CN117807149A - Data visualization method, device, server, storage medium and program product - Google Patents

Abstract

Embodiments of the present disclosure relate to a data visualization method, apparatus, server, storage medium, and program product. The method comprises the following steps: processing unstructured data of a vehicle to be simulated by using a preset data processing plug-in to obtain structured data; obtaining visual configuration information by using a preset visual plug-in and the structured data; and rendering the structured data by using a preset fusion visualization engine and the visualization configuration information to obtain the visualized content. The method can be used for processing unstructured data and applying the processed unstructured data to simulation test.

Description

Data visualization method, device, server, storage medium and program product

Technical Field

The embodiment of the disclosure relates to the technical field of automatic driving, in particular to a data visualization method, a data visualization device, a server, a storage medium and a program product.

Background

In the field of autopilot, simulation testing is critical to assessing unmanned server performance. The visualization system allows for a large number of tests in the autopilot system to evaluate autopilot performance, safety and adaptability. The visual system provides an intuitive tool for simulation, and high-efficiency monitoring of system performance is realized by restoring an actual scene in the 3D world. This provides critical support for system design and improvement, enabling researchers and engineers to gain more insight into the performance of an autopilot system.

Conventional autopilot simulation visualization tools have some limitations in processing unstructured data. Current simulation visualization tools often face challenges in handling diverse unstructured data.

Disclosure of Invention

The embodiment of the disclosure provides a data visualization method, a device, a server, a storage medium and a program product, which can be used for processing unstructured data and applying the processed unstructured data to simulation test.

In a first aspect, embodiments of the present disclosure provide a data visualization method, the method comprising:

processing unstructured data of a vehicle to be simulated by using a preset data processing plug-in to obtain structured data;

obtaining visual configuration information by using a preset visual plug-in and structured data;

and rendering the structured data by using a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

In a second aspect, embodiments of the present disclosure provide a data visualization apparatus, the apparatus comprising:

the data processing module is used for processing unstructured data of the vehicle to be simulated by utilizing a preset data processing plug-in to obtain structured data;

The determining module is used for obtaining visual configuration information by utilizing a preset visual plug-in and structured data;

and the rendering module is used for rendering the structured data by utilizing a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

In a third aspect, embodiments of the present disclosure provide a server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of the first aspect described above when executing the computer program.

In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of the first aspect described above.

In a fifth aspect, embodiments of the present disclosure provide a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect described above.

The data visualization method, the device, the server, the storage medium and the program product provided by the embodiment of the disclosure firstly process unstructured data of a vehicle to be simulated by using a preset data processing plug-in to obtain structured data; then, obtaining visual configuration information by using a preset visual plug-in and structured data; finally, rendering the structured data by utilizing a preset fusion visualization engine and visualization configuration information to obtain visualized contents; in the method, the unstructured data can be standardized into structured data through the data processing plug-in, which is helpful for a simulation system to process various data types consistently, and the consistency and comparability of the data are improved, so that the processed unstructured data can be applied to simulation tests.

Drawings

FIG. 1 is an application environment diagram of a data visualization method in one embodiment;

FIG. 2 is a flow diagram of a method of visualizing data in accordance with one embodiment;

FIG. 3 is a flow chart of a method of visualizing data in another embodiment;

FIG. 4 is a flow chart of a method of visualizing data in another embodiment;

FIG. 5 is a flow chart of a method of visualizing data in another embodiment;

FIG. 6 is a flow chart of a method of visualizing data in another embodiment;

FIG. 7 is a flow chart of a method of visualizing data in another embodiment;

FIG. 8 is a block diagram of a data visualization device in one embodiment;

fig. 9 is an internal structural diagram of a server in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the embodiments of the present disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the disclosed embodiments and are not intended to limit the disclosed embodiments.

First, before the technical solution of the embodiments of the present disclosure is specifically described, a description is given of a technical background or a technical evolution context on which the embodiments of the present disclosure are based. In general, in the field of autopilot, the current technical background includes visualization systems that allow for extensive testing of an unmanned server in a virtual environment to evaluate its performance, safety and adaptability. However, the effectiveness of simulation testing depends on an accurate simulation of the simulation scenario and a comprehensive analysis of the test data. The visual system provides complete interaction for the simulation system, the system is displayed in the 3D world to carry out visual real restoration, and a direct and efficient tool is provided for the simulation process. Under the technical background, the applicant finds that the comprehensive analysis and utilization of various unstructured data are helpful for improving the simulation test effect through long-term model simulation research and development and collection, demonstration and verification of experimental data. However, how to better analyze and utilize unstructured data is a current challenge to be addressed. The applicant has made a great deal of inventive work on this problem and introduced a series of solutions by means of the following examples. It is worth emphasizing that the process of analyzing and utilizing unstructured data is not only a technical challenge, but requires a deep understanding of the complexity and diversity of driving scenarios. Applicant's work is not only technical but also includes a deep understanding of the actual scenario and data to ensure that the extracted information and simulation results effectively reflect the characteristics of the real driving environment.

The following describes a technical scheme related to an embodiment of the present disclosure in conjunction with a scenario in which the embodiment of the present disclosure is applied.

