Dynamic data charting method and apparatus

Abstract

The application provides a dynamic data chart statistical method and device, and relates to the technical field of big data. The method comprises the following steps: receiving a data query request of a user; querying full data corresponding to the data query request from a server backend according to the data query request; storing the full data in a data visualization chart library corresponding to a pull-down box selector of a front-end interface; the pull-down box selector provides a plurality of statistical index options of user-specified data in the full data for the user; after the user selects a first statistical index option from the pull-down box selector, extracting the user-specified data from the data visualization chart library, inputting the user-specified data and the first statistical index option into a data statistical analysis model, and outputting a first statistical analysis result; visually rendering the first statistical analysis result to generate a first visualization chart; and showing the first visualization chart to the user.

Description

Technical Field

[0001] This invention relates to the field of big data technology, and more particularly to a dynamic data charting and statistical method and apparatus. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Statistical analysis of data can reveal problems within the data. However, this process requires manual analysis to identify the statistical indicators and present the results. Manual analysis of these indicators is inefficient, prone to errors, and consumes significant human resources. Furthermore, accessing the server backend before each data analysis introduces network risks and time overhead. Summary of the Invention

[0004] This invention proposes a dynamic data chart statistical method to improve the efficiency and accuracy of data chart statistical analysis, reduce costs, avoid network risks, and reduce time consumption, including:

[0005] Receive user data query requests;

[0006] Based on the data query request, retrieve the full data corresponding to the data query request from the server backend;

[0007] All data is stored in the data visualization chart library corresponding to the drop-down selector on the front-end interface; the drop-down selector provides users with multiple statistical indicator options for user-specified data in the full dataset;

[0008] After the user selects the first statistical indicator option from the drop-down list selector, the user-specified data is extracted from the data visualization chart library. The user-specified data and the first statistical indicator option are input into the data statistical analysis model, and the first statistical analysis result is output. The data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results.

[0009] The results of the first statistical analysis are visualized and rendered to generate the first visualization chart;

[0010] Present the first visual chart to the user.

[0011] This invention provides a dynamic data chart statistical device to improve the efficiency and accuracy of data chart statistical analysis, reduce costs, avoid network risks, and reduce time consumption. The device includes:

[0012] The request receiving module is used to receive data query requests from users;

[0013] The data query module is used to retrieve the full set of data corresponding to the data query request from the server backend based on the data query request.

[0014] The data storage module is used to store the full data into the data visualization chart library corresponding to the drop-down box selector of the front-end interface; the drop-down box selector provides users with multiple statistical indicator options for user-specified data in the full data.

[0015] The first statistical analysis module is used to extract user-specified data from the data visualization chart library after the user selects the first statistical indicator option from the drop-down box selector, input the user-specified data and the first statistical indicator option into the data statistical analysis model, and output the first statistical analysis result; the data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options and corresponding historical statistical analysis results.

[0016] The first chart generation module is used to visualize and render the first statistical analysis results, and generate the first visualization chart.

[0017] The first chart display module is used to present the first visual chart to the user.

[0018] An embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a dynamic data chart statistical method.

[0019] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a dynamic data chart statistical method.

[0020] This invention provides a computer program product, which includes a computer program that, when executed by a processor, implements a dynamic data chart statistical method.

[0021] This invention addresses the problems of low efficiency, low accuracy, and high labor costs associated with manual data analysis and statistical indicators in existing technologies. Furthermore, each data query request requires access to the server backend, introducing network risks and time overhead. This invention receives user data query requests; retrieves the full dataset corresponding to the query request from the server backend; stores the full dataset in a data visualization chart library corresponding to a dropdown selector on the front-end interface; the dropdown selector provides the user with multiple statistical indicator options for user-specified data within the full dataset; after the user selects a first statistical indicator option from the dropdown selector, the user-specified data is extracted from the data visualization chart library, and the user-specified data and the first statistical indicator option are input into a data statistical analysis model, outputting a first statistical analysis result; the data statistical analysis model is trained on a first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results; the first statistical analysis result is visualized and rendered to generate a first visualization chart; and the first visualization chart is then displayed to the user. This invention enables the analysis of user-specified data, the generation of statistical analysis results and visualization charts, improving the efficiency and accuracy of data chart statistical analysis, reducing costs, querying the full data corresponding to the data query request from the server backend, and storing the full data in the data visualization chart library, which can avoid network risks and reduce time consumption. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the dynamic data chart statistics method in an embodiment of the present invention;

