Data visualization analysis method and device, storage medium and electronic equipment

Abstract

The invention provides a data visualization analysis method and device, a storage medium and electronic equipment, and the method comprises the steps: configuring a corresponding data protocol according to the basic information of the data protocol; based on the configured data protocol, analyzing the data file to obtain analyzed parameter information; and performing visual analysis on the parameter information to obtain a visual analysis result corresponding to the parameter information. Therefore, the data protocol can be configured in a user-defined manner, the data can be analyzed under various protocols, the visual result can be displayed, the applicability is wide, and the display effect is relatively visual.

Description

Technical Field

[0001] This invention relates to the field of big data, and in particular to a data visualization and analysis method, apparatus, storage medium, and electronic device. Background Art

[0002] In aviation software applications, data visualization tools play a crucial role. They are key tools for ground analysis teams to understand and analyze flight data. However, these tools face a series of problems in practical applications, while also demonstrating their unique functions and certain limitations. For example, aviation data is diverse, involving various parameters such as engine parameters, vibration data, and fuel data. These parameters all need to be analyzed through visualization to intuitively show the underlying patterns and trends. How to achieve data visualization analysis with diverse functions and protocol compatibility has become a pressing technical problem for researchers in this field. Summary of the Invention

[0003] In view of the above problems, the present invention provides a data visualization analysis method, apparatus, storage medium and electronic device that overcomes or at least partially solves the above problems.

[0004] Firstly, a data visualization analysis method includes:

[0005] Configure the corresponding data protocol based on the basic information of the data protocol;

[0006] Based on the configured data protocol, the data file is parsed to obtain the parsed parameter information;

[0007] The parameter information is visualized and analyzed to obtain the visualization analysis results corresponding to the parameter information.

[0008] Optionally, configuring the corresponding data protocol based on the basic information of the data protocol includes:

[0009] Based on the basic information of the data protocol, analyze and determine the corresponding data protocol configuration table, data parameter parsing configuration table, and discrete quantity bitwise parsing table.

[0010] Based on the data protocol configuration table, the data parameter parsing configuration table, and the discrete quantity bitwise parsing table, a template configuration for the corresponding data protocol is established.

[0011] Optionally, the data file is parsed based on the configured data protocol to obtain the parsed parameter information, including:

[0012] Select the corresponding data protocol template based on the data file;

[0013] The data file is parsed using the template of the data protocol to obtain the parsed parameter information. Specifically, the data file is parsed using the data parameter parsing configuration table to obtain physical quantity parameters and corresponding over-limit parameter information, and the data file is parsed using the discrete quantity bitwise parsing table to obtain discrete quantity parameters and corresponding alarm parameter information.

[0014] Optionally, after parsing the data file based on the configured data protocol to obtain the parsed parameter information, the method further includes:

[0015] The parameter information is stored in a structured manner in the corresponding data table.

[0016] Optionally, the step of performing visualization analysis on the parameter information to obtain the visualization analysis results corresponding to the parameter information includes:

[0017] The parameter information is subjected to visualization analysis to obtain a visualization curve, which serves as the visualization analysis result corresponding to the parameter information; the visualization analysis process includes at least curve plotting; the curve plotting is used to generate the corresponding parameter curve.

[0018] Optionally, after performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes:

[0019] The visualization curve is processed by at least one of the following operations: curve setting, curve measurement, curve adjustment, and curve calculation, so that the visualized curve after processing meets the user's needs. The curve setting is used to change at least one of the following attributes of the visualization curve: curve style, curve color, curve point style, and vertical axis dependence. The curve measurement is used to perform at least one of the following mapping operations on the view of the visualization curve: mouse point selection, cross-sectional data monitoring, and curve interval measurement. The curve adjustment is used to perform at least one of the following adjustment operations on the visualization curve: curve scaling, vertical offset adjustment, data alignment, and automatic memory. The curve calculation is used to display a formula editor in a pop-up window on the view of the visualization curve, set the curve formula for existing parameters, and draw a curve that fits the curve formula.

[0020] Optionally, after performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes:

[0021] Based on the out-of-limit parameter information or alarm parameter information shown in the parameter information, mark the corresponding alarm points for the visualized curve.

[0022] Optionally, after performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes:

[0023] The view of the visualization curve is set to change the view parameters of the view. The view parameters include at least one or more of the following: grid lines, main grid line spacing, sub-grid line spacing, background color, foreground color, legend display, line width, origin setting, and coordinate axis range.

[0024] Generate the corresponding image file based on the modified visualization curve;

[0025] Based on the curve type of the modified visualization curve and the parameter information, generate the filename of the image file;

[0026] Save the image file to the specified path.

[0027] Optionally, after performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes:

[0028] An analysis operation is performed on a curve display interface containing multiple visualized curves, including at least one of the following: creating labels, scaling curves, normalizing curves, and marking alarm points, so that the curve display interface after the analysis operation meets the user's needs. The label creation is used to generate text labels corresponding to each visualized curve in the curve display interface. The curve scaling is used to change the shape of the mouse selection area on the curve display interface. The curve normalization is used to normalize multiple visualized curves. The alarm point marking is used to mark alarm points corresponding to predefined alarm signals on the curve display interface.

