CN109878509A - Monoblock type tank car Multi-source Information Fusion rollover method for early warning based on fuzzy logic - Google Patents
Monoblock type tank car Multi-source Information Fusion rollover method for early warning based on fuzzy logic Download PDFInfo
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Abstract
The monoblock type tank car Multi-source Information Fusion that the invention proposes a kind of based on fuzzy logic is turned on one's side method for early warning, three rollover characterization parameters are defined first and calculate separately the corresponding probability estimating rollover and occurring, then the probability calculation model based on fuzzy logic is established, the optimum probability for estimating rollover generation is finally calculated and classification carry out not early warning.The rollover method for early warning proposed by the invention kinetics equation complicated without consideration and vehicle body parameter only need the multiple inexpensive sensor informations of redundancy processing, then obtain estimating the optimum probability of rollover generation by the probability calculation model based on fuzzy logic.This method analysis data are comprehensive, with Probability Forms by the dangerous precise quantification of rollover, driver can be made to take preventive measures as early as possible, reduce the generation of rollover event in monoblock type tank car there are early warning accurate, timely when lesser rollover danger.
Description
Technical field
The present invention relates to a kind of monoblock type tank car rollover method for early warning, more particularly, to a kind of entirety based on fuzzy logic
Formula tank car Multi-source Information Fusion rollover method for early warning, belongs to technical field of vehicle safety.
Background technique
In recent years, flourishing with Transportation Industry, monoblock type tank car already becomes the weight of dangerous material highway transportation
Want carrier.Position of centre of gravity is higher, wheelspan is relatively small and vulnerable to liquid perturbation due to, monoblock type tank car is wet and slippery in ice and snow
Under road surface, turning radius be too small and the operating conditions such as travel speed is larger, easily turn on one's side, so as to cause dangerous material leakage, cause
Environmental pollution and traffic congestion, it is serious to damage life and social property.According to the correlation of National Highway Traffic Safety Administration
Statistical data shows that in all motorbus or transport vehicle traffic accident, the extent of injury of rollover event is only second to collision thing
Therefore occupy the 2nd.Therefore, the method for research monoblock type tank car rollover early warning, has biggish social effect to traffic safety
And practical value.
In monoblock type tank car rollover early warning field, common characterization parameter of turning on one's side has angle of heel, side acceleration and cross
To load transfer rate, most of existing rollover method for early warning is based on single rollover characterization parameter, by side when vehicle driving
The actual value for turning over characterization parameter is compared with preset threshold value, the early warning when actual value is more than threshold value.Due to monoblock type tank car
Body gesture variation is fast when driving, stability is low, and causes the factor of rollover more, although these methods can play centainly
The forewarning function of degree, but there is a problem of early warning inaccuracy, if while preset rollover threshold it is larger, when actual value is close
When threshold value, it is dangerous that monoblock type tank car has existed rollover, therefore when actual value is more than threshold value early warning again, it is possible that early warning is not
Timely problem.
Summary of the invention
Turn on one's side in monoblock type tank car driving process early warning inaccuracy and not in time aiming at the problem that, propose and a kind of be based on mould
The monoblock type tank car Multi-source Information Fusion rollover method for early warning of fuzzy logic.This method analysis data are comprehensive, will turn on one's side dangerous accurate
Quantization accurately, can timely carry out stagewise early warning under different operating conditions, to reduce rollover occurrence risk, improve and drive
Safety.
