Assessment result visualization methods, apparatuses, devices, and media

Abstract

The present disclosure provides a result evaluation visualization method, device, equipment and medium, comprising: calling a corresponding target scene template from a scene template library according to an evaluation scene type of multi-dimensional evaluation data; fusing user-defined data operation parameters and the target scene template to generate chart core configuration parameters; constructing a multi-layer chart structure according to the chart core configuration parameters; performing multi-layer rendering on the multi-layer chart structure to obtain an initial visualization chart; adding a user interaction module comprising a dimension filtering unit, a weight adjusting unit, a data traceability unit and a chart exporting unit in the initial visualization chart to generate a visualization display chart corresponding to the multi-dimensional evaluation data. Thus, an adaptive visualization scheme is quickly generated through a scene template, and the configuration time is reduced; the original data, the correlation and the key warning are simultaneously displayed through the multi-layer, the key information is highlighted while the visualization information is displayed in layers, and the browsing effect is effectively improved.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of chart visualization, and more specifically, to a method, apparatus, device, and medium suitable for visualizing evaluation results. Background Technology

[0002] Visualizing assessment results is an important part of data analysis and decision support. By integrating multi-dimensional assessment data with interactive controls, it allows users to more intuitively grasp the overall distribution and detailed characteristics of the assessment results.

[0003] In related technologies, fixed chart templates are mainly used to display evaluation results. As the dimensions and volume of evaluation data increase, problems such as chaotic visualization information hierarchy and lack of prominence of key results can easily occur, affecting the browsing experience. Summary of the Invention

[0004] The embodiments described herein provide a method, apparatus, device, and medium for visualizing evaluation results, overcoming the aforementioned problems.

[0005] Firstly, based on the content of this disclosure, a method for visualizing evaluation results is provided, including: Based on the evaluation scenario type of the multi-dimensional evaluation data, the corresponding target scenario template is called from the scenario template library; the target scenario template is pre-configured with basic chart types, indicator display levels and color mapping rules; The user-defined data operation parameters are combined with the target scene template to generate the core configuration parameters of the chart; A multi-layer chart structure is constructed based on the core configuration parameters of the chart. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display the multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to issue warnings for key data. The multi-layer chart structure is rendered in multiple layers to obtain an initial visual chart; Add a user interaction module containing dimension filtering, weight adjustment, data tracing, and chart export units to the initial visualization chart to generate a visualization chart corresponding to the multi-dimensional evaluation data.

[0006] Secondly, according to the present disclosure, an evaluation result visualization device is provided, comprising: The calling module is used to call the corresponding target scenario template from the scenario template library based on the assessment scenario type of the multi-dimensional assessment data; the target scenario template is pre-configured with basic chart types, indicator display levels and color mapping rules; The fusion module is used to fuse user-defined data operation parameters with the target scene template to generate core chart configuration parameters; The construction module is used to construct a multi-layer chart structure based on the core configuration parameters of the chart. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display the multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to issue warnings for key data. The rendering module is used to perform multi-layer rendering of the multi-layer chart structure to obtain an initial visual chart; The generation module is used to add a user interaction module containing a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit to the initial visualization chart, thereby generating a visualization chart corresponding to the multi-dimensional evaluation data.

[0007] Thirdly, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the evaluation result visualization method as described in any of the above embodiments.

[0008] Fourthly, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the evaluation result visualization method as described in any of the above embodiments.

[0009] The evaluation result visualization method provided in this application embodiment calls the corresponding target scenario template from the scenario template library according to the evaluation scenario type of multi-dimensional evaluation data. The target scenario template is pre-configured with basic chart types, indicator display levels, and color mapping rules. User-defined data operation parameters are integrated with the target scenario template to generate core chart configuration parameters. A multi-layer chart structure is constructed based on the core chart configuration parameters. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to warn of key data. The multi-layer chart structure is rendered to obtain an initial visualization chart. A user interaction module containing dimension filtering units, weight adjustment units, data tracing units, and chart export units is added to the initial visualization chart to generate a visualization display chart corresponding to the multi-dimensional evaluation data. In this way, a suitable visualization scheme can be quickly generated through scenario-based templates, reducing configuration time. By displaying original data, correlation relationships, and key warnings simultaneously through multiple layers, key information is highlighted while achieving layered display of visualization information, thereby effectively improving the browsing experience.

