves the system's usability and practicality, reduces human intervention, and improves work efficiency.

[0067] The automated visualization screen configuration method provided in this application automatically matches the visualization component template by parsing the data source type, and uses an algorithm to generate the optimal layout solution to achieve full process automation from data access to visualization display, avoiding the low efficiency and high cost problems of traditional manual configuration. The method first uses a plug-in architecture and a dynamic protocol adaptation layer to achieve flexible access to multi-source heterogeneous data, and significantly improves data quality and system compatibility through rule-driven data cleaning and standardization processes; then, based on data feature extraction and rule base matching mechanism, it automatically selects the optimal visualization component, effectively reducing the configuration threshold and the degree of manual intervention; further, based on screen size, component priority and data characteristics, a dynamic layout algorithm is used to automatically generate the optimal display solution, solving the problems of poor flexibility and low adaptability of traditional fixed templates or manual layout methods. This automated visualization screen configuration method not only improves the efficiency and intelligence level of data visualization configuration, but also significantly optimizes the user experience, has good scalability and practical value, and is suitable for the rapid construction and dynamic operation and maintenance needs of data screens in various industry scenarios.

[0068] In some embodiments of the present application, the dynamic protocol adaptation layer performs parsing based on the data source type, specifically:

[0069] The multiple data sources include at least one of relational databases, non-relational databases, real-time data streams, and API interfaces;

[0070] If the data source is a relational database, the JDBC or ODBC protocol is used to execute the DESCRIBE statement to obtain table structure information and parse field types and constraints. If the data source is a non-relational database, the JSON Schema is inferred by querying a single document, and the nested structure is parsed based on a recursive algorithm to obtain field types. If the data source is a real-time data stream, the initial data is parsed based on Kafka's Topic metadata or message Schema, and the data type is dynamically corrected. If the data source is an API interface, the response content is determined based on the Content-Type field, and the JSON data is parsed using the Jackson / Gson library, or the XML data is parsed using DOM.

[0071] To ensure the system can flexibly and efficiently access data from diverse sources, a dynamic protocol adaptation layer enables automatic identification and parsing of multiple data sources (such as relational databases, non-relational databases, real-time data streams, and API interfaces). Targeted data structure parsing and field type identification are performed based on the specific characteristics of the data source, providing a solid foundation for subsequent data cleaning, standardization, and visualization.

[0072] Specifically, the system first identifies the type of data source and selects the corresponding parsing method based on the different data source types. If the data source is a relational database (such as MySQL, Oracle), it uses the JDBC or ODBC protocol to connect to the database, executes the DESCRIBE statement to obtain table structure information, and parses the field types and constraints for subsequent data cleaning and standardization. If the data source is a non-relational database (such as MongoDB), it infers the JSON Schema by querying a single document, and parses the nested structure based on a recursive algorithm to obtain the field type, ensuring that complex data structures can also be accurately parsed. If the data source is a real-time data stream (such as a message queue based on Kafka), the initial data is parsed based on the Topic metadata or message Schema. The system can dynamically correct the data type, adapt to changes in the data format, and ensure the real-time and accuracy of the data. If the data source is an API interface, the format of the response content is determined based on the Content-Type field in the HTTP response header. For JSON data, Jackson or Gson libraries are used for parsing; for XML data, valid information is extracted through the DOM parser.

[0073] This technical solution achieves intelligent identification and parsing of multiple data sources through a dynamic protocol adaptation layer. It supports access to multiple types of data sources, enabling the system to seamlessly integrate with diverse data environments, whether traditional SQL databases, emerging NoSQL databases, or real-time data streams and API interfaces, significantly improving the flexibility and efficiency of data access. It also employs specialized parsing strategies for different types of data sources, enabling the identification and parsing of data sources. This greatly simplifies the data preprocessing process, ensures accurate parsing of data structures and field types, reduces parsing errors caused by differences in data formats, improves work efficiency, and enhances overall data quality.