The data visualization method provided by the embodiment of the disclosure can be applied to a system architecture as shown in fig. 1. The system architecture includes a vehicle end 101 and a server 102. The server 102 may be a stand-alone server or a server cluster formed by a plurality of servers. The vehicle end 101 is provided with a communication component, which can communicate with the server 102 in a wireless manner, and the communication manner between the vehicle end 101 and the server 102 is not limited in the embodiments of the present disclosure.

In one embodiment, as shown in fig. 2, a data visualization method is provided, and this embodiment is illustrated by applying the method to a server, and is implemented through interaction between a vehicle end and the server. In this embodiment, the method includes the steps of:

s202, unstructured data of the vehicle to be simulated are processed by using a preset data processing plug-in, and structured data are obtained.

Wherein the data processing plug-in is a software component for processing, converting and analyzing data. In the autopilot domain, the data processing plug-in may process unstructured data through predefined algorithms and methods, converting it into structured data for further analysis and application. The following are some of the functions and steps contained in the data processing plug-in:

(1) Data cleaning and preprocessing: noise, outliers and missing values in unstructured data are processed, and quality and consistency of the data are improved.

(2) Feature extraction: useful features are extracted from unstructured data, which can capture important information in the data. In autopilot, features may relate to the position, speed, direction, etc. of an object.

(3) Data fusion: the data from the different sensors is integrated to provide a more comprehensive view. For example, camera and lidar data are fused to obtain more accurate object position information.

(4) And (3) signal processing: and performing signal processing on the time sequence data to filter, reduce noise or extract frequency domain information. In autopilot, this may involve handling the vehicle motion state.

(5) Image processing: image data is processed, including object detection, image segmentation, etc., to extract useful information in the image.

(6) And (3) voice recognition: and voice data is identified, and voice instructions are converted into text forms, so that subsequent processing is facilitated.

(7) And (3) time sequence data analysis: the time series data is analyzed, including trend analysis, periodicity analysis, etc., to understand the evolution process of the data.

(8) Data conversion: the processed data is converted into a structured format, such as a table or database, for convenient storage and subsequent analysis.

(9) Correlation analysis: the correlations between the different data sources are analyzed to gain more insight. For example, a relationship between sensor data and a vehicle state is analyzed.

(10) Metadata record: metadata in the data processing process is recorded, including information of algorithms used, parameter settings, processing dates, etc., so as to trace back and reproduce the processing steps.

Unstructured data refers to data that is not organized in a fixed format, lacking a distinct data model or table structure. Unlike structured data, unstructured data is not suitable for storage in conventional relational database tables. Such data typically contains different kinds and forms of information, such as camera radar, positioning, sensing algorithms, planning algorithms, control algorithms, etc. data. The following are unstructured data types that may be generated by an autonomous vehicle:

(1) Sensor data:

1) Laser radar data: and the point cloud data acquired by the laser radar sensor is used for constructing an environment map and detecting obstacles.

2) Microwave radar data: the radar sensor provides information on the position, speed, etc. of the target in the environment.

3) Camera image: the images captured by the vision sensor are used for real-time vision perception, lane detection, object recognition and the like.

4) Infrared camera data: for obtaining information outside the visible range in low light or severe weather conditions.

(2) Vehicle state data:

1) Vehicle CAN bus data: including vehicle speed, acceleration, steering angle, etc.

2) GPS data: global positioning information of the vehicle is provided for navigation and geographic location identification.

(3) Communication data:

1) V2X (vehicle-to-infrastructure) communication data: data communicated with the road infrastructure and other vehicles.

2) V2V (vehicle-to-vehicle) communication data: data communicated with other vehicles.

(3) Log file:

1) System log: the operation status, error information, etc. of each system of the vehicle are recorded.

2) Sensor data log: and recording the original data output by the sensor, so that the subsequent analysis and debugging are convenient.

(4) Voice command and feedback:

speech recognition data: if the vehicle supports voice interaction, voice commands and voice feedback of the system will be included.

In the embodiment of the disclosure, unstructured data of the vehicle to be simulated can be processed by using a preset data processing plug-in to obtain structured data.

In some embodiments, first, rich unstructured data is obtained from sensors simulating a vehicle. And then, carrying out sensor data fusion through a preset data processing plug-in, and organically integrating the data from different sensors. And then, carrying out feature extraction on the fused data by utilizing a preset data processing plug-in to more deeply understand and mine the inherent information of the data.

For example, for lidar data, shape and density information of the point cloud may be extracted to obtain a detailed three-dimensional structure of the environment. For radar data, signal processing technology can be used for object identification, and the position and motion state of an object can be captured.

Finally, to further improve the understandability and usability of the data, the information resulting from the feature extraction is mapped to a structured data format. For example, a structured data table may be created that contains environmental information about the vehicle and detailed information about the vehicle's state. Through such a structured form, subsequent data analysis, processing and visual presentation can be more conveniently performed.

The sensor of the simulation vehicle can comprise a laser radar, a camera, a microwave radar and the like. The above-described process of sensor fusion may include time synchronization, coordinate system conversion, etc., so that data from different sensors work cooperatively.