[0010] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the drawings described below only relate to some embodiments of this disclosure and are not intended to limit this disclosure, wherein: Figure 1 This is a flowchart illustrating an evaluation result visualization method provided in this disclosure.

[0012] Figure 2 This is a schematic diagram of the structure of an evaluation result visualization device provided in this disclosure.

[0013] Figure 3 This is a schematic diagram of the structure of a computer device provided in this disclosure.

[0014] It should be noted that the elements in the attached diagram are schematic and not drawn to scale. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are also within the scope of protection of this disclosure.

[0016] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this subject matter pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having the meaning consistent with their meaning in the context of the specification and in the relevant art, and shall not be interpreted in an idealized or overly formal form unless otherwise explicitly defined herein. As used herein, the statement of “connecting” or “coupling” two or more parts together shall mean that these parts are directly joined together or joined through one or more intermediate components.

[0017] The term "embodiment" as used herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of the phrase "embodiment" in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0018] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists, A and B exist simultaneously, or B exists. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship. Terms such as "first" and "second" are only used to distinguish one component (or part of a component) from another component (or another part of a component).

[0019] In the description of this application, unless otherwise stated, "multiple" means two or more (including two), and similarly, "multiple groups" means two or more (including two groups).

[0020] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0021] Figure 1 This is a flowchart illustrating an evaluation result visualization method provided in an embodiment of this disclosure. Figure 1 As shown, the specific process of the evaluation result visualization method includes: S110. Based on the evaluation scenario type of the multi-dimensional evaluation data, call the corresponding target scenario template from the scenario template library.

[0022] The target scenario template pre-configured basic chart types, indicator display levels, and color mapping rules. Basic chart types are pre-defined and describe the correspondence between chart types and display formats, such as radar charts + bar charts for risk assessment and heatmaps + scatter plots for outcome assessment. Indicator display levels prioritize indicators based on their importance, limiting the display weight and prominence of different indicators in the chart; core assessment indicators receive higher weight, while auxiliary indicators are displayed less prominently. Color mapping rules associate indicator assessment scores with display colors, such as green for high scores and red for low scores.

[0023] In some embodiments, before calling the corresponding target scene template from the scene template library according to the evaluation scene type of the multi-dimensional evaluation data, the method further includes: removing outliers and filling in missing values ​​in the multi-dimensional evaluation data; and processing the multi-dimensional evaluation data according to a preset standard format.

[0024] The preset standard format pre-defines the data naming rules, data storage paths, and data precision thresholds for different dimensions of evaluation data. When imputing missing values, numerical indicators can be imputed using the mean, while categorical indicators can be imputed using the mode.

[0025] S120 integrates user-defined data operation parameters with target scenario templates to generate core chart configuration parameters.

[0026] Among them, user-defined data operation parameters may include, but are not limited to, target display dimensions, weight adjustment coefficients, and interaction triggering methods.

[0027] Users can select the desired chart components from the chart template library through a drag-and-drop interactive visual layout editor, and place them into the canvas area to achieve a WYSIWYG layout. For each type of chart template in the template library, the system uses a metadata-driven configuration extraction mechanism to extract common attribute sets (such as axis styles, legend configurations, color themes, font specifications, etc., applicable to multiple chart types) and specific attribute sets (such as the corner radius of bar charts, the line width and marker style of line charts, the sector starting angle and label leader line position of pie charts, etc., applicable only to specific chart types).

[0028] In addition, the system provides a dynamically generated structured configuration interface, where users can assign and adjust public and private attributes separately. The system maintains an attribute merging engine that performs deep merging and conflict resolution of the two configurations based on the selected chart template and corresponding attribute mapping rules, ultimately generating unified chart JSON description data.