In order to achieve the above object, the invention provides the following technical scheme:
Monoblock type tank car Multi-source Information Fusion rollover method for early warning based on fuzzy logic, includes the following steps:
Step 1: defining three rollover characterization parameters and calculates separately the corresponding probability estimating rollover and occurring
Monoblock type tank car rollover characterization parameter are as follows: angle of heel α, side acceleration A and leaf spring pressure horizontal transfer rate L, plate
Spring pressure horizontal transfer rate L calculation formula are as follows:
In formula, FliIt is the pressure that leaf spring is subject on the left of i-th axle of monoblock type tank car, FriIt is monoblock type tank car i-th
The pressure that leaf spring is subject on the right side of root axle, i are monoblock type tank car axle location numbers, and i=1,2 ..., n, n are monoblock types
The total axle quantity of tank car；
The sensor of selection has MEMS gyroscope and several pressure sensors, obtains side according to MEMS gyroscope output information
Leaf spring pressure horizontal transfer rate is calculated according to pressure sensor output information and formula (1) in inclination alpha and side acceleration A
L；MEMS gyroscope is fixed on monoblock type tank car center chassis, and pressure sensor is installed on the junction of leaf spring and wheel,
Pressure sensor quantity depends on monoblock type tank car two sides leaf spring number；
It defines and judges the probability of rollover generation for P based on angle of heel α1,
In formula, α is the angle of heel of monoblock type tank car, αTFor preset angle of heel threshold value, αT> 0,0≤P1≤ 1, P1Decimal
Retain two effective digitals after point；
It defines and judges the probability of rollover generation for P based on side acceleration A2,
In formula, A is the side acceleration of monoblock type tank car, ATFor preset side acceleration threshold value, AT> 0,0≤P2≤
1, P2Retain two effective digitals after decimal point；
It defines and judges the probability of rollover generation for P based on leaf spring pressure horizontal transfer rate L3,
In formula, L is the leaf spring pressure horizontal transfer rate of monoblock type tank car, L >=0, LTLaterally turn for preset leaf spring pressure
Shifting rate threshold value, LT> 0,0≤P3≤ 1, P3Retain two effective digitals after decimal point；
Step 2: the probability calculation model based on fuzzy logic is established
1) clear input variable and output variable
By P in step 11、P2And P3As the input variable of computation model, will estimate optimum probability that rollover occurs as
Output variable；
2) blurring of precise volume
Blurring is that input variable numerical value is converted to the process of each fuzzy set degree of membership, is the first of fuzzy logic
Step, when blurring, need to consider following point；
1. the fuzzy set of selected input variable
Select three fuzzy sets, i.e., it is small, in, it is big, letter indicates to be followed successively by S, M, B；
2. determining the subordinating degree function of fuzzy set
The range of input variable is all 0~1, therefore the corresponding subordinating degree function of three fuzzy sets is defined as follows:
In formula, fS(x) subordinating degree function for being fuzzy set S, fM(x) subordinating degree function for being fuzzy set M, fB(x) it is
The subordinating degree function of fuzzy set B, two effective digitals are retained after three subordinating degree function value decimal points, and x indicates each input change
The corresponding probability of amount, 0≤x≤1；
For three input variable P1、P2And P3, available according to subordinating degree function: fS(P1)、fM(P1)、fB(P1), fS
(P2)、fM(P2)、fB(P2), fS(P3)、fM(P3)、fB(P3)；
3) fuzzy reasoning
Fuzzy rule is based on the mature experience of driver and rollover simulation results, it is believed that three input probabilities
There are two above reach set B when, then it is assumed that estimate rollover generation probability reached set B；Reach M collection there are two probability
When a unification probability reaches set B, also think that estimating the probability that rollover occurs has reached set B；Three probability all reach M set
When, then it is assumed that it estimates the probability that rollover occurs and has reached M set；A S collection unification probability, which is in, there are two probability reaches set B
When, then it is assumed that it estimates the probability that rollover occurs and has reached M set；Reach a M collection unification probability there are two probability and is in S set
When, then it is assumed that it estimates the probability that rollover occurs and has reached M set；One probability reaches one probability of set B and reaches the unification of M collection
Probability reaches S set, also thinks that estimating the probability that rollover occurs has reached M set；S collection, which is in, there are two probability unifies a probability
Gather in M or S, then it is assumed that estimate the probability that rollover occurs and reached S set；
Due to carrying out combinational fuzzy conditional statement using with operation in rule, the degree of membership of each rule output result is logical
Min function is crossed to be calculated；Rule 1: if P1In S set and P2In S set and P3Gather in S, then output result
It is in S set to estimate the probability that rollover occurs, due to P1Degree of membership for S set is fS(P1), P2For the degree of membership of S set
For fS(P2), P3Degree of membership for S set is fS(P3), therefore the degree of membership for exporting result is min (fS(P1)、fS(P2)、fS
(P3)), other rules and so on share 27 rules；
4) ambiguity solution strategy is determined
Using gravity model appoach as ambiguity solution strategy, calculation formula is as follows:
In formula, R is the output variable of computation model, that is, estimates the optimum probability that rollover occurs, FSjIt is in fuzzy rule
J rule exports the degree of membership of result, OWjIt is the weight of fuzzy set in j-th strip rule output result in fuzzy rule, weight
Usually take the median of each set, i.e. OW (S)=0.25, OW (M)=0.5, OW (B)=0.75；
Step 3: calculating the optimum probability for estimating rollover generation and classification carry out not early warning
By P1、P2And P3Three are estimated the probability calculation model established in the probability input step two that rollover occurs, and are obtained pre-
Estimate the optimum probability R that rollover occurs, early warning is not carried out according to the size fractionation of R, warning module is by voice alerting unit and buzzing
Device composition, is fixed in driver's cabin, and early warning rule is as follows:
When not alarming, voice alerting unit and buzzer do not work；When level-one is alarmed, voice alerting unit is played: " wishing good health
It is complete to drive ", buzzer low-frequency vibration；When secondary alarm, voice alerting unit is played: " please drive with caution ", the shake of buzzer intermediate frequency
It is dynamic；When three-level is alarmed, voice alerting unit is played: " dangerous, i.e., will to turn on one's side ", buzzer high-frequency vibration.