[0074] According to one embodiment of the present application, before obtaining initial data from multiple data sources, the method further includes:

[0075] Set up a dynamic connection pool, adjust the number of data source connections based on data source load information, and achieve load balancing among multiple data sources through a weighted round-robin algorithm;

[0076] Handle abnormal data sources by setting up an exception handling mechanism.

[0077] Before acquiring initial data from various data sources, the system incorporates a dynamic connection pool and exception handling mechanism to ensure the stability and efficiency of the data access process. The dynamic connection pool automatically adjusts the number of connections based on data source load information and utilizes a weighted round-robin algorithm to balance load across multiple instances, addressing high concurrency and load fluctuations. The exception handling mechanism is designed to promptly identify and address data source anomalies, ensuring system reliability.

[0078] Specifically, the system dynamically increases or decreases the number of connections in the connection pool based on real-time data source load information (such as the current number of connections and response time). For example, it increases the number of connections during periods of high load to improve throughput, while reducing it during periods of low load to conserve resources. For data sources with multiple instances, a weighted round-robin algorithm is used to distribute requests. Each instance is assigned a weight based on its performance and load capacity. The system uses these weights to distribute requests across instances, achieving load balancing and preventing overloading of a single instance. An exception handling mechanism is implemented. When encountering a data source anomaly (such as a network timeout or connection failure), the system first attempts to immediately retry several times (for example, in increments of three). This helps resolve temporary issues caused by transient failures. If recovery is unsuccessful after multiple retries, a circuit breaker mechanism is activated, temporarily halting requests to the data source and initiating fallback solutions (such as using cached data or degrading service) to prevent the failure from spreading and affecting the entire system. All exception events and their handling processes are logged to facilitate subsequent analysis and optimization. This log information can help developers quickly identify the root cause of the problem and take appropriate remediation measures.

[0079] By setting up a dynamic connection pool and exception handling mechanism, the load between data source instances can be effectively balanced, preventing service interruptions caused by overloading a single instance. At the same time, the exception handling mechanism can respond quickly when a failure occurs, maximizing the maintenance of the system's normal operation and improving overall stability and availability. In addition, by dynamically adjusting the size of the connection pool, the system can flexibly allocate resources according to actual needs, ensuring sufficient processing capacity during peak periods while saving costs during off-peak periods and improving resource utilization. The built-in exception handling mechanism can automatically detect and handle common problems, reducing reliance on manual monitoring and intervention, alleviating the workload of the operation and maintenance team, and making the system easier to manage and maintain.

[0080] In some embodiments of the present application, a visual display component is obtained by matching the feature information through a rule library, specifically:

[0081] Determine the visual display component corresponding to the feature information in the rule base through a matching model, where the matching model includes at least one of an exact matching model, a fuzzy matching model, a data proportion dynamic adjustment model, and a personalized matching model;

[0082] The feature information includes at least any one of a data dimension, a value type, a data feature, and a matching template identification number.

[0083] In order to achieve efficient and automated conversion from data to visualization, improve configuration efficiency, and ensure that the selected components can accurately and intuitively express data characteristics, this application uses a rule library and matching model to automatically select the most appropriate visualization display components based on the extracted data feature information.

[0084] Specifically, the system first analyzes the standard data set and extracts key feature information, including but not limited to data dimensions (such as single dimension, multi-dimensional), value types (such as numeric type, text type, time series, etc.), data features (such as whether it contains timestamps, geographic coordinates, etc.) and matching template identification numbers.

[0085] The rule base stores a variety of predefined visualization component templates and their applicable conditions. Based on the extracted feature information, the system uses different matching models to find the best match in the rule base. The matching models include at least the following: when the feature information fully meets the requirements of a visualization component template, the exact matching model is directly selected as the matching result; if no fully matching entry is found, fuzzy matching is performed through the fuzzy matching model in the order of data dimension, numerical type, and data feature to find the closest template; for data sets that contain multiple features at the same time (such as time and geographic location), the system will dynamically adjust the weights of different candidate templates based on the data proportion of each feature, giving priority to the template with the highest score. For example, when the initial data contains both time and geographic data, the weights of the geographic map and the time series diagram are dynamically adjusted according to the proportion of the initial data. At the same time, the system allows users to manually modify the matching results, and records user preferences to optimize subsequent matching strategies, gradually building a personalized matching model.