In some embodiments, the data processing plug-ins may include a speech recognition plug-in and an image processing plug-in. First, voice and image data may be acquired from a simulated vehicle. The speech data is then converted into structured text information by applying a speech recognition plug-in, making it easier to process and understand. Meanwhile, the image processing plug-in is utilized to carry out multi-level processing on the real-time image, including target detection, object identification and the like, so as to extract key information from the image.

Finally, the voice text data and the image processing result are fused to form a comprehensive structured data set. This dataset contains not only text information in speech but also visual content obtained from image processing.

The voice data may be voice instructions from passengers in the vehicle, and the image data may be a real-time image from the surroundings of the vehicle.

In some embodiments, the data processing plug-ins may include time-sequential data processing plug-ins. First, time series data is acquired from a simulated vehicle. Next, a series of preprocessing steps including time series analysis and filtering are performed by applying a time series data processing plug-in to improve accuracy and reliability of the data. Then, key features are extracted from the processed time series data, which may involve calculating a rate of change of the vehicle speed, a peak value of the acceleration, a periodic change of the sensor output, and the like. Finally, the extracted timing characteristics are converted into structured data. For example, a table may be created that contains time series data features, where each column corresponds to a feature and each row corresponds to data at a point in time.

The time series data is a data set arranged in time sequence, and information of time variation of one or more variables is recorded. For example, the time series data may include various sensor outputs of the vehicle, control instructions, information of the vehicle state, etc. over time. Specifically, the time series data may include the following:

(1) Sensor output: the outputs of various sensors mounted on the vehicle, such as lidar, cameras, microwave radar, etc., are covered. Such data may include information such as obstacle detection, image recognition, distance measurement, etc.

(2) Vehicle state: status information of the vehicle itself, such as vehicle speed, acceleration, steering angle, etc., is recorded. These data reflect the state of motion of the vehicle during the simulation.

(3) Control instructions: including control commands to the vehicle such as acceleration, braking, steering, etc. These instructions may affect the motion profile of the vehicle.

(4) Environmental information: changes in the simulation environment, such as road conditions, weather conditions, etc., are recorded. Such information may affect the behavior of the vehicle and the performance of the sensors.

S204, obtaining the visual configuration information by using the preset visual plug-in and the structured data.

Where visualizing a configuration generally involves presenting system configuration, parameter settings, or other relevant information in graphical or graphical form so that a user can more intuitively understand and adjust the configuration. Taking a visualization configuration of a visualization system as an example, the visualization configuration of the visualization system may include:

1. map configuration: the method comprises the steps of setting a map of a simulation environment and defining geographical information such as road networks, intersections, barriers and the like.

2. Vehicle configuration: including defining vehicle parameters in the simulation, such as vehicle model, mass, tire friction coefficient, etc.

3. Sensor configuration: including setting up various sensors used in simulation, such as lidar, cameras, microwave radar, etc.

4. Parameter configuration of a control algorithm: parameters of an autopilot control algorithm used in the simulation, such as a path planning algorithm, a speed control algorithm, etc., are defined.

5. Simulation environment configuration: including setting up roads, obstacle layout, etc.

Wherein the visualization plug-ins are software components for creating graphical user interfaces in an application or system that enable a developer to expose data, configuration information, or system states in an intuitive manner. These plug-ins may encompass a variety of graphical and interactive elements including charts, maps, dashboards, adjustment sliders, buttons, etc. to facilitate easier understanding and manipulation of information by the user.

In the embodiment of the disclosure, first, the simulation system analyzes the characteristics of the structured data to understand the fields, types and relationships contained in the structured data. Next, depending on the nature of the structured data, a suitable visualization plug-in is selected. For example, a map visualization plug-in is adapted to display geospatial information, a chart visualization plug-in is adapted to numeric data, and a dashboard visualization plug-in is adapted to monitor real-time parameters.

The fields in the structured data are then mapped into corresponding attributes in the visualization plug-in. Each field may correspond to a different element in the visualization plug-in, such as coordinates, color, size, etc.

For each selected visualization plug-in, its properties and appearance are configured so that the information it presents meets the user's needs. And finally, integrating the configuration information to form visual configuration information.

S206, rendering the structured data by using a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

The fusion visualization engine integrates different visualization components, tools or libraries into one system to realize a more comprehensive and highly interactive visualization effect. The fusion visualization engine can provide a more flexible and comprehensive visualization experience while simplifying the complexity of development and maintenance.

In an autopilot simulation configuration, the converged visualization engine may include multiple sub-engines, each of which is responsible for a different task to cooperatively implement a comprehensive visualization effect. The function of these sub-engines may cover the following aspects:

(1) Rendering sub-engines: and presenting the fused structured data in an intuitive mode. This may include real-time rendering, shadow effects, texture mapping, etc. to create a realistic simulated scene.

(2) Configuration sub-engines: the visual configuration information is processed such that the structured data is correctly mapped onto the visual elements and appropriate graphical effects are generated according to the configuration rules.

(3) Meter tray engine: real-time monitoring and display of system states including vehicle speed, sensor output, etc. are realized.

