An intelligent data visualization platform and application

Abstract

The present invention provides an intelligent data visualization platform and application, belonging to the field of intelligent visualization technology, including: a data acquisition module: autonomously identifying and collecting instruction data of different adapter callers in N1 adjacent time periods from multiple data sources, and obtaining the index of each adapter caller through an intelligent preprocessing process; a data visualization module: retrieving the best visualization method corresponding to each indicator from the indicator-type database, and automatically generating a customized visualization template for each adapter caller, each visualization template containing a number of arbitrarily constructed visualization components; a user interaction module: autonomously capturing the user's interaction details based on the data input module, and intelligently matching with the visualization template to create an interface interaction template. The performance indicators of the adapter caller are intelligently collected from the data source, and customized data visualization templates are automatically generated and matched in combination with user feedback, thereby improving the efficiency of data analysis and user experience.

Description

Technical Field

[0001] The present invention relates to the field of intelligent visualization technology, and in particular to an intelligent data visualization platform and application. Background Art

[0002] With the development of the big data era, the rapid increase in data volume has made data processing and analysis more complicated. Data visualization, as an intuitive data display and analysis method, has become an important tool for explaining and conveying data insights. However, traditional data visualization platforms have problems such as low data collection efficiency, insufficient innovation of visualization templates, and insufficient user interactivity when facing large-scale and complex data. There are already many data visualization platforms on the market, but they still need to be improved in data collection, visualization template innovation, and user interaction. Therefore, a comprehensive and intelligent data visualization platform is needed to generate targeted and creative visualization results to meet the needs of data analysis and visualization in the current big data environment.

[0003] Therefore, the present invention provides an intelligent data visualization platform and application. Summary of the invention

[0004] The present invention provides an intelligent data visualization platform and application, which is used to collect instruction data from multiple data sources and pre-process it to provide accurate data for subsequent analysis, obtain a visualization method matching the indicator data of each adapter caller, and create a corresponding visualization template, so that users can intuitively display and analyze data through the template. The visualization template contains any visualization components that can be flexibly combined and customized according to their needs. This helps users to conduct in-depth data analysis and exploration according to specific circumstances and improve the visualization effect of data. By capturing the user's interaction details and matching them with the visualization template to create an interface interaction template, data can be interacted in real time and changed dynamically. The efficiency of data analysis and user experience are improved.

[0005] The present invention provides an intelligent data visualization platform, comprising:

[0006] Data collection module: The platform autonomously identifies and collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources, pre-processes the collected instruction data through an intelligent pre-processing process, and obtains the indicators of each adapter caller based on the pre-processing results;

[0007] Data visualization module: The system intelligently retrieves the best visualization method for each performance indicator from the indicator-type database, and automatically generates a customized visualization template for each adapter caller, where each visualization template contains several arbitrarily constructed visualization components;

[0008] User interaction module: Based on the data input module, the user's interaction details are captured autonomously, and then matched with the visualization template to create an interface interaction template.

[0009] Preferably, the data acquisition module comprises:

[0010] Data acquisition configuration unit: identifies the port types and characteristic parameters of different data sources based on preset configuration files and identification mechanisms, generates identification codes, and obtains connection information based on preset identification code configuration tables;

[0011] Rule acquisition unit: based on different adapter caller types and preset type-rule database, acquires collection rules corresponding to the adapter caller;

[0012] Parameter acquisition unit: determines the adapter caller type under the connection information of different data sources, and configures data collection parameters to the corresponding data source based on the collection rules corresponding to the adapter caller type;

[0013] Data collection unit: collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources according to the data collection parameters;

[0014] Data preprocessing unit: cleans, screens, converts and desensitizes the collected instruction data based on an intelligent preprocessing process;

[0015] The indicator acquisition unit organizes the processed data to obtain multiple parameters of each adapter caller in N1 adjacent time periods, and determines the indicators of each adapter caller in N1 adjacent time periods based on the multiple parameters.

[0016] Preferably, the indicator acquisition unit includes:

[0017] Data sorting block: based on the processed data, obtain the relevant processing parameters of each adapter caller for each call request in N1 adjacent time periods;

[0018] Parameter calculation block: based on the relevant processing parameters of each adapter caller processing each call request in each adjacent time period, N1 response coefficients of each adapter caller are calculated;

[0019] Indicator acquisition block: Map the N1 response coefficients of each adapter caller based on the coefficient-indicator data table to obtain the N1 indicators of each adapter caller.

[0020] Preferably, the parameter calculation block includes:

[0021] The first calculation sub-block: determines the time delay of the transmission line of each call request, the data length of the call request, the line transmission rate of the transmission line, and the line distance between the corresponding adapter caller and the sender of the call request based on the relevant processing parameters, and calculates the total transmission time for each adapter caller to receive all the call requests in the corresponding adjacent time period:

[0022] ;in, It represents the total transmission time of all the call requests received by the i-th adapter caller in the k-th adjacent time period. is the time delay of the transmission line for the i-th adapter caller to receive the call request j in the k-th adjacent time period, is the data length of the call request j, represents the line transmission rate at which the i-th adapter caller receives call request j in the k-th adjacent time period, is the line distance between the i-th adapter caller and the sender of call request j in the k-th adjacent time period; is the number of call requests received by the i-th adapter caller in the k-th adjacent time period;

[0023] The second calculation sub-block: determines the processing time of the adapter caller to process the call request j and the resource ratio used for the call request j based on the relevant processing parameters, and calculates the service time of each adapter caller in each adjacent time period:

[0024] ;in, is the service time of the i-th adapter caller in the k-th adjacent time period, is the processing time of the i-th adapter caller processing call request j in the k-th adjacent time period, is the ratio of resources used by the i-th adapter caller for call request j in the k-th adjacent time period, and , is the concurrent request factor of the i-th adapter caller processing call request j in the k-th adjacent time period, and ;