[0029] S130. Construct a multi-layer chart structure based on the core chart configuration parameters.

[0030] The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key alert layer. The basic data layer displays multi-dimensional evaluation data; the correlation analysis layer displays the correlation coefficients between data, such as using thick lines to show the correlation coefficients between indicators; and the key alert layer provides warnings for key data, such as using flashing animations to mark indicators below a threshold.

[0031] In some embodiments, a multi-layer chart structure is constructed based on the chart's core configuration parameters, including: Multi-dimensional evaluation data is mapped to a base canvas to generate a base data layer for displaying the evaluation data. Linear correlation coefficients between different dimensions of evaluation data are calculated, sorted by correlation strength, and then superimposed on the base data layer to generate a correlation analysis layer that presents the relationships between different dimensions of evaluation data. Anomalies are filtered from the multi-dimensional evaluation data based on a preset anomaly threshold. Highlighted warning labels are added to the display locations of evaluation data exceeding the preset anomaly threshold and superimposed on the correlation analysis layer to generate a key warning layer. A multi-layered chart structure is generated based on the base data layer, the correlation analysis layer, and the key warning layer.

[0032] The linear correlation coefficients between the data from different dimensions can be calculated using the Pearson correlation coefficient formula. After calculation, the absolute values ​​of the coefficients are extracted and sorted from largest to smallest.

[0033] The basic data layer can be used as the bottom layer, the correlation analysis layer as the layer above the basic data layer, and the key warning layer as the layer above the key analysis layer, which facilitates multi-layer display.

[0034] In addition, the multi-layer chart structure includes spatial layout attributes: the chart's display position in the dashboard or report page (X / Y coordinates or grid row and column index), size (width, height), margins, alignment, and hierarchy order, supporting responsive adaptive layout. Visual style attributes: provide color schemes (supporting global theme color, single-series color, conditional color mapping, and gradient configuration), chart element styles (rounded corners of columns, line width, area fill transparency, pie chart sector separation, etc.), axis styles (axis lines, tick marks, grid lines, and label formats), and the display and positioning of legends, titles, and subtext. Text specification attributes: allow independent configuration of the font family, font size, font weight, font color, line height, and text rotation angle for various text types within the chart (titles, axis labels, data labels, legend items) to adapt to different information levels and visual readability requirements.

[0035] S140. Render the multi-layer chart structure using multiple layers to obtain the initial visual chart.

[0036] The SVG (Scalable Vector Graphics) vector drawing technology can be used to render each layer and generate an initial visual chart.

[0037] In some embodiments, multi-layer rendering is performed on the multi-layer chart structure to obtain an initial visual chart, including: Set different display transparency levels for the basic data layer, the correlation analysis layer, and the key warning layer respectively; and call the graphics rendering interface in sequence from bottom to top to render the basic data layer, the correlation analysis layer, and the key warning layer independently to obtain the initial visualization chart.

[0038] Specifically, the display transparency of the key warning layer can be set to be lower than that of the correlation analysis layer, and the display transparency of the correlation analysis layer can be set to be lower than that of the basic data layer. This allows for the differentiation of information levels among the layers through differences in transparency, highlighting key warning content, while retaining basic and correlation analysis information as background references to prevent content from different layers from obscuring each other and interfering with interpretation.

[0039] S150. Add a user interaction module to the initial visualization chart, which includes a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit, to generate a visualization chart corresponding to the multi-dimensional evaluation data.

[0040] The dimension filtering unit allows users to select indicators to add or remove dimensions; the weight adjustment unit allows users to adjust indicator weights using a slider and update chart data in real time; the data traceability unit allows users to click on data nodes to display the original scoring basis and evaluator information; and the chart export unit supports exporting in PDF, PNG, and editable SVG formats.