(4) Time axis sub-engine: recording and playback of the emulated scene is supported. The user can trace back the simulation process through the time axis, so that the analysis and the configuration adjustment are convenient.

In the embodiment of the disclosure, the fusion visualization engine analyzes the visualized configuration information obtained in the above embodiment, and identifies each configuration item including map configuration, sensor configuration, vehicle parameters, control algorithm parameters, and the like.

Then, based on the parsed configuration information, various visualization components are initialized, including maps, charts, dashboards, etc. For example, map visualization tools may be invoked according to map configuration, render city maps, and display relevant simulated environmental elements, such as road networks, obstacles, vehicle locations, and the like. Meanwhile, a graphic element plug-in or a custom engine is utilized to display a radar scanning area and a camera view according to sensor configuration information. Further, the instrument panel engine is started, and information such as the vehicle speed, sensor output and the like on the instrument panel is displayed according to the real-time data.

Finally, the rendered map, chart, dashboard, etc. components are integrated into one user interface.

It should be noted that, the visual content needs to be updated in real time, so that the user can timely acquire the real-time data change in the simulation process, and the user experience is improved.

In the data visualization method, unstructured data of a vehicle to be simulated are processed by using a preset data processing plug-in to obtain structured data; then, obtaining visual configuration information by using a preset visual plug-in and structured data; finally, rendering the structured data by utilizing a preset fusion visualization engine and visualization configuration information to obtain visualized contents; in the method, the unstructured data can be standardized into structured data through the data processing plug-in, which is helpful for a simulation system to process various data types consistently, and the consistency and comparability of the data are improved, so that the processed unstructured data can be applied to simulation tests.

In one embodiment, as shown in fig. 3, S202 in the above embodiment may include the following steps:

s302, information extraction is carried out on unstructured data by using regular expressions in the data processing plug-in, and intermediate data are obtained.

Wherein, regular expression is a tool for describing string patterns. It can search, match and manipulate strings.

In the embodiment of the disclosure, the unstructured data is extracted by using a regular expression in the data processing plug-in to obtain intermediate data.

For example, if the unstructured data is VehicleID:123 _， Speed:60，Location:37.7749，-122.4194。

The designed regular expression may be:

VehicleID:(\d+),\s*Speed:(\d+),\s*Location:(-？\d+\.\d+,-？\d+\.\d+)。

this regular expression will match the entire string and extract the vehicle ID, speed and location information using the capture set.

And finally, extracting the interested information according to the matching result of the regular expression. For example, intermediate data extracted from "VehicleID:123, speed:60, location:37.7749, -122.4194" includes:

vehicle ID 123

Speed of 60

37.7749, -122.4194.

It should be noted that, the specific regular expression design may need to be fine-tuned according to the actual situation.

S304, mapping the intermediate data to obtain structured data.

In the disclosed embodiments, the fields that need to be mapped are identified from the intermediate data, i.e., which information needs further processing is determined. For example, in the intermediate data mentioned in the above embodiment, it may be necessary to identify fields of the vehicle ID, the speed, the position, and the like. Each identified field is then mapped to a corresponding field in the defined data structure. This involves associating elements in the intermediate data with the data model of the system. For example, the vehicle ID is mapped to the "vehicle ID" field, the Speed is mapped to the "Speed" field, and the Location information is mapped to the "Location" field containing latitude and longitude.

Finally, the mapped and possibly data type converted fields are organized into a satisfactory structure according to the defined data structure. This may be in the form of a manifest, a table, or other data structure, depending on the defined data model.

In the above embodiments, intermediate information may be extracted from unstructured data through regular expressions in the data processing plug-in, which helps to improve the quality of the data. In addition, regular expressions are typically faster in processing speed, and are particularly suited for processing large amounts of unstructured data. In the simulation test, key information can be rapidly extracted from the original data, and the efficiency of data processing is improved. Intermediate data extracted by regular expressions may be normalized in format. Such normalization facilitates unifying the representation of the data, making it easier for the structured data to map to a standard format defined by the system, improving the consistency of the data.

In one embodiment, as shown in fig. 4, S304 in the above embodiment may include the following steps:

s402, cleaning the intermediate data to obtain cleaned data.

In the embodiment of the disclosure, the intermediate data is subjected to cleaning processing, and cleaned data is obtained. When performing a cleaning process on intermediate data to obtain cleaned data, a series of steps are typically performed to improve the quality and consistency of the intermediate data. The following are some of the cleaning steps that may be involved:

(1) Removing the duplicate values: duplicate records in intermediate data are detected and removed to reduce the effects of duplicate information.

(2) Processing the missing values: for missing values present in the intermediate data, it is contemplated that processing may be performed using suitable methods, such as filling in default values, replacing with mean/median values, etc., or deleting records containing missing values.

(3) Processing outliers: and detecting and processing abnormal values in the intermediate data, and ensuring the accuracy of the data. This may include limiting the range of data, culling outliers, etc.

(4) Format normalization: for fields containing date, time or other specific formats, format normalization is performed to improve data consistency.

(5) Processing nonstandard characters: and (3) clearing out irregular characters or special characters in the intermediate data, so that the cleanness and usability of the data are improved.