[0025] The third calculation sub-block: based on the calculated total transmission time and service time of each adapter caller receiving all call requests in each adjacent time period, calculate the response time of each adapter caller for all call requests in each adjacent time period:

[0026] ;in, is the response time of the ith adapter caller to all call requests in the kth adjacent time period;

[0027] The fourth calculation sub-block: calculates the response coefficient of each adapter caller in each adjacent time period:

[0028] ;in, represents the response coefficient of the i-th adapter caller in the k-th adjacent time period, represents the number of call requests that the i-th adapter caller can process simultaneously in the k-th adjacent time period, and , It represents the maximum response time of the response time corresponding to each call request of the i-th adapter caller in the k-th adjacent time period. It represents the minimum response time of each call request corresponding to the response time of the i-th adapter caller in the k-th adjacent time period. It represents the average response time of the ith adapter caller for all call requests in the kth adjacent time period. It represents the standard deviation of the response time of all call requests of the ith adapter caller in the kth adjacent time period. , They represent the dynamic adjustment coefficient of the i-th adapter caller in the k-th adjacent time period, is the preset constraint value, and ; Indicates the historical maximum number of requests processed by the ith adapter caller in the kth adjacent time period.

[0029] Preferably, the data visualization module includes:

[0030] Mapping and matching unit: in the indicator-type database, N1 indicators corresponding to the response coefficient of each adapter caller are mapped and matched with the component type respectively, and the component parameters of each indicator based on the component type are obtained, and the best visualization method is determined based on the component parameters of each indicator based on the component type and the component type;

[0031] Template acquisition unit: automatically generates a customized visualization template based on the optimal visualization method, and names the corresponding visualization template according to preset rules for the visualization component types and component parameters included in the visualization template.

[0032] Preferably, the user interaction module includes:

[0033] Data input unit: write an event listener in the interactive interface to automatically monitor and obtain the user's interaction details in multiple time periods, wherein the interaction details are related to the interaction operation and the specific information of the operation;

[0034] Intention acquisition unit: parses the interaction details of users in different time periods to acquire user intentions, and determines template requirement keywords based on user intentions to perform a first correlation calculation between the template requirement keywords and each visualization template name;

[0035] Template acquisition unit: sort all first correlation calculation results in descending order, and screen the first N2 templates based on the sorting results to evaluate each screened template;

[0036] Interaction template creation unit: creates N2 templates according to the evaluation results to obtain interface interaction templates.

[0037] Preferably, the data input unit comprises:

[0038] Area division block: obtain the function of each user's interaction interface, and calculate the correlation of the functions of the user interaction interface to divide the interaction interface into different functional areas, where each functional area contains a group of related functional elements;

[0039] First event listener block: set a unique first event listener for each functional area;

[0040] Logic setting block: obtaining the function attributes of each function area through the element-attribute data table, and setting the first event processing logic based on the function attributes of each function area;

[0041] Second event listener block: create a second event listener in the event processing logic of the first event listener; inner listener building block: create an inner listener in the event processing logic of the second event listener; event order setting block: set the event listening order based on the nested relationship of all event listeners;

[0042] Information acquisition block: automatically monitors and acquires the user's interaction details in multiple time periods based on the event listeners and event listening sequences.

[0043] Preferably, the inner layer monitoring building block includes:

[0044] Quantity determination sub-block: determining the number of logic levels in the event processing logic of the second event listener;

[0045] Consistency judgment sub-block: set a level identifier to the level where each inner listener is located, and when the level identifier is consistent with the number of logical levels, stop creating the inner listener.

[0046] Preferably, the application comprises: applying any one of the intelligent data visualization platforms described above.

[0047] Compared with the prior art, the beneficial effects of the present application are as follows: it is used to collect instruction data from multiple data sources and pre-process it to provide accurate data for subsequent analysis, obtain the visualization method that matches it according to the indicator data of each adapter caller, and create a corresponding visualization template, so that users can intuitively display and analyze data through the template. The visualization template contains any visualization components that can be built, and users can flexibly combine and customize them according to their needs. This helps users conduct in-depth data analysis and exploration according to specific circumstances and improve the visualization of data. By capturing the user's interaction details and matching them with the visualization template to create an interface interaction template, data can interact in real time and change dynamically. Improve the efficiency of data analysis and user experience.

[0048] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0049] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a structural diagram of an intelligent data visualization platform in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] The embodiment of the present invention provides an intelligent data visualization platform, including:

[0054] Example 1

[0055] Reference Figure 1 The present invention provides an intelligent data visualization platform, including:

[0056] Data collection module: autonomously identifies and collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources, and obtains the indicators of each adapter caller through an intelligent preprocessing process;

[0057] Data visualization module: retrieves the best visualization method for each indicator from the indicator-type database, and automatically generates customized visualization templates for each adapter caller, where each visualization template contains several arbitrarily constructed visualization components;

[0058] User interaction module: autonomously captures user interaction details based on the data input module, and intelligently matches with the visualization template to create an interface interaction template. In this embodiment, multiple data sources refer to sources of data obtained from different data providers or data storage locations. For example, data is obtained from multiple data sources such as databases, API interfaces, log files, etc.;

[0059] In this embodiment, different adapter callers refer to different adapters or callers used in the data collection process, which are used to extract data from various data sources and are configured according to different data source types and interface specifications. For example, to obtain data from a database and an API interface, one adapter caller is used for the database to execute SQL queries and extract data, and another adapter caller is used for the API interface to send requests and obtain returned data;

[0060] In this embodiment, instruction data refers to raw data collected from a data source in the form of instructions or commands, such as sensor data, database query results, data returned by an API call, etc.