[0086] Based on the matching results, a visualization component instance is automatically generated, basic parameter configuration is performed, and the data is bound to generate the display component. For example, for a line chart component, the system automatically sets the horizontal axis to the time dimension and the vertical axis to the value dimension, and sets the appropriate scale range based on the data range.

[0087] By using multiple matching models, the system can accurately select the most suitable visualization component template for different types of data features. The automated processing from data feature identification to component selection eliminates the need for manual trial of various component types, greatly simplifying the operational process and improving work efficiency while ensuring the optimal matching and scientific nature of the display effect. Whether it is simple single-dimensional data or complex multi-dimensional data sets, the system can intelligently recommend appropriate visualization forms based on specific features. It has high flexibility and adaptability and supports a wide range of business scenario requirements. The personalized matching model allows users to adjust matching results according to their own preferences, records user modification behavior, and continuously optimizes matching strategies, which helps to form a more intelligent and efficient matching mechanism, thereby providing a more personalized service experience.

[0088] In some embodiments of the present application, the target screen physical parameters are obtained, the priority of the visual display components is determined based on the data importance and business requirements, and the component layout coordinate scheme is obtained based on the target screen physical parameters and component priority through an adaptive layout algorithm, specifically:

[0089] The adaptive layout algorithm includes at least any one of a grid layout algorithm, a flow layout algorithm and a free layout algorithm; the display area is allocated based on the component priority, wherein the display area is positively correlated with the priority; the association between the first component and the second component is determined, and the position and / or area of ​​the visual display component is allocated based on the priority of the visual display component; the physical parameters include at least any one of screen size, resolution, aspect ratio and display area division.

[0090] To improve the layout flexibility and aesthetics of the visualization screen, ensure that key information can be displayed in the most prominent position, and adapt to screen devices of different sizes, the system intelligently generates the optimal component layout coordinate solution through an adaptive layout algorithm based on the physical parameters and priorities of the target screen.

[0091] The system automatically obtains the specific physical parameters of the target screen, including but not limited to screen size, resolution, aspect ratio, and display area division. Based on the importance of the data and business needs, the system prioritizes the generated visualization components. For example, core indicator components (such as total sales) are usually given higher priority to ensure that they are in a more prominent position; while auxiliary analysis components (such as regional sales share) may be arranged in a secondary position. Based on the physical parameters of the target screen and the importance of the components and business needs, the system intelligently generates the optimal component layout coordinate solution through an adaptive layout algorithm. In addition, the system determines the correlation between different components by analyzing data features and business logic, and determines the relative position relationship between the first component and the second component based on the degree of correlation (the degree of correlation can be judged by the matching degree of the field description of the data). When the correlation between components is high, the system will pay special attention to their relative position relationship and try to arrange them adjacent to each other to enhance information coherence and readability.

[0092] The grid layout algorithm divides the screen into fixed grid units and allocates corresponding grid areas based on component priority. High-priority components occupy more grid units to ensure a larger and more prominent display area. The flow layout algorithm dynamically adjusts the order and size of components based on their priority and relevance. High-priority components are placed first and as close to the visual focus as possible, while lower-priority components fill the remaining space in turn. The free layout algorithm allows users to manually adjust the position and size of components, while combining system-recommended best practices to provide greater flexibility and customization capabilities.

[0093] This solution leverages multiple adaptive layout algorithms to dynamically adjust component positions and sizes based on actual physical parameters, ensuring the layout remains optimal regardless of screen size. Through a prioritization mechanism, the system places the most critical information where it's most noticeable, helping users quickly grasp the most important data points and make more accurate decisions.

