A data analysis and visualization method for road test data

Abstract

The present invention discloses a data analysis and visualization method for road test data, belonging to the field of data analysis. The method comprises: obtaining the original road test data and performing data screening after preliminary processing; performing scene analysis according to the screened data; writing data extraction rules for the screened data according to the results of the scene analysis and in combination with user needs; modularizing the test results and reports after data analysis of the screened data in combination with the extraction rules; programming the extraction rules, and the program is used to generate corresponding visualization images after extracting data according to the extraction rules. The present invention can realize rapid analysis of road test data and data visualization.

Description

Technical Field

[0001] The invention belongs to the field of data analysis, and in particular, relates to a data analysis and visualization method for road test data. Background Art

[0002] At present, vehicles are developing rapidly towards intelligence and greenness, and related technologies are becoming more mature. Road tests are the basis for improving vehicle driving characteristics. Vehicle verification and durability assessment rely on the collection, processing and analysis of road test data.

[0003] Because the amount of road test data collected is large and the analysis is complicated. Even with good algorithms, processing is time-consuming and labor-intensive. When encountering urgent projects, especially benchmarking analysis, we often hope to get certain analysis soon after the road test. In reality, on the contrary, data processing is slow and statistical analysis is insufficient.

[0004] Chinese patent CN202311611733.4 discloses a method and system for analyzing and processing road test data, which includes: collecting road test data and storing the road test data in a non-relational database; checking whether the road test data meets the quality rules, and performing plain text analysis on the road test data that meets the quality rules; and analyzing the road test data after the plain text analysis, and storing the analysis results in the non-relational database. Through the method, intelligent driving data can be efficiently managed and applied.

[0005] However, this method focuses on the boundary control of data, but the timeliness is still insufficient. In addition, the results are slow, and special data still needs to spend a long time to complete the data visualization. Summary of the invention

[0006] The present invention aims to overcome the deficiencies of the prior art and proposes a data analysis and visualization method for road test data to achieve the following objectives: to achieve rapid analysis of road test data and data visualization.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a data analysis and visualization method of road test data, the method comprising:

[0008] Step S1, obtaining the original road test data and performing preliminary processing and data screening;

[0009] Step S2: performing scene analysis based on the screened data;

[0010] Step S3, compiling data extraction rules for the screened data according to the results of the scenario analysis and in combination with user needs;

[0011] Step S4, combining the extraction rules, modularizing the test results and reports after data analysis of the screened data;

[0012] Step S5: Program the extraction rules, wherein the program is used to generate corresponding visualization images after extracting data according to the extraction rules.

[0013] Preferably, the step S1 comprises:

[0014] Step S11, converting the acquired original road test data into a digital format file for storage;

[0015] Step S12, preliminarily processing the original road test data, and saving the newly added data and the original road test data in the same digital format file after the preliminarily processing;

[0016] Step S13: The user establishes a data screening template according to his / her own needs, and screens the original road test data through the data screening template.

[0017] Preferably, in step S2, the scenario analysis includes application scenario analysis and measurement parameter scenario analysis, wherein the application scenario analysis includes analysis of national highways, expressways and differential roads; and the measurement parameter scenario analysis includes emission analysis, thermal management analysis and fuel consumption analysis.

[0018] Preferably, in step S3, for national roads, expressways and differential roads, the extracted data include road conditions and vehicle speed data; for emission analysis, the extracted data include warm-up conditions, rapid acceleration or rapid deceleration data; for thermal management analysis, the extracted data include coolant temperature, intercooler rear intake temperature and fan speed; for fuel consumption analysis, the extracted data include injection patterns, injection parameters and power economy.

[0019] Preferably, in step S4, the data is modularized to include a general module for data collection and recording, a data analysis module for road test operation conditions, and a fuel consumption analysis module affected by special working conditions.