[0061] In this embodiment, preprocessing refers to cleaning, converting, filtering and other operations on the collected instruction data to facilitate subsequent data processing and visualization, including data cleaning, data format conversion, missing value processing, etc.;

[0062] In this embodiment, the intelligent preprocessing process refers to a series of automated data processing steps designed to convert raw data into a format that is more suitable for analysis and visualization. This process includes steps such as data cleaning, screening, conversion, and desensitization. For example: an intelligent preprocessing process that automatically detects and corrects outliers in time series data collected from sensors, screens out data within the past 24 hours, converts timestamps from UNIX time to standard date and time format, and replaces personal identifiers of all users with anonymous IDs; in this embodiment, the indicators of each adapter caller are key indicators extracted from the instruction data of each adapter caller after intelligent preprocessing, including: numerical, textual or other types of data, used to describe and measure the performance, status and other relevant information of the adapter caller, for example: obtaining relevant indicators of the adapter caller, including: response time, success rate, concurrent processing capability, data integrity, etc.,

[0063] These metrics serve as a measure of the performance and status of the adapter.

[0064] In this embodiment, the indicator-type database refers to a database that stores the mapping relationship between each indicator and the corresponding visualization method. By querying the indicator-type database, the visualization method matching each indicator is obtained to facilitate the subsequent creation of visualization templates. For example, the concurrent processing capability corresponds to a line chart, the data integrity indicator corresponds to a bar chart, etc.

[0065] In this embodiment, the best visualization method is to select the most suitable data visualization type and technology according to specific data characteristics, analysis purpose and target audience. For example, for time series data, the purpose of visualization is to show the trend of sales, and the best visualization method is determined to be a line chart;

[0066] In this embodiment, the visualization template based on each adapter caller refers to a visualization template for displaying adapter caller data created based on the indicators and visualization method of each adapter caller, and each visualization template contains multiple visualization components to display different indicators and relationships. For example: create a visualization template based on concurrent processing capability, data integrity, and response time indicators, use a line chart to display the changing trend of concurrent processing capability, use a bar chart to display the distribution of data integrity, and use a scatter plot to display the relationship between response time and concurrent processing capability, etc.;

[0067] In this embodiment, the plurality of arbitrarily constructed visualization components refer to visualization elements that can be freely combined and configured in the visualization template. For example, components such as bar charts, line charts, and tables can be freely combined to display different data indicators and relationships.

[0068] In this embodiment, the data input module refers to a module for capturing user input data or information, for example, by arranging a listener on the interface to obtain data input in the form, text box, slider, etc.;

[0069] In this embodiment, the interface interaction template refers to an interface template created by matching the user's interaction information with the visualization template, and displays data and charts according to the user's interaction changes and trends. For example, the user can select different time ranges through sliders and select indicators to be displayed through check boxes. When the user performs interactive operations, the interface interaction template will update the data and charts according to the user's selections and operations to display the data changes and trends that the user is interested in. For example, if the user selects the time range of the past week, the interface interaction template will update the line chart and bar chart accordingly, and only display the data within the time range;

[0070] In this embodiment, the interaction details are the specific interaction behaviors of the user when using the application or website, including operations such as clicking, sliding, and inputting, as well as specific information of the operations, such as: time, location, frequency, etc. of the operations.

[0071] The working principle and beneficial effects of the above technical solution are as follows: by collecting instruction data from multiple data sources and preprocessing it, accurate data is provided for subsequent analysis. According to the indicator data of each adapter caller, the visualization method matching it is obtained, and the corresponding visualization template is created, so that users can intuitively display and analyze data through the template. The visualization template contains any visualization components that can be built, and users can flexibly combine and customize them according to their needs. This helps users conduct in-depth data analysis and exploration according to specific circumstances and improve the visualization of data. By capturing the user's interaction details and matching them with the visualization template to create an interface interaction template, data can interact in real time and change dynamically. The efficiency of data analysis and user experience are improved.

[0072] Example 2

[0073] The present invention provides an intelligent data visualization platform, a data acquisition module, including:

[0074] Data acquisition configuration unit: autonomously identifies the port types and characteristic parameters of different data sources based on preset configuration files and identification mechanisms, generates identification codes, and obtains connection information based on preset identification code configuration tables;

[0075] Rule acquisition unit: based on different adapter caller types and preset type-rule database, acquires collection rules corresponding to the adapter caller;

[0076] Parameter acquisition unit: determines the adapter caller type under the connection information of different data sources, and configures data collection parameters to the corresponding data source based on the collection rules corresponding to the adapter caller type;

[0077] Data collection unit: collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources according to the data collection parameters;

[0078] Data preprocessing unit: cleans, screens, converts and desensitizes the collected instruction data based on an intelligent preprocessing process;

[0079] The indicator acquisition unit organizes the processed data to obtain multiple parameters of each adapter caller in N1 adjacent time periods, and determines the indicators of each adapter caller in N1 adjacent time periods based on the multiple parameters.

[0080] In this embodiment, the preset configuration file is a set of pre-defined settings, which contains various parameters and identification mechanisms required for data collection, including the type of data source, port number, communication protocol, characteristic parameters, etc. The configuration file allows the system to identify the characteristics of different data sources and generate a unique identification code for each data source. Based on this identification code, the system can retrieve the necessary connection information, such as IP address, port, user name, password, etc. from the preset identification code configuration table in order to establish a data connection. For example: a configuration file defines an SQL database data source, which specifies the database type as MySQL, the port as 3306, and also includes the access credentials for the database. The system reads this configuration file, generates an identification code, and uses this identification code to retrieve the connection information so that it can connect to the MySQL database and collect data;

[0081] In this embodiment, different data source port types are port types used by different data sources. For example, a database may use a database connection port, and an API interface may use an HTTP or HTTPS port. For example, one data source is a MySQL database, and the port type it uses is 3306; another data source is a Twitter API, and the port type it uses is HTTPS port 443.

