Graded early warning system for rollover of heavy-duty truck based on time-varying interactive kalman filtering and early warning method thereof

Abstract

Disclosed is a graded early warning system for rollover of heavy-duty truck based on time-varying interactive Kalman filtering and early warning method thereof. The system includes an infrared speed measuring module, a gyroscope, a humidity signal acquisition sensor, a bluetooth data transmission module and a main control chip. The infrared speed measuring module, the gyroscope, the humidity signal acquisition sensor are respectively connected with input ends of the main control chip through control lines. A video output end of the main control chip is connected with a liquid crystal display through video signal line. The main control chip is wirelessly connected with a mobile phone through the bluetooth data transmission module.

Claims

1. A graded early warning system for rollover of heavy-duty truck based on time-varying interactive Kalman filtering, comprising: an infrared speed measuring module; a gyroscope; a humidity signal acquisition sensor; a bluetooth data transmission module; and a main control chip; wherein the infrared speed measuring module, the gyroscope, the humidity signal acquisition sensor are respectively connected with input ends of the main control chip through control lines; a video output end of the main control chip is connected with a liquid crystal display through video signal line; the main control chip is wirelessly connected with a mobile phone through the bluetooth data transmission module, wherein the infrared speed measuring module, the gyroscope and the humidity signal acquisition sensor are configured for collecting index data of vehicle acceleration, degree offset, road humidity respectively, wherein the main control chip is configured for: using a rigid body model of an automobile as physical model for analyzing the rollover threshold of truck under real-time road condition; obtaining the index data of vehicle acceleration, degree offset, road humidity; constructing a graded time-varying interactive Kalman filtering model; completing date processing by using the time-varying interactive Kalman filtering and integration of data mining technology of quaternion algorithm by: processing the index data of vehicle acceleration, degree offset, road humidity according to the Kalman filtering model for compensating sensor drift and measurement noise, obtaining an initial attitude under static conditions by using a three-axis accelerometer and a three-axis magnetometer, processing the initial attitude by the quaternion algorithm, and obtaining a current attitude of the truck based on the processed index data of vehicle acceleration, degree offset, road humidity, together with the processed initial attitude; carrying out mechanical analysis of the rigid body model to obtain condition equation of rollover; and predicting a rollover of the truck based on the current attitude of the truck and the condition equation of rollover.

2. The early warning system of claim 1, wherein the gyroscope is a physical parameter instrument for collecting acceleration for serial data.

3. The early warning system of claim 1, wherein the humidity signal acquisition sensor is a climate parameter instrument for collecting temperature and humidity.

4. The early warning system of claim 1, wherein the bluetooth data transmission module is configured for realizing wireless information transmission between the main control chip and the mobile phone.

5. The early warning system of claim 1, wherein the main control chip is a chip with functions of completing system programming and algorithm design, and the model number is STC89c52RC.

6. A method for predicting rollover of heavy-duty truck based on time-varying interactive Kalman filtering, the method comprising: using a rigid body model of an automobile as physical model for analyzing a rollover threshold of truck under real-time road condition; collecting index data of vehicle acceleration, degree offset, road humidity through an infrared speed measuring module, a gyroscope and a humidity signal acquisition sensor; constructing a graded time-varying interactive Kalman filtering model; concentrating the collected index data on a main control chip and completing date processing by using time-varying interactive Kalman filtering and integration of data mining technology of quaternion algorithm, comprising: processing the index data of vehicle acceleration, degree offset, road humidity according to the Kalman filtering model for compensating sensor drift and measurement noise, obtaining an initial attitude under static conditions by using a three-axis accelerometer and a three-axis magnetometer; processing the initial attitude by the quaternion algorithm; obtaining a current attitude of the truck based on the processed index data of vehicle acceleration, degree offset, road humidity, together with the processed initial attitude; carrying out mechanical analysis of the rigid body model to obtain condition equation of rollover; predicting a rollover of the truck based on the current attitude of the truck and the condition equation of rollover; and achieving information transmission between the main control chip and a mobile APP by means of a bluetooth interface, and further realizing graded early warning management of the driving condition of the truck.