[0094] In some embodiments of the present application, the method further comprises:

[0095] Dynamically configure the visual configuration interface through interactive actions;

[0096] Wherein, the visual configuration interface includes at least any one of a component library area, a screen canvas area and a property editing area;

[0097] The interaction action includes at least one of component adjustment, style modification, data binding and interaction setting.

[0098] In order to enhance users' control over visual layout and achieve personalized customization and real-time feedback, a visual configuration interface is introduced, enabling users to perform flexible operations based on the component library, canvas area and property editing area in the graphical interface.

[0099] The integrated graphical configuration interface provided by the system mainly includes the following three functional areas: component library area, screen canvas area and property editing area.

[0100] The user can perform one or a combination of the following interactive operations in the above interface. Specifically,

[0101] Components are adjusted to change their position and size by dragging;

[0102] The style is modified to adjust the component color, font, border and other styles through the property panel;

[0103] Data binding is to select the data source field and bind it to the component;

[0104] Interaction settings are used to configure the interactive behaviors of components, such as click events, data filtering, etc.

[0105] All configuration operations will be reflected in the screen preview area in real time, and users can view the configuration effects instantly.

[0106] This step provides a dynamic configuration function based on interactive actions, and adjusts the layout through a direct operation interface, allowing users to quickly adjust the component layout according to specific needs, greatly improving the flexibility and adaptability of the system. It not only solves the problem of inconvenient layout adjustment in traditional data visualization tools, reduces the complexity and time consumption of traditional manual configuration, but also greatly improves the interactivity and user experience of the system, providing enterprises with a more powerful and flexible data display platform.

[0107] According to one embodiment of the present application, the method further includes:

[0108] Establish a data subscription mechanism to monitor data sources and obtain real-time updated data;

[0109] The real-time update data is refreshed through the component refresh strategy to obtain the refresh component and the new layout coordinate solution of the refresh component.

[0110] In order to achieve the system's continuous monitoring and real-time update of data sources, and dynamically refresh the visualization components and their layout coordinates according to the changes in real-time updated data, to ensure that the screen display content always reflects the latest business status, by establishing a data subscription mechanism and component refresh strategy, the system can automatically perceive data changes without human intervention, and update the visualization component content and location layout in a timely manner, thereby improving the real-time, intelligence and user experience of the visualization system.

[0111] Specifically, the system continuously monitors the data source through a monitoring mechanism (such as event-driven, polling, long connection, etc.). Once a data change is detected (such as a new record, a field value change, etc.), the data update process is triggered.

[0112] After the data subscription mechanism obtains the latest data, the system executes the above steps, cleaning and standardizing the latest data according to the format of the standard data set to ensure that it can be used for subsequent visualization refresh operations. Based on the changes in the content of the real-time data, the system determines which visualization components need to be refreshed (such as data indicator changes exceeding the threshold, the emergence of new dimensions, etc.) and generates a corresponding refresh component list. For refreshed components, the system not only updates their display content (such as values, chart trends, etc.), but may also re-evaluate their priority on the screen based on the new data characteristics and adjust their layout coordinates accordingly.

[0113] Develop different component refresh strategies based on the type and scope of data changes. Specifically,

[0114] A partial refresh updates only the affected components;

[0115] Linked refresh means automatically refreshing related components when the data of a component changes;

[0116] Full screen refresh is triggered when the data structure changes significantly.

[0117] This solution, through the establishment of a data subscription mechanism, enables real-time monitoring and automatic updates of data sources, ensuring that screen displays always reflect the latest business dynamics, preventing display failures or information omissions due to data changes, and improving the timeliness and accuracy of decision support. Furthermore, the component refresh strategy not only updates content but also dynamically adjusts the layout based on new data characteristics, demonstrating the system's ability to autonomously respond to data changes. This enhances the overall system's intelligence and automation, effectively addressing issues with traditional visualization systems, such as poor real-time performance, delayed updates, and frequent manual intervention.

[0118] The technical means of dynamically updating visualization components and their layout coordinates based on the component refresh strategy not only significantly improves the intelligence level and responsiveness of the visualization system, but also provides a more flexible and efficient solution for data display in complex business scenarios, with good practical value and technological advancement significance.