[0082] In this embodiment, the characteristic parameter information of the port refers to the specific characteristics and configuration parameters of each data source port, including the user name, password, host address, etc. of the database. For example, for a MySQL database, the port characteristic parameter information may include the user name "root", the password "password", and the host address "localhost";

[0083] In this embodiment, the identification code is compiled to the corresponding data source to generate a unique identification code according to the port type and characteristic parameter information of the data source, so as to identify the data source. For example, for a MySQL database, the identification code generated according to the port type and characteristic parameter information may be "mysql_localhost_3306";

[0084] In this embodiment, the preset identification code configuration table is a pre-set table that stores identification codes of different data sources and corresponding connection information. For example, the preset identification code configuration table contains connection information corresponding to "mysql_localhost_3306", including the database address, user name, password, etc.;

[0085] In this embodiment, the connection information is obtained according to the preset identification code configuration table, and the connection information corresponding to each data source is used to establish a connection with the data source. For example, according to the connection information of "mysql_localhost_3306" in the preset identification code configuration table, a connection with a MySQL database is established.

[0086] In this embodiment, the preset type-rule database is a preset database that stores different adapter caller types and corresponding collection rules. For example, the preset type-rule database includes collection rules corresponding to the "database adapter", including selection tables, columns, and filtering conditions, etc.;

[0087] In this embodiment, the collection rules corresponding to the adapter caller are obtained from the preset type-rule database according to the type of the adapter caller. For example, for the database adapter, the collection rules are obtained from the preset type-rule database, including selecting the "username" and "email" columns in the "users" table.

[0088] In this embodiment, the adapter caller type under the connection information of different data sources refers to selecting the adapter caller type suitable for the data source according to the connection information of different data sources. For example, two different data sources: one is a MySQL database and the other is a Twitter API. For the MySQL database, the database adapter caller type is selected, and the database connection information, such as the host address, user name, password, etc., is used to establish a connection with the MySQL database, and an SQL query statement is executed to obtain data. For the Twitter API, the API adapter caller type is selected, and the API connection information, such as the API key, request address, etc., is used to obtain the data returned by the API by sending an HTTP request.

[0089] In this embodiment, configuring data collection parameters to the corresponding data source based on the collection rules corresponding to the adapter caller type is to configure data collection parameters to the corresponding data source according to the type of the adapter caller and the collection rules, including: executing SQL queries or sending API requests, etc. For example: according to the collection rules of the database adapter, the data collection parameters for executing "SELECT username, email FROM users" are configured to the MySQL database.

[0090] In this embodiment, the command data of different adapter callers in N1 adjacent time periods are collected from multiple data sources according to the configured data collection parameters. For example, according to the data collection parameters of the database adapter, the "username" and "email" data of the past week are collected from the MySQL database.

[0091] In this embodiment, the cleaned and filtered data is sorted out based on the data of the cleaned and filtered data to obtain relevant processing parameters of each adapter caller for each call request in N1 adjacent time periods.

[0092] The working principle and beneficial effects of the above technical solution: By configuring different adapter callers and collection rules, it is possible to intelligently obtain instruction data from different data sources and perform intelligent preprocessing on the data, providing preparation for subsequent data processing and visualization, improving data reliability and accuracy, reducing the workload of manual operations, and improving data processing efficiency.

[0093] Example 3

[0094] The present invention provides an intelligent data visualization platform, an indicator acquisition unit, comprising:

[0095] Data sorting block: based on the processed data, obtain the relevant processing parameters of each adapter caller for each call request in N1 adjacent time periods;

[0096] Parameter calculation block: based on the relevant processing parameters of each adapter caller processing each call request in each adjacent time period, N1 response coefficients of each adapter caller are calculated;

[0097] Indicator acquisition block: Map the N1 response coefficients of each adapter caller based on the coefficient-indicator data table to obtain the N1 indicators of each adapter caller.

[0098] In this embodiment, the relevant processing parameters refer to the relevant parameters of each adapter caller for each call request in N1 adjacent time periods obtained based on the cleaned and filtered data, describing the characteristics, attributes or other relevant information of the call request, including: the time delay of the transmission line of the adapter receiving the call request, the data length of the call request, the line transmission rate of the transmission line, the line distance between the adapter caller and the sender of the call request, etc.

[0099] In this embodiment, the response coefficient is an indicator calculated based on the relevant processing parameters of each adapter caller processing each call request in each adjacent time period, and is used to represent the performance of the adapter caller in processing requests.

[0100] In this embodiment, mapping based on the coefficient-indicator data table refers to a predefined table or mapping relationship, which maps the N1 response coefficients of each adapter caller to specific indicator data, so as to convert the response coefficients into understandable and visual indicator data. For example, assuming that the coefficient-indicator data table defines that the coefficients in the range of 0-1 represent low fluctuations, the coefficients in the range of 1-2 represent medium fluctuations, and the coefficients in the range of 2-3 represent high fluctuations. Then, when mapping the response coefficients of the adapter caller to the indicator data, the corresponding quality indicators, such as low, medium, and high, can be obtained according to the mapping relationship.

[0101] The working principle and beneficial effects of the above technical solution are as follows: the relevant processing parameters of each adapter caller in N1 adjacent time periods are obtained through the data sorting block, the performance and quality of the adapter caller are evaluated more intuitively, and the N1 response coefficients of each adapter caller are calculated through the parameter calculation block. Finally, the response coefficient is mapped to the coefficient-indicator data table through the indicator acquisition block to obtain N1 indicators for each adapter caller, which helps users to deeply understand the working conditions and performance bottlenecks of the adapter caller, and then optimize and improve it. The effect or quality of the adapter caller is quantified and visualized.

