Time-series data-oriented word cloud generation display method and system

Abstract

The application provides a word cloud generation display method and system for time series data, comprising: reading user configuration, registering a word cloud service interface, generating a user dictionary and a stop word dictionary; a user selects a range time period parameter, and calls the word cloud service interface in parallel to obtain word cloud data; the word cloud service interface first obtains word cloud data from a database, and if the word cloud data does not exist, generates the word cloud data; the front end merges the word cloud data, calculates color and size attributes, and renders into a word cloud graph. By adopting a configured word cloud registration service structure, the application realizes service of the word cloud, facilitates developers to customize a word cloud data source to quickly build a word cloud service; by realizing a range time axis sliding control, the application enables a user to not need to select a range time from a time selection box, thereby improving user experience; and by adopting a front end parallel word cloud data merging mode, the application reduces the calculation pressure of a server.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of word cloud generation of time series data, in particular, to a word cloud generation display method and system for time series data. BACKGROUND

[0002] A cloud chart is a visualization technology for displaying the content of a text or a collection of texts. It displays high-frequency items through font size and color, and can show the development of a certain field. At present, there are mainly two categories of cloud charts according to the displayed items, namely tag cloud chart and word cloud chart. The former relies on user tags for analysis, and the latter mainly analyzes the full text. The main problem of the current cloud chart technology is that it cannot effectively reveal the theme. On the one hand, there are many synonyms or near-synonyms in the displayed items, and on the other hand, there are some items irrelevant to the field to be analyzed or general items.

[0003] Various logs will be generated in work, such as daily work content, Git submission, operation records, etc. The main content of these logs can be intuitively displayed through a word cloud.

[0004] At present, there are few patents and papers related to word cloud of time series data. In the prior art, the Institute of Scientific and Technical Information of China discloses a "generation method of stepless dynamic evolution theme cloud chart based on automatic indexing" in a patent with application number CN113378512A. The method uses a time axis to dynamically display different time word clouds, but does not describe the specific implementation of the time axis and does not provide a method for selecting word clouds within the range of the time axis.

[0005] Adrian et al. proposed "CodeTimeline: Storytelling with Versioning Data", which is close to what we want to do. It proposes to use a time axis to display the word cloud composed of commit of each version code submission of a project, but it only focuses on a specific theme.

[0006] Therefore, it is necessary to propose a new technical solution to improve the above technical problems. SUMMARY

[0007] In view of the defects in the prior art, the purpose of the present application is to provide a word cloud generation display method and system for time series data.

[0008] According to the word cloud generation display method for time series data provided by the present application, the method comprises the following steps:

[0009] Step S1: reading user configuration, registering word cloud service interface, generating user dictionary and stop word dictionary;

[0010] Step S2: the user selects the range time period parameter, and calls the word cloud service interface in parallel to obtain word cloud data;

[0011] Step S3: the word cloud service interface first obtains word cloud data from a database, and if the word cloud data does not exist, generates the word cloud data;

[0012] Step S4: the front end merges the word cloud data, calculates color and size attributes, and renders a word cloud diagram.

[0013] Preferably, the step S3 of generating word cloud data includes the following steps:

[0014] Step S3.1: obtaining original data from a user-configured word cloud data source;

[0015] Step S3.2: performing word segmentation on the original data according to a user dictionary, and filtering according to a stop word dictionary after word segmentation;

[0016] Step S3.3: counting word frequency, and normalizing and denoising to form word cloud weights to constitute word cloud data;

[0017] Step S3.4: saving to a database and returning.

[0018] Preferably, the user configuration includes word cloud data source configuration, user dictionary configuration, and stop word dictionary configuration; the word cloud data source configuration, user dictionary configuration, and stop word dictionary configuration are each a data source list, each data source supports multiple sources including an interface, an SQL statement, a file, and plain text as well as parameters; and original data is obtained from the data source by specifying parameter data when calling;

[0019] The data source configuration is a list, and each data source configuration includes data acquisition interface and parameter information;

[0020] The word cloud configuration includes the maximum number of words and a normalization algorithm;

[0021] The user dictionary is a list, supports multiple sources of user dictionaries, and the structure of the stop word dictionary configuration is consistent with that of the user dictionary;

[0022] The word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting word cloud data, and supports defining whether to persist, a callback function attribute;

[0023] The data source supports nesting, that is, one data source can use data obtained by itself as multiple parameters to batch obtain data from another data source and aggregate the data as the result of the data source;

[0024] One data source obtains batch data from another data source to generate word cloud data.

