Commercial vehicle test data monitoring analysis system and method based on Internet of Vehicles big data

Abstract

The invention relates to a commercial vehicle test data monitoring analysis system and method based on Internet of Vehicles big data, the system comprises an overview module, a vehicle state module, a test analysis module, a driving operation module and a vehicle-mounted video module, CAN bus data and video streams are collected through a vehicle-mounted terminal, and the CAN bus data and the video streams are classified and stored in a time sequence database after being analyzed; the system creatively integrates the functions of vehicle state real-time monitoring, multi-dimensional test data analysis, driving behavior analysis and identification, test data entry and video remote viewing, adopts modular design and big data analysis technology, solves the problems of single function and data islanding of the existing system, remarkably improves the test management efficiency and decision accuracy, and is suitable for popularization and application. And operation risks are reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle intelligent networking, and in particular to a commercial vehicle test data monitoring and analysis system and method based on vehicle networking big data. BACKGROUND

[0002] With the continuous expansion of commercial vehicle test scale and the increasing complexity of test projects, comprehensive monitoring, in-depth analysis and visual display of vehicle data have become the key to improving test efficiency and optimizing vehicle performance. However, the monitoring and analysis systems in the prior art are often single-function, lack of comprehensiveness and intelligence, and are difficult to meet the diversified needs of commercial vehicle test data analysis; data island: lack of integration, the data between different systems are often isolated from each other, it is difficult to realize the sharing and comprehensive analysis of data, resulting in low information utilization rate; insufficient automation: the existing system has limited automation degree in data analysis and decision support, and cannot realize the automation of analysis results. SUMMARY

[0003] The purpose of the present application is to overcome the shortcomings of the prior art and provide a commercial vehicle test data monitoring and analysis system and method based on vehicle networking big data.

[0004] The present application is implemented by adopting the following technical solutions:

[0005] The commercial vehicle test data monitoring and analysis system based on vehicle networking big data comprises:

[0006] The overview module is used to display the total number of test vehicles, the current position distribution and the stay state; identify and highlight the long-period non-operational vehicles and the vehicles without data return; display the vehicles operating on the day, the specific operating mileage and the vehicle test project profile; dynamically present the vehicle operation trajectory and the serious fault alarm information affecting the normal operation of the whole vehicle, wherein the vehicle operation trajectory comprises the vehicle operation route, the stay point and the passing area;

[0007] The serious fault alarm information comprises engine fault, gearbox fault and vehicle-mounted electrical appliance fault information;

[0008] The vehicle use project profile comprises vehicle intelligent networking test, energy consumption test, reliability test, highland and cold region test;

[0009] The vehicle state module is used to acquire and display the vehicle state in real time, wherein the vehicle state comprises vehicle positioning information and key operation parameters; and provide time sequence diagram display of vehicle operation data, wherein the key operation parameters comprise vehicle speed, engine speed, water temperature, ambient temperature, altitude, fuel consumption, torque and battery state;

[0010] The test analysis module compares the trend display through the vehicle speed, engine speed and torque parameters calculation and columnar distribution graph, generates a big data analysis report of different vehicles and each time range, and the big data analysis report includes vehicle power performance, operation parameters and abnormal identification results.

[0011] The abnormal identification results include whether the vehicle has high water temperature, fan direct connection, overspeed, over-revolution abnormal identification results.

[0012] The driving operation module identifies the driver's abnormal driving behavior, scores the driver's driving behavior through gear shifting frequency, brake frequency and emergency brake frequency, slope vehicle control, vehicle overspeed and long time full throttle sub-items, outputs the driving behavior score, evaluates the driving condition according to the driving behavior score, then generates a driving behavior score report, and visualizes each sub-item score and driving behavior score through a radar chart.

[0013] The driver's abnormal driving behavior includes smoking, inattentive driving and fatigue driving behavior during driving.

[0014] The vehicle-mounted video module remotely and real-timely checks the in-vehicle and out-of-vehicle videos.

[0015] The commercial vehicle test data monitoring and analysis method based on vehicle networking big data is applied to the commercial vehicle test data monitoring and analysis system based on vehicle networking big data as described above, and comprises:

[0016] Vehicle operation data acquisition: the vehicle CAN line speed, engine speed and torque parameter data are transmitted back through the network by the vehicle data acquisition terminal, and then stored in the object storage OSS (OSS), and the storage format is ASC format data.

