Vehicle operation data visualization method and system, computer equipment and storage medium

Abstract

The invention relates to a vehicle operation data visualization method and system, computer equipment and a storage medium. The method comprises the steps of obtaining vehicle operation data; extracting a statistical feature set of the vehicle operation data; determining a target chart type according to the statistical feature set; and outputting a visual chart according to the target chart type, wherein the visual chart is used for displaying the vehicle operation data. According to the method, the appropriate target chart type can be intelligently analyzed according to the statistical feature set of the vehicle operation data, and the visual chart is automatically generated according to the target chart type to display the vehicle operation data, so that a user is assisted in analyzing the data, the data analysis efficiency can be improved, and the user experience is improved. And the accuracy of an analysis result is improved to a certain extent.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a vehicle operation data visualization method, system, computer device and storage medium. Background Art

[0002] Vehicle calibration is an important process to ensure vehicle performance, safety, compliance and optimize user experience. It is mainly used in the development and testing phase of autonomous vehicles to ensure that various sensors and algorithms can accurately perceive and understand the surrounding environment, thereby achieving safe and reliable autonomous driving.

[0003] The vehicle calibration process specifically includes: collecting the vehicle's operating data in real time under various working conditions through the vehicle's CAN (Controller Area Network) bus interface. Engineers manually select charts suitable for displaying operating data based on their experience, analyze the collected operating data in combination with the charts, and adjust and optimize various vehicle parameters based on the analysis results.

[0004] Engineers need to spend time to conduct a preliminary analysis of the operating data before they can select a chart that is suitable for displaying the operating data. Appropriate charts can help engineers get analysis results faster, but if engineers choose inappropriate charts, it will affect their judgment, reduce their analysis efficiency, and even affect the analysis results. Based on this, there are high requirements for engineers' abilities. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a vehicle operation data visualization method, system, computer device and storage medium that can intelligently analyze the appropriate target chart type and automatically generate a visualization chart according to the target chart type to display the vehicle operation data.

[0006] In a first aspect, the present application provides a method for visualizing vehicle operation data, the method comprising:

[0007] Obtain vehicle operation data;

[0008] Extracting a set of statistical features of the vehicle operation data;

[0009] Determining a target chart type according to the statistical feature set;

[0010] A visual chart is output according to the target chart type, where the visual chart is used to display the vehicle operation data.

[0011] In one embodiment, the vehicle operating data includes actual operating data of the vehicle under target operating conditions and predicted operating data obtained based on the actual operating data.

[0012] In one embodiment, the obtaining of vehicle operation data includes:

[0013] In response to the actual operation data received in real time, the actual operation data is input into a data prediction model to obtain predicted operation data output by the data prediction model.

[0014] In one embodiment, outputting a visual chart according to the target chart type includes:

[0015] Determine whether there is an output visualization chart;

[0016] If not, outputting a first visual chart according to the target chart type;

[0017] If so, when the actual chart type of the output visualization chart is the same as the target chart type, update the actual operation data and the predicted operation data displayed by the output visualization chart; if the actual chart type of the output visualization chart is different from the target chart type, output a second visualization chart according to the target chart type.

[0018] In one embodiment, determining the target chart type according to the statistical feature set includes:

[0019] The statistical feature set is input into a chart recommendation model to obtain a target chart type output by the chart recommendation model.

[0020] In one embodiment, the chart recommendation model is trained based on first training data and a random forest model, the first training data includes a statistical feature set sample extracted based on a vehicle operation data sample, and the first label corresponding to the first training data includes a chart type suitable for the statistical feature set sample.

[0021] In one embodiment, the data prediction model is trained based on second training data and a long short-term memory model, the second training data includes first actual operating data of the vehicle under multiple set operating conditions and a first time point for collecting the first actual operating data, the second label corresponding to the second training data includes second actual operating data of the vehicle under the same set operating conditions and a second time point for collecting the second actual operating data, and the second time point is later than the first time point.