[0029] Optionally, the curve plotting process includes:

[0030] Retrieve structured parameter information from the database;

[0031] Iterate through each parameter shown in the parameter information to obtain the data type and parameter value corresponding to each parameter;

[0032] Normalize the parameter value corresponding to each of the above parameters;

[0033] Based on the data type corresponding to each parameter, determine the curve type corresponding to each parameter;

[0034] Based on the curve type corresponding to each parameter and the normalized parameter value, a parameter curve corresponding to each parameter is generated.

[0035] Secondly, a data visualization and analysis device includes: a protocol configuration unit, a parameter parsing unit, and a visualization and analysis unit;

[0036] The protocol configuration unit is used to configure the corresponding data protocol according to the basic information of the data protocol;

[0037] The parameter parsing unit is used to parse the data file based on the configured data protocol to obtain the parsed parameter information;

[0038] The visualization analysis unit is used to perform visualization analysis on the parameter information and obtain the visualization analysis results corresponding to the parameter information.

[0039] Thirdly, a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the data visualization and analysis method described in any of the preceding claims.

[0040] Fourthly, an electronic device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other via the bus; the processor is used to call program instructions in the memory to execute the data visualization analysis method described in any of the preceding claims.

[0041] By employing the above technical solutions, the present invention provides a data visualization analysis method, apparatus, storage medium, and electronic device. This allows for the configuration of corresponding data protocols based on basic data protocol information; the parsing of data files based on the configured data protocols to obtain parsed parameter information; and the visualization analysis of the parameter information to obtain the corresponding visualization analysis results. Therefore, it can be seen that the present invention allows for custom configuration of data protocols, enabling data parsing under various protocols and displaying visualization results. This not only provides wide applicability but also offers a relatively intuitive display effect.

[0042] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. Attached Figure Description

[0043] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0044] Figure 1A flowchart illustrating a data visualization and analysis method provided in an embodiment of this application;

[0045] Figure 2 A flowchart illustrating another data visualization and analysis method provided in this application embodiment;

[0046] Figure 3 A flowchart illustrating another data visualization and analysis method provided in this application embodiment;

[0047] Figure 4 A flowchart illustrating another data visualization and analysis method provided in this application embodiment;

[0048] Figure 5 A flowchart illustrating another data visualization and analysis method provided in this application embodiment;

[0049] Figure 6 A schematic diagram of the architecture of a data visualization and analysis device provided in an embodiment of this application;

[0050] Figure 7 This application provides an implementation process for a configurable data parsing technology.

[0051] Figure 8 A schematic diagram of the architecture of an aviation business system provided in this application embodiment;

[0052] Figure 9 This is a schematic diagram illustrating the implementation process of curve drawing provided in an embodiment of this application. Detailed Implementation

[0053] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0054] In this application, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0055] like Figure 1 The diagram shown is a flowchart of a data visualization analysis method provided in an embodiment of this application, including the following steps.

[0056] S101: Configure the corresponding data protocol based on the basic information of the data protocol.

[0057] The basic information of the data protocol can be entered by the user or obtained directly from the database.

[0058] In some examples, a data protocol refers to the format, process, permissions, and security requirements defined in the data communication transmission, storage, and exchange process to ensure data consistency and compliance. For example, the data protocol of an engine electronic controller includes field variables such as communication cycle, flight altitude, and speed. The data protocol formats involved in this embodiment include text files and binary files.

[0059] Optionally, based on the basic information of the data protocol, configure the corresponding data protocol implementation process, which can be found in [reference needed]. Figure 2 The steps shown are accompanied by corresponding explanations.

[0060] S102: Based on the configured data protocol, parse the data file to obtain the parsed parameter information.

[0061] The data file is the file to be processed (e.g., a data file to be processed in the aviation field). After configuring the corresponding data protocol, the data file is parsed using the template configuration of the data protocol (which can be regarded as the configured data protocol) to obtain the parsed parameter information.

[0062] Optionally, the process of parsing the data file based on the configured data protocol to obtain the parsed parameter information can be found in [reference needed]. Figure 3 The steps shown are accompanied by corresponding explanations.

[0063] Combination Figure 2 and Figure 3 The method shown can build a configurable data parsing technology for data files. The implementation process of this technology can be summarized as follows: Figure 7 As shown.

[0064] S103: Perform visual analysis on the parameter information to obtain the corresponding visual analysis results.

[0065] After obtaining the parameter information of the data file, users can select the parameters to be analyzed, plot curves by associating the selected parameters, and adjust the style of the curves to achieve statistical analysis of the selected parameter data.

[0066] Optionally, the process of performing visualization analysis on parameter information to obtain the visualization analysis results corresponding to the parameter information can be as follows: performing visualization analysis on parameter information to obtain a visualization curve, which serves as the visualization analysis result corresponding to the parameter information; the visualization analysis process includes at least curve drawing; curve drawing is used to generate the corresponding parameter curve.

[0067] Optionally, the implementation process for curve drawing can be found in [link to documentation]. Figure 4 The steps shown are accompanied by corresponding explanations.

[0068] It should be noted that after obtaining the visualization curve corresponding to the parameter information, various analysis operations can be performed on the visualization curve to meet user needs. These analysis operations can include view settings, curve processing, and data analysis.