[0024] Figure 2 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0025] Figure 3 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0026] Figure 4 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0027] Figure 5 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0028] Figure 6This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0029] Figure 7 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention;

[0030] Figure 8 This is a schematic diagram of the dynamic data chart statistics device in an embodiment of the present invention;

[0031] Figure 9 This is a specific example diagram of the dynamic data chart statistics device in the embodiments of the present invention;

[0032] Figure 10 This is a specific example diagram of the dynamic data chart statistics device in the embodiments of the present invention;

[0033] Figure 11 This is a specific example diagram of the dynamic data chart statistics device in the embodiments of the present invention;

[0034] Figure 12 This is a specific example diagram of the dynamic data chart statistics device in the embodiments of the present invention;

[0035] Figure 13 This is a specific example diagram of the dynamic data chart statistics device in the embodiments of the present invention;

[0036] Figure 14 This is a schematic diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0038] In this document, the term "and / or" merely describes a relationship, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0039] In the description of this specification, the terms "comprising," "including," "having," and "containing" are open-ended terms, meaning that they include but are not limited to. The terms "an embodiment," "a specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example that are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0040] The principles and spirit of the present invention will be explained in detail below with reference to several representative embodiments.

[0041] Figure 1 This is a flowchart illustrating the dynamic data chart statistical method according to an embodiment of the present invention. Figure 1 As shown, the method includes:

[0042] Step 101: Receive the user's data query request;

[0043] Step 102: Based on the data query request, retrieve the full data corresponding to the data query request from the server backend;

[0044] Step 103: Store all data into the data visualization chart library corresponding to the drop-down box selector on the front-end interface; the drop-down box selector provides users with multiple statistical indicator options for user-specified data in the full data.

[0045] Step 104: After the user selects the first statistical indicator option from the drop-down list selector, the user-specified data is extracted from the data visualization chart library. The user-specified data and the first statistical indicator option are input into the data statistical analysis model, and the first statistical analysis result is output. The data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results.

[0046] Step 105: Visualize the results of the first statistical analysis to generate the first visualization chart;

[0047] Step 106: Show the user the first visualization chart.

[0048] Depend on Figure 1As shown in the process, this embodiment of the invention receives a user's data query request, retrieves the full data corresponding to the data query request from the server backend, and stores the full data in the data visualization chart library corresponding to the drop-down selector on the front-end interface, which can reduce network risks and time overhead. The drop-down selector provides the user with multiple statistical indicator options for user-specified data in the full data. Since too many statistical indicators are not suitable to be displayed in the visualization chart, as this would reduce the readability of the chart and make it difficult to compare related statistical indicators, the user can customize the statistical indicators to be displayed in the chart. After the user selects the first statistical indicator option from the drop-down selector, the user-specified data is extracted from the data visualization chart library, and the user-specified data and the first statistical indicator option are input into the data statistical analysis model to output the first statistical analysis result. The data statistical analysis model is trained on a first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results. The first machine learning model is a natural language processing model. The first statistical analysis result is visualized and rendered to generate a first visualization chart, which is then displayed to the user. This invention enables the analysis of user-specified data, the generation of statistical analysis results and visualization charts, improving the efficiency and accuracy of data chart statistical analysis, reducing costs, querying the full data corresponding to the data query request from the server backend, storing the full data in the data visualization chart library, avoiding network risks, and reducing time overhead.

[0049] To provide a clearer explanation of the above dynamic data chart statistical methods, each step will be explained in detail below.

[0050] Figure 2 This is a specific example diagram of the dynamic data chart statistics method in an embodiment of the present invention. (Reference) Figure 2 The detailed processing procedure for determining the relevant statistical indicators is as follows:

[0051] Step 201: After extracting user-specified data from the data visualization chart library, the first statistical indicator option is input into the statistical indicator analysis model, and the second statistical indicator option related to the first statistical indicator option is output; the statistical indicator analysis model is trained on the second machine learning model based on the historical statistical indicator options and the correlation between the historical statistical indicator options.