[0020] Preferably, the general module of the data collection and recording analyzes the conventional data extracted according to the data extraction rule, and the conventional data includes mileage, fuel consumption, non-zero average speed, speed standard deviation, maximum speed, average engine speed, average fan speed, average cooling water temperature, average oil temperature, driving condition proportion, air conditioning condition proportion, gear-engaged coasting proportion, neutral coasting proportion, unit 100-kilometer cycle work, driver, trailer, and vehicle sequence;

[0021] The data analysis module of the road test operation status is used to analyze the influence of driving intensity, acceleration, deceleration and constant speed on fuel consumption according to the road test operation status data extracted by the data extraction rule;

[0022] The fuel consumption analysis module affected by special operating conditions is used to calculate the fuel consumption accumulated over time under special operating conditions based on the fuel consumption data under special operating conditions extracted by the data extraction rules, and analyze the contribution of these special operating conditions to the fuel consumption; the special operating conditions include idling conditions, non-driving conditions and heating conditions.

[0023] Preferably, step S5 comprises:

[0024] Step 51, read configuration and common program class files, wherein the files include numpy library, matplotlib library, pandas library, sklearn library and pylab library;

[0025] Step 52: after extracting the configuration file, select and establish a graphic module program according to user needs, wherein the program includes a scatter plot module, a bubble plot module, a bubble comparison plot module, a proportion plot module, a proportion comparison plot module and a universal characteristic curve module;

[0026] Step 53: During the programming process, after the program code is modified and rewritten based on the data extraction rules, code checking is performed;

[0027] Step 54, define the image generation of the program, obtain the image in image format, and realize visualization.

[0028] Preferably, the step S52 includes:

[0029] Step 521, read the filtered data file;

[0030] Step 522: based on the need for comparison, select the proportion diagram module and the comparison module; based on the need for connection, select the scatter diagram and bubble diagram modules; based on the need for displaying engine characteristics, select the universal characteristic diagram;

[0031] Step 523: Establish a corresponding program according to the selected graphic module.

[0032] Preferably, the data analysis of steps S1 to S4 is performed using Excel tools.

[0033] Preferably, the step S5 is programmed using Python.

[0034] The technical effects of the present invention are:

[0035] (1) The present invention involves data screening, scenario analysis, extraction, and modular analysis. The entire process fully combines user needs and can simultaneously perform a comprehensive and multi-angle analysis of road test data, greatly facilitating users.

[0036] (2) The present invention can process road test data in a targeted manner according to user needs, thereby improving work efficiency and achieving rapid data analysis and visualization.

[0037] (3) The data analysis process of the present invention is simple and efficient, and can be realized based on Excel tools to facilitate user operation. At the same time, data visualization uses Python, which has simple syntax and powerful functions. The data visualization method of the present invention can be packaged into a program for repeated use, thereby improving work efficiency and being used for user operation.

[0038] (4) The data visualization image module program of the present invention includes a variety of commonly used data analysis charts, which can meet the user's comprehensive data analysis and display needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The present invention is a flowchart of a method for analyzing and visualizing road test data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The following is a further detailed description of the specific implementation of the present invention by describing the embodiments with reference to the accompanying drawings, with the purpose of helping those skilled in the art to have a more complete, accurate and in-depth understanding of the inventive concept and technical solution of the present invention and to facilitate its implementation. In order to make the technical solution of the present invention clearer, the present invention is explained by the following embodiments.

[0041] As mentioned above, in this embodiment, the data analysis and processing adopts Excel tool. Excel is the most commonly used data processing tool in the prior art, which supports processing a large amount of data at the same time and supports a variety of built-in formulas and functions. Users can perform complex mathematical calculations, statistical analysis, data screening and logical judgment. In specific implementation, other data analysis tools can also be selected according to actual conditions.

[0042] In this embodiment, Python is used for editing and application of the program. As a high-level programming language, Python has a wide range of third-party libraries, such as NumPy, Pandas, Matplotlib, TensorFlow, Django, etc., which can meet the needs of different fields; and Python performs well in scientific computing and data analysis. In specific implementation, other programming languages ​​can also be selected according to actual conditions.