[0022] In a second aspect, the present application further provides a vehicle operation data visualization system for implementing the vehicle operation data visualization method described in the first aspect. The system comprises:

[0023] A data acquisition module, used to acquire vehicle operation data;

[0024] A feature extraction module, used to extract a set of statistical features of the vehicle operation data;

[0025] A chart type recommendation module, used to determine a target chart type according to the statistical feature set;

[0026] A visualization module is used to output a visualization chart according to the target chart type, and the visualization chart is used to display the vehicle operation data.

[0027] In a third aspect, the present application provides a computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the vehicle operation data visualization method described in the first aspect is implemented.

[0028] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the vehicle operation data visualization method described in the first aspect is implemented.

[0029] On the basis of conforming to the common sense in the art, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present application.

[0030] The above-mentioned vehicle operation data visualization method, system, computer device and storage medium can achieve the following beneficial effects: intelligently analyzing the appropriate target chart type based on the statistical feature set of the vehicle operation data, and automatically generating a visualization chart according to the target chart type to display the vehicle operation data, assisting users in analyzing the data, which can improve the efficiency of data analysis and improve the accuracy of the analysis results to a certain extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 A schematic diagram of a process of visualizing vehicle operation data in one embodiment;

[0032] Figure 2 A schematic diagram of a process of visualizing vehicle operation data in one embodiment;

[0033] Figure 3 A schematic diagram of a module of a vehicle operation data visualization system in one embodiment;

[0034] Figure 4 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] It should be noted that the diagrams provided in the present embodiment only illustrate the basic concept of the present application in a schematic manner. The diagrams only show the components related to the present application rather than the number, shape and size of the components in the actual implementation. The form, quantity and proportion of each component in the actual implementation can be changed at will, and the component layout form may also be more complicated. The structure, proportion, size, etc. illustrated in the drawings of the present specification are only used to match the content disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions that can be implemented in the present application, so they have no technical substantive significance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effect that can be produced by the present application and the purpose that can be achieved, should still fall within the scope of the technical content disclosed in the present application. At the same time, the terms such as "upper", "lower", "left", "right", "middle" and "one" quoted in the present specification are only for the convenience of narration, and are not used to limit the scope of the implementation of the present application. The change or adjustment of the relative relationship, without substantial change of the technical content, should also be regarded as the scope of the implementation of the present application.

[0037] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the text does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] As shown in this document, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural, unless the context clearly indicates an exception. Generally speaking, the terms "include" and "comprise" only indicate that the steps and elements that have been clearly identified are included, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0039] The definitions of inclusion in this document, such as the terms "having", "may have", "including" or "may include" used herein, indicate the existence of the corresponding functions, operations, elements, etc. herein, and do not limit the existence of one or more other functions, operations, elements, etc. In addition, it should be understood that the terms "including" or "having" used herein indicate the existence of the features, numbers, steps, operations, elements, components, or a combination thereof described in the specification, and do not exclude the existence or addition of one or more other features, numbers, steps, operations, elements, components, or a combination thereof.

[0040] In the embodiments of the present application, prefixes such as "first" and "second" are used only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present application does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "multiple" is two or more.

[0041] The vehicle operation data visualization method provided in the present application intelligently analyzes a suitable target chart type according to a set of statistical features of the vehicle operation data, and automatically generates a visualization chart according to the target chart type to display the vehicle operation data, thereby assisting users in analyzing the data, thereby improving the efficiency of data analysis, and improving the accuracy of the analysis results to a certain extent.

[0042] In one embodiment, the vehicle operation data visualization method provided by the present application is as follows: Figure 1 As shown, it includes steps S101 to S104:

[0043] S101, obtaining vehicle operation data;

[0044] S102, extracting a statistical feature set of vehicle operation data;

[0045] S103, determining a target chart type according to a set of statistical features;

[0046] S104. Output a visualization chart according to the target chart type, where the visualization chart is used to display vehicle operation data.

[0047] The target chart type may be determined based on a pre-trained chart recommendation model. In one embodiment, step S103 includes:

[0048] The statistical feature set is input into a chart recommendation model to obtain a target chart type output by the chart recommendation model.

[0049] Exemplarily, the chart recommendation model is trained based on first training data and a random forest model, the first training data includes a statistical feature set sample extracted based on a vehicle operation data sample, and the first label corresponding to the first training data includes a chart type suitable for the statistical feature set sample.