[0017] Vehicle data analysis and storage: for the ASC format data transmitted back by the vehicle CAN line, first decode according to the vehicle signal analysis table, convert the decoded data to decimal data, and classify according to the converted data category, then store the classified data in the TimescaleDB time series database.

[0018] Video data reception: the in-vehicle and out-of-vehicle video data are transmitted by the vehicle data transmission terminal, and the in-vehicle and out-of-vehicle video stream is real-timely received and pushed by the streaming media server.

[0019] Operation parameter calculation: the vehicle state, operation trajectory and key operation parameters are analyzed and calculated by the background calculation system, and stored in the TimescaleDB time series database for front-end display system calling display.

[0020] Whole system architecture, build background computing system and VUE front end framework, build commercial vehicle test vehicle data monitoring and front end display system.

[0021] Compared with the prior art, the present application has the following beneficial technical effects:

[0022] Significantly improve monitoring efficiency: through integrated system design, realize the comprehensive, real-time monitoring of test vehicle data, greatly improve the monitoring efficiency;

[0023] Enhance data analysis capability: use big data technology and artificial intelligence algorithm, carry out deep mining and analysis on vehicle data, provide strong support for vehicle performance optimization and test management;

[0024] Improve decision accuracy: through visual data display and intelligent decision assistance, help users make more accurate and efficient decisions;

[0025] Reduce operating cost and safety risk: real-time monitoring of vehicle state and driving behavior, timely discovery and processing of potential problems, effectively reduce operating cost and safety risk. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The present application is an explosive structure diagram. DETAILED DESCRIPTION

[0027] In order to make the personnel in the technical field better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor shall belong to the scope of protection of the present application.

[0028] As shown in Figure 1 The commercial vehicle test data monitoring and analysis system based on Internet of Vehicles big data comprises:

[0029] The overview module is used for displaying the total number of test vehicles, current position distribution and stay state; identifying and highlighting long-period non-operational vehicles and no-data return vehicles; displaying the current operational vehicles, specific operational mileage and vehicle test project overview; dynamically presenting the vehicle operation trajectory and serious fault alarm information affecting the normal operation of the whole vehicle, wherein the vehicle operation trajectory comprises vehicle operation route, stay point and passing area.

[0030] The serious fault alarm information comprises engine fault, gearbox fault and vehicle-mounted electrical appliance fault information.

[0031] The vehicle use item profile includes vehicle intelligent network connection testing, energy consumption testing, reliability testing, plateau and cold region testing.

[0032] The vehicle state module acquires and displays vehicle states in real time, the vehicle states including vehicle positioning information and key operation parameters, and provides time sequence diagram display of vehicle operation data, wherein the key operation parameters include vehicle speed, engine speed, water temperature, ambient temperature, altitude, fuel consumption, torque and battery state.

[0033] The test analysis module compares and displays trends through vehicle speed, engine speed and torque parameter calculation and columnar distribution diagram, generates big data analysis reports of different vehicles and time ranges by inputting different vehicle numbers and time ranges, and the big data analysis reports include vehicle power performance, operation parameters and abnormality identification results.

[0034] The abnormality identification results include abnormality identification results of whether the vehicle has high water temperature, fan direct connection, overspeed and over-revolution.

[0035] The driving operation module identifies abnormal driving behaviors of drivers, scores driving behaviors of drivers through gear shifting times, brake frequency and sudden brake frequency, slope vehicle control, vehicle overspeed and long time full throttle sub-items, outputs driving behavior scores, evaluates driving conditions according to driving behavior scores, then generates a driving behavior scoring report, and visually displays each sub-item score and driving behavior score through a radar chart.

[0036] The abnormal driving behaviors of the driver include smoking, inattentive driving and fatigue driving behaviors during driving.

[0037] The vehicle-mounted video module remotely and real-timely checks in-vehicle and out-vehicle videos.

[0038] The commercial vehicle test data monitoring and analysis method based on vehicle networking big data is applied to the commercial vehicle test data monitoring and analysis system based on vehicle networking big data, and includes the following steps.