(6) Unnecessary spaces are removed: unnecessary spaces in the fields are removed to improve consistency of the data format.

(7) Unified data unit: if different units are involved in the intermediate data, unit conversion or unified units may be required to improve the consistency of the data in subsequent processing.

S404, performing data type conversion on the cleaned data to obtain converted data.

In the embodiment of the disclosure, the data type of the cleaned data is converted to obtain converted data. In data type conversion of the cleaned data, the goal is to have each field with the correct data type to better match the defined structured data.

In the process of carrying out data type conversion on the cleaned data, firstly, the characteristics of the cleaned data need to be known, the noise is removed, the missing value is processed, and the format is more standard. Subsequently, for each field, a data type conversion is performed to ensure consistency and accuracy of the data. For example, it may be desirable to convert a string type field to a number or a date field to a date and time data type.

S406, mapping the converted data to obtain structured data.

In the embodiment of the present disclosure, in the process of mapping the converted data to obtain the structured data, the fields that need to be mapped need to be identified first. This step typically involves comparing to a predefined data structure to determine which information is of interest to the system and requires further processing. Each field generally corresponds to a corresponding field in the data structure, improving the integrity and consistency of the data.

Each identified field is then mapped to a corresponding field in a predefined data structure. This mapping process corresponds elements in the intermediate data to a predefined data model so that the data can be properly understood and processed. This may involve assigning each field a unique identifier so that they can be accurately referenced in subsequent processing.

The mapped fields are organized according to a predefined data structure. This step allows the generated structured data to conform to the desired format of the system, allowing subsequent processing to proceed efficiently.

In the above embodiment, the cleaning process can remove the problems of noise, invalid characters, blank spaces and the like in unstructured data, and improve the consistency of the data. The cleaned data is normalized, which is helpful for unifying the representation mode of the data, so that the structured data is easier to map, and the consistency and the usability of the data are improved; through the mapping process, the obtained structured data conforms to a predefined data model, so that the structured data can be understood and processed.

In one embodiment, as shown in fig. 5, S204 in the above embodiment may include the following steps:

s502, determining a target visual plug-in from a plurality of candidate visual plug-ins stored in advance according to the structured data.

The pre-stored visualization plug-ins may cover various visualization types, such as charts, maps, dashboards, etc.

In the disclosed embodiment, first, the structured data is analyzed to understand the information and fields contained therein. These visualization plug-ins are classified to better match the characteristics of the structured data. Each visualization plug-in is capability-matched so that it can handle specific fields and information in the structured data. Different visual plug-ins may be suitable for different types of data presentation, and thus it is desirable to select plug-ins that can meet the requirements of structured data. The structured data is mapped onto a matching visualization plug-in according to its characteristics.

It is assumed that the structured data contains vehicle ID, speed and position information. By analyzing the data, it is determined that speed and location information are key fields. In the pre-stored visualization plug-ins, there may be histograms, maps, dashboards, etc. Depending on the nature of the data, it may be appropriate to select a map plug-in, as the map plug-in can effectively reveal the vehicle's location information in geographic space and express speed in an intuitive manner. Thus, the map plug-in becomes a target visualization plug-in to best meet the needs of the structured data.

S504, obtaining the visual configuration information by utilizing the configuration rules in the target visual plug-in.

In the disclosed embodiments, each visualization plug-in typically has its own configuration rules that define how structured data is interpreted and displayed prior to generating the visualizations. This includes rules in terms of field mapping, color settings, label definition, etc. In the whole process, the fields in the structured data need to be precisely mapped with the configuration rules of the visual plug-in.

Taking the map plug-in as an example, assuming that the selected target visualization plug-in is a map plug-in, the configuration rules include mapping the vehicle ID to a marker point on the map, the speed information to the color of the marker point, and the position information to the position of the marker point. By executing these configuration rules, the generated visual configuration information may include map types, mark point patterns, color mapping rules, and the like.

In the above embodiment, by selecting an appropriate target visualization plug-in according to the structured data, a more customized and adaptive visualization effect can be provided. Different plug-ins may provide specific visualization schemes for different data types and requirements, so that the visualization better meets the expectations of the user.

In one embodiment, as shown in fig. 6, S502 in the above embodiment may include the following steps:

s602, extracting the data type identifier from the structured data.

Wherein the data type identifier is a field or identifier included in the structured data for indicating the type of data.

In the embodiment of the present disclosure, the data type identifier is extracted from the structured data, for example, the extracted data type identifier is a "visual type" field, and the value of the extracted data type identifier may be "map", "char", "table", or the like. Wherein the visualization type field is used to identify the visualization type of the data. This field serves as a key flag in the structured data to identify the type of visualization of the data, and to instruct in what way the data is visually presented.

In particular, this field contains information about what type of visualization the data is applicable to. For example, if the value of "visual type" is "map", then the simulation system can determine that this data is most suitable for visual presentation on the map. Conversely, if the value is "chart", the simulation system may choose to present the data in the form of a chart.

S604, determining a target visual plugin matched with the data type identifier from a plurality of candidate visual plugins.