[0041] Data binding and mapping attributes, as well as interaction and behavior attributes between charts, can be configured in the interactive module. Data binding and mapping attributes are responsible for establishing associations between the data fields required by the chart and the visualization elements. This includes performing data cleaning, missing value handling, outlier removal, and data type standardization on the raw data to generate a data source that meets the requirements of the chart's data model. It also supports integration with various heterogeneous data sources, including static JSON objects, database query result sets, and RESTful API return fields. Users can flexibly map data fields to the corresponding visual channel configuration items of the chart through visual drag-and-drop (dragging fields to the configuration area) or expression binding (writing field mapping expressions), such as: horizontal axis (category fields), vertical axis (numerical fields), grouping / stacking fields, chart size, and color saturation. Configuring chart interaction and behavior attributes can enhance users' data exploration capabilities; enabled interaction mechanisms include: triggering events by clicking on data items in the chart (such as selection, hover tooltip); drill-down operations (drilling down to finer-grained data, or jumping to related detail pages); chart linkage (filtering or highlighting other charts or components within the same dashboard to achieve cross-chart collaborative filtering); zooming and panning (enlarging or panning a data area); and data brushing (selecting subsets by box selection or lasso to achieve real-time filtering).

[0042] In some embodiments, a user interaction module including a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit is added to the initial visualization chart to generate a visualization chart corresponding to the multi-dimensional evaluation data, including: Separate an independent interactive sidebar from the canvas area of ​​the initial visualization chart, and deploy dimension filtering unit, weight adjustment unit, data tracing unit and chart export unit in sequence in the interactive sidebar.

[0043] The system includes a dimension filtering unit for filtering multi-dimensional evaluation data, a weight adjustment unit for adjusting the display proportion of different dimensions of evaluation data, a data tracing unit for tracing the data collection source of multi-dimensional evaluation data, and a chart export unit for exporting visualization charts.

[0044] The dimension filtering unit can be deployed above the weight adjustment unit, the weight adjustment unit can be deployed above the data tracing unit, and the data tracing unit can be deployed above the chart export unit, making it convenient for users to perform optional operations.

[0045] In some embodiments, the method further includes: in response to a trigger operation of the dimension filtering unit, filtering out the corresponding target evaluation data from the multi-dimensional evaluation data; and / or: in response to a trigger operation of the weight adjustment unit, adjusting the display ratio of different dimension evaluation data in the multi-dimensional evaluation data; and / or: in response to a trigger operation of the data tracing unit, querying the data collection source of the target evaluation data; and / or: in response to a trigger operation of the chart export unit, exporting a visualization chart according to a preset chart format.

[0046] Users can filter evaluation data using the trigger dimension filtering unit, adjust the display proportion of different dimensions of evaluation data using the weight adjustment unit, trace the data collection source using the data tracing unit, and export visualization charts using the chart export unit. This allows users to flexibly adjust the display dimensions and presentation methods of evaluation results according to their own needs, while verifying the reliability of data sources. This enhances the intuitiveness and credibility of multi-dimensional evaluation result presentation, meeting the visualization and export needs of evaluation results in different scenarios.

[0047] In some embodiments, the method further includes updating the visualization charts based on the changed multidimensional evaluation data when a change in the multidimensional evaluation data is detected.

[0048] In real-time monitoring scenarios, the latest data can be automatically retrieved and the chart display refreshed according to a specified period by setting the polling interval, thus meeting the needs of real-time monitoring scenarios.

[0049] In this embodiment, based on the assessment scenario type of the multi-dimensional evaluation data, the corresponding target scenario template is called from the scenario template library. The target scenario template is pre-configured with basic chart types, indicator display levels, and color mapping rules. User-defined data operation parameters are integrated with the target scenario template to generate core chart configuration parameters. A multi-layer chart structure is constructed based on the core chart configuration parameters, including a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to warn of key data. The multi-layer chart structure is rendered to obtain an initial visualization chart. A user interaction module containing dimension filtering units, weight adjustment units, data tracing units, and chart export units is added to the initial visualization chart to generate a visualization display chart corresponding to the multi-dimensional evaluation data. In this way, a suitable visualization solution can be quickly generated through scenario-based templates, reducing configuration time. By simultaneously displaying raw data, correlation relationships, and key warnings through multiple layers, key information is highlighted while achieving layered display of visualization information, thereby effectively improving the browsing experience.