[0069] In some examples, view settings are used to modify and adjust view information such as the background, spacing, and axes of the visualized curve.

[0070] In some examples, curve manipulation is used to modify the style and color of a visualized curve, as well as to adjust the scaling, offset, and translation of the visualized curve, and to statistically analyze data information for a specific interval within the visualized curve.

[0071] In some examples, data analytics is used to create new curve labels, scale and normalize curves, and flag outlier data points.

[0072] Optionally, the curve processing can be implemented as follows: performing at least one of the following processing operations on the visualized curve: curve setting, curve measurement, curve adjustment, and curve calculation, so that the visualized curve after processing meets the user's needs; curve setting is used to change at least one of the following attributes of the visualized curve: curve style, curve color, curve point style, and vertical axis dependence; curve measurement is used to implement at least one of the following surveying operations on the view of the visualized curve: mouse point selection, cross-sectional data monitoring, and curve interval measurement; curve adjustment is used to perform at least one of the following adjustment operations on the visualized curve: curve scaling, vertical offset adjustment, data alignment, and automatic memory; curve calculation is used to display a formula editor in a pop-up window on the view of the visualized curve, set the curve formula for the existing parameters, and draw a curve that fits the curve formula.

[0073] In some examples, the curve settings allow you to configure the relevant functional properties of the visualized curve, as shown in Table 1.

[0074] Table 1

[0075] Function illustrate Select curve Switch the currently selected curve Curve style Set the style of the currently selected curve to a polyline, a dotted line, or a smoothed fitted curve. Curve Color Set the color of the currently selected curve. Dot style Sets the style of the points on the currently selected curve, including shapes such as solid circles, hollow circles, triangles, and squares. Vertical axis relies on Sets whether the ordinate of the currently selected curve is based on the left or right axis.

[0076] In some examples, the curve scaling shown in the curve adjustment is used to analyze subtle fluctuations in the visualized curve. The zoom function can be used to adjust the vertical display range of the curve to see the changes in the visualized curve more clearly.

[0077] In a possible implementation, enter parameters in the "Lower Limit / Higher Limit" field of "Curve Processing", click the "Scale" button, change the scale of the selected visualization curve in the Y-axis direction. After scaling, the lower limit coordinate of the visualization curve is aligned with the bottom of the Y-axis, and the higher limit coordinate is aligned with the top of the Y-axis. Entering "0 / 0" restores the original scale. This adjustment is also applied to the parameter curve of another data channel.

[0078] In a possible implementation, curve scaling is performed based on a set lower limit and a higher limit, and the calculation formula is shown in formula (1).

[0079] (1)

[0080] In formula (1), These are the scaled parameter values. These are the original parameter values. The set upper limit, The set minimum value.

[0081] In some examples, the vertical offset adjustment shown in the curve adjustment is used to separate overlapping visualization curves when analyzing multiple overlapping visualization curves, so as to see the trend of each visualization curve clearly.

[0082] In a possible implementation, the parameter is entered in the "Vertical Offset" field of "Curve Processing", and the "Offset" button is clicked to translate the selected curve in the Y-axis direction. Positive numbers translate upwards, negative numbers translate downwards, and "0" cancels the offset. This adjustment is also applied to the parameter curve of another data channel. Specifically, the calculation formula for the vertical offset is shown in formula (2).

[0083] (2)

[0084] In formula (2), These are the parameter values ​​after vertical offset. These are the original parameter values. The offset entered by the user.

[0085] In some examples, the data alignment shown in the curve adjustment is used so that if the time deviation between parameter 2 and parameter 1 is known, the value can be directly entered for adjustment. Click the "Fine Adjustment" button in "Curve Processing" and all curves of parameter 2 will be shifted according to the time in the value box next to the button. The unit of this input box is "seconds". A positive number indicates a shift to the right and a negative number indicates a shift to the left.

[0086] In a possible implementation, each time the “fine adjustment” button is clicked, all curves of parameter 2 will be adjusted according to the translation amount. The adjustment of parameter 2 will be maintained until a new data file is loaded. New curves will be drawn according to this adjustment. Specifically, the calculation formula for fine adjustment is shown in formula (3).

[0087] (3)

[0088] In formula (3), This is the finely adjusted x-axis time coordinate value. This represents the original time coordinate value of the x-axis. Fine-tuning translation amount set by the user.

[0089] In some examples, the automatic memory shown in the curve adjustment means that adjustments made to the position and scale of the visualization curve are automatically recorded in the current parameter group information. In the future, whenever the visualization curve is added to this group, the visualization curve will be automatically drawn using this setting.

[0090] In some examples, the mouse point selection shown in the curve measurement allows the view of the visualized curve to sense mouse movement, displaying the name of the visualized curve and the value of that point when the mouse approaches a point on the curve.

[0091] In some examples, the cross-sectional data monitoring shown in curve measurement allows a measurement ruler to be displayed in the view of the visualized curve. The ruler can be dragged left and right with the mouse. The right side of the view window displays a curve value monitor, which shows the parameter names of the curve and the curve parameter values ​​at the time of the ruler position in a list.