Compared with prior art, the invention has the advantages that and the utility model has the advantages that
1. the sensor that method for early warning of the invention uses is at low cost, calculation method is clear, is convenient for large-scale promotion.
2. present invention analysis data are comprehensive, method for early warning is enabled to monitor the roll stability of monoblock type tank car on-line,
Well adapt to various possible environment.
3. method for early warning of the invention will be turned on one's side dangerous precise quantification with Probability Forms, can monoblock type tank car presence it is smaller
Rollover danger when it is accurate, carry out stagewise early warning in time.
Detailed description of the invention
Fig. 1 is method for early warning master-plan flow chart provided by the invention.
Fig. 2 is source of early warning schematic view of the mounting position.
Fig. 3 is single pressure sensor schematic view of the mounting position.
Fig. 4 is the subordinating degree function figure of fuzzy set.
Specific embodiment
Technical solution provided by the invention is described in detail below with reference to specific embodiment, it should be understood that following specific
Embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
The monoblock type tank car Multi-source Information Fusion that the invention proposes a kind of based on fuzzy logic is turned on one's side method for early warning, first
It defines three rollover characterization parameters and calculates separately the corresponding probability estimating rollover and occurring, then establish based on fuzzy logic
Probability calculation model, finally calculates the optimum probability for estimating rollover generation and classification carry out not early warning.Side proposed by the invention
Method for early warning is turned over without considering complicated kinetics equation and vehicle body parameter, only needs the multiple inexpensive sensor letters of redundancy processing
Breath, then obtain estimating the optimum probability turned on one's side and occurred by the probability calculation model based on fuzzy logic.This method analyzes data
Comprehensively, will be turned on one's side dangerous precise quantification with Probability Forms, can when monoblock type tank car is dangerous there are lesser rollover accurately and
When early warning, so that driver is taken preventive measures as early as possible, reduce the generation of rollover event.The master-plan process of method for early warning is as schemed
Shown in 1, comprising the following specific steps
Step 1: defining three rollover characterization parameters and calculates separately the corresponding probability estimating rollover and occurring
When angle of heel and side acceleration increase to certain value, monoblock type tank car can just turn on one's side, the two rollover tables
Sign parameter can intuitively assess the lateral stability of monoblock type tank car, and acquisition modes are simple, therefore present invention selection is whole
Body formula tank car angle of heel α and side acceleration A are rollover characterization parameter.The transverse load rate of transform is common rollover characterization ginseng
Number, however the load measurement of wheel is extremely difficult under the state that runs at high speed, and the transverse load rate of transform is caused to calculate inaccuracy.For
Using the monoblock type tank car of Leaf Spring Suspension, since the load of leaf spring pressure and wheel has certain pair at wheel
It should be related to, and leaf spring branch bilateral symmetry, leaf spring pressure can also be obtained directly by sensor, therefore this
Invention replaces wheel weight with leaf spring pressure, proposes this rollover characterization parameter of leaf spring pressure horizontal transfer rate L, meter
Calculate formula are as follows:
In formula, FliIt is leaf spring pressure on the left of i-th axle of monoblock type tank car, FriIt is i-th, monoblock type tank car
Leaf spring pressure on the right side of axle, i are monoblock type tank car axle location numbers, and i=1,2 ..., n, n are monoblock type tank cars
Total axle quantity.