[0025] Preferably, the word cloud service interface parses the user configuration, and registers the word cloud service according to the user configuration;

[0026] Merging the dictionary obtained from the file and the data source into the actual user dictionary and the stop dictionary;

[0027] Reading the data source configuration, registering the word cloud service interface using the id and parameters therein, when the user accesses the service interface, first obtaining the historical generated word cloud data from the word cloud data persistence module, if the data does not exist or the version is inconsistent, obtaining the data from the data source, performing word segmentation according to the user dictionary and the stop dictionary, and counting the word frequency, generating the word cloud data according to the word cloud configuration, and saving the word cloud data using the word cloud data persistence module.

[0028] Preferably, the word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, and uses SQLite; the persistence module separately creates a data table for each word cloud data source configuration of the user, uses the ID and parameter name of the data source as the table name, and constructs an index for the parameter list specified by the data source.

[0029] The application also provides a word cloud generation and display system for time series data, which comprises the following modules:

[0030] Module M1: reading the user configuration, registering the word cloud service interface, and generating the user dictionary and the stop dictionary;

[0031] Module M2: the user selects the range time period parameter, and calls the word cloud service interface in parallel to obtain the word cloud data;

[0032] Module M3: the word cloud service interface first obtains the word cloud data from the database, and if the data does not exist, generates the word cloud data;

[0033] Module M4: the front end merges the word cloud data, calculates the color and size attributes, and renders into a word cloud graph.

[0034] Preferably, the word cloud data generated by the module M3 comprises the following modules:

[0035] Module M3.1: obtaining the original data from the word cloud data source configured by the user;

[0036] Module M3.2: performing word segmentation on the original data according to the user dictionary, and filtering according to the stop dictionary after the word segmentation;

[0037] Module M3.3: counting the word frequency, and normalizing and denoising the word frequency into the word cloud weight to constitute the word cloud data;

[0038] Module M3.4: saving into the database and returning.

[0039] Preferably, the user configuration includes a word cloud data source configuration, a user dictionary configuration and a stop dictionary configuration; the word cloud data source configuration, the user dictionary configuration and the stop dictionary configuration are each a data source list, each data source supports multiple sources including interfaces, SQL statements, files and pure texts and parameters; raw data is obtained from the data source by specifying parameter data when calling;

[0040] The data source configuration is a list, and each data source configuration includes data acquisition interface and parameter information;

[0041] The word cloud configuration includes the maximum number of words and normalization algorithms;

[0042] The user dictionary is a list, and multiple user dictionaries are supported; the structure of the stop dictionary configuration is consistent with that of the user dictionary;

[0043] The word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting word cloud data, and supports defining whether to persist, callback function attributes;

[0044] The data source supports nesting, that is, one data source can use the data obtained by itself as multiple parameters to batch obtain data from another data source and aggregate the data as the result of the data source;

[0045] One data source obtains batch data from another data source to generate word cloud data.

[0046] Preferably, the word cloud service interface parses the user configuration and registers the word cloud service according to the user configuration;

[0047] The dictionaries obtained from files and data sources are merged into actual user dictionaries and stop dictionaries;

[0048] The data source configuration is read, and the id and parameters therein are used to register the word cloud service interface; when a user accesses the service interface, the word cloud data generated historically is obtained from the word cloud data persistence module; if there is no such data or the version is inconsistent, data is obtained from the data source, word segmentation is performed according to the user dictionary and the stop dictionary, word frequency is counted, word cloud data is generated according to the word cloud configuration, and the word cloud data is saved using the word cloud data persistence module.