[0092] In some examples, the curve interval measurement shown in the curve measurement allows the measurement rectangle to be displayed in the view of the visualized curve. The mouse can drag the left and right borders of the rectangle to move it left and right to select the measurement range. The software will measure and display the length of time and the maximum, minimum, average, and maximum rate of change of the curve within the interval (which can be equal to the maximum rate of change / communication cycle). The results of the curve measurement can also be exported in a specified format. Specifically, the formula for calculating the maximum value of the curve interval can be found in formula (4), the formula for calculating the minimum value of the curve interval can be found in formula (5), the formula for calculating the average value of the curve interval can be found in formula (6), and the formula for calculating the maximum rate of change of the curve interval can be found in formula (7).

[0093] (4)

[0094] In formula (4), Set the starting point index of the selected interval curve. The endpoint index of the selected interval curve. This selects all data points of the interval curve, and max(·) is the operation to retrieve the maximum value.

[0095] (5)

[0096] In formula (5), Set the starting point index of the selected interval curve. The endpoint index of the selected interval curve. This selects all data points of the interval curve, and min(·) is the operation to take the minimum value.

[0097] (6)

[0098] In formula (6), Set the starting point index of the selected interval curve. The endpoint index of the selected interval curve. This selects the data points within the selected interval curve.

[0099] (7)

[0100] In formula (7), , Indicates the first interval curve in the selected interval. The data point and the The absolute difference between the data points is used, and max(·) is the operation to find the maximum value. It is the communication cycle corresponding to the data protocol.

[0101] In some examples, curve calculation is used as follows: Click the "Curve Calculation" button in "Curve Processing" to display the formula editor. The formula editor's operation panel displays a parameter list and arithmetic symbol buttons. The parameter list shows all parameters from the data file; the function descriptions of the operator buttons are shown in Table 2.

[0102] Table 2

[0103]

[0104] In one possible implementation, the formula editor supports editing custom formulas. Specifically, clicking on a parameter name in the parameter list adds that parameter name to the far right of the formula, and clicking on an arithmetic symbol button adds that arithmetic symbol to the far right of the formula.

[0105] In one possible implementation, the formula editor displays the formula in real time in the "Calculate Formula" field and allows users to enter the formula name in the "New Curve Name" field. After editing, clicking the "Calculate" button performs the calculation, and the edited parameter names and formulas are recorded in the formula selection list.

[0106] In a possible implementation, all formulas defined in the formula editor can be substituted with parameters to generate new visualization curves.

[0107] Optionally, the implementation process for view settings can be found in [link to documentation]. Figure 5 The steps shown are accompanied by corresponding explanations.

[0108] Optionally, the data analysis process can be as follows: performing at least one of the following analysis operations on the curve display interface containing multiple visualization curves: creating labels, scaling curves, normalizing curves, and marking alarm points, so that the curve display interface after the analysis operation meets the user's needs; creating labels is used to generate text labels corresponding to each visualization curve in the curve display interface; scaling curves is used to change the shape of the mouse selection area on the curve display interface; normalizing curves is used to normalize multiple visualization curves; marking alarm points is used to mark alarm points corresponding to predefined alarm signals on the curve display interface.

[0109] In some examples, for creating labels, the "Data Analysis" function bar allows users to create new labels for any visualized curve, modify the label style, and delete labels. Specifically, clicking the "Create Label" button, selecting the curve in the curve view, and pressing the left mouse button displays the corresponding curve's label at the mouse hover position. The label is the curve's name, and its color matches the curve's color. Users can also set the label's color by clicking a color swatch or entering a color value, and set the label's font style and size via a dropdown menu. Its design is similar to common file editors, conforming to user habits.

[0110] In one possible implementation, a single label is displayed on each visual curve in the curve display interface. Clicking the left mouse button again on the visual curve repositions the label. Holding down the left mouse button on a label allows you to move the mouse and drag the label's position. The label's orientation remains relative to the visual curve; this relative orientation persists even after panning the image or scaling the curve.

[0111] In some examples, for curve scaling, you can select "Rectangular Scaling," "Horizontal Scaling," or "Vertical Scaling" in the "Data Analysis" toolbar to change the shape of the area selected by the mouse cursor.

[0112] In a possible implementation, rectangular scaling can be understood as: pressing the right mouse button, dragging the mouse to the right, enlarging the display range of the horizontal and vertical axes of the curve according to the rectangular area selected by the mouse, and dragging the mouse to the left to restore the curve.

[0113] In a possible implementation, horizontal scaling can be understood as: pressing the right mouse button, dragging the mouse to the right to enlarge the horizontal axis display range of the curve according to the rectangular area selected by the mouse, and dragging the mouse to the left to restore the curve.

[0114] In a possible implementation, vertical scaling can be understood as: pressing the right mouse button, dragging the mouse to the right, enlarging the display range of the vertical axis of the curve according to the rectangular area selected by the mouse, and dragging the mouse to the left to restore the curve.

[0115] In some examples, for curve normalization, the visualization curve is plotted as a percentage based on the maximum and minimum values, as shown in formula (8).

[0116] (8)

[0117] In formula (8), These are the normalized parameter values. These are the original parameter values. It is the maximum value among all data points in the interval. It is the minimum value among all data points in the interval.