[0102] Example 4

[0103] The present invention provides an intelligent data visualization platform, a parameter calculation block, comprising:

[0104] The first calculation sub-block: determines the time delay of the transmission line of each call request, the data length of the call request, the line transmission rate of the transmission line, and the line distance between the corresponding adapter caller and the sender of the call request based on the relevant processing parameters, and calculates the total transmission time for each adapter caller to receive all the call requests in the corresponding adjacent time period:

[0105]

[0106] in, It represents the total transmission time of all the call requests received by the i-th adapter caller in the k-th adjacent time period. is the time delay of the transmission line for the i-th adapter caller to receive the call request j in the k-th adjacent time period, is the data length of the call request j, represents the line transmission rate at which the i-th adapter caller receives call request j in the k-th adjacent time period, is the line distance between the i-th adapter caller and the sender of call request j in the k-th adjacent time period; is the number of call requests received by the i-th adapter caller in the k-th adjacent time period;

[0107] The second calculation sub-block: determines the processing time of the adapter caller to process the call request j and the resource ratio used for the call request j based on the relevant processing parameters, and calculates the service time of each adapter caller in each adjacent time period:

[0108]

[0109] in, is the service time of the i-th adapter caller in the k-th adjacent time period, is the processing time of the i-th adapter caller processing call request j in the k-th adjacent time period, is the ratio of resources used by the i-th adapter caller for call request j in the k-th adjacent time period, and , is the concurrent request factor of the i-th adapter caller processing call request j in the k-th adjacent time period, and ;

[0110] The third calculation sub-block: based on the calculated total transmission time and service time of each adapter caller receiving all call requests in each adjacent time period, calculate the response time of each adapter caller for all call requests in each adjacent time period:

[0111]

[0112] in, is the response time of the ith adapter caller to all call requests in the kth adjacent time period;

[0113] The fourth calculation sub-block: calculates the response coefficient of each adapter caller in each adjacent time period:

[0114]

[0115] in, represents the response coefficient of the i-th adapter caller in the k-th adjacent time period, represents the number of call requests that the i-th adapter caller can process simultaneously in the k-th adjacent time period, and , It represents the maximum response time of the response time corresponding to each call request of the i-th adapter caller in the k-th adjacent time period. It represents the minimum response time of each call request corresponding to the response time of the i-th adapter caller in the k-th adjacent time period. It represents the average response time of the ith adapter caller for all call requests in the kth adjacent time period. It represents the standard deviation of the response time of all call requests of the ith adapter caller in the kth adjacent time period. , They represent the dynamic adjustment coefficient of the i-th adapter caller in the k-th adjacent time period, is the preset constraint value, and ; Indicates the historical maximum number of requests processed by the ith adapter caller in the kth adjacent time period.

[0116] In this embodiment, the time delay of the transmission line refers to the time delay required from the sending end to the receiving end during the data transmission process, which is affected by factors such as network congestion and transmission distance. For example, an adapter is used to transmit video data, and the time delay of the transmission line indicates the transmission time of the video data from the sending end to the receiving end;

[0117] In this embodiment, the data length of the call request refers to the length of the data contained in each call request. The data length indicates the complexity or data size of each call request. For example, an adapter caller is used to process image processing requests, and the data length of one call request is 100KB;

[0118] In this embodiment, the line transmission rate of the transmission line refers to the amount of data that can be transmitted per second by the transmission line. The line transmission rate can affect the efficiency and speed of data transmission. For example, an adapter is used to transmit audio data, and the line transmission rate of the transmission line can represent the amount of audio data transmitted per second. The line transmission rate of the transmission line is 1 Gbps.

[0119] In this embodiment, the line distance between the adapter caller and the sender of the call request refers to the physical distance between the adapter caller and the sender. For example, an adapter caller is used in a remote monitoring system, and the line distance between the adapter caller and the sender represents the physical distance between the monitoring device and the adapter caller.

[0120] In this embodiment, the processing time of the adapter caller processing the call request refers to the time required for the adapter caller to process a call request, indicating the processing efficiency and speed of the adapter caller. For example, if an adapter caller is used to process an image processing request, the processing time of the adapter caller processing the call request may indicate the time for processing the image.

[0121] In this embodiment, the resource ratio refers to the resource ratio allocated by the adapter caller to process a call request, which is used to control the resource utilization and performance of the adapter caller. For example, the adapter caller 020 is used to process database query requests. The resource ratio used by the call request represents the number of database connections allocated by the adapter caller 020 for each query request. The number of database connections allocated for each query request is 10.

[0122] In this embodiment, the service time of each adapter caller refers to the time each adapter caller spends processing a call request in each adjacent time period. For example, if an adapter caller is used to process a network request, the service time of each adapter caller may represent the time required to process each request.

[0123] In this embodiment, the concurrent request factor refers to a coefficient value set by the adapter caller when processing a call request to control the concurrent processing capability of the adapter caller by taking into account the factors of processing other requests at the same time. A concurrent request factor of 10 means that the adapter caller can process 10 requests at the same time.

[0124] In this embodiment, the response coefficient of each adapter caller refers to a coefficient that measures the response capability of each adapter caller to a call request, reflecting factors such as the processing capability, resource utilization, and response time of the adapter caller. For example, a response coefficient of 0.8 indicates that the response capability of the adapter caller is 80%.

[0125] The working principle and beneficial effects of the above technical solution: By calculating the time delay, data length, line transmission rate and line distance of the transmission line, as well as the processing time, resource ratio and service time of the adapter caller, the response time and response coefficient of each adapter caller to the call request can be obtained. This can more intuitively evaluate the performance and quality of the adapter caller and provide users with the convenience of data visualization. At the same time, by dynamically adjusting parameters such as coefficients and the maximum number of historical processed requests, users can optimize the performance and manage resources of the adapter caller. The performance and quality of the adapter caller can be quantified and evaluated.