[0052] Step 202: Input the user-specified data and the second statistical indicator option into the data statistical analysis model, and output the second statistical analysis results;

[0053] Step 203: Visualize the results of the second statistical analysis to generate a second visualization chart;

[0054] Step 204: Show the user the second visualization chart.

[0055] In one embodiment of the present invention, after extracting user-specified data from a data visualization chart library, a first statistical indicator option is input into a statistical indicator analysis model, which outputs a second statistical indicator option related to the first statistical indicator option. The user-specified data and the second statistical indicator option are then input into a data statistical analysis model, which outputs a second statistical analysis result. This second statistical analysis result is then visualized and rendered to generate a second visualization chart. For example, the first statistical indicator option includes financial risk issue indicators and financial loss issue indicators. Inputting these two statistical indicators into the statistical indicator analysis model outputs statistical indicators related to these two statistical indicators, i.e., financial issue indicators. Inputting the user-specified data and financial issue indicators into the data statistical analysis model yields the quantity corresponding to each financial issue indicator in the user-specified data. Based on the quantity corresponding to each financial issue indicator in the user-specified data, visualization rendering is performed to obtain a visualization chart. The chart displays the user-specified data name, each financial issue indicator, and the quantity corresponding to each financial issue indicator.

[0056] Figure 3 This is a specific example diagram of the dynamic data chart statistics method in an embodiment of the present invention. (Reference) Figure 3 The statistical indicator analysis model was trained and tested in the following manner:

[0057] Step 301: Obtain historical statistical indicator options and the relationships between historical statistical indicator options, and establish a sample set; determine the training set and test set from the sample set;

[0058] Step 302: Train the second machine learning model using the training set, and test the trained second machine learning model using the test set;

[0059] Step 303: The second machine learning model that passes the test is identified as the statistical indicator analysis model.

[0060] In one embodiment of the present invention, the statistical indicator analysis model is trained on a second machine learning model based on historical statistical indicator options and the relationships between them. The second machine learning model is a text classification model, such as TextCNN or DPCNN. Specifically, word embedding is used to convert historical statistical indicator options into numerical vectors, supporting subsequent convolutional pooling operations. Based on the relationships between historical statistical indicator options, the options are categorized to establish a sample set, from which training and test sets are determined. The historical statistical indicator options in the training set are used as input to the text classification model, and the categories of these options are used as output. The text classification model is then trained, and the test set is used to test it. The text classification model that passes the test is identified as the statistical indicator analysis model.

[0061] Figure 4 This is a specific example diagram of the dynamic data chart statistics method in an embodiment of the present invention. (Reference) Figure 4 The data statistical analysis model was trained and tested as follows:

[0062] Step 401: Obtain the specified data of historical users, the statistical indicator options selected by historical users, and the corresponding historical statistical analysis results to establish a sample set; determine the training set and test set from the sample set;

[0063] Step 402: Train the first machine learning model using the training set, and test the trained first machine learning model using the test set.

[0064] Step 403: The first machine learning model that passes the test is identified as the data statistical analysis model.

[0065] In one embodiment of the present invention, after the user selects the first statistical indicator option from the drop-down list selector, the user-specified data is extracted from the data visualization chart library. Since the user-specified data is long text data, a data statistical analysis model is used to obtain the statistical indicators of the user-specified data. The statistical indicators of the user-specified data are matched with the user-selected first statistical indicator option to determine the statistical result of the first statistical indicator option.

[0066] In one embodiment of the present invention, a data statistical analysis model is trained on a first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results. The first machine learning model is a natural language processing model, such as a Long Short-Term Memory (LSTM) network model. Specifically, historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results are obtained to establish a sample set. A training set and a test set are determined from the sample set. The historical user-specified data and historical user-selected statistical indicator options in the training set are used as inputs to the natural language processing model. The historical statistical analysis results in the training set are used as outputs to train the natural language processing model. The trained natural language processing model is then tested using the test set, and the natural language processing model that passes the test is determined as the statistical indicator analysis model.