In the disclosed embodiment, first, the data types supported by each candidate visualization plug-in are known. The data type identification is mapped to the data type supported by the visualization plug-in. Such that each data type identifier is supported by a corresponding visualization plug-in. Each visualization plug-in is capability-matched so that it can efficiently process structured data that matches the data type identification.

For example, assume that there are two visualization plug-ins, one supporting a histogram presentation of numeric data and the other supporting a line presentation of time series data. If the data type identification matches numeric data, then the histogram plug-in may be a target visualization plug-in. If the data type identification matches the time series data, the line graph plug-in may be a target visualization plug-in. The plug-ins are selected to ensure that their capabilities match the characteristics of the data.

In the above embodiment, extracting the data type identifier allows the simulation system to automatically identify the type of structured data. According to the data type identification, the simulation system can intelligently select the visualization plug-in matched with the data type identification, so that the self-adaptive visualization of different types of data is realized. And selecting a target visual plug-in according to the data type identifier, so that the visual effect is more in line with the characteristics of the data. This helps to improve the quality and understandability of the visualization, making it easier for the user to understand and analyze the data.

In one embodiment, as shown in fig. 7, S206 in the above embodiment may include the following steps:

s702, determining a target rendering mode according to the visual configuration information; the target rendering mode includes at least one of a two-dimensional rendering mode and a three-dimensional rendering mode.

The two-dimensional rendering mode refers to a rendering mode used in the field of visualization, wherein data is presented in a plane form. In this mode, the visual content is typically based on a planar coordinate system, such as a Cartesian coordinate system, mapping data into a two-dimensional space. In a two-dimensional rendering mode, common forms of visualization include:

(1) Line graph: for displaying the trend of the change over time or other variables.

(2) Scatter plot: the relationship between the two variables is demonstrated by the positions of the points, each of which is determined by two values.

(3) Bar graph: the numerical sizes of the different categories or groups are represented by the height of the rectangle.

(4) Pie chart: the whole is divided into sectors, the area of each sector representing the proportion of the corresponding category.

(5) Map: the data in the geographic space is shown in the form of a planar map.

The two-dimensional rendering mode is adapted to exhibit simple relationships, trends or distributions.

The three-dimensional rendering mode refers to a rendering mode used in the field of visualization, wherein data is presented in a three-dimensional space form. Unlike the two-dimensional rendering mode, the three-dimensional rendering mode allows the visualized contents to have more depth and realism by representing data in a three-dimensional coordinate system.

In three-dimensional rendering mode, common forms of visualization include:

(1) Three-dimensional scattered point cloud: the relationship between the three variables is represented in three-dimensional space by the positions of the points.

(2) Three-dimensional curved surface diagram: the complex relationship between the three variables is demonstrated by the shape of the surface.

(3) Three-dimensional histogram: similar to a two-dimensional histogram, but represented in three-dimensional space.

(4) Three-dimensional map: data in a geographic space is presented in three-dimensional space, including the height and shape of the earth's surface.

(5) Three-dimensional perspective view: by using perspective and shadow effects, the graphics are made to look more stereoscopic.

The three-dimensional rendering mode is suitable for exhibiting more complex data relationships and structures, especially when the characteristics of the data involve more than three variables. This mode is generally more realistic and helps to understand the stereo structure of the data in depth.

In an embodiment of the present disclosure, a rendering mode to be used is determined from the visual configuration information. The target rendering mode may include a two-dimensional rendering mode and a three-dimensional rendering mode, or a combination of both modes. The specific choice of which mode depends on the nature of the data and the purpose of the visualization.

(1) Dimension of data: if the data contains more than three variables, it may be more appropriate to use a three-dimensional rendering mode.

(2) Visualization purposes: different rendering modes are suitable for different visualization purposes. For example, a three-dimensional rendering mode may be more suitable for exhibiting stereo structures and complex relationships.

And S704, rendering the visualized data by using a fusion visualization engine in a target rendering mode to obtain the visualized content.

In the embodiment of the present disclosure, first, various configuration parameters including a target rendering mode are parsed from the visual configuration information. And then, initializing a fusion visualization engine according to the configuration information obtained by analysis. The data is mapped onto the graphical elements according to the fields of the structured data. And finally, carrying out actual rendering on the mapped data by using an initialized rendering engine to generate final visual content.

The above-described initializing a fusion visualization engine may include selecting the correct rendering engine, loading the required graphics library or tool, and configuring the rendering environment. Mapping data to graphical elements as described above may include placing data points in the correct locations, applying color mapping, adjusting the shape of the chart, and so forth. The actual rendering process described above may include drawing graphics on a screen, creating animation effects, displaying marked points on a map, and the like.

The layout adjustment and style setting can also be performed on the generated visual content according to the need. This may include modifying chart size, adjusting label position, changing color theme, etc.

In the above-described embodiments, the target rendering modes include two-dimensional and three-dimensional rendering modes, allowing diversified visual effects to be exhibited. This provides a more flexible way of presentation for different types of data. The three-dimensional rendering mode can provide a more vivid and visual effect, so that a user can more comprehensively understand the spatial relationship and structure of data. This helps to improve the user's deep understanding of the data. By selecting the target rendering mode, the system is able to better express the complexity of the data. For datasets containing a large amount of information and relationships, three-dimensional rendering modes may provide a more comprehensive expression, improving the expressive power of the data. And rendering the visual data by using a target rendering mode by using a fusion visual engine, so that the structured data can be effectively converted into visual contents which can be understood by a user.