[0050] Exemplarily, the specific training process includes steps S201 to S204:

[0051] S201. Obtain a large amount of vehicle operation data samples.

[0052] For example, the original operation data of each control module is collected through the vehicle's CAN bus interface. The collected original operation data is cleaned and normalized to remove outliers and noise to obtain actual operation data that meets the requirements. The normalization process can use methods such as minimum-maximum normalization and standardization.

[0053] The target operating conditions include, but are not limited to, road type, road slope, weather temperature, humidity, traffic flow, etc. The specific target operating conditions can be selected according to the actual needs of vehicle calibration.

[0054] Vehicle operation data generally includes operating data of multiple dimensions obtained by testing the vehicle under target operating conditions, including but not limited to motor speed, motor power, motor torque, vehicle mass, vehicle speed, battery charge, battery voltage, battery current, accelerator pedal information, brake pedal information, steering wheel steering angle, signal light information, etc.

[0055] S202: extracting a statistical feature set sample of each vehicle operation data sample as training data.

[0056] According to the above-mentioned operation data of different dimensions, their statistical features can be extracted respectively, including but not limited to sample size, maximum value, minimum value, maximum difference, median, number of missing values, mean value, variance, standard deviation, trimmed mean, coefficient of variation, skewness, kurtosis, interquartile range, autocorrelation coefficient, data trend, normality test results, etc. That is, the statistical feature set corresponding to the vehicle operation data includes one or more statistical features.

[0057] S203: Mark each group of statistical feature set samples with a suitable chart type label as training data.

[0058] Chart types include but are not limited to histograms, box plots, scatter plots, line charts, pie charts, bar charts, heat maps, radar charts, density maps, box plots, etc.

[0059] Among them, histograms are used to show the distribution of data. Box plots are used to show the statistical characteristics of data, such as median, quartiles, outliers, etc. Scatter plots are used to show the relationship between two variables. Line charts are often used for time series data. Pie charts show the proportion of different categories. Bar charts are used to show the comparison between categories. Heat maps are used to show the heat of matrix data. Radar charts are used to display multivariate data. Density maps are used to show the distribution density of data. Box plots combine the characteristics of box plots and density maps to show data distribution.

[0060] S204: Perform model training based on the statistical feature set samples and the corresponding chart type labels to obtain a chart recommendation model.

[0061] For example, the statistical features corresponding to the operating data of each dimension are combined into a statistical feature set and input into the chart recommendation model to obtain a target chart type suitable for the operating data of this dimension. That is, if the vehicle operating data includes operating data of multiple dimensions, the target chart type corresponding to each data dimension can be obtained. Based on this, multiple visualization charts can be generated to respectively display the operating data of different dimensions.

[0062] The random forest model is an ensemble learning method that can usually achieve high prediction accuracy by integrating the prediction results of multiple decision trees. It can effectively process large-scale data sets, including high-dimensional data and data sets with a large number of features. It can also reduce data dimensions by randomly selecting features and handle missing values ​​in the data without filling in the data in advance.

[0063] Based on this, the above chart recommendation model can be obtained based on the above training data and random forest model training. The parameter iteration process and model verification process in the model training process belong to common knowledge in the field and will not be repeated here.

[0064] In the actual application process of vehicle calibration, engineers can currently only analyze based on the actual operating data obtained from the calibration test. Therefore, it is necessary to obtain the actual operating data obtained from the vehicle test under the target working conditions as the vehicle operating data multiple times and perform multiple analyses, which takes a long time and has low calibration efficiency. This application adds a data prediction mechanism to this, which can obtain future predicted operating data based on the actual operating data, and simultaneously display the actual operating data and predicted operating data through visual charts.

[0065] In one embodiment, the vehicle operation data further includes predicted operation data obtained based on the actual operation data.

[0066] Exemplarily, step S101 includes:

[0067] In response to the actual operation data received in real time, the actual operation data is input into the data prediction model to obtain the predicted operation data output by the data prediction model.

[0068] Among them, the data prediction model can be pre-trained.