[0050] In summary, this embodiment modularizes common charts required in assessment operations, primarily applying them to general-purpose chart components for assessment data visualization. Based on a pre-packaged, extensible chart template library (including but not limited to bar charts, line charts, pie charts, radar charts, scatter plots, dashboards, and heatmaps), it can quickly build diverse visualization charts adapted to different assessment scenarios. The components adopt a decoupled "template + configuration" architecture, internally containing a visualization rendering engine and a hierarchical attribute configuration model. Within the software system, the various levels of chart attributes can be freely configured according to actual business scenarios through a graphical interface or configuration interface. This allows for the rapid generation of adapted visualization solutions through scenario-based templates, reducing configuration time; multi-layered structures simultaneously display raw data, relationships, and key warnings; rich interactive functions support personalized adjustments and in-depth data exploration; intelligent data preprocessing ensures the accuracy and reliability of visualization; and modular design supports multiple assessment scenarios and diverse needs.

[0051] Figure 2 This is a schematic diagram of the structure of an evaluation result visualization device provided in this embodiment. The evaluation result visualization device may include: Module 210 is used to call the corresponding target scenario template from the scenario template library based on the assessment scenario type of the multi-dimensional assessment data; the target scenario template is pre-configured with basic chart types, indicator display levels and color mapping rules.

[0052] The fusion module 220 is used to merge user-defined data operation parameters with target scene templates to generate chart core configuration parameters.

[0053] Module 230 is used to construct a multi-layer chart structure based on the core chart configuration parameters. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to issue warnings for key data.

[0054] Rendering module 240 is used to perform multi-layer rendering on multi-layer chart structures to obtain an initial visual chart.

[0055] The generation module 250 is used to add a user interaction module containing dimension filtering units, weight adjustment units, data tracing units, and chart export units to the initial visualization chart, and generate a visualization display chart corresponding to the multi-dimensional evaluation data.

[0056] In some embodiments, the construction module 230 is specifically used for: Multi-dimensional evaluation data is mapped to a base canvas to generate a base data layer for displaying the evaluation data. Linear correlation coefficients between different dimensions of evaluation data are calculated, sorted by correlation strength, and then superimposed on the base data layer to generate a correlation analysis layer that presents the relationships between different dimensions of evaluation data. Anomalies are filtered from the multi-dimensional evaluation data based on a preset anomaly threshold. Highlighted warning labels are added to the display locations of evaluation data exceeding the preset anomaly threshold and superimposed on the correlation analysis layer to generate a key warning layer. A multi-layered chart structure is generated based on the base data layer, the correlation analysis layer, and the key warning layer.

[0057] In some embodiments, the rendering module 240 is specifically used for: Set different display transparency levels for the basic data layer, the correlation analysis layer, and the key warning layer respectively; and call the graphics rendering interface in sequence from bottom to top to render the basic data layer, the correlation analysis layer, and the key warning layer independently to obtain the initial visualization chart.

[0058] In some embodiments, the generation module 250 is specifically used for: An independent interactive sidebar is created from the canvas area of ​​the initial visualization chart. Within this sidebar, a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit are deployed sequentially. The dimension filtering unit is used to filter multi-dimensional evaluation data, the weight adjustment unit is used to adjust the display proportion of different dimensions of evaluation data, the data tracing unit is used to trace the data collection source of multi-dimensional evaluation data, and the chart export unit is used to export visualization charts.

[0059] In some embodiments, it further includes: a first processing module.

[0060] The first processing module is used to remove outliers and fill in missing values ​​in the multi-dimensional evaluation data; and to process the multi-dimensional evaluation data according to a preset standard format.

[0061] In some embodiments, a second processing module is also included.