[0043] like Figure 1 As shown, this embodiment proposes a data analysis and visualization method for road test data, the method comprising:

[0044] Step S1, obtaining the original road test data and performing preliminary processing and data screening;

[0045] Step S2: performing scene analysis based on the screened data;

[0046] Step S3, compiling data extraction rules for the screened data according to the results of the scenario analysis and in combination with user needs;

[0047] Step S4, combining the extraction rules, modularizing the test results and reports after data analysis of the screened data;

[0048] Step S5: Program the extraction rules, wherein the program is used to generate corresponding visualization images after extracting data according to the extraction rules.

[0049] Specifically, step S1, obtaining the original road test data and performing preliminary processing and then screening the data. Step S1 of this embodiment includes:

[0050] Step S11, converting the acquired original road test data into a digital format file for storage. The road test data collection volume is large, so when acquiring the original road test data, it can be collected in batches according to a preset fixed frequency, and after the collection is completed, all the data are converted into a digital format file for storage. Since Excel is used in this embodiment, the digital format can be selected as .xls or .xlsx.

[0051] Step S12: Perform preliminary processing on the original road test data, and save the newly added data and the original road test data in the same digital format file after preliminary processing. The original road test data is usually various sensor data on the vehicle during the road test experiment. After importing into Excel, the user can perform preliminary processing on these data, such as average value calculation, summation calculation, etc. The data after preliminary processing is also saved in Excel to facilitate comparative analysis between data.

[0052] Step S13, the user establishes a data screening template according to his own needs, and filters the original road test data through the data screening template. Since the original road test data is large in volume and noisy, and not all data need to be used in subsequent analysis and visualization, the user can establish a data screening template according to his own needs. In the data screening template, the user can set the screening conditions, such as only selecting data within a fixed time period, only selecting data from designated sensors of the vehicle, etc. At the same time, the data screening template can be saved for subsequent reuse by users with the same needs. The original road test data is filtered according to the data screening template, so that the noise can be eliminated, the data can be retained in a targeted manner, and the efficiency of subsequent data analysis and visualization can be improved.

[0053] Step S2, performing scenario analysis based on the filtered data. Specifically, the scenario analysis of this embodiment includes application scenario analysis and measurement parameter scenario analysis, wherein the application scenario analysis includes analysis of national highways, expressways, and differential roads, etc.; the measurement parameter scenario analysis includes emission analysis, thermal management analysis, and fuel consumption analysis, etc. In short, the purpose of scenario analysis is to classify the filtered road test data, such as which data belongs to road test data under national highways, which data is used for fuel consumption analysis, etc., so as to facilitate subsequent data analysis and visualization.

[0054] Step S3, compile data extraction rules for the filtered data according to the results of the scenario analysis and in combination with user needs. For example, if the user needs to analyze national roads, expressways, and differential roads, the extracted data includes road conditions, vehicle speed data, etc. For emission analysis, the user needs to focus on the working conditions that affect emissions, and the extracted data includes warm-up conditions, rapid acceleration or rapid deceleration data, etc. For thermal management analysis, the user needs to extract data including coolant temperature, intercooler rear intake temperature, and fan speed, etc. For fuel consumption analysis, the extracted data includes injection rules, injection parameters, power economy, etc.

[0055] Step S4, combining the extraction rules, modularizing the test results and reports after data analysis of the screened data. In order to enable users to intuitively view the test results and reports after data analysis, this embodiment adopts a modular approach, and multiple modules can simultaneously analyze according to the extracted data, thereby realizing rapid road test data analysis. In this embodiment, the data modularization includes a general module for data acquisition and recording, a data analysis module for road test operation status, and a fuel consumption analysis module affected by special working conditions.

[0056] Specifically, the general module of the data collection and recording is used to record the routine data extracted by the data extraction rules, including mileage, fuel consumption, non-zero average vehicle speed, vehicle speed standard deviation, maximum vehicle speed, average engine speed, average fan speed, average cooling water temperature, average oil temperature, driving condition ratio, air-conditioning condition ratio, gear-engaged coasting ratio, neutral coasting ratio, unit 100-kilometer cycle work, driver, trailer, and vehicle sequence.