[0039] Vehicle operation data acquisition: vehicle speed, engine speed and torque parameter data of vehicle CAN line are returned and received through a network by a vehicle data acquisition terminal, and then stored in an object storage (OSS) in an ASC format data.

[0040] Vehicle data analysis and storage: for ASC format data returned by the vehicle CAN line, first decode according to a vehicle signal analysis table, convert the decoded data into decimal data, classify according to the converted data categories, and then store the classified data in a TimescaleDB time sequence database.

[0041] Video data receiving, transmitting in-vehicle and out-of-vehicle video data through a vehicle data backhaul terminal, and receiving and pushing in-vehicle and out-of-vehicle video streams in real time through a streaming media server for display;

[0042] Operation parameter calculation, involving vehicle status, operation trajectory, and key operation parameters, is analyzed and calculated by a background calculation system and stored in a TimescaleDB time series database for calling and display by a front-end display system;

[0043] Overall system architecture, building a background calculation system and a VUE front-end framework, and building a commercial vehicle test vehicle data monitoring and front-end display system.

Claims

1. A commercial vehicle test data monitoring and analysis system based on Internet of Vehicles big data, characterized in that, The application comprises: an overview module for displaying the total number of test vehicles, current position distribution and parking state; identifying and highlighting long-period non-operational vehicles and vehicles without data return; displaying the vehicles operating on the day, specific operating mileage and vehicle test project profile; dynamically presenting the vehicle operation trajectory and serious fault alarm information affecting the normal operation of the vehicle, wherein the vehicle operation trajectory comprises the vehicle operation route, parking point and passing area; the serious fault alarm information comprises engine fault, gearbox fault and vehicle-mounted electrical appliance fault information; wherein the vehicle use project profile comprises vehicle intelligent network connection test, energy consumption test, reliability test, plateau and cold region test; a vehicle state module for real-time acquisition and display of vehicle state, wherein the vehicle state comprises vehicle positioning information and key operating parameters; a time series diagram of vehicle operation data is provided for display, wherein the key operating parameters comprise vehicle speed, engine speed, water temperature, ambient temperature, altitude, fuel consumption, torque and battery state; a test analysis module for comparing the operation data through vehicle speed, engine speed and torque parameter calculation and bar chart trend display; different vehicle numbers and time ranges are input to generate a big data analysis report of different vehicles and each time range, wherein the big data analysis report comprises vehicle power performance, operating parameters and abnormal identification results; the abnormal identification results comprise abnormal identification results of whether the vehicle has high water temperature, fan direct connection, overspeed and over-revolution; a driving operation module for identifying abnormal driving behaviors of drivers; the driving behaviors of the drivers are scored through gear shifting frequency, brake frequency and sudden brake frequency, vehicle control on slope, vehicle overspeed and long-time full throttle; a driving behavior score is output; the driving condition is evaluated according to the driving behavior score; then a driving behavior score report is generated; and the scores of each sub-item and the driving behavior score are visually displayed through a radar chart; the abnormal driving behaviors of the drivers comprise smoking, inattentive driving and fatigue driving behaviors during driving; a vehicle-mounted video module for remotely and real-timely checking the in-vehicle and out-vehicle videos.

2. The commercial vehicle test data monitoring and analysis method based on Internet of Vehicles big data, applied to the commercial vehicle test data monitoring and analysis system based on Internet of Vehicles big data in claim 1, characterized in that, The application comprises: vehicle operation data acquisition, vehicle speed, engine speed and torque parameter data of vehicle CAN line are returned and received through a network by a vehicle data acquisition terminal, and then stored in an object storage (OSS) in an ASC format data; vehicle data analysis and storage, ASC format data returned by the vehicle CAN line is decoded according to a vehicle signal analysis table, and the decoded data is converted into decimal data; the data is classified according to the converted data categories, and then stored in a TimescaleDB time series database; video data reception, in-vehicle and out-vehicle video data are transmitted by a vehicle data return terminal, and in-vehicle and out-vehicle video streams are real-timely received and pushed by a streaming media server; operation parameter calculation, vehicle state, operation trajectory and key operating parameters are analyzed and calculated by a background calculation system, and stored in the TimescaleDB time series database for calling and display by a front-end display system. The whole system architecture, build background computing system and VUE front-end framework, build commercial vehicle test vehicle data monitoring and front-end display system.

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