[0069] In one embodiment, the data prediction model is trained based on second training data and a long short-term memory model, the second training data includes first actual operating data of the vehicle under multiple set operating conditions and a first time period for collecting the first actual operating data, the second label corresponding to the second training data includes second actual operating data of the vehicle under the same set operating condition and a second time period for collecting the second actual operating data, and the time point in the second time period is later than the time point in the first time period.

[0070] Exemplarily, the specific training process includes steps S301 to S303:

[0071] S301. Acquire actual operating data of a vehicle tested under various working conditions and time information of collecting the actual operating data.

[0072] For example, the original operation data of each control module is collected through the vehicle's CAN bus interface, and the time point of collecting each original operation data is recorded. The collected original operation data is cleaned and normalized to remove outliers and noise to obtain actual operation data that meets the requirements. The normalization process can use methods such as minimum-maximum normalization and standardization.

[0073] S302 : The actual running data belonging to the same test and the corresponding time information are divided into training data and labels corresponding to the training data according to time periods.

[0074] For example, the actual operation data corresponding to the first time period is used as training data, and the actual operation data corresponding to the second time period after the first time period is used as the label corresponding to the training data, wherein the time points in the second time period are all later than the time points in the first time period.

[0075] S303: Perform model training using the training data obtained in step S202 and the labels corresponding to the training data to obtain the above data prediction model.

[0076] The Long Short-Term Memory (LSTM) model is a time-recurrent neural network model that controls the flow of information by introducing input gates, forget gates, and output gates, allowing the model to selectively retain or discard information, thereby effectively alleviating the gradient vanishing and gradient exploding problems. The LSTM memory cell can store information for a long time, which enables the model to capture long-distance dependencies in the sequence.

[0077] Based on this, the above data prediction model can be obtained based on the above training data and LSTM model training. The parameter iteration process and model verification process in the model training process belong to common knowledge in the field and will not be repeated here.

[0078] In addition, during the actual application process, the above-mentioned chart recommendation model and data prediction model can also use the input actual operation data as training data and user feedback to continuously train the model to improve the prediction accuracy of the model.

[0079] In one embodiment, step S101 may receive vehicle operation data in real time, and the visualization chart should also be updated in real time according to the newly received actual operation data and the predicted operation data obtained based on the actual operation data. That is, step S104 includes the following steps:

[0080] Determine whether there is an output visualization chart;

[0081] If not, outputting the first visual chart according to the target chart type;

[0082] If so, when the actual chart type of the output visualization chart is the same as the target chart type, the actual operation data and predicted operation data displayed by the output visualization chart are updated; when the actual chart type of the output visualization chart is different from the target chart type, a second visualization chart is output according to the target chart type.

[0083] Based on this, engineers can receive and intuitively feel the actual operating data of the vehicle during the calibration process in real time, and combined with the corresponding predicted operating data, they can obtain analysis results faster and more accurately, avoiding repeated testing, chart selection, and analysis processes. This can significantly improve analysis efficiency and accuracy and shorten the vehicle calibration cycle.

[0084] Exemplarily, when the actual operation data is received for the first time, it is input into a pre-trained data prediction model that meets the prediction accuracy requirements to obtain output predicted operation data. The actual operation data and the predicted operation data are taken as a whole to extract a set of overall statistical features. The set of statistical features is input into a pre-trained chart recommendation model that meets the recommendation accuracy requirements to obtain an output first chart type.

[0085] Generate a visualization chart according to the first chart type to show Figure 2 The vehicle operation data shown. The actual operation data of the vehicle is represented by a solid line, and the predicted operation data is represented by a dotted line. The specific representation method can be set and adjusted according to actual needs.

[0086] In response to receiving the new actual operation data, the new actual operation data is also input into the data prediction model to obtain the output new predicted operation data. The new actual operation data and the new predicted operation data are taken as a whole to extract the overall statistical feature set. The statistical feature set is input into the chart recommendation model to obtain the output second chart type.

[0087] Since there is an output visualization chart, it is necessary to determine whether the first chart type is the same as the second chart type.