[0057] The data analysis module of the road test operation status is used to analyze the influence of driving intensity, acceleration, deceleration and uniform speed on fuel consumption according to the road test operation status data extracted according to the data extraction rules. In this embodiment, for heavy-duty commercial vehicles, based on GB / T 38146.2019 "China Automobile Driving Conditions Part 2: Heavy-duty Commercial Vehicles", the road test operation status data extracted according to the data extraction rules include maximum speed, maximum acceleration, maximum deceleration, average speed, average operating speed, average acceleration in the acceleration section, average deceleration in the deceleration section, relative positive acceleration, acceleration ratio, deceleration ratio, idle ratio, uniform speed ratio, speed standard deviation, acceleration standard deviation, acceleration fuel consumption ratio, deceleration fuel consumption ratio, idle fuel consumption ratio and uniform speed fuel consumption ratio.

[0058] The fuel consumption analysis module affected by special operating conditions is used to calculate the fuel consumption accumulated over time under special operating conditions based on the fuel consumption data under special operating conditions extracted by the data extraction rules, and analyze the contribution of these special operating conditions to fuel consumption; the special operating conditions include idling conditions, non-driving conditions and heating conditions.

[0059] Step S5: Program the extraction rules, wherein the program is used to generate corresponding visualization images after extracting data according to the extraction rules. Specifically, step S5 of this embodiment includes:

[0060] Step 51, read configuration and common program class files, wherein the files include numpy library, matplotlib library, pandas library, sklearn library and pylab library, etc.;

[0061] Step 52, after extracting the configuration file, select and establish a graphic module program according to user needs, the program includes a scatter plot module, a bubble plot module, a bubble comparison plot module, a proportion plot module, a proportion comparison plot module and a universal characteristic curve module.

[0062] Specifically, the step S52 includes:

[0063] Step 521, read the filtered data file;

[0064] Step 522, based on the need for comparison, select the proportion map module and the comparison module; based on the need for connection, select the scatter plot and bubble map modules; based on the need to display engine characteristics, select the universal characteristic map. For example, when performing fuel consumption analysis, it is necessary to compare the fuel consumption data at different mileages to reflect the fuel consumption difference. At this time, the proportion map module and the comparison module can be selected. It is worth mentioning that the fuel consumption data at different mileages can be obtained by interpolation, thereby improving the data and improving the reliability of the data. In the Python program, the interpolation method is also a common technique used by those skilled in the art, and the present invention will not be repeated here.

[0065] In Python programs, scatter plots and bubble plots can be directly called from existing databases; for percentage plots, the self-written function interval_statistics(data, intervals) is used to define parameter intervals and obtain corresponding statistical values. The data variable represents the data set for interval statistics; the intervals variable is used to store statistical information for each interval.

[0066] The universal characteristic diagram is obtained by fitting the universal characteristics. The fitting methods of universal characteristics include linear fitting (taking logarithms and then performing linear analysis, this method has large errors) and polynomial fitting (for example, for the analysis of engine fuel consumption, fuel consumption can be regarded as a polynomial function of the engine effective torque Me and engine speed n). In Python, the fitting of universal characteristics usually uses the polynomial fitting method (usually 3rd order), and then uses contour mapping to realize the universal characteristic diagram.

[0067] Step 523: Establish a corresponding program according to the selected graphic module. In this embodiment, all graphic module programs can be packaged into one program, so that data analysis and image visualization can be quickly realized during actual use.

[0068] Step 53: During the programming process, the program code is modified and rewritten based on the data extraction rules, and then the code is checked. The Jupyter notebook tool is a commonly used code checking tool that can identify syntax errors, logic errors and potential defects in a timely manner, helping users to fix problems in a timely manner, thereby improving the work efficiency of data analysis and visualization.

[0069] Step 54, define the image generation of the program, obtain an image in an image format (such as .jpg and .png, etc.), and finally realize visualization.

[0070] The present invention is described above by way of example in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-mentioned methods. As long as various non-substantial improvements are made using the method concept and technical solution of the present invention; or the above-mentioned concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A data analysis and visualization method for road test data, characterized in that: The method comprises: Step S1, obtaining the original road test data and performing preliminary processing and data screening; Step S2: performing scene analysis based on the screened data; Step S3, compiling data extraction rules for the screened data according to the results of the scenario analysis and in combination with user needs; Step S4, combining the extraction rules, modularizing the test results and reports after data analysis of the screened data; Step S5: Program the extraction rules, wherein the program is used to generate corresponding visualization images after extracting data according to the extraction rules.