[0118] In some examples, for marking alarm points, all fault alarm occurrence times can be marked on a trend graph based on defined alarm signals. In the trend graph, a red line is used to mark the time points where alarms occurred; the line is perpendicular to the time axis. At the top of the marked line, the total number of alarm signals at that time is displayed. Clicking on an alarm number lists the name of the alarm signal. Clicking "Ignore Alarm Points" hides all markers on the graph.

[0119] In some examples, the method shown in the embodiments of this application can be applied to an aviation business system for visualizing aviation data files. Specifically, the architecture of the aviation business system can be found in [reference needed]. Figure 8 As shown, the system includes a data analysis and processing module and a data analysis and visualization module. The system allows users to provide basic information about new data protocols and input it into the data analysis and processing module, which will then complete the configuration of the data protocols. Figure 8 The data import shown refers to the user collecting relevant data, selecting a template corresponding to the data protocol, and importing the data files in batches into the data analysis and processing module. The data analysis and processing module will parse the data files according to the configured data protocol and save the parsed parameter information to the database. The data analysis visualization module provides three service functions: view settings, curve processing, and data analysis.

[0120] In possible implementations Figure 8 The system demonstrated possesses the ability to handle multiple data protocols, thus breaking through the limitations of traditional tools in data compatibility. By parsing different data protocols, the visualization tool in this study can transform complex data into intuitive charts, improving not only data readability but also enhancing information delivery efficiency. Furthermore, the system allows users to customize chart styles according to personal preferences or specific analytical needs, including but not limited to elements such as color, font, and layout, to achieve optimal visual presentation. Moreover, the system goes beyond simply displaying data; it also incorporates powerful statistical analysis functions. This means users can directly perform statistical operations such as summarizing data, calculating averages, and standard deviations within the charts without switching to other software or tools, significantly improving work efficiency.

[0121] In some examples, the data visualization analysis method shown in the embodiments of this application can achieve the following technical innovations: (1) Configurable data reading and parsing technology: Configurable data reading and parsing technology is designed based on different manufacturers' data protocols. No code modification is required, and data protocol compatibility can be achieved through interface configuration; (2) Customizable data visualization technology: Data visualization realizes custom configuration, allowing users to modify and adjust curve background, curve style, curve color, curve coordinate position, etc., to present the data to users more intuitively; (3) Customizable data analysis technology: Coordinate axis normalization processing is realized for multiple curves, which facilitates comparative analysis of multiple parameter information. Statistical calculation and scaling processing of data are realized, which facilitates users to understand data details. Alarm points are marked for abnormal data, which facilitates users to process and maintain the data.

[0122] In some examples, the data visualization analysis method shown in the embodiments of this application can achieve the following beneficial effects: (1) It is flexible, accurate and adaptable to user habits, with a variety of curve style modification and adjustment functions, and human-computer interaction is convenient and effective; (2) It can be compatible with multiple data protocols and can achieve configurable parsing without modifying the source code; (3) It is more sensitive to aviation data, understands the specific business process of aero-engines, and the data analysis results are more accurate.

[0123] The processes shown in S101-S103 above utilize the configured data protocol to parse the user-input data file to obtain parameter information. By performing visual analysis on the parameter information, the data protocol can be customized to parse data under various protocols and display the visualization results. This not only has wide applicability but also provides a more intuitive display effect.

[0124] like Figure 2 The diagram shown is a flowchart of another data visualization and analysis method provided in this application embodiment, including the following steps.

[0125] S201: Based on the basic information of the data protocol, analyze and determine the corresponding data protocol configuration table, data parameter parsing configuration table, and discrete quantity bitwise parsing table.

[0126] The data protocol configuration table stores basic information about the sample data protocol, such as protocol name, data packet length, and data type.

[0127] In some examples, the data parameter parsing configuration table is used to store basic information about data parsing parameters, including parameter name, parameter unit, data type, etc.

[0128] In some examples, a bitwise parsed table for discrete quantities is used to store basic information about discrete quantity parameters, including the discrete quantity name, discrete quantity unit, data type, etc.

[0129] S202: Based on the data protocol configuration table, data parameter parsing configuration table, and discrete bit-by-bit parsing table, establish the template configuration for the corresponding data protocol.

[0130] Among them, based on the data protocol configuration table, the data parameter parsing configuration table, and the discrete bit-by-bit parsing table, a template configuration (which can be called a template) for the corresponding data protocol is established, which can facilitate the rapid import of data by using the template configuration.

[0131] The processes shown in S201-S202 above, by determining the data protocol configuration table, data parameter parsing configuration table, and discrete bit-by-bit parsing table, suggest template configurations that facilitate data import.

[0132] like Figure 3 The diagram shown is a flowchart of another data visualization and analysis method provided in this application embodiment, including the following steps.

[0133] S301: Select the template for the corresponding data protocol based on the data file.

[0134] The process involves using a template for a predefined data protocol, importing a data file, selecting the corresponding data protocol template, and then structuring the data according to the defined data protocol format to parse out the required parameter information and store the results in the database.

[0135] S302: Use the template of the data protocol to parse the data file and obtain the parsed parameter information.

[0136] Specifically, the data file is parsed using the data parameter parsing configuration table to obtain physical quantity parameters and corresponding over-limit parameter information, and the data file is parsed using the discrete quantity bit-by-bit parsing table to obtain discrete quantity parameters and corresponding alarm parameter information.