[0088] If they are the same, it means that the previously generated visualization chart will continue to be used, and only the actual operation data and predicted operation data displayed in the visualization chart need to be updated. That is, the visualization chart retains the original actual operation data, adds new actual operation data, and replaces the old predicted operation data with the new predicted operation data. Based on this, users can view the increasing actual operation data and dynamically changing predicted operation data in real time.

[0089] If they are not the same, it means that the previously generated visualization chart is no longer applicable, and a new visualization chart needs to be generated according to the second chart type, and the original actual operation data and the new actual operation data are displayed, and the new predicted operation data are displayed at the same time. Based on this, a more appropriate chart can be dynamically selected to display the vehicle operation data according to the changes.

[0090] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0091] Corresponding to the vehicle operation data visualization method, the present application also provides a vehicle operation data visualization system.

[0092] In one embodiment, Figure 3 As shown, the vehicle operation data visualization system includes:

[0093] The data acquisition module 401 is used to acquire vehicle operation data;

[0094] A feature extraction module 402, used to extract a set of statistical features of vehicle operation data;

[0095] A chart type recommendation module 403, used to determine a target chart type according to a set of statistical features;

[0096] The visualization module 404 is used to output a visualization chart according to a target chart type, and the visualization chart is used to display vehicle operation data.

[0097] For the specific definition of the vehicle operation data visualization system, please refer to the definition of the vehicle operation data visualization method above, which will not be repeated here. Each module in the above-mentioned vehicle operation data visualization system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0098] The present application also provides a computer device. In one embodiment, the computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the vehicle operation data visualization method in the above embodiment are implemented.

[0099] In one embodiment, the computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for visualizing vehicle operation data is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0100] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0101] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the vehicle operation data visualization method in the above-mentioned embodiment are implemented.

[0102] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0103] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0104] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for visualizing vehicle operation data, characterized in that: The vehicle operation data visualization method comprises: Obtain vehicle operation data; Extracting a set of statistical features of the vehicle operation data; Determining a target chart type according to the statistical feature set; A visual chart is output according to the target chart type, where the visual chart is used to display the vehicle operation data.

2. The vehicle operation data visualization method according to claim 1, characterized in that: The vehicle operation data includes actual operation data of the vehicle under target operating conditions and predicted operation data obtained based on the actual operation data.

3. The data visualization method according to claim 2, characterized in that: The obtaining of vehicle operation data comprises: In response to the actual operation data received in real time, the actual operation data is input into a data prediction model to obtain predicted operation data output by the data prediction model.

4. The vehicle operation data visualization method according to claim 2, characterized in that: The outputting a visual chart according to the target chart type includes: Determine whether there is an output visualization chart; If not, outputting a first visual chart according to the target chart type; If so, when the actual chart type of the output visualization chart is the same as the target chart type, update the actual operation data and the predicted operation data displayed by the output visualization chart; if the actual chart type of the output visualization chart is different from the target chart type, output a second visualization chart according to the target chart type.

5. The vehicle operation data visualization method according to claim 1, characterized in that: The step of determining the target chart type according to the statistical feature set includes: The statistical feature set is input into a chart recommendation model to obtain a target chart type output by the chart recommendation model.

6. The vehicle operation data visualization method according to claim 5, characterized in that: The chart recommendation model is trained based on first training data and a random forest model, the first training data includes a statistical feature set sample extracted based on a vehicle operation data sample, and the first label corresponding to the first training data includes a chart type suitable for the statistical feature set sample.

7. The method for visualizing vehicle operation data according to claim 3, characterized in that: The data prediction model is trained based on second training data and a long short-term memory model, the second training data includes first actual operating data of the vehicle under multiple set operating conditions and a first time point for collecting the first actual operating data, the second label corresponding to the second training data includes second actual operating data of the vehicle under the same set operating conditions and a second time point for collecting the second actual operating data, the second time point being later than the first time point.

8. A vehicle operation data visualization system, characterized in that: The vehicle operation data visualization system comprises: A data acquisition module, used to acquire vehicle operation data; A feature extraction module, used to extract a set of statistical features of the vehicle operation data; A chart type recommendation module, used to determine a target chart type according to the statistical feature set; A visualization module is used to output a visualization chart according to the target chart type, and the visualization chart is used to display the vehicle operation data.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

Images