[0049] Preferably, the word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, and uses SQLite; the persistence module separately creates a data table for each word cloud data source configuration of a user, uses the ID and parameter name of the data source as the table name, and constructs an index for the parameter list specified by the data source.

[0050] Compared with the prior art, the application has the following beneficial effects:

[0051] 1. This invention realizes the service-oriented nature of word clouds by adopting a configurable word cloud registration service structure, which makes it convenient for developers to quickly build word cloud services by customizing word cloud data sources;

[0052] 2. By implementing a range time axis sliding control, this invention eliminates the need for users to select a time range from a time selection box, thus improving the user experience.

[0053] 3. This invention reduces the computational burden on the server by employing a front-end parallel acquisition and merging method for word cloud data;

[0054] 4. By employing configurable user dictionaries and stop dictionaries, this invention allows users to contribute dictionaries, thereby improving the word cloud's segmentation performance in specific domains. Attached Figure Description

[0055] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0056] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention.

[0058] Example 1

[0059] According to the present invention, a method for generating and displaying word clouds based on time-series data includes the following steps:

[0060] Step S1: Read user configuration, register word cloud service interface, and generate user dictionary and stop dictionary;

[0061] Step S2: The user selects the range / time period parameter, and the word cloud service interface is called in parallel to obtain word cloud data;

[0062] Step S3: The word cloud service interface first retrieves word cloud data from the database; if the data does not exist, it generates word cloud data.

[0063] Step S3.1: Obtain raw data from the user-configured word cloud data source;

[0064] Step S3.2: Segment the raw data according to the user dictionary, and then filter it according to the stop dictionary;

[0065] Step S3.3: Count the word frequency of the segmented words, normalize and denoise to word cloud weight, and form word cloud data.

[0066] Step S3.4: Save to the database and return.

[0067] Step S4: Merge the word cloud data in the front end, calculate the color and size attributes, and render into a word cloud graph.

[0068] The user configuration includes word cloud data source configuration, user dictionary configuration, and stop dictionary configuration; the word cloud data source configuration, user dictionary configuration, and stop dictionary configuration are each a data source list, each data source supports multiple sources including interfaces, SQL statements, files, and plain text as well as parameters; the original data is obtained from the data source by specifying the parameter data when calling; the data source configuration is a list, each data source configuration includes data acquisition interface and parameter information; the word cloud configuration includes the maximum number of words and normalization algorithm; the user dictionary is a list, supporting multiple sources of user dictionaries, and the structure of the stop dictionary configuration is consistent with that of the user dictionary; the word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting the word cloud data, while supporting the definition of whether to persist, callback function attributes; the data source supports nesting, that is, one data source can use the data it obtains as multiple parameters to batch obtain data from another data source and aggregate the results of the data source; one data source obtains batch data from another data source to generate word cloud data.

[0069] The word cloud service interface parses the user configuration and registers the word cloud service according to the user configuration; the word cloud service interface merges the dictionaries obtained from the file and the data source into the actual user dictionary and the stop dictionary; the word cloud service interface reads the data source configuration, uses the id and parameters therein to register the word cloud service interface, and when the user accesses the service interface, the word cloud data generated historically is obtained from the word cloud data persistence module first, if there is no such data or the version is inconsistent, data is obtained from the data source, segmented according to the user dictionary and the stop dictionary, and the word frequency is counted, the weight is calculated according to the word cloud configuration to generate the word cloud data, and the word cloud data is saved using the word cloud data persistence module.

[0070] The word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, using SQLite; the persistence module will create a data table separately for each word cloud data source configuration of the user, using the ID and parameter name of the data source as the table name, and building an index on the parameter list specified by the data source.

[0071] Example 2

[0072] Embodiment 2 is a preferred example of Embodiment 1, which more specifically illustrates the present application.

[0073] The application also provides a word cloud generation display system for time series data, which comprises the following modules.