[0062] The second processing module is used to, in response to the trigger operation of the dimension filtering unit, filter out the corresponding target evaluation data from the multi-dimensional evaluation data; and / or, in response to the trigger operation of the weight adjustment unit, adjust the display ratio of different dimension evaluation data in the multi-dimensional evaluation data; and / or, in response to the trigger operation of the data tracing unit, query the data collection source of the target evaluation data; and / or, in response to the trigger operation of the chart export unit, export a visualization chart according to a preset chart format.

[0063] In some embodiments, an update module is also included.

[0064] The update module is used to update the visualization charts based on the changed multi-dimensional evaluation data when changes are detected.

[0065] The evaluation result visualization device provided in this disclosure can execute the above method embodiments. Its specific implementation principle and technical effect can be found in the above method embodiments, and will not be repeated here.

[0066] This application also provides a computer device. Please refer to the following for details. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0067] The computer device includes a memory 310 and a processor 320 that are interconnected via a system bus. It should be noted that only a computer device with memory 310 and processor 320 is shown in the figure; however, it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented alternatively. Those skilled in the art will understand that the computer device described herein is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0068] Computer devices can include desktop computers, laptops, handheld computers, and cloud servers. These devices allow for human-computer interaction with users through keyboards, mice, remote controls, touchpads, or voice-activated devices.

[0069] The memory 310 includes at least one type of readable storage medium, including non-volatile memory or volatile memory, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. RAM may include static RAM or dynamic RAM. In some embodiments, the memory 310 may be an internal storage unit of a computer device, such as the hard disk or memory of the computer device. In other embodiments, the memory 310 may also be an external storage device of the computer device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, or flash card equipped on the computer device. Of course, the memory 310 may include both internal storage units and external storage devices of the computer device. In this embodiment, the memory 310 is typically used to store the operating system and various application software installed on the computer device, such as the program code of the method described above. In addition, the memory 310 can also be used to temporarily store various types of data that have been output or will be output.

[0070] Processor 320 is typically used to perform overall operations of a computer device. In this embodiment, memory 310 is used to store program code or instructions, including computer operation instructions, and processor 320 is used to execute the program code or instructions stored in memory 310 or process data, such as program code that runs the methods described above.

[0071] In this article, the bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. This bus system can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0072] Another embodiment of this application also provides a computer-readable medium, which may be a computer-readable signal medium or a computer-readable medium. A processor in a computer reads computer-readable program code stored in the computer-readable medium, enabling the processor to execute the functional actions specified in each step or combination of steps in the above method; and to generate means for implementing the functional actions specified in each block or combination of blocks in the block diagram.

[0073] Computer-readable media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared memory or semiconductor systems, devices or apparatuses, or any suitable combination thereof, wherein the memory is used to store program code or instructions, the program code including computer operation instructions, and the processor is used to execute the program code or instructions of the above-described methods stored in the memory.

[0074] The definitions of memory and processor can be found in the description of the foregoing computer device embodiments, and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0076] In the various embodiments of this application, the functional units or modules can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" as described in this application does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims listing several means, several units of these means may be embodied by the same item of hardware. The use of "first," "second," and "third," etc., does not indicate any order and these words should be interpreted as names. Unless otherwise specified, the steps in the above embodiments should not be construed as limiting the order of execution.

[0079] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for visualizing evaluation results, characterized in that, include: Based on the evaluation scenario type of the multi-dimensional evaluation data, the corresponding target scenario template is called from the scenario template library; The target scenario template is pre-configured with basic chart types, indicator display levels, and color mapping rules; The user-defined data operation parameters are combined with the target scene template to generate the core configuration parameters of the chart; A multi-layer chart structure is constructed based on the core configuration parameters of the chart. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display the multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to issue warnings for key data. The multi-layer chart structure is rendered in multiple layers to obtain an initial visual chart; Add a user interaction module containing dimension filtering, weight adjustment, data tracing, and chart export units to the initial visualization chart to generate a visualization chart corresponding to the multi-dimensional evaluation data.