[0067] In one embodiment of the present invention, before the user selects the first statistical indicator option from the drop-down list selector, it is necessary to obtain all statistical indicators in string form from the server backend; convert the string form of all statistical indicators into array form of all statistical indicator options, and embed the array form of all statistical indicator options into the drop-down list selector; at the same time, since there are many statistical indicators, fuzzy matching can be performed based on the first statistical indicator option selected by the user, making it more convenient to select statistical indicators from the drop-down list selector.

[0068] In practice, the full statistical indicators in string format are obtained from the server backend. Since full statistical indicators in string format cannot be directly embedded in the dropdown selector (el-select), they need to be converted into array format. The array format full statistical indicators are then used as the selection options in the dropdown selector. Taking two statistical indicator options as an example: the server backend string statistical indicators are obtained as follows: "'abc', financial risk issue; 'xyz', financial loss issue;" where 'abc' and 'xyz' are the position numbers of the statistical indicators. The replace method is used to process the symbols in the string statistical indicators, and the split method is used to divide the string statistical indicators. The divided string statistical indicators are then converted into array format, for example: [{key:abc,value:'financial risk issue'},{key:xyz,value:'financial loss issue'}]. The financial risk issue and financial loss issue are then used as the selection options in the dropdown selector.

[0069] In one embodiment of the present invention, for step 105, the first statistical analysis result is converted into a JSON object; the JSON object of the first statistical analysis result is visualized and rendered to generate a first visualization chart.

[0070] In practice, the first statistical analysis result is converted into a JSON object. The ECharts control library is then used to visualize and render this JSON object, generating the first visual chart. ECharts is a JavaScript-based data visualization chart library. For example, if the first statistical analysis result shows 20 financial risk issues and 11 financial loss issues in user-specified report A, the ECharts control library's `setOption` method is used to configure chart parameters and generate tables, bar charts, and pie charts. Since the ECharts control library interface cannot generate tables from the first statistical analysis result as a string, the result is converted into a JSON object, which is then used to display the data in a table. Furthermore, before generating the first visual chart using the ECharts control library, it's necessary to determine the number of statistical indicator options selected by the user from the dropdown menu. If the number is 0, meaning the user did not select any statistical indicator options from the dropdown menu, all statistical indicators are displayed in the chart. When the user changes the first statistical indicator option selected from the drop-down list, the user-specified data and the first statistical indicator option are re-entered into the data statistical analysis model, the first statistical analysis result is output, and the first visualization chart is regenerated.

[0071] Figure 5 , Figure 6 , Figure 7 This is a specific example diagram of the dynamic data chart statistics method in the embodiments of the present invention.

[0072] In this embodiment of the invention, a user-specified Report A is extracted from a data visualization chart library. The user selects five statistical indicators from a drop-down list: financial risk issues, financial loss issues, non-performing asset disposal issues, safe production and operation issues, and centralized procurement issues. Report A, along with these five statistical indicators, is input into a data statistical analysis model. The model outputs the analysis results for each statistical indicator. Specifically, in Report A, there are 20 financial risk issues, 11 financial loss issues, 18 non-performing asset disposal issues, 25 safe production and operation issues, and 3 centralized procurement issues. Based on the above statistical analysis results, a table is generated using the ECharts control library, referencing... Figure 5 You can also use the ECharts control library to generate corresponding bar charts and pie charts. (See reference...) Figure 6 , Figure 7 This allows users to view and analyze data intuitively.

[0073] In one embodiment of the present invention, in order to enable data traceability and provide a data drill-down function, the first visualization chart includes a traceability option for the first statistical analysis result. After the user selects the traceability option for the first statistical analysis result from the first visualization chart, the user is shown the data content corresponding to the traceability option for the first statistical analysis result in the user-specified data. Furthermore, in another embodiment of the present invention, the first visualization chart can be exported using the xlsx, xlsx-style, or file-saver methods. The export process does not require server backend support and offers high flexibility.

[0074] In practice, after the user clicks on the first statistical analysis result data in the first visualization chart, the data is drilled down, and the drill-down area can be customized. The user is then shown the data content corresponding to the traceability option of the first statistical analysis result in the user-specified data. For example, in a table generated for a user-specified Report A containing 20 financial risk issues and 11 financial loss issues, clicking on the data "20" in the table extracts the data corresponding to the financial risk issues in Report A and displays it to the user.