[0074] Module M1: reading user configuration, registering word cloud service interface, generating user dictionary and stop word dictionary;

[0075] Module M2: user selects range time period parameters, and calls word cloud service interface in parallel to obtain word cloud data;

[0076] Module M3: the word cloud service interface first obtains word cloud data from the database, and if the word cloud data does not exist, the word cloud data is generated;

[0077] Module M3.1: obtaining original data from the word cloud data source configured by the user;

[0078] Module M3.2: performing word segmentation on the original data according to the user dictionary, and filtering according to the stop word dictionary after word segmentation;

[0079] Module M3.3: word frequency is counted, and the word cloud weight is normalized and denoised to constitute word cloud data;

[0080] Module M3.4: saving to the database and returning.

[0081] Module M4: merging word cloud data in the front end, calculating color and size attributes, and rendering into a word cloud graph.

[0082] The user configuration comprises word cloud data source configuration, user dictionary configuration and stop word dictionary configuration; the word cloud data source configuration, user dictionary configuration and stop word dictionary configuration each are a data source list, each data source supports multiple sources including interface, SQL statement, file and pure text and parameters; the original data is obtained from the data source by specifying parameters when calling; the data source configuration is a list, each data source configuration comprises data acquisition interface and parameter information; the word cloud configuration comprises the maximum number of words and normalization algorithm; the user dictionary is a list, supporting multiple sources of user dictionaries, the structure of the stop word dictionary configuration is consistent with that of the user dictionary; the word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting word cloud data, and simultaneously supports defining whether to persist, callback function attribute; the data source supports nesting, that is, one data source can use the data obtained by itself as multiple parameters to batch obtain data from another data source and aggregate the data into the result of the data source; one data source obtains batch data from another data source to generate word cloud data.

[0083] The word cloud service interface parses the user configuration, and registers the word cloud service according to the user configuration; the word dictionaries are obtained from files and data sources and merged into an actual user dictionary and a stop dictionary; the data source configuration is read, and the id and parameters therein are used to register the word cloud service interface; when the user accesses the service interface, the word cloud data generated historically is obtained from the word cloud data persistence module first; if the word cloud data does not exist or the version is inconsistent, data is obtained from the data source, segmented according to the user dictionary and the stop dictionary, and the word frequency is counted; the weight is calculated according to the word cloud configuration to generate word cloud data, and the word cloud data is saved by using the word cloud data persistence module.

[0084] The word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, and uses SQLite; the persistence module separately creates a data table for each word cloud data source configuration of a user, uses the ID and parameter name of the data source as the table name, and builds an index for the parameter list specified by the data source.

[0085] Example 3

[0086] Embodiment 3 is a preferred example of Embodiment 1, which more specifically illustrates the present application.

[0087] The present application can quickly generate time period word clouds for various logs and other time series data by configuring a backend word cloud data generation service. Meanwhile, a range time axis selection UI component is realized based on a sliding control, and the log word cloud data of multiple time periods is merged in the front end, the size, color and rotation angle data are calculated, and the word cloud is displayed.

[0088] User configuration: The user configuration mainly includes data source configuration, word cloud configuration, user dictionary configuration and stop dictionary configuration, etc. The data source configuration is a list, each data source configuration includes data acquisition interface and parameter information, etc.; the word cloud configuration includes the maximum number of words, the layout algorithm of the word cloud, etc.; the user dictionary is a list, and supports multiple sources of user dictionaries, such as files, interfaces, direct data, etc., and the structure of the stop dictionary configuration is consistent with that of the user dictionary.

[0089] Word cloud data service module: This module parses the user configuration and registers the word cloud service according to the user configuration.

[0090] ①Parse the user dictionary configuration and the stop dictionary configuration: obtain the word dictionaries from files, interfaces, etc. and merge them into an actual user dictionary and a stop dictionary.

[0091] ② Analyze the registration data source configuration: read the data source configuration, use the id and parameters in it to register the word cloud service interface, when the user accesses the service interface, first get the historical generated word cloud data from the word cloud data persistence module, if it does not exist or the version is inconsistent, get the data from the interface defined by the data source, perform word segmentation according to the user dictionary and stop dictionary, and count the word frequency, generate word cloud data according to the word cloud configuration, and save it using the word cloud data persistence module.

[0092] ③Support nested data acquisition from data source, one data source can acquire batch data from another data source to generate word cloud data.