[0119] like Figure 2 As shown, the second embodiment of the present application provides an automated visual screen configuration device, including:

[0120] The data preprocessing module 110 is adapted to obtain initial data from various data sources, parse and process the data based on the data source type through the dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set;

[0121] The component automatic generation module 120 is adapted to extract feature information based on a standard data set, the feature information including data type, data dimension, data distribution, etc., and obtain a visual display component by matching the feature information through a rule base;

[0122] The layout intelligent planning module 130 obtains the physical parameters of the target screen and is suitable for determining component priorities based on data importance and business requirements, and obtains a component layout coordinate solution based on the target screen physical parameters and component priorities through an adaptive layout algorithm.

[0123] The device for automated visual screen configuration provided in the second aspect of the present application can implement the automated visual screen configuration method in any embodiment of the first aspect above, and thus can achieve any technical effect in the above automated visual screen configuration method, which will not be repeated here.

[0124] An embodiment of the third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the automated visual screen configuration method of any embodiment of the first aspect is implemented.

[0125] Figure 3 An example of a physical structure diagram of an electronic device is shown below. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830, and a communication bus 840, wherein the processor 810, the communication interface 820, and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the automated visualization screen configuration method in any embodiment of the first aspect, the method comprising:

[0126] Step 100: Acquire initial data from multiple data sources, parse and process the data source type through a dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set.

[0127] Step 200: Extract feature information based on a standard data set, and match the feature information through a rule base to obtain a visual display component.

[0128] Step 300: Obtain the physical parameters of the target screen, determine the priorities of the components to be displayed, and obtain a component layout coordinate solution through an adaptive layout algorithm, the physical parameters of the target screen, and the component priorities.

[0129] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0130] In another aspect, the present invention further provides a computer program product, comprising a computer program. The computer program may be stored on a non-transitory computer-readable storage medium. When the computer-readable storage medium is executed by a processor, the computer is capable of executing the automated visualization screen configuration method provided by the above methods, the method comprising:

[0131] Step 100: Acquire initial data from multiple data sources, parse and process the data source type through a dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set.

[0132] Step 200: Extract feature information based on a standard data set, and match the feature information through a rule base to obtain a visual display component.

[0133] Step 300: Obtain the physical parameters of the target screen, determine the priorities of the components to be displayed, and obtain a component layout coordinate solution through an adaptive layout algorithm, the physical parameters of the target screen, and the component priorities.

[0134] In another aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program is implemented to perform the automated visualization screen configuration method provided by the above methods, the method comprising:

[0135] Step 100: Acquire initial data from multiple data sources, parse and process the data source type through a dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set.

[0136] Step 200: Extract feature information based on a standard data set, and match the feature information through a rule base to obtain a visual display component.

[0137] Step 300: Obtain the physical parameters of the target screen, determine the priorities of the components to be displayed, and obtain a component layout coordinate solution through an adaptive layout algorithm, the physical parameters of the target screen, and the component priorities.

[0138] Finally, the present invention also provides a non-volatile computer storage medium having computer executable instructions stored thereon. When the computer executable instructions are executed by a processor, the automated visualization screen configuration method provided by the above methods is implemented. The method includes:

[0139] Step 100: Acquire initial data from multiple data sources, parse and process the data source type through a dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set.

[0140] Step 200: Extract feature information based on a standard data set, and match the feature information through a rule base to obtain a visual display component.

[0141] Step 300: Obtain the physical parameters of the target screen, determine the priorities of the components to be displayed, and obtain a component layout coordinate solution through an adaptive layout algorithm, the physical parameters of the target screen, and the component priorities.

[0142] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

[0143] The foregoing is merely an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included in the protection scope of the present application.

Claims

1. An automated visual screen configuration method, characterized in that: include: Obtain initial data from multiple data sources, parse and process the data source type through the dynamic protocol adaptation layer, and clean and standardize the parsed data to obtain a standard data set; Extracting feature information based on a standard data set and matching the feature information with a pre-configured rule base to obtain a visual display component; The target screen physical parameters are acquired, the priorities of the visual display components are determined, and a component layout coordinate solution is obtained through an adaptive layout algorithm, the target screen physical parameters, and the priorities.