[0137] It should be noted that during the data file parsing process, if the parsed data parameter is a physical quantity (i.e., a physical quantity parameter), then the out-of-limit information (i.e., out-of-limit parameter information) needs to be configured for that data parameter. If the parsed data parameter is a discrete quantity (i.e., a discrete quantity parameter), then the alarm information (i.e., alarm parameter information) needs to be configured for that data parameter. During parsing, the data parameter is analyzed, automatically determining whether the data parameter has alarms or exceeds limits, and marking the corresponding alarm points in the visualization interface. The parsed data parameter is then categorized according to the business scenario, and the categorized data is structured and stored in different data tables in the database, thereby further improving the efficiency of data analysis.

[0138] Optionally, after parsing the data file based on the configured data protocol to obtain the parsed parameter information, the parameter information can also be structured and stored in the corresponding data table.

[0139] The processes shown in S301-S302 above can use the template of the data protocol to parse the data file to obtain parameter information, thereby enabling rapid data import.

[0140] like Figure 4 The diagram shown is a flowchart of another data visualization and analysis method provided in this application embodiment, including the following steps.

[0141] S401: Retrieve structured parameter information from the database.

[0142] After a user successfully imports a data file, a preset parameter selection list (composed of multiple structured parameter information from the database) will display the names of all parsed parameters, allowing the user to select the parameter information to be analyzed.

[0143] In some examples, if the parameter information includes discrete bit configuration information for the relevant parameters, the relevant parameters in the parameter selection list will include a secondary parameter list. Clicking the arrow before the parameter name will expand the list and show all discrete bit signal parameter names.

[0144] S402: Iterate through the parameters shown in each parameter information to obtain the data type and parameter value corresponding to each parameter.

[0145] Specifically, by iterating through each parameter name in the parameter selection list, the corresponding parameter ID is obtained, and then the data type and parameter value corresponding to the parameter ID are obtained.

[0146] S403: Normalize the parameter value corresponding to each parameter.

[0147] In this process, the parameter values ​​corresponding to each parameter are normalized to ensure the consistency of the data in terms of measurement units, which makes the parameter curves that are subsequently established more aesthetically pleasing.

[0148] S404: Determine the curve type corresponding to each parameter based on the data type corresponding to each parameter.

[0149] The data type includes either physical quantity or discrete quantity. Specifically, the curve type corresponding to physical quantity is a trend curve (e.g., trend graph), and the curve type corresponding to discrete quantity is a distribution curve (e.g., distribution graph).

[0150] S405: Generate the parameter curve corresponding to each parameter based on the curve type and the normalized parameter value.

[0151] In some examples, the corresponding curve type and parameter value are obtained based on the parameter name selected by the user, and the parameter value is normalized to make the generated parameter curve more aesthetically pleasing. Secondly, while generating the parameter curve, out-of-limit or alarm parameter information can be obtained based on the data type, and corresponding alarm points can be marked on the parameter curve to more intuitively remind the user of the current parameter status.

[0152] Optionally, after performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, alarm points can also be marked on the visual curve according to the out-of-limit parameter information or alarm parameter information shown in the parameter information.

[0153] In some examples, the process of curve plotting can also be summarized as follows: Figure 9 As shown.

[0154] The processes shown in S401-S405 above generate corresponding parameter curves based on the data type and parameter values ​​indicated in the parameter information, which serve as visualization curves for the parameter information, thereby realizing the visualization of the parameter information.

[0155] like Figure 5 The diagram shown is a flowchart of another data visualization and analysis method provided in this application embodiment, including the following steps.

[0156] S501: Configure the view of the visualized curve to change the view parameters.

[0157] The view parameters include at least one or more of the following: grid lines, main grid line spacing, sub-grid line spacing, background color, foreground color, legend display, line width, origin setting, and coordinate axis range.

[0158] In some examples, the view style can be modified in the "View Options" function bar. The view settings are used to change the view parameters of the visualized curves. See Table 3 for specific view parameters.

[0159] Table 3

[0160] Function illustrate Grid lines Set the gridline style in the curve view, including: no gridlines, solid lines (main spacing lines are solid, sub-spacing lines are dashed), dashed lines, horizontal solid lines (main spacing lines are solid, sub-spacing lines are dashed), horizontal dashed lines, and vertical solid lines (main spacing lines are solid, sub-spacing lines are dashed), vertical dashed lines. Grid line main spacing Set the grid line spacing corresponding to the main tick of the main coordinate axis. Grid line spacing Set the grid line spacing corresponding to the main coordinate axis ticks. Background color Set the background color of the curve view Foreground Set the coordinate axes, cursor text, and legend font color in the curve view. Legend shows Set whether to display the graph legend. Line width Setting the curve width will not work if it exceeds the default maximum width. Origin setting Set the coordinate origin (x, y) values. coordinate axis range By clicking the arrows in different directions, you can shift the coordinate axes in the corresponding directions.

[0161] S502: Generate the corresponding image file based on the modified visualization curve.

[0162] S503: Generate the filename of the image file based on the curve type and parameter information of the modified visualization curve.