7. The graded warning method of claim 6, wherein the method further comprises: coding by a monitoring and management platform in a control center, and packaging and releasing the code as an application installation package; connecting the running program with the main control chip by calling the a bluetooth port; receiving the hexadecimal date output by the sensors across mobile phone platform and displaying the current driving status of the truck by combining geographic information; (1) construction of a time-varying interactive Kalman filtering model constructing a time-varying interactive Kalman filtering model based on the acquired data belonging to time-varying data and interacting between data and outputting a waveform curve; (2) attitude algorithm of data A.sub.X,A.sub.y,A.sub.z) A.sub.xL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH) O.sub.x,O.sub.y,O.sub.z) O.sub.xL(O.sub.xH),O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH) RollL(RollH),PitchL(PitchH),YawL(YawH) calculating the time-varying data by designing a quaternion method to obtain the calculated data; obtaining an initial attitude (QUOTEA.sub.x,A.sub.y,A.sub.z)A.sub.x,A.sub.y,A.sub.z) under static conditions by using a three-axis accelerometer and a three-axis magnetometer; processing the initial data by the quaternion in the process of converting the initial data into the measured data; wherein QUOTEA.sub.XL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH)A.sub.xL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH) are high byte and low byte of acceleration in the X-axis, Y-axis, and Z-axis, respectively; and an attitude of(QUOTEO.sub.X,O.sub.y,O.sub.z)O.sub.x,O.sub.y,O.sub.z) is obtained under the motion condition; wherein QUOTEO.sub.XL(O.sub.xH),O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH)O.sub.zL(O.sub.xH),O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH) are high byte and low byte of acceleration on X-axis, Y-axis, and Z-axis, respectively; wherein QUOTERollL(RollH),PitchL(PitchH),YawL(YawH)RollL(RollH),PitchL(PitchH),YawL(YawH) are high byte and low byte of angle on the X-axis, Y-axis, and Z-axis, respectively; (3) graded early warning management system to reflect graded warning management, dividing a prompt into five grades according to a driving condition: no warning, first to third level warning and fourth level warning; truck drivers are given safe driving, be careful, dangerous or extremely dangerous prompts correspondingly, and a distress signal is automatically issued when necessary.

Description

(1) In the drawings: 1. infrared speed measuring module; 2. gyroscope; 3. humidity signal acquisition sensor; 4. main control chip; 5. bluetooth data transmission module; 6. liquid crystal display; 7. mobile phone; 8. weather and altitude; 9. real-time vehicle status; 10. positioning and navigation; 11. early warning response.

DETAILED DESCRIPTION OF EMBODIMENTS

(2) As shown in FIGS. 1-5, a graded early warning system for rollover of heavy-duty truck based on time-varying interactive Kalman filtering can be used for detecting and monitoring vehicle conditions, including: an infrared speed measuring module 1, a gyroscope 2, a humidity signal acquisition sensor 3, a bluetooth data transmission module 5 and a main control chip 4. The infrared speed measuring module 1, the gyroscope 2, the humidity signal acquisition sensor 3 are respectively connected with input ends of the main control chip 4 through control lines. A video output end of the main control chip 4 is connected with a liquid crystal display 6 through video signal line. The main control chip 4 is wirelessly connected with a mobile phone 7 through the bluetooth data transmission module 5.

(3) The gyroscope 2 is a physical parameter instrument for collecting acceleration for serial data. The humidity signal acquisition sensor is a climate parameter instrument for collecting temperature and humidity. The bluetooth data transmission module 5 is configured for realizing wireless information transmission between the main control chip and the mobile phone. The main control chip is a chip with functions of completing system programming and algorithm design, and the model number is STC89c52RC.

(4) The present device uses the multi-disciplinary knowledge system to construct the basic theoretical model, and verifies the feasibility of the system by means of experimental data, and perfects the system through repeated practical operation data. The interface of APP of the mobile phone 7 of the system includes weather and altitude 8, real-time vehicle status 9, positioning and navigation 10, early warning response 11.

(5) A graded early warning method for rollover of heavy-duty truck based on time-varying interactive Kalman filtering includes: using a rigid body model of an automobile as physical model for analyzing the rollover threshold of truck under real-time condition; collecting index data of vehicle acceleration, degree offset, road humidity through several kinds of sensors; concentrating the collected data on the main control chip, and completing date processing by using time-varying interactive Kalman filtering and integration of data mining technology of quaternion algorithm; carrying out mechanical analysis of the rigid body model to obtain condition equation of rollover, namely, conditions for determining a rollover of the truck; and achieving information transmission between the main control chip and an APP of the mobile phone 7 by means of a bluetooth data transmission module, and further realizing graded early warning management of the driving condition of the truck.