2. The automated visual screen configuration method according to claim 1, characterized in that: The dynamic protocol adaptation layer performs parsing based on the data source type, specifically: The multiple data sources include at least one of relational databases, non-relational databases, real-time data streams, and API interfaces; If the data source is a relational database, use the JDBC or ODBC protocol to execute the DESCRIBE statement to obtain table structure information and parse field types and constraints; If the data source is a non-relational database, the JSON Schema is inferred by querying a single document and the nested structure is parsed based on a recursive algorithm to obtain the field type. If the data source is a real-time data stream, the initial data is parsed based on Kafka's Topic metadata or message schema, and the data type is dynamically modified; If the data source is an API interface, the response content is determined based on the Content-Type field, and JSON data is parsed using the Jackson / Gson library, or XML data is parsed using DOM.

3. The automated visual screen configuration method according to claim 2, characterized in that: Before acquiring initial data from multiple data sources, the method further includes: Set up a dynamic connection pool, adjust the number of data source connections based on data source load information, and achieve load balancing among multiple data sources through a weighted round-robin algorithm.

4. The automated visual screen configuration method according to claim 1, characterized in that: The visual display component is obtained by matching the feature information through the rule base, specifically: Determine the visual display component corresponding to the feature information in the rule base through a matching model, where the matching model includes at least one of an exact matching model, a fuzzy matching model, a data proportion dynamic adjustment model, and a personalized matching model; The feature information includes at least any one of a data dimension, a value type, a data feature, and a matching template identification number.

5. The automated visual screen configuration method according to claim 1, characterized in that: The target screen physical parameters are obtained, and the priority of the visual display components is determined based on the data importance and business requirements. The component layout coordinate scheme is obtained based on the target screen physical parameters and priority through the adaptive layout algorithm, specifically: The adaptive layout algorithm includes at least one of a grid layout algorithm, a flow layout algorithm, and a free layout algorithm; allocating positions and / or areas of the visual display components based on their priorities; The visual display component includes at least a first component and a second component, determining the correlation between the first component and the second component, and when the correlation is higher than a preset threshold, determining the relative position relationship between the first component and the second component; The physical parameters include at least any one of screen size, resolution, aspect ratio and display area division.

6. The automated visual screen configuration method according to claim 1, characterized in that: The method further comprises: Dynamically configure the visual configuration interface through interactive actions; Wherein, the visual configuration interface includes at least any one of a component library area, a screen canvas area and a property editing area; The interaction action includes at least one of component adjustment, style modification, data binding and interaction setting.

7. The automated visual screen configuration method according to claim 1, characterized in that: The method further comprises: Establish a data subscription mechanism to monitor the initial data source and obtain real-time data; Refresh the real-time data through the component refresh strategy to obtain the refresh component and the new layout coordinate solution of the refresh component.

8. An automated visual screen configuration device, characterized in that: include: The data preprocessing module is suitable for obtaining initial data from various data sources, parsing and processing the data based on the data source type through the dynamic protocol adaptation layer, and cleaning and standardizing the parsed data to obtain a standard data set; The component automatic generation module is suitable for extracting feature information based on standard data sets, including data type, data dimension, data distribution, etc., and matching the feature information based on the rule base to obtain visual display components; The intelligent layout planning module obtains the physical parameters of the target screen and is suitable for determining component priorities based on data importance and business needs. It uses an adaptive layout algorithm to obtain component layout coordinate solutions based on the target screen physical parameters and component priorities.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the automated visualization screen configuration method according to any one of claims 1 to 7 is implemented.

10. A non-volatile computer storage medium having computer executable instructions stored thereon, characterized in that: When the computer executable instructions are executed by a processor, the automated visualization screen configuration method according to any one of claims 1 to 7 is implemented.

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