[0163] The filename generated based on the curve type and parameter information can be “xxx_yyyy-MM-dd HH:mm:ss”. Specifically, “xxx” is a combination of the curve type and parameter name shown in the parameter information, and “yyyy-MM-dd HH:mm:ss” is the date and time of saving the image file. The curve graph in the view window is saved to the specified path in the specified format.

[0164] S504: Save the image file to the specified path.

[0165] If a file with the same name exists in the specified path, a symbol (such as a number) will be automatically added to the end of the file name to avoid overwriting the existing image file.

[0166] The processes described in S501-S504 above enable the setting of views for visualized curves and the saving of image files.

[0167] like Figure 6 The diagram shown is a schematic representation of the architecture of a data visualization and analysis device provided in an embodiment of this application, which includes the following units.

[0168] The protocol configuration unit 100 is used to configure the corresponding data protocol according to the basic information of the data protocol.

[0169] Optionally, the protocol configuration unit 100 is specifically used to: analyze and determine the data protocol configuration table, data parameter parsing configuration table, and discrete bit-by-bit parsing table of the corresponding data protocol based on the basic information of the data protocol; and establish the template configuration of the corresponding data protocol based on the data protocol configuration table, data parameter parsing configuration table, and discrete bit-by-bit parsing table.

[0170] The parameter parsing unit 200 is used to parse the data file based on the configured data protocol to obtain the parsed parameter information.

[0171] Optionally, the parameter parsing unit 200 is specifically used for: selecting the corresponding data protocol template according to the data file; using the data protocol template to parse the data file and obtain the parsed parameter information, wherein the data file is parsed using the data parameter parsing configuration table to obtain physical quantity parameters and corresponding over-limit parameter information, and the data file is parsed using the discrete quantity bit-by-bit parsing table to obtain discrete quantity parameters and corresponding alarm parameter information.

[0172] Optionally, the parameter parsing unit 200 is also used to: store the parameter information in a structured manner in the corresponding data table.

[0173] The visualization analysis unit 300 is used to perform visualization analysis on parameter information and obtain the visualization analysis results corresponding to the parameter information.

[0174] Optionally, the visualization analysis unit 300 is specifically used for: performing visualization analysis on parameter information to obtain a visualization curve as the visualization analysis result corresponding to the parameter information; the visualization analysis process includes at least curve drawing; curve drawing is used to generate the corresponding parameter curve.

[0175] Optionally, the visualization analysis unit 300 is further configured to: perform at least one of the following processing operations on the visualized curve: curve setting, curve measurement, curve adjustment, and curve calculation, so that the visualized curve after processing meets the user's needs; curve setting is configured to change at least one of the following attributes of the visualized curve: curve style, curve color, curve point style, and vertical axis dependence; curve measurement is configured to perform at least one of the following surveying operations on the view of the visualized curve: mouse point selection, cross-sectional data monitoring, and curve interval measurement; curve adjustment is configured to perform at least one of the following adjustment operations on the visualized curve: curve scaling, vertical offset adjustment, data alignment, and automatic memory; curve calculation is configured to display a formula editor in a pop-up window on the view of the visualized curve, set curve formulas for existing parameters, and draw a curve that fits the curve formula.

[0176] Optionally, the visualization analysis unit 300 is also used to: mark the corresponding alarm points for the visualization curve based on the out-of-limit parameter information or alarm parameter information shown in the parameter information.

[0177] Optionally, the visualization analysis unit 300 is also used to: set the view of the visualization curve to change the view parameters, which include at least one or more of the following: grid lines, main grid line spacing, sub-grid line spacing, background color, foreground color, legend display, line width, origin setting, and coordinate axis range; generate a corresponding image file based on the changed visualization curve; generate the image file name according to the curve type and parameter information of the changed visualization curve; and save the image file to a specified path.

[0178] Optionally, the visualization analysis unit 300 is also used to: perform at least one of the following analysis operations on the curve display interface containing multiple visualization curves: creating labels, scaling curves, normalizing curves, and marking alarm points, so that the curve display interface after the analysis operation meets the user's needs; creating labels is used to generate text labels corresponding to each visualization curve in the curve display interface; scaling curves is used to change the shape of the mouse selection area on the curve display interface; normalizing curves is used to normalize multiple visualization curves; marking alarm points is used to mark alarm points corresponding to predefined alarm signals on the curve display interface.

[0179] Optionally, the process of curve plotting implemented by the visualization analysis unit 300 includes: obtaining multiple structured parameter information from the database; traversing the parameters shown in each parameter information to obtain the data type and parameter value corresponding to each parameter; normalizing the parameter value corresponding to each parameter; determining the curve type corresponding to each parameter based on the data type corresponding to each parameter; and generating the parameter curve corresponding to each parameter based on the curve type corresponding to each parameter and the normalized parameter value.

[0180] Each of the units described above uses a configured data protocol to parse the user-input data file to obtain parameter information. By visually analyzing the parameter information, the data protocol can be customized to parse data under various protocols and display the visualization results. This not only has wide applicability but also provides a more intuitive display effect.

[0181] This application also provides a computer-readable storage medium including a stored program, wherein the program executes the data visualization and analysis method provided in this application.