[0075] It should be noted that although the operation of the method of the present invention has been described in a specific order in the above embodiments and figures, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0076] The implementation of the dynamic data charting and statistical device can refer to the implementation of the above method, and repeated details will not be elaborated further. The term "module" or "unit" used below can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0077] Based on the same inventive concept, this invention also proposes a dynamic data chart statistical device, such as... Figure 8 As shown, the device includes:

[0078] The request receiving module 801 is used to receive data query requests from users.

[0079] The data query module 802 is used to query the full data corresponding to the data query request from the server backend according to the data query request;

[0080] The data storage module 803 is used to store the full data into the data visualization chart library corresponding to the drop-down box selector of the front-end interface; the drop-down box selector provides the user with multiple statistical indicator options for user-specified data in the full data.

[0081] The first statistical analysis module 804 is used to extract user-specified data from the data visualization chart library after the user selects the first statistical indicator option from the drop-down box selector, input the user-specified data and the first statistical indicator option into the data statistical analysis model, and output the first statistical analysis result; the data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options and corresponding historical statistical analysis results.

[0082] The first chart generation module 805 is used to visualize and render the first statistical analysis results to generate the first visualization chart.

[0083] The first chart display module 806 is used to display the first visual chart to the user.

[0084] Figure 9 This is a specific example diagram of the dynamic data chart statistics device in an embodiment of the present invention. For example... Figure 9 As shown, in one embodiment of the present invention, Figure 8 The dynamic data chart statistical device shown also includes:

[0085] The relevant indicator determination module 901 is used to extract user-specified data from the data visualization chart library, input the first statistical indicator option into the statistical indicator analysis model, and output the second statistical indicator option related to the first statistical indicator option; the statistical indicator analysis model is obtained by training the second machine learning model based on the historical statistical indicator options and the correlation between the historical statistical indicator options.

[0086] The second statistical analysis module 902 is used to input user-specified data and second statistical indicator options into the data statistical analysis model and output the second statistical analysis results.

[0087] The second chart generation module 903 is used to visualize and render the second statistical analysis results, and generate a second visualization chart.

[0088] The second chart display module 904 is used to display a second visual chart to the user.

[0089] Figure 10 This is a specific example diagram of the dynamic data chart statistics device in an embodiment of the present invention. For example... Figure 10 As shown, in one embodiment of the present invention, Figure 9 The dynamic data chart statistical device shown also includes:

[0090] The second model training module 1001 is used to train and test the statistical indicator analysis model in the following manner:

[0091] Obtain historical statistical indicator options and the relationships between them, and establish a sample set; determine the training set and test set from the sample set.

[0092] The second machine learning model is trained using the training set and tested using the test set.

[0093] The second machine learning model that passed the test was identified as the statistical indicator analysis model.

[0094] Figure 11 This is a specific example diagram of the dynamic data chart statistics device in an embodiment of the present invention. For example... Figure 11 As shown, in one embodiment of the present invention, Figure 8 The dynamic data chart statistical device shown also includes:

[0095] The first model training module 1101 is used to obtain historical user-specified data, historical user-selected statistical indicator options and corresponding historical statistical analysis results, and establish a sample set; determine the training set and test set from the sample set; train the first machine learning model using the training set, and test the trained first machine learning model using the test set; and determine the first machine learning model that passes the test as the data statistical analysis model.

[0096] Figure 12 This is a specific example diagram of the dynamic data chart statistics device in an embodiment of the present invention. For example... Figure 12 As shown, in one embodiment of the present invention, Figure 8 The dynamic data chart statistical device shown also includes:

[0097] The indicator acquisition module 1201 is used to acquire full statistical indicators in string format from the server backend.

[0098] The format conversion module 1202 is used to convert full statistical indicators in string format into full statistical indicator options in array format, and to embed the full statistical indicator options in array format into the drop-down list selector.

[0099] In one embodiment of the present invention, the first chart generation module 805 is specifically used for:

[0100] Convert the first statistical analysis results into a JSON object;

[0101] The JSON object of the first statistical analysis result is rendered to generate the first visualization chart.