[0126] Example 5

[0127] The present invention provides an intelligent data visualization platform, a data visualization module, including:

[0128] Mapping and matching unit: in the indicator-type database, N1 indicators corresponding to the response coefficient of each adapter caller are mapped and matched with the component type respectively, and the component parameters of each indicator based on the component type are obtained, and the best visualization method is determined based on the component parameters of each indicator based on the component type and the component type;

[0129] Template acquisition unit: automatically generates a customized visualization template based on the optimal visualization method, and names the corresponding visualization template according to preset rules for the visualization component types and component parameters included in the visualization template.

[0130] In this embodiment, mapping matching refers to matching the N1 indicators corresponding to the response coefficient of each adapter caller with the component type to obtain the component parameters of each indicator under different component types, for example: adapter caller 020 is used to process data analysis requests, where the response coefficient is 0.8. According to mapping matching, the indicator corresponding to the response coefficient is matched with the component type, and the component parameter corresponding to indicator A is X, and the component parameter corresponding to indicator B is Y.

[0131] In this embodiment, the component parameter refers to the parameter value of the indicator obtained according to the response coefficient of the adapter caller and the mapping match under different component types, which is used to create a visualization template. For example, according to the result of the mapping match, the component parameter corresponding to indicator A is X, and the component parameter corresponding to indicator B is Y. When creating a visualization template, the component parameter X can be applied to the bar chart component, and the component parameter Y can be applied to the line chart component.

[0132] In this embodiment, creating a visualization template based on the optimal visualization method is to create a visualization template of the adapter caller according to the component parameters and visualization component types obtained by mapping matching. For example, according to the results of mapping matching, component parameter X is applied to the bar chart component, and component parameter Y is applied to the line chart component. Then, a visualization template can be created, including a bar chart and a line chart component, and component parameters X and Y are used for data display respectively.

[0133] In this embodiment, the naming of the corresponding visualization template is achieved by naming the created visualization template according to a preset rule. For example, according to the preset rule, the visualization template created above is named "data analysis template".

[0134] The working principle and beneficial effects of the above technical solution: Through the application of mapping matching and component parameters, the best visualization method is obtained according to the response capability and indicator requirements of the adapter caller, and a suitable visualization template is automatically generated, which improves the intelligence of the data visualization platform, reduces the configuration workload of users, and ensures that the generated visualization template matches the requirements of the adapter caller. The accuracy and efficiency of data display are improved.

[0135] Example 6

[0136] The present invention provides an intelligent data visualization platform, a user interaction module, including:

[0137] Data input unit: write an event listener in the interactive interface to automatically monitor and obtain the user's interaction details in multiple time periods, wherein the interaction details are related to the interaction operation and the specific information of the operation;

[0138] Intention acquisition unit: parses the interaction details of users in different time periods to acquire user intentions, and determines template requirement keywords based on user intentions to perform a first correlation calculation between the template requirement keywords and each visualization template name;

[0139] Template acquisition unit: sort all first correlation calculation results in descending order, and screen the first N2 templates based on the sorting results to evaluate each screened template;

[0140] Interaction template creation unit: creates N2 templates according to the evaluation results to obtain interface interaction templates.

[0141] In this embodiment, the event listener refers to a program written in the interactive interface, which is used to automatically monitor and obtain the user's interactive operations in multiple time periods and the specific information of the operations. The event listener is used to capture the user's interactive behaviors in real time, such as clicks, scrolls, inputs, and other operations. For example, in the interactive interface, an event listener is written. When the user clicks on the visualization chart 5 within a certain time period and selects a specific data filtering condition, the event listener can record the specific information of this operation: the coordinates of the click, the selected filtering condition, etc.;

[0142] In this embodiment, user intention refers to obtaining the user's intention or purpose by analyzing the user interaction information in different time periods, for example: based on the user operation information recorded by the third event listener of the module 5 of the third interface, it is analyzed that the user clicked on the visualization chart 55 in the time period 858-4 and selected a specific data filtering condition, and it is determined that the user intends to perform data analysis and display under specific conditions for the visualization chart 55;

[0143] In this embodiment, the demand keyword is a template demand keyword determined according to the user's intention, and the template name is matched to find the template most relevant to the user's needs. For example, according to the user's intention, it is inferred that the user needs to perform data analysis and display, and "data analysis" is used as the demand keyword;

[0144] In this embodiment, the first correlation calculation is to perform a first correlation calculation on the template requirement keyword and each visualization template name to determine the degree of correlation between them. For example, for a visualization template named "data analysis template", the requirement keyword "data analysis" is performed on the template name to obtain a correlation score of 10.

[0145] In this embodiment, each screening template is evaluated based on the result of the first correlation calculation, and the screened templates are evaluated. The purpose is to determine whether each template meets user needs based on the correlation score and other indicators, and select the most suitable template. For example: for the screened "data analysis template", the template is evaluated based on the correlation score and other evaluation indicators, such as visualization effect, interactive performance, etc., to determine whether it meets user needs.

[0146] The working principle and beneficial effects of the above technical solution: By monitoring the user interaction details and parsing the user intention through the event listener, the user needs are automatically acquired and understood. By converting the user intention into the demand keywords and performing relevant calculations with the visualization template name, the templates related to the user needs can be quickly screened out. By evaluating the screened templates, the interface interaction template that best suits the user needs can be selected. The intelligence level of the data visualization platform is improved, while ensuring that the generated interface interaction template matches the user needs, improving the user experience and the accuracy of data display.

[0147] Example 7

[0148] The present invention provides an intelligent data visualization platform, a data input unit, comprising:

[0149] Area division block: obtain the function of each user's interaction interface, and calculate the correlation of the functions of the user interaction interface to divide the interaction interface into different functional areas, where each functional area contains a group of related functional elements;

[0150] First event listener block: set a unique first event listener for each functional area;

[0151] Logic setting block: obtaining the function attributes of each function area through the element-attribute data table, and setting the first event processing logic based on the function attributes of each function area;

[0152] Second event listener block: create a second event listener in the event processing logic of the first event listener; inner listener building block: create an inner listener in the event processing logic of the second event listener; event order setting block: set the event listening order based on the nested relationship of all event listeners;

[0153] Information acquisition block: automatically monitors and acquires the user's interaction details in multiple time periods based on the event listeners and event listening sequences.