[0182] This application also provides an electronic device, including a processor, a memory, and a bus. The processor and the memory are connected via the bus. The memory is used to store a program, and the processor is used to run the program. During program execution, the data visualization and analysis method provided in this application is performed.

[0183] While several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this application. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0184] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A data visualization analysis method, characterized in that, include: Configure the corresponding data protocol based on the basic information of the data protocol; Based on the configured data protocol, the data file is parsed to obtain the parsed parameter information; The parameter information is visualized and analyzed to obtain the visualization analysis results corresponding to the parameter information.

2. The method according to claim 1, characterized in that, Configure the corresponding data protocol based on the basic information of the data protocol, including: Based on the basic information of the data protocol, analyze and determine the corresponding data protocol configuration table, data parameter parsing configuration table, and discrete quantity bitwise parsing table. Based on the data protocol configuration table, the data parameter parsing configuration table, and the discrete quantity bitwise parsing table, a template configuration for the corresponding data protocol is established.

3. The method according to claim 1, characterized in that, Based on the configured data protocol, the data file is parsed to obtain the parsed parameter information, including: Select the corresponding data protocol template based on the data file; The data file is parsed using the template of the data protocol to obtain the parsed parameter information. Specifically, the data file is parsed using the data parameter parsing configuration table to obtain physical quantity parameters and corresponding over-limit parameter information, and the data file is parsed using the discrete quantity bitwise parsing table to obtain discrete quantity parameters and corresponding alarm parameter information.

4. The method according to claim 1, characterized in that, After parsing the data file based on the configured data protocol to obtain the parsed parameter information, the method further includes: The parameter information is stored in a structured manner in the corresponding data table.

5. The method according to claim 1, characterized in that, The step of performing visualization analysis on the parameter information to obtain the visualization analysis results corresponding to the parameter information includes: The parameter information is subjected to visualization analysis to obtain a visualization curve, which serves as the visualization analysis result corresponding to the parameter information; the visualization analysis process includes at least curve plotting; the curve plotting is used to generate the corresponding parameter curve.

6. The method according to claim 5, characterized in that, After performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes: The visualization curve is processed by at least one of the following operations: curve setting, curve measurement, curve adjustment, and curve calculation, so that the visualized curve after processing meets the user's needs. The curve setting is used to change at least one of the following attributes of the visualization curve: curve style, curve color, curve point style, and vertical axis dependence. The curve measurement is used to perform at least one of the following mapping operations on the view of the visualization curve: mouse point selection, cross-sectional data monitoring, and curve interval measurement. The curve adjustment is used to perform at least one of the following adjustment operations on the visualization curve: curve scaling, vertical offset adjustment, data alignment, and automatic memory. The curve calculation is used to display a formula editor in a pop-up window on the view of the visualization curve, set the curve formula for existing parameters, and draw a curve that fits the curve formula.

7. The method according to claim 5, characterized in that, After performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes: Based on the out-of-limit parameter information or alarm parameter information shown in the parameter information, mark the corresponding alarm points for the visualized curve.

8. The method according to claim 5, characterized in that, After performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes: The view of the visualization curve is set to change the view parameters of the view. The view parameters include at least one or more of the following: grid lines, main grid line spacing, sub-grid line spacing, background color, foreground color, legend display, line width, origin setting, and coordinate axis range. Generate the corresponding image file based on the modified visualization curve; Based on the curve type of the modified visualization curve and the parameter information, generate the filename of the image file; Save the image file to the specified path.

9. The method according to claim 5, characterized in that, After performing visual analysis on the parameter information to obtain a visual curve as the visualization analysis result corresponding to the parameter information, the method further includes: An analysis operation is performed on a curve display interface containing multiple visualized curves, including at least one of the following: creating labels, scaling curves, normalizing curves, and marking alarm points, so that the curve display interface after the analysis operation meets the user's needs. The label creation is used to generate text labels corresponding to each visualized curve in the curve display interface. The curve scaling is used to change the shape of the mouse selection area on the curve display interface. The curve normalization is used to normalize multiple visualized curves. The alarm point marking is used to mark alarm points corresponding to predefined alarm signals on the curve display interface.

10. The method according to claim 5, characterized in that, The process of drawing the curve includes: Retrieve structured parameter information from the database; Iterate through each parameter shown in the parameter information to obtain the data type and parameter value corresponding to each parameter; Normalize the parameter value corresponding to each of the above parameters; Based on the data type corresponding to each parameter, determine the curve type corresponding to each parameter; Based on the curve type corresponding to each parameter and the normalized parameter value, a parameter curve corresponding to each parameter is generated.

11. A data visualization and analysis device, characterized in that, include: Protocol configuration unit, parameter parsing unit, and visualization analysis unit; The protocol configuration unit is used to configure the corresponding data protocol according to the basic information of the data protocol; The parameter parsing unit is used to parse the data file based on the configured data protocol to obtain the parsed parameter information; The visualization analysis unit is used to perform visualization analysis on the parameter information and obtain the visualization analysis results corresponding to the parameter information.

12. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the data visualization and analysis method as described in any one of claims 1 to 10.

13. An electronic device, characterized in that, The electronic device includes at least one processor, at least one memory connected to the processor, and a bus; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the data visualization analysis method as described in any one of claims 1 to 10.

Images