2. The method according to claim 1, characterized in that, A multi-layer chart structure is constructed based on the core chart configuration parameters, including: The multi-dimensional evaluation data is mapped to a basic canvas to generate a basic data layer for displaying the evaluation data. Calculate the linear correlation coefficient between evaluation data of different dimensions, sort the linear correlation coefficients between evaluation data of different dimensions according to the correlation strength, and then superimpose them on the basic data layer to generate a correlation analysis layer for presenting the correlation between evaluation data of different dimensions. The multi-dimensional evaluation data is filtered for anomalies based on a preset anomaly threshold. Highlighted warning labels are added to the display positions of evaluation data that exceed the preset anomaly threshold and overlaid on the correlation analysis layer to generate a key warning layer. A multi-layer chart structure is generated based on the basic data layer, the correlation analysis layer, and the key warning layer.

3. The method according to claim 1, characterized in that, The multi-layer chart structure is rendered using multi-layer rendering to obtain an initial visual chart, including: Different display transparency levels are set for the basic data layer, the correlation analysis layer, and the key warning layer, respectively; Following the bottom-up layer stacking order, the graphics rendering interface is called sequentially to independently render the basic data layer, the correlation analysis layer, and the key warning layer to obtain the initial visualization chart.

4. The method according to claim 1, characterized in that, Add a user interaction module containing dimension filtering, weight adjustment, data tracing, and chart export units to the initial visualization chart to generate a visualization chart corresponding to the multi-dimensional evaluation data, including: An independent interactive sidebar is divided from the canvas area of ​​the initial visualization chart, and a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit are deployed sequentially in the interactive sidebar. The dimension filtering unit is used to filter the multi-dimensional evaluation data by dimension; the weight adjustment unit is used to adjust the display ratio of different dimension evaluation data; the data tracing unit is used to trace the data collection source of the multi-dimensional evaluation data; and the chart export unit is used to export the visualization chart.

5. The method according to claim 1, characterized in that, Based on the evaluation scenario type according to the multi-dimensional evaluation data, before calling the corresponding target scenario template from the scenario template library, the following steps are also included: Outlier removal and missing value completion are performed on the multi-dimensional evaluation data; and the multi-dimensional evaluation data is formatted according to a preset standard format.

6. The method according to claim 1, characterized in that, Also includes: In response to the triggering operation of the dimension filtering unit, the corresponding target evaluation data is filtered out from the multi-dimensional evaluation data; and / or; In response to the triggering operation of the weight adjustment unit, the display proportion of different dimension evaluation data in the multi-dimensional evaluation data is adjusted; And / or; in response to a trigger operation on the data tracing unit, query the data acquisition source of the target evaluation data; And / or; in response to a trigger operation on the chart export unit, export the visualization chart according to a preset chart format.

7. The method according to claim 1, characterized in that, Also includes: When a change is detected in the multi-dimensional evaluation data, the visualization chart is updated based on the changed multi-dimensional evaluation data.

8. A device for visualizing evaluation results, characterized in that, include: The calling module is used to call the corresponding target scene template from the scene template library based on the evaluation scene type of the multi-dimensional evaluation data; The target scenario template is pre-configured with basic chart types, indicator display levels, and color mapping rules; The fusion module is used to fuse user-defined data operation parameters with the target scene template to generate core chart configuration parameters; The construction module is used to construct a multi-layer chart structure based on the core configuration parameters of the chart. The multi-layer chart structure includes: a basic data layer, a correlation analysis layer, and a key warning layer. The basic data layer is used to display the multi-dimensional evaluation data, the correlation analysis layer is used to display the correlation coefficients between data, and the key warning layer is used to issue warnings for key data. The rendering module is used to perform multi-layer rendering of the multi-layer chart structure to obtain an initial visual chart; The generation module is used to add a user interaction module containing a dimension filtering unit, a weight adjustment unit, a data tracing unit, and a chart export unit to the initial visualization chart, thereby generating a visualization chart corresponding to the multi-dimensional evaluation data.

9. A computer device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the evaluation result visualization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When a computer program is executed by a processor, it implements the evaluation result visualization method as described in any one of claims 1 to 7.

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