[0093] Word cloud data persistence module: the data persistence module uses a NoSQL database to persistently store word cloud data, and provides functions such as insertion, saving, updating and deletion. According to the user's data source configuration, a collection (table) will be created separately for each data source, and an index will be constructed using id+parameter.

[0094] Time series word cloud visualization interaction module: this module mainly includes a range time axis sliding control, when the user slides the control, it will request word cloud data for each time period from the word cloud data service module interface in parallel, and merge the data within the time period range selected by the user on the front end to display as a word cloud graph. The range time axis sliding control is based on the sliding control in the general UI component library, and its value is generally a number. In this implementation, the maximum value is taken as the current time period, the default is to select the current time period, and a value of 1 indicates a time period of 1. The currently selected time period is displayed through a persistent tooltip. At the same time, by forcibly updating the selected node when the user drags, the drag of fixed time periods such as a quarter or a year is realized.

[0095] The word cloud generation and display method for time series data provided by the application comprises the following steps:

[0096] Step 1: read user configuration, register word cloud service interface, generate user dictionary and stop dictionary;

[0097] Step 2: the user selects the range time period and other parameters, and calls the word cloud service interface in parallel to obtain word cloud data;

[0098] Step 3: the word cloud service interface first gets word cloud data from the database, if it does not exist, it generates word cloud data;

[0099] Step 4: merge word cloud data on the front end, calculate color size and other attributes, and render into a word cloud graph;

[0100] Among them, the generation of word cloud data in step 3 includes the following steps:

[0101] Step 3.1: Obtain raw data from the data interface configured by the user;

[0102] Step 3.2: Tokenize the raw data according to the user dictionary, and filter according to the stop word dictionary after tokenization;

[0103] Step 3.3: Count the word frequency of the tokenized data, and normalize and denoise to word cloud weight to form word cloud data;

[0104] Step 3.4: Save to the database and return.

[0105] Those skilled in the art can understand this embodiment as a more specific description of Embodiment 1 and Embodiment 2.

[0106] Those skilled in the art know that, in addition to implementing the system provided by the present application and each device, module, unit thereof in the form of pure computer readable program code, the same function can also be achieved by logically programming the method steps to make the system provided by the present application and each device, module, unit thereof in the form of logic gate, switch, application specific integrated circuit, programmable logic controller and embedded microcontroller. Therefore, the system provided by the present application and each device, module, unit thereof can be considered as a hardware component, and the devices, modules and units included therein for achieving various functions can also be considered as structures within the hardware component; the devices, modules and units for achieving various functions can also be considered as both software modules for achieving the method and structures within the hardware component.

[0107] The specific embodiments of the present application are described above. It should be understood that the present application is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essential content of the present application. In the case of no conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for generating and displaying word clouds based on time-series data, characterized in that, The method comprises the following steps: Step S1: read user configuration, register word cloud service interface, generate user dictionary and stop dictionary; Step S2: the user selects the range time period parameter, and calls the word cloud service interface in parallel to obtain word cloud data; Step S3: the word cloud service interface first obtains word cloud data from the database, and if there is no word cloud data, generates word cloud data; Step S4: the front end merges word cloud data, calculates color size attributes, and renders into a word cloud graph; The word cloud data generated in step S3 comprises the following steps: Step S3.1: obtain original data from the word cloud data source configured by the user; Step S3.2: perform word segmentation on the original data according to the user dictionary, and filter according to the stop dictionary after word segmentation; Step S3.3: count the word frequency, and normalize and denoise to form word cloud weight to constitute word cloud data; Step S3.4: save to the database and return; The user configuration comprises word cloud data source configuration, user dictionary configuration and stop dictionary configuration; the word cloud data source configuration, user dictionary configuration and stop dictionary configuration are each a data source list, each data source supports multiple sources including interface, SQL statement, file and pure text and parameters; the original data is obtained from the data source by specifying parameter data when calling; The data source configuration is a list, and each data source configuration comprises data acquisition interface and parameter information; The word cloud configuration comprises the maximum number of words and normalization algorithm; The user dictionary is a list, and supports multiple sources of user dictionaries; the structure of the stop dictionary configuration is consistent with that of the user dictionary; The word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting word cloud data, and supports defining whether to persist, callback function attribute; The data source supports nesting, that is, one data source can use the data obtained by itself as multiple parameters to batch obtain data from another data source and aggregate the data as the result of the data source; One data source obtains batch data from another data source to generate word cloud data.