[0102] Figure 13This is a specific example diagram of the dynamic data chart statistics device in an embodiment of the present invention. For example... Figure 13 As shown, in one embodiment of the present invention, the first visualization chart includes: a source tracing option for the first statistical analysis result; Figure 8 The dynamic data chart statistical device shown also includes:

[0103] The data tracing module 1301 is used to display the data content corresponding to the tracing option of the first statistical analysis result in the user-specified data after the user selects the tracing option of the first statistical analysis result from the first visualization chart.

[0104] It should be noted that although several modules of the dynamic data charting and statistical device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules described above can be embodied in one module. Conversely, the features and functions of one module described above can be further divided and embodied by multiple modules.

[0105] Based on the aforementioned inventive concept, such as Figure 14 As shown, the present invention also proposes a computer device 1400, including a memory 1401, a processor 1402, and a computer program 1403 stored in the memory 1401 and executable on the processor 1402. When the processor 1402 executes the computer program 1403, it implements the aforementioned dynamic data chart statistical method.

[0106] Based on the aforementioned inventive concept, the present invention proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the aforementioned dynamic data chart statistical method.

[0107] Based on the aforementioned inventive concept, the present invention proposes a computer program product, which includes a computer program that, when executed by a processor, implements a dynamic data chart statistical method.

[0108] This invention addresses the problems of low efficiency, low accuracy, and high labor costs associated with manual data analysis and statistical indicators in existing technologies. Furthermore, each data query request requires access to the server backend, introducing network risks and time overhead. This invention receives user data query requests; retrieves the full dataset corresponding to the query request from the server backend; stores the full dataset in a data visualization chart library corresponding to a dropdown selector on the front-end interface; the dropdown selector provides the user with multiple statistical indicator options for user-specified data within the full dataset; after the user selects a first statistical indicator option from the dropdown selector, the user-specified data is extracted from the data visualization chart library, and the user-specified data and the first statistical indicator option are input into a data statistical analysis model, outputting a first statistical analysis result; the data statistical analysis model is trained on a first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results; the first statistical analysis result is visualized and rendered to generate a first visualization chart; and the first visualization chart is then displayed to the user. This invention enables the analysis of user-specified data, the generation of statistical analysis results and visualization charts, improving the efficiency and accuracy of data chart statistical analysis, reducing costs, querying the full data corresponding to the data query request from the server backend, and storing the full data in the data visualization chart library, which can avoid network risks and reduce time consumption.

[0109] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0110] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0111] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0113] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic data chart statistical method, characterized in that, include: Receive user data query requests; Based on the data query request, retrieve the full data corresponding to the data query request from the server backend; All data is stored in the data visualization chart library corresponding to the drop-down selector on the front-end interface; the drop-down selector provides users with multiple statistical indicator options for user-specified data in the full dataset; After the user selects the first statistical indicator option from the drop-down list selector, the user-specified data is extracted from the data visualization chart library. The user-specified data and the first statistical indicator option are input into the data statistical analysis model, and the first statistical analysis result is output. The data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results. The results of the first statistical analysis are visualized and rendered to generate the first visualization chart; Show the user the first visual chart; The data statistical analysis model was trained and tested in the following manner: Obtain specified data from historical users, statistical indicator options selected by historical users, and corresponding historical statistical analysis results to establish a sample set; determine the training set and test set from the sample set. The first machine learning model is trained using the training set, and the trained first machine learning model is tested using the test set. The first machine learning model that passes the test is selected as the data statistical analysis model.

2. The method according to claim 1, characterized in that, Also includes: After extracting user-specified data from the data visualization chart library, the first statistical indicator option is input into the statistical indicator analysis model, and the second statistical indicator option related to the first statistical indicator option is output; the statistical indicator analysis model is trained on the second machine learning model based on the historical statistical indicator options and the correlation between the historical statistical indicator options. Input the user-specified data and the second statistical indicator option into the data statistical analysis model, and output the second statistical analysis results; The results of the second statistical analysis are visualized and rendered to generate a second visualization chart. Show users a second visualization chart.