[0154] In this embodiment, the function of each user's interactive interface refers to identifying and obtaining each function or operation therein according to the user's interactive interface. For example, the interactive interface of user C includes functional elements such as a bar chart, a line chart, and a filter condition input box, wherein the bar chart is used to display data distribution, the line chart is used to display trend changes, and the filter condition input box is used to specify data filtering conditions;

[0155] In this embodiment, different functional areas are divided into different functional areas according to the relevance of the functions of each user interaction interface. For example, according to the function of the user interaction interface, the bar chart and line chart are used as one functional area, and the filter condition input box is used as another functional area.

[0156] In this embodiment, a unique first event listener is set for each functional area. The first event listener is used to monitor the user's operations on the main outermost functional elements in the functional area and trigger the corresponding event processing logic. For example: a unique first event listener is set for the bar chart and line chart in the functional area C to monitor the user's operations on these two charts.

[0157] In this embodiment, the element-attribute data table is used to obtain the functional attributes of each functional area and determine the first event processing logic. For example, the element-attribute data table is used to determine the attributes of the bar chart and the line chart, including the data source, display style, etc. According to these attributes, the first event processing logic is set, including updating the chart data according to the data source, drawing the chart according to the display style, etc.;

[0158] In this embodiment, the second event listener is created in the event processing logic of the first event listener to monitor the operations in the first event processing logic and trigger the corresponding event processing logic. For example, in the event processing logic of the first event listener in the function B-50 area, a second event listener is created to monitor the user's data filtering operations on the chart and trigger the corresponding event processing logic.

[0159] In this embodiment, an inner listener is created in the event processing logic of the second event listener to monitor operations in the second event listener and trigger corresponding event processing logic. For example, in the event processing logic of the second event listener in the function D-50 area, an inner listener is created to monitor user operations on the filter condition input box and trigger corresponding event processing logic.

[0160] In this embodiment, the nesting relationship and event listening order of all event listeners are based on the nesting relationship of all event listeners, and the order of event listening is set. The first event listener is located in the outermost layer, the second event listener is located in the event processing logic of the first event listener, and the inner layer listener is located in the event processing logic of the second event listener.

[0161] The working principle and beneficial effects of the above technical solution: Through components such as area division blocks, event monitoring blocks and information acquisition blocks, the functions of the user interaction interface are acquired and processed. By dividing the interactive interface into different functional areas and setting a unique event listener for each functional area, accurate monitoring and processing of user operations are achieved. Complex interaction logic is achieved by setting nested event listeners and event monitoring sequences. The intelligence level of the data visualization platform is improved, so that users can perform interactive operations more conveniently and accurately obtain user operation information, thereby providing more accurate data visualization services.

[0162] Example 8

[0163] The present invention provides an intelligent data visualization platform, an inner layer monitoring building block, including:

[0164] Quantity determination sub-block: determining the number of logic levels in the event processing logic of the second event listener;

[0165] Consistency judgment sub-block: set a level identifier to the level where each inner listener is located, and when the level identifier is consistent with the number of logical levels, stop creating the inner listener.

[0166] In this embodiment, the number of logical levels refers to the number of logical levels of inner listeners in the event processing logic of the second event listener. For example, a logical level number of 3 indicates that at most 3 inner listeners can be created in the event processing logic of the second event listener.

[0167] In this embodiment, setting the level identifier is to set a level identifier in the level where each inner listener is located. For example, in the event processing logic of the second event listener, three inner listeners are created, and the level identifiers are set to 1, 2, and 3 respectively. When the level identifier of the inner listener is consistent with the number of logical levels 3, stop creating more inner listeners.

[0168] The working principle and beneficial effects of the above technical solution: The inner-layer listening building block realizes the creation control of the inner-layer listener through the quantity determination sub-block and the consistency judgment sub-block. The number of levels of the inner-layer listener is limited by determining the number of logical levels to prevent the increase of logical complexity due to too many levels. By setting the level identifier and comparing it with the number of logical levels to determine whether it is necessary to stop creating the inner-layer listener, the creation process of the inner-layer listener is controlled. The flexibility and controllability of the data visualization platform are improved, making the creation and use of the inner-layer listeners more efficient and reasonable. At the same time, by controlling the number and hierarchical relationship of the inner-layer listeners, unnecessary calculations and resource consumption can be reduced. The performance and response speed of the platform are improved.

[0169] Example 9

[0170] The present invention provides an application, including: an intelligent data visualization platform described in any one of application embodiments 1-8.

[0171] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent data visualization platform, characterized in that: include: Data collection module: autonomously identifies and collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources, and obtains the indicators of each adapter caller through an intelligent preprocessing process; Data visualization module: retrieves the best visualization method for each indicator from the indicator-type database, and automatically generates customized visualization templates for each adapter caller, where each visualization template contains several arbitrarily constructed visualization components; User interaction module: Based on the data input module, it autonomously captures the user's interaction details and intelligently matches them with the visualization template to create an interface interaction template; Wherein, the user interaction module includes: Data input unit: write an event listener in the interactive interface to automatically monitor and obtain the user's interaction details in multiple time periods, wherein the interaction details are related to the interaction operation and the specific information of the operation; Intention acquisition unit: parses the interaction details of users in different time periods to acquire user intentions, and determines template requirement keywords based on user intentions to perform a first correlation calculation between the template requirement keywords and each visualization template name; Template acquisition unit: sort all first correlation calculation results in descending order, and screen the first N3 templates based on the sorting results to evaluate each screened template; Interaction template creation unit: creates N3 templates according to the evaluation results to obtain interface interaction templates; Wherein, the data input unit comprises: Area division block: obtain the function of each user's interaction interface, and calculate the correlation of the functions of the user interaction interface to divide the interaction interface into different functional areas, where each functional area contains a group of related functional elements; First event listener block: set a unique first event listener for each functional area; Logic setting block: obtaining the function attributes of each function area through the element-attribute data table, and setting the first event processing logic based on the function attributes of each function area; Second event listener block: create a second event listener in the event processing logic of the first event listener; inner listener building block: create an inner listener in the event processing logic of the second event listener; event order setting block: set the event listening order based on the nested relationship of all event listeners; Information acquisition block: automatically monitors and acquires the user's interaction details in multiple time periods based on the event listeners and event listening sequences.