2. The time-series data oriented word cloud generation presentation method according to claim 1, characterized in that, The word cloud service interface parses the user configuration and registers the word cloud service according to the user configuration; Merge the dictionaries obtained from the file and the data source into the actual user dictionary and the stop dictionary; Read the data source configuration, use the id and parameters therein to register the word cloud service interface, when the user accesses the service interface, first obtain the word cloud data generated by the word cloud data persistence module, if there is no word cloud data or the version is inconsistent, obtain data from the data source, perform word segmentation according to the user dictionary and the stop dictionary, count the word frequency, calculate the weight to generate word cloud data according to the word cloud configuration, and save using the word cloud data persistence module.

3. The time-series data oriented word cloud generation presentation method according to claim 2, characterized in that, The word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, and uses SQLite; the persistence module separately creates a data table for each word cloud data source configuration of the user, uses the ID and parameter name of the data source as the table name, and builds an index for the parameter list specified by the data source.

4. A word cloud generation presentation system for time series data, characterized by, The system comprises the following modules: Module M1: read user configuration, register word cloud service interface, generate user dictionary and stop dictionary; Module M2: the user selects the range time period parameter, and calls the word cloud service interface in parallel to obtain word cloud data; Module M3: the word cloud service interface first obtains word cloud data from the database, and generates word cloud data if it does not exist; Module M4: the front end merges word cloud data, calculates color and size attributes, and renders into a word cloud diagram; The word cloud data generated by the module M3 includes the following modules: Module M3.1: obtain original data from the user-configured word cloud data source; Module M3.2: perform word segmentation on the original data according to the user dictionary, and filter according to the stop word dictionary after word segmentation; Module M3.3: count word frequency, and normalize and denoise to word cloud weight to form word cloud data; Module M3.4: save to the database and return; The user configuration includes word cloud data source configuration, user dictionary configuration, and stop word dictionary configuration; the word cloud data source configuration, user dictionary configuration, and stop word dictionary configuration are each a data source list, each data source supports multiple sources including interfaces, SQL statements, files, and plain text, and parameters; the original data is obtained from the data source by specifying parameters when calling; The data source configuration is a list, and each data source configuration includes data acquisition interface and parameter information; The word cloud configuration includes the maximum number of words and normalization algorithms; The user dictionary is a list, supporting multiple user dictionary sources, and the structure of the stop word dictionary configuration is consistent with that of the user dictionary; The word cloud data source needs to specify the unique ID of the data source as the registered word cloud service interface path and the data table name for persisting word cloud data, and supports defining whether to persist, callback function attributes; The data source supports nesting, that is, one data source can use the data it obtains as multiple parameters to batch obtain data from another data source and aggregate the results of the data source; A data source obtains batch data from another data source to generate word cloud data.

5. The time-series data oriented word cloud generation presentation system according to claim 4, wherein, The word cloud service interface parses the user configuration and registers the word cloud service according to the user configuration; Merge the dictionaries from files and data sources into actual user dictionaries and stop word dictionaries; Read the data source configuration, use the id and parameters in it to register the word cloud service interface, when the user accesses the service interface, first obtain the word cloud data generated from the word cloud data persistence module, if it does not exist or the version is inconsistent, obtain data from the data source, perform word segmentation according to the user dictionary and stop word dictionary, and count word frequency, calculate weight to generate word cloud data according to the word cloud configuration, and save using the word cloud data persistence module.

6. The time-series data oriented word cloud generation presentation system according to claim 5, wherein, The word cloud data persistence module supports SQL and NoSQL persistent storage of word cloud data, using SQLite; the persistence module will create a data table for each word cloud data source configuration of the user, using the ID and parameter name of the data source as the table name, and building an index on the parameter list specified by the data source.

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