The sensor that the present invention selects has MEMS gyroscope and several pressure sensors.According to MEMS gyroscope output information
Angle of heel α and side acceleration A are obtained, it is lateral that leaf spring pressure is calculated according to pressure sensor output information and formula (1)
Rate of transform L.Sensor mounting location is as shown in Fig. 2, MEMS gyroscope is fixed on monoblock type tank car center chassis, pressure sensor
It is installed on the junction of leaf spring and wheel, pressure sensor quantity depends on monoblock type tank car two sides leaf spring number,
The specific installation site of single pressure sensor is as shown in Figure 3.
It defines and judges the probability of rollover generation for P based on angle of heel α1,
In formula, α is the angle of heel of monoblock type tank car, αTFor preset angle of heel threshold value, αT> 0,0≤P1≤ 1, P1Decimal
Retain two effective digitals after point.
It defines and judges the probability of rollover generation for P based on side acceleration A2,
In formula, A is the side acceleration of monoblock type tank car, ATFor preset side acceleration threshold value, AT> 0,0≤P2≤
1, P2Retain two effective digitals after decimal point.
It defines and judges the probability of rollover generation for P based on leaf spring pressure horizontal transfer rate L3,
In formula, L is the leaf spring pressure horizontal transfer rate of monoblock type tank car, L >=0, LTLaterally turn for preset leaf spring pressure
Shifting rate threshold value, LT> 0,0≤P3≤ 1, P3Retain two effective digitals after decimal point.
Step 2: the probability calculation model based on fuzzy logic is established
1) clear input variable and output variable
Since the present invention establishes the purpose of computation model, to be that three rollover characterization parameters of fusion treatment are corresponding estimate rollover
The probability of generation, so as to it is accurate, timely estimate rollover risk, therefore by P in step 11、P2And P3As computation model
Input variable, will estimate rollover occur optimum probability as output variable.
2) blurring of precise volume
Blurring is that input variable numerical value is converted to the process of each fuzzy set degree of membership, is the first of fuzzy logic
Step, when blurring, need to consider following point.
1. the fuzzy set of selected input variable
The accuracy of probability calculation model when describing each input variable, can be improved with more fuzzy set, however
It is comparatively laborious when formulating fuzzy rule, therefore simplicity and flexibility need to be taken into account when selected fuzzy set.
Since input variable is all probability value in the present invention, so select three fuzzy sets, i.e., it is small, in, big, alphabet
Show and is followed successively by S, M, B.
2. determining the subordinating degree function of fuzzy set
The shape of subordinating degree function is steeper, then resolution ratio is higher, and output sensitivity is also higher；The variation of subordinating degree function
Slower, then sensitivity is lower, since the range of input variable in the present invention is all 0~1, therefore the corresponding person in servitude of three fuzzy sets
Category degree function is defined as follows:
In formula, fS(x) subordinating degree function for being fuzzy set S, fM(x) subordinating degree function for being fuzzy set M, fB(x) it is
The subordinating degree function of fuzzy set B, two effective digitals are retained after three subordinating degree function value decimal points, and x indicates each input change
The corresponding probability of amount, the subordinating degree function of 0≤x≤1, fuzzy set are as shown in Figure 4.
For three input variable P1、P2And P3, available according to subordinating degree function: fS(P1)、fM(P1)、fB(P1), fS
(P2)、fM(P2)、fB(P2), fS(P3)、fM(P3)、fB(P3)。
3) fuzzy reasoning
Fuzzy condition statement is write as a fuzzy reasoning table generally according to practical experience in fuzzy logic.It is considered herein that
When three input probabilities are there are two set B is reached above, then it is assumed that the probability for estimating rollover generation has reached set B；Have two
A probability reaches M collection when unifying probability and reaching set B, also thinks that estimating the probability that rollover occurs has reached set B；Three general
When rate all reaches M set, then it is assumed that estimate the probability that rollover occurs and reached M set；There are two probability to be in the unification of S collection generally
When rate reaches set B, then it is assumed that estimate the probability that rollover occurs and reached M set；Reach M collection there are two probability and unifies a probability
When in S set, then it is assumed that estimate the probability that rollover occurs and reached M set；One probability reaches one probability of set B and reaches
M collection unifies a probability and reaches S set, also thinks that estimating the probability that rollover occurs has reached M set；S collection is in there are two probability
Unify a probability and be in M or S set, then it is assumed that estimates the probability that rollover occurs and reached S set.Specific fuzzy rule such as following table
It is shown:
Fuzzy reasoning table has determined 27 rules, due to carrying out combinational fuzzy conditional statement using with operation in rule,
The degree of membership of each rule output result is calculated by min function.Rule 1: if P1In S set and P2Gather in S
With P3Gather in S, then output result is to estimate the probability that rollover occurs to be in S set, due to P1For the degree of membership of S set
For fS(P1), P2Degree of membership for S set is fS(P2), P3Degree of membership for S set is fS(P3), therefore export being subordinate to for result
Degree is min (fS(P1)、fS(P2)、fS(P3)), other rules and so on.