(6) The graded early warning process for rollover of heavy-duty truck based on time-varying interactive Kalman filtering includes: coding by a monitoring and management platform in a control center by NJS; packaging and releasing the code as an application installation package in HBUILDER; connecting the running program with the main control chip by calling a bluetooth data transmission module, receiving the hexadecimal date output by the sensors by applying HTML5 streaming across mobile phone platform and displaying the current driving status of the truck by combining the geographic information of BAIDU api, constructing a graded time-varying interactive Kalman filtering model by the attitude angle measurement system of the infrared speed measuring module and the gyroscope, fusing signal of the accelerometer and the gyroscope, effectively compensating the effects of sensor drift and measurement noise on the infrared speed measuring module and the gyroscope on the basis of the dynamic data acquisition test; thus reducing the attitude angle measurement error, and avoiding the defects of fast parameter change and being difficult to eliminate the deviation value; the specific implementation steps are as follows:

(7) (1) construction of a time-varying interactive Kalman filtering model

(8) constructing a time-varying interactive Kalman filtering model based on the acquired data belonging to time-varying data and interacting between data, and outputting a waveform curve;

(9) (2) attitude algorithm of data

(10) A.sub.X,A.sub.y,A.sub.z)

(11) A.sub.xL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH)

(12) O.sub.x,O.sub.y,O.sub.z)

(13) O.sub.xL(O.sub.xH),O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH)

(14) RollL(RollH),PitchL(PitchH),YawL(YawH)

(15) calculating the time-varying data by designing a quaternion method to obtain the calculated data; obtaining an initial attitude (QUOTEA.sub.x,A.sub.y,A.sub.z)A.sub.x,A.sub.y,A.sub.z) under static conditions by using a three-axis accelerometer and a three-axis magnetometer; processing the initial data by the quaternion in the process of converting the initial data into the measured data; wherein QUOTEA.sub.XL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH)A.sub.xL(A.sub.xH),A.sub.yL(A.sub.yH),A.sub.zL(A.sub.zH) are high byte and low byte of acceleration in the X-axis, Y-axis, and Z-axis, respectively; and an attitude of (QUOTEO.sub.X,O.sub.y,O.sub.z)O.sub.x,O.sub.y,O.sub.z) is obtained under the motion condition; wherein QUOTEO.sub.XL(O.sub.xH), O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH)O.sub.zL(O.sub.xH),O.sub.yL(O.sub.yH),O.sub.zL(O.sub.zH) are high byte and low byte of acceleration on X-axis, Y-axis, and Z-axis, respectively; wherein

(16) QUOTERollL(RollH),PitchL(PitchH),YawL(YawH)RollL(RollH),PitchL(PitchH),YawL(YawH) are high byte and low byte of angle on the X-axis, Y-axis, and Z-axis, respectively;

(17) (3) graded early warning management system

(18) to reflect graded early warning management, dividing a prompt into five grades according to a driving condition: no warning, first to third level warning and fourth level warning; truck drivers are given safe driving, be careful, dangerous or extremely dangerous prompts correspondingly, and a distress signal is automatically issued when necessary.

(19) The present invention realizes the close combination between the single chip microcomputer and the mobile APP by means of the combination of software and hardware and makes design of this system become a fully functional prediction system by the application of visualization platform to guarantee the safety of the driver. The creative point of this system mainly reflects in the following aspects:

(20) (1) graded early warning: graded early warning prompts for rollover are given according to driving status of different safety grades of the vehicle, and a distress signal is automatically issued when necessary.

(21) (2) real-time monitoring: the speed and the external environment of the vehicle is changing at any time during the process of driving. The predicted value for rollover of the vehicle can be processed and obtained by collecting external data by the sensor to guarantee safety of the driver.

(22) (3) comprehensive data: index, such as driving acceleration, angle offset, road humidity, temperature, steering wheel offset, and time-varying speed are proposed to achieve zero error, and it can be processed in different environments, such as rain, snow, fog and haze.

(23) (4) economical and practical: it maximums the consideration for public standard and avoids high cost; part of the software is developed with standard of market product, and it has good portability and practicability.

(24) (5) creative technology: the present system considers that the acquired data belongs to time-varying data and there is interaction among data. It can accurately output the current attitude of module under dynamic environment, reduce the measurement noise and improve the measurement accuracy by combining the attitude algorithm device and the time-varying interactive Kalman filtering algorithm. The gravity field is used for filtering modification, which avoids the drift in angle measurement. The measurement data is accurate to 0.01 degrees, therefore, the stability is extremely high.