In one embodiment, the foregoing embodiment may further include the steps of:

and adjusting the visual content by using a preset dynamic layout module to obtain target display content.

In the embodiment of the disclosure, the generated visual content is adjusted by using a preset dynamic layout module so as to meet the personalized requirements of the user and obtain the final target display content.

First, a user may select a dynamic layout module on the interface that typically provides rich operational options including adding, deleting, modifying the size and location of the visual elements, and the like. For example, the operation of adding one camera element in the upper left corner of the interface can be achieved by simple interactions.

The user may then edit the properties of the newly added element, including size, camera data source displayed, etc. This editing process may be in real time, i.e., the simulation system will immediately respond to and update the interface to display new or modified visual elements as the user performs the editing operation.

In the above embodiment, the dynamic layout module allows the user to customize the layout setting, so that the user can adjust the style and structure of the display content according to personal preference or task requirement, and the flexibility of the layout is improved.

The following provides a detailed embodiment to illustrate the process of the data visualization method in the present application, and on the basis of the foregoing embodiment, the implementation process of the method may include the following:

S1, extracting information from unstructured data by using regular expressions in a data processing plug-in to obtain intermediate data.

S2, cleaning the intermediate data to obtain cleaned data.

And S3, performing data type conversion on the cleaned data to obtain converted data.

And S4, mapping the converted data to obtain structured data.

S5, extracting the data type identifier from the structured data.

S6, determining a target visual plug-in matched with the data type identifier from a plurality of candidate visual plug-ins.

S7, obtaining visual configuration information by utilizing configuration rules in the target visual plug-in.

S8, determining a target rendering mode according to the visual configuration information; the target rendering mode includes at least one of a two-dimensional rendering mode and a three-dimensional rendering mode.

And S9, rendering the visualized data by using a target rendering mode by using a fusion visualization engine to obtain visualized contents.

And S10, adjusting the visual content by using a preset dynamic layout module to obtain target display content.

In the above embodiment, by using the regular expression to perform information extraction (S1) on unstructured data, and subsequent cleaning (S2), data type conversion (S3) and mapping (S4), the accuracy and consistency of the finally obtained structured data are improved. This helps to reduce the occurrence of erroneous or misleading information during the visualization process. By extracting the data type identification (S5) and the matching target visualization plug-in (S6), the most suitable visualization tool can be selected according to the characteristics of the data. This increases the flexibility of visualization, enabling the simulation system to accommodate different types of data and user requirements. With configuration rules in the target visualization plug-in (S7), the simulation system is able to customize the visual appearance and behavior according to the user' S needs. This provides greater flexibility to meet the personalized requirements of different users for visual presentations. By determining target rendering modes (S8), including two-dimensional and three-dimensional rendering modes, visual content can be presented in a more diverse manner. This helps to more fully understand the data. And rendering the visualized data by using a target rendering mode by using a fusion visualization engine (S9), and performing consistent rendering on different data types. So that the user can more easily compare and analyze different data sets. The visual content is adjusted by using a preset dynamic layout module (S10), so that the visual content can be adapted according to different display environments and equipment characteristics, and the display effect of the visual content in different scenes is improved.

It should be understood that, although the steps in the flowcharts of fig. 2 to 7 are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least a portion of the steps of fig. 2-7 may include steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily occur sequentially, but may be performed alternately or alternately with other steps or at least a portion of the steps or stages in other steps.

In one embodiment, as shown in fig. 8, there is provided a data visualization apparatus including: a data processing module 11, a determination module 12 and a rendering module 13, wherein:

the data processing module 11 is used for processing unstructured data of the vehicle to be simulated by utilizing a preset data processing plug-in to obtain structured data;

a determining module 12, configured to obtain visual configuration information by using a preset visual plug-in and structured data;

And the rendering module 13 is used for rendering the structured data by utilizing a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

In another embodiment, another data visualization apparatus is provided, and the data processing module 11 may include:

the extraction unit is used for extracting information from unstructured data by using a regular expression in the data processing plug-in to obtain intermediate data;

and the mapping processing unit is used for carrying out mapping processing on the intermediate data to obtain the structured data.

In another embodiment, another data visualization apparatus is provided, and on the basis of the above embodiment, the mapping processing unit may include:

the cleaning processing subunit is used for cleaning the intermediate data to obtain cleaned data;

the conversion processing subunit is used for carrying out data type conversion on the cleaned data to obtain converted data;

and the mapping processing subunit is used for mapping the converted data to obtain the structured data.

In another embodiment, another data visualization apparatus is provided, and the determining module 12 may include:

The plug-in determining unit is used for determining a target visual plug-in from a plurality of pre-stored candidate visual plug-ins according to the structured data;

and the configuration information determining unit is used for obtaining the visual configuration information by utilizing the configuration rules in the target visual plug-in.