4) ambiguity solution strategy is determined
The output of fuzzy reasoning is the result is that a fuzzy set, and the output result of fuzzy logic must be a determining number
Value.The monodrome that can most represent this fuzzy set relatively, referred to as ambiguity solution or mould are taken in the fuzzy set that reasoning obtains
Paste judgement.Most common two kinds of ambiguity solution methods are maximum membership degree method and gravity model appoach.Maximum membership degree method is to take all obscure
That maximum value of degree of membership as output, realize simply, but does not account for it by this method in set or subordinating degree function
The influence of the lesser value of his degree of membership, it is representative bad.The output result of gravity model appoach is more reasonable, be easy to generate one it is smooth
Curved surface is exported, the robustness for improving computation model is conducive to.The present invention is using gravity model appoach as ambiguity solution strategy, and calculation formula is such as
Under:
In formula, R is the output variable of computation model, that is, estimates the optimum probability that rollover occurs, FSjIt is in fuzzy reasoning table
J-th strip rule exports the degree of membership of result, OWjIt is the weight of fuzzy set in j-th strip rule output result in fuzzy reasoning table,
Weight usually takes the median of each set, i.e. OW (S)=0.25, OW (M)=0.5, OW (B)=0.75.
Step 3: calculating the optimum probability for estimating rollover generation and classification carry out not early warning
By P1、P2And P3Three are estimated the probability calculation model established in the probability input step two that rollover occurs, and are obtained pre-
Estimate the optimum probability R that rollover occurs, early warning is not carried out according to the size fractionation of R, warning module is by voice alerting unit and buzzing
Device composition, is fixed in driver's cabin, and early warning rule is as follows:
When not alarming, voice alerting unit and buzzer do not work；When level-one is alarmed, voice alerting unit is played: " wishing good health
It is complete to drive ", buzzer low-frequency vibration；When secondary alarm, voice alerting unit is played: " please drive with caution ", the shake of buzzer intermediate frequency
It is dynamic；When three-level is alarmed, voice alerting unit is played: " dangerous, i.e., will to turn on one's side ", buzzer high-frequency vibration.
Claims (1)
- The method for early warning 1. the monoblock type tank car Multi-source Information Fusion based on fuzzy logic is turned on one's side, which is characterized in that including walking as follows It is rapid:Step 1: defining three rollover characterization parameters and calculates separately the corresponding probability estimating rollover and occurringMonoblock type tank car rollover characterization parameter are as follows: angle of heel α, side acceleration A and leaf spring pressure horizontal transfer rate L, leaf spring pressure Power horizontal transfer rate L calculation formula are as follows:In formula, FliIt is the pressure that leaf spring is subject on the left of i-th axle of monoblock type tank car, FriIt is i-th vehicle of monoblock type tank car The pressure that leaf spring is subject on the right side of axis, i are monoblock type tank car axle location numbers, and i=1,2 ..., n, n are monoblock type tank cars Total axle quantity；The sensor of selection has MEMS gyroscope and several pressure sensors, obtains angle of heel according to MEMS gyroscope output information Leaf spring pressure horizontal transfer rate L is calculated according to pressure sensor output information and formula (1) in α and side acceleration A； MEMS gyroscope is fixed on monoblock type tank car center chassis, and pressure sensor is installed on the junction of leaf spring and wheel, pressure Force snesor quantity depends on monoblock type tank car two sides leaf spring number；It defines and judges the probability of rollover generation for P based on angle of heel α1,In formula, α is the angle of heel of monoblock type tank car, αTFor preset angle of heel threshold value, αT> 0,0≤P1≤ 1, P1It is protected after decimal point Stay two effective digitals；It defines and judges the probability of rollover generation for P based on side acceleration A2,In formula, A is the side acceleration of monoblock type tank car, ATFor preset side acceleration threshold value, AT> 0,0≤P2≤ 1, P2It is small Retain two effective digitals after several points；It defines and judges the probability of rollover generation for P based on leaf spring pressure horizontal transfer rate L3,In formula, L is the leaf spring pressure horizontal transfer rate of monoblock type tank car, L >=0, LTFor preset leaf spring pressure horizontal transfer rate threshold Value, LT> 0,0≤P3≤ 1, P3Retain two effective