2. The method for analyzing and visualizing road test data according to claim 1, characterized in that: The step S1 comprises: Step S11, converting the acquired original road test data into a digital format file for storage; Step S12, preliminarily processing the original road test data, and saving the newly added data and the original road test data in the same digital format file after the preliminarily processing; Step S13: The user establishes a data screening template according to his / her own needs, and screens the original road test data through the data screening template.

3. The method for analyzing and visualizing road test data according to claim 1, characterized in that: In step S2, the scenario analysis includes application scenario analysis and measurement parameter scenario analysis, wherein the application scenario analysis includes analysis of national highways, expressways and differential roads; the measurement parameter scenario analysis includes emission analysis, thermal management analysis and fuel consumption analysis.

4. The method for analyzing and visualizing road test data according to claim 3, characterized in that: In step S3, for national roads, expressways and differential roads, the extracted data include road conditions and vehicle speed data; for emission analysis, the extracted data include warm-up conditions, rapid acceleration or rapid deceleration data; for thermal management analysis, the extracted data include coolant temperature, intercooler rear intake temperature and fan speed; for fuel consumption analysis, the extracted data include injection patterns, injection parameters and power economy.

5. The method for analyzing and visualizing road test data according to claim 1, characterized in that: In step S4, the data is modularized to include a general module for data collection and recording, a data analysis module for road test operation conditions, and a fuel consumption analysis module affected by special working conditions.

6. The method for analyzing and visualizing road test data according to claim 5, characterized in that: The general module of the data collection and recording analyzes the conventional data extracted according to the data extraction rules, and the conventional data includes mileage, fuel consumption, non-zero average speed, speed standard deviation, maximum speed, average engine speed, average fan speed, average cooling water temperature, average oil temperature, driving condition ratio, air conditioning condition ratio, gear-engaged coasting ratio, neutral coasting ratio, unit 100-kilometer cycle work, driver, trailer, and vehicle sequence; The data analysis module of the road test operation status is used to analyze the influence of driving intensity, acceleration, deceleration and constant speed on fuel consumption according to the road test operation status data extracted by the data extraction rule; The fuel consumption analysis module affected by special operating conditions is used to calculate the fuel consumption accumulated over time under special operating conditions based on the fuel consumption data under special operating conditions extracted by the data extraction rules, and analyze the contribution of these special operating conditions to the fuel consumption; the special operating conditions include idling conditions, non-driving conditions and heating conditions.

7. The method for analyzing and visualizing road test data according to claim 1, characterized in that: The step S5 comprises: Step 51, read configuration and common program class files, wherein the files include numpy library, matplotlib library, pandas library, sklearn library and pylab library; Step 52: after extracting the configuration file, select and establish a graphic module program according to user needs, wherein the program includes a scatter plot module, a bubble plot module, a bubble comparison plot module, a proportion plot module, a proportion comparison plot module and a universal characteristic curve module; Step 53: During the programming process, after the program code is modified and rewritten based on the data extraction rules, code checking is performed; Step 54, define the image generation of the program, obtain the image in image format, and realize visualization.

8. The method for analyzing and visualizing road test data according to claim 7, characterized in that: The step S52 comprises: Step 521, read the filtered data file; Step 522: based on the need for comparison, select the proportion diagram module and the comparison module; based on the need for connection, select the scatter diagram and bubble diagram modules; based on the need for displaying engine characteristics, select the universal characteristic diagram; Step 523: Establish a corresponding program according to the selected graphic module.

9. A method for analyzing and visualizing road test data according to any one of claims 1 to 6, characterized in that: The data analysis from step S1 to step S4 is performed using Excel tool.

10. A method for analyzing and visualizing road test data according to claim 1, 7 or 8, characterized in that: The step S5 is programmed using Python.

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