In another embodiment, another data visualization apparatus is provided, and on the basis of the above embodiment, the above plug-in determining unit may include:

an extraction subunit, configured to extract a data type identifier from the structured data;

and the matching subunit is used for determining a target visual plug-in matched with the data type identifier from the plurality of candidate visual plug-ins.

In another embodiment, another data visualization apparatus is provided, and the rendering module 13 may include:

a mode determining unit for determining a target rendering mode according to the visual configuration information; the target rendering mode includes at least one of a two-dimensional rendering mode and a three-dimensional rendering mode;

and the rendering unit is used for rendering the visual data by using a target rendering mode by utilizing the fusion visual engine to obtain visual contents.

In another embodiment, another data visualization apparatus is provided, where, on the basis of the foregoing embodiment, the foregoing apparatus may further include:

And the adjustment module is used for adjusting the visual content by utilizing the preset dynamic layout module to obtain target display content.

For specific limitations of the data visualization device, reference may be made to the above limitation of the data visualization method, and no further description is given here. The various modules in the data visualization apparatus described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in a server, or may be stored in software in a memory in an electronic device, so that the processor may call and execute operations corresponding to the above modules.

Fig. 9 is a block diagram of a server 1400 shown in accordance with an exemplary embodiment. With reference to fig. 9, server 1400 includes a processing component 1420 that further includes one or more processors and memory resources, represented by memory 1422, for storing instructions or computer programs, such as application programs, executable by the processing component 1420. The application programs stored in memory 1422 can include one or more modules, each corresponding to a set of instructions. Further, the processing component 1420 is configured to execute instructions to perform the method of data visualization described above.

The server 1400 may also include a power component 1424 configured to perform power management of the device 1400, a wired or wireless network interface 1426 configured to connect the device 1400 to a network, and an input/output (I/O) interface 1428. The server 1400 may operate an operating system based on storage 1422, such as Window14 14erverTM,Mac O14 XTM,UnixTM,LinuxTM,FreeB14DTM or the like.

In an exemplary embodiment, a storage medium is also provided that includes instructions, such as memory 1422 including instructions, that can be executed by a processor of server 1400 to perform the above-described methods. The storage medium may be a non-transitory computer readable storage medium, which may be, for example, ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In an exemplary embodiment, a computer program product is also provided, which, when being executed by a processor, may implement the above-mentioned method. The computer program product includes one or more computer instructions. When loaded and executed on a computer, these computer instructions may implement some or all of the methods described above, in whole or in part, in accordance with the processes or functions described in embodiments of the present disclosure.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided by the present disclosure may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples merely represent a few implementations of the disclosed examples, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made to the disclosed embodiments without departing from the spirit of the disclosed embodiments. Accordingly, the protection scope of the disclosed embodiment patent should be subject to the appended claims.

Claims (11)

1. A method of visualizing data, the method comprising:

processing unstructured data of a vehicle to be simulated by using a preset data processing plug-in to obtain structured data;

obtaining visual configuration information by using a preset visual plug-in and the structured data;

and rendering the structured data by using a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

2. The method according to claim 1, wherein the processing unstructured data of the vehicle to be simulated with a preset data processing plug-in to obtain structured data comprises:

Extracting information from the unstructured data by using a regular expression in the data processing plug-in to obtain intermediate data;

and mapping the intermediate data to obtain the structured data.

3. The method according to claim 2, wherein the mapping the intermediate data to obtain the structured data comprises:

cleaning the intermediate data to obtain cleaned data;

performing data type conversion on the cleaned data to obtain converted data;

and mapping the converted data to obtain the structured data.

4. The method according to claim 1, wherein the obtaining visual configuration information using a preset visual plug-in and the structured data includes:

determining a target visual plug-in from a plurality of candidate visual plug-ins stored in advance according to the structured data;

and obtaining the visual configuration information by utilizing the configuration rules in the target visual plug-in.

5. The method of claim 4, wherein the determining a target visualization plug-in from a pre-stored plurality of candidate visualization plug-ins based on the structured data comprises:

Extracting a data type identifier from the structured data;

and determining the target visual plugin matched with the data type identifier from a plurality of candidate visual plugins.

6. The method according to claim 1, wherein the rendering the structured data using the preset fusion visualization engine and the visualization configuration information to obtain the visualized content includes:

determining a target rendering mode according to the visual configuration information; the target rendering mode comprises at least one of a two-dimensional rendering mode and a three-dimensional rendering mode;

and rendering the visual data by using the fusion visual engine in the target rendering mode to obtain the visual content.

7. The method according to claim 1, wherein the method further comprises:

and adjusting the visual content by using a preset dynamic layout module to obtain target display content.

8. A data visualization apparatus, the apparatus comprising:

the data processing module is used for processing unstructured data of the vehicle to be simulated by utilizing a preset data processing plug-in to obtain structured data;

The determining module is used for obtaining visual configuration information by utilizing a preset visual plug-in and the structured data;

and the rendering module is used for rendering the structured data by utilizing a preset fusion visualization engine and the visualization configuration information to obtain the visualized content.

9. A server comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1 to 7 when the computer program is executed.

10. A storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the method of any of claims 1 to 7.

11. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.