3. The method according to claim 2, characterized in that, The statistical indicator analysis model was trained and tested in the following manner: Obtain historical statistical indicator options and the relationships between them, and establish a sample set; determine the training set and test set from the sample set. The second machine learning model is trained using the training set and tested using the test set. The second machine learning model that passed the test was identified as the statistical indicator analysis model.

4. The method according to claim 1, characterized in that, Before the user selects the first statistic option from the dropdown selector, the following is also included: Retrieve full statistical metrics from the server backend in string format; Convert the full statistics in string format into full statistics options in array format, and embed the full statistics options in array format into the drop-down selector.

5. The method according to claim 1, characterized in that, The results of the first statistical analysis are visualized and rendered to generate the first visualization chart, including: Convert the first statistical analysis results into a JSON object; The JSON object of the first statistical analysis result is rendered to generate the first visualization chart.

6. The method according to claim 1, characterized in that, The first visualization chart includes: the option to trace the source of the first statistical analysis results; After showing the user the first visualization, it also includes: After the user selects the source tracing option for the first statistical analysis result from the first visualization chart, the user is shown the data content corresponding to the source tracing option for the first statistical analysis result in the user-specified data.

7. A dynamic data charting and statistical device, characterized in that, include: The request receiving module is used to receive data query requests from users; The data query module is used to retrieve the full set of data corresponding to the data query request from the server backend based on the data query request. The data storage module is used to store the full data into the data visualization chart library corresponding to the drop-down box selector of the front-end interface; the drop-down box selector provides users with multiple statistical indicator options for user-specified data in the full data. The first statistical analysis module is used to extract user-specified data from the data visualization chart library after the user selects the first statistical indicator option from the drop-down box selector, input the user-specified data and the first statistical indicator option into the data statistical analysis model, and output the first statistical analysis result; the data statistical analysis model is obtained by training the first machine learning model based on historical user-specified data, historical user-selected statistical indicator options and corresponding historical statistical analysis results. The first chart generation module is used to visualize and render the first statistical analysis results, and generate the first visualization chart. The first chart display module is used to display the first visual chart to the user; The first model training module is used to obtain historical user-specified data, historical user-selected statistical indicator options, and corresponding historical statistical analysis results to establish a sample set. Determine the training and test sets from the sample set; The first machine learning model is trained using the training set, and the trained first machine learning model is tested using the test set. The first machine learning model that passes the test is selected as the data statistical analysis model.

8. The apparatus according to claim 7, characterized in that, Also includes: The relevant indicator determination module is used to extract user-specified data from the data visualization chart library, input the first statistical indicator option into the statistical indicator analysis model, and output the second statistical indicator option related to the first statistical indicator option; the statistical indicator analysis model is obtained by training the second machine learning model based on the historical statistical indicator options and the correlation between the historical statistical indicator options. The second statistical analysis module is used to input user-specified data and second statistical indicator options into the data statistical analysis model and output the second statistical analysis results. The second chart generation module is used to visualize and render the results of the second statistical analysis, and generate a second visualization chart. The second chart display module is used to display a second visual chart to the user.

9. The apparatus according to claim 8, characterized in that, Also includes: The second model training module is used to obtain historical statistical indicator options, the correlation between historical statistical indicator options, and to establish a sample set. Determine the training and test sets from the sample set; The second machine learning model is trained using the training set and tested using the test set. The second machine learning model that passed the test was identified as the statistical indicator analysis model.

10. The apparatus according to claim 7, characterized in that, Also includes: The metrics acquisition module is used to acquire all statistical metrics in string format from the server backend. The format conversion module is used to convert full statistical indicators in string format into full statistical indicator options in array format, and then embed the full statistical indicator options in array format into the drop-down selector.

11. The apparatus according to claim 7, characterized in that, The first chart generation module is specifically used for: Convert the first statistical analysis results into a JSON object; The JSON object of the first statistical analysis result is rendered to generate the first visualization chart.

12. The apparatus according to claim 7, characterized in that, The first visualization chart includes: the option to trace the source of the first statistical analysis results; it also includes: The data tracing module is used to display the data content corresponding to the tracing option of the first statistical analysis result in the user's specified data after the user selects the tracing option from the first visualization chart.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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