2. The intelligent data visualization platform according to claim 1, characterized in that: The data acquisition module comprises: Data acquisition configuration unit: autonomously identifies the port types and characteristic parameters of different data sources based on preset configuration files and identification mechanisms, generates identification codes, and obtains connection information based on preset identification code configuration tables; Rule acquisition unit: based on different adapter caller types and preset type-rule database, acquires collection rules corresponding to the adapter caller; Parameter acquisition unit: determines the adapter caller type under the connection information of different data sources, and configures data collection parameters to the corresponding data source based on the collection rules corresponding to the adapter caller type; Data collection unit: collects instruction data of different adapter callers in N1 adjacent time periods from multiple data sources according to the data collection parameters; Data preprocessing unit: cleans, screens, converts and desensitizes the collected instruction data based on an intelligent preprocessing process; The indicator acquisition unit organizes the processed data to obtain multiple parameters of each adapter caller in N1 adjacent time periods, and determines the indicators of each adapter caller in N1 adjacent time periods based on the multiple parameters.

3. The intelligent data visualization platform according to claim 2, characterized in that: The indicator acquisition unit includes: Data sorting block: based on the processed data, obtain the relevant processing parameters of each adapter caller for each call request in N1 adjacent time periods; Parameter calculation block: based on the relevant processing parameters of each adapter caller processing each call request in each adjacent time period, N1 response coefficients of each adapter caller are calculated; Indicator acquisition block: Map the N1 response coefficients of each adapter caller based on the coefficient-indicator data table to obtain the N1 indicators of each adapter caller.

4. The intelligent data visualization platform according to claim 3, characterized in that: The parameter calculation block includes: The first calculation sub-block: determines the time delay of the transmission line of each call request, the data length of the call request, the line transmission rate of the transmission line, and the line distance between the corresponding adapter caller and the sender of the call request based on the relevant processing parameters, and calculates the total transmission time for each adapter caller to receive all the call requests in the corresponding adjacent time period: ;in, It represents the total transmission time of all the call requests received by the i-th adapter caller in the k-th adjacent time period. is the time delay of the transmission line for the i-th adapter caller to receive the call request j in the k-th adjacent time period, is the data length of the call request j, represents the line transmission rate at which the i-th adapter caller receives call request j in the k-th adjacent time period, is the line distance between the i-th adapter caller and the sender of call request j in the k-th adjacent time period; is the number of call requests received by the i-th adapter caller in the k-th adjacent time period; The second calculation sub-block: determines the processing time of the adapter caller to process the call request j and the resource ratio used for the call request j based on the relevant processing parameters, and calculates the service time of each adapter caller in each adjacent time period: ;in, is the service time of the i-th adapter caller in the k-th adjacent time period, is the processing time of the i-th adapter caller processing call request j in the k-th adjacent time period, is the ratio of resources used by the i-th adapter caller for call request j in the k-th adjacent time period, and , is the concurrent request factor of the i-th adapter caller processing call request j in the k-th adjacent time period, and ; The third calculation sub-block: based on the calculated total transmission time and service time of each adapter caller receiving all call requests in each adjacent time period, calculate the response time of each adapter caller to all call requests in each adjacent time period: ;in, is the response time of the ith adapter caller to all call requests in the kth adjacent time period; The fourth calculation sub-block: calculates the response coefficient of each adapter caller in each adjacent time period: ;in, represents the response coefficient of the i-th adapter caller in the k-th adjacent time period, represents the number of call requests that the i-th adapter caller can process simultaneously in the k-th adjacent time period, and , It represents the maximum response time of the response time corresponding to each call request of the i-th adapter caller in the k-th adjacent time period. It represents the minimum response time of each call request corresponding to the response time of the i-th adapter caller in the k-th adjacent time period. It represents the average response time of the ith adapter caller for all call requests in the kth adjacent time period. It represents the standard deviation of the response time of all call requests of the ith adapter caller in the kth adjacent time period. , They represent the dynamic adjustment coefficient of the i-th adapter caller in the k-th adjacent time period, is the preset constraint value, and ; Indicates the historical maximum number of requests processed by the ith adapter caller in the kth adjacent time period.

5. The intelligent data visualization platform according to claim 4, characterized in that: The data visualization module comprises: Mapping and matching unit: in the indicator-type database, N1 indicators corresponding to the response coefficient of each adapter caller are mapped and matched with the component type respectively, and the component parameters of each indicator based on the component type are obtained, and the best visualization method is determined based on the component parameters of each indicator based on the component type and the component type; Template acquisition unit: automatically generates a customized visualization template based on the optimal visualization method, and names the corresponding visualization template according to preset rules for the visualization component types and component parameters included in the visualization template.

6. The intelligent data visualization platform according to claim 1, characterized in that: The inner layer monitoring building block includes: Quantity determination sub-block: determining the number of logic levels in the event processing logic of the second event listener; Consistency judgment sub-block: set a level identifier to the level where each inner listener is located, and when the level identifier is consistent with the number of logical levels, stop creating the inner listener.

7. An application, characterized in that: An intelligent data visualization platform according to any one of claims 1 to 6 is applied.

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