digitals after decimal point；Step 2: the probability calculation model based on fuzzy logic is established1) clear input variable and output variableBy P in step 11、P2And P3As the input variable of computation model, the optimum probability of rollover generation will be estimated as output Variable；2) blurring of precise volumeBlurring is that input variable numerical value is converted to the process of each fuzzy set degree of membership, is the first step of fuzzy logic, It needs to consider following point when blurring；1. the fuzzy set of selected input variableSelect three fuzzy sets, i.e., it is small, in, it is big, letter indicates to be followed successively by S, M, B；2. determining the subordinating degree function of fuzzy setThe range of input variable is all 0~1, therefore the corresponding subordinating degree function of three fuzzy sets is defined as follows:In formula, fS(x) subordinating degree function for being fuzzy set S, fM(x) subordinating degree function for being fuzzy set M, fBIt (x) is fuzzy The subordinating degree function of set B, two effective digitals are retained after three subordinating degree function value decimal points, and x indicates each input variable institute Corresponding probability, 0≤x≤1；For three input variable P1、P2And P3, available according to subordinating degree function: fS(P1)、fM(P1)、fB(P1), fS(P2)、fM (P2)、fB(P2), fS(P3)、fM(P3)、fB(P3)；3) fuzzy reasoningFuzzy rule be by the mature experience of driver and rollover simulation results based on, it is believed that three input probabilities have two It is a or more when reaching set B, then it is assumed that estimate the probability that rollover occurs and reached set B；There are two probability to reach the unification of M collection When a probability reaches set B, also think that estimating the probability that rollover occurs has reached set B；When three probability all reach M set, Then think that estimating the probability that rollover occurs has reached M set；When be in an a probability of S collection unification there are two probability and reach set B, then Think that estimating the probability that rollover occurs has reached M set；When reaching M collection there are two probability and unify probability and be in S and gather, then recognize M set is reached to estimate the probability that rollover occurs；One probability, which reaches one probability of set B and reaches M collection and unify probability, to be reached Gather to S, also thinks that estimating the probability that rollover occurs has reached M set；A S collection unification probability, which is in, there are two probability is in M Or S set, then it is assumed that estimate the probability that rollover occurs and reached S set；Due to carrying out combinational fuzzy conditional statement using with operation in rule, the degree of membership of each rule output result passes through Min function is calculated；Rule 1: if P1In S set and P2In S set and P3Gather in S, then output result is It estimates the probability that rollover occurs and is in S set, due to P1Degree of membership for S set is fS(P1), P2Degree of membership for S set is fS (P2), P3Degree of membership for S set is fS(P3), therefore the degree of membership for exporting result is min (fS(P1)、fS(P2)、fS(P3)), Other rules and so on share 27 rules；4) ambiguity solution strategy is determinedUsing gravity model appoach as ambiguity solution strategy, calculation formula is as follows:In formula, R is the output variable of computation model, that is, estimates the optimum probability that rollover occurs, FSjIt is that j-th strip is advised in fuzzy rule Then export the degree of membership of result, OWjIt is the weight of fuzzy set in j-th strip rule output result in fuzzy rule, weight is usual Take the median of each set, i.e. OW (S)=0.25, OW (M)=0.5, OW (B)=0.75；Step 3: calculating the optimum probability for estimating rollover generation and classification carry out not early warningBy P1、P2And P3Three are estimated the probability calculation model established in the probability input step two that rollover occurs, and obtain estimating side The optimum probability R for turning over generation does not carry out early warning according to the size fractionation of R, and warning module is by voice alerting unit and buzzer group At, be fixed in driver's cabin, early warning rule it is as follows:When not alarming, voice alerting unit and buzzer do not work；When level-one is alarmed, voice alerting unit is played: " please driving safely Sail ", buzzer low-frequency vibration；When secondary alarm, voice alerting unit is played: " please drive with caution ", the vibration of buzzer intermediate frequency；Three When grade alarm, voice alerting unit is played: " dangerous, i.e., will to turn on one's side ", buzzer high-frequency vibration.
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