CN101934808A - Train control method and device of train control system - Google Patents

Abstract

The embodiment of the invention discloses train control method and device of a train control system, which relate to the technical field of communication and solve the technical problems that the train control method is complicated and loss of train control is easy to cause in the prior art. The method of the embodiment of the invention mainly comprises the following steps of: obtaining a fitting parameter according to the related factor of train driving, wherein the related factor of the train driving is a parameter or a coefficient related to the train driving process; and obtaining train control data according to the fitting parameter and the related factor of train driving. The embodiment of the invention is applied to the train control system.

Description

The car controlling method and the device of train control system

Technical field

The present invention relates to the row control techniques, relate in particular to a kind of car controlling method and device of train control system, belong to communication technical field.

Background technology

At CTCS (Chinese Train Control System; China's train operation control system) in-3 grades of train control system; by ATP (Automatic Train Protection; train is protected automatically) subsystem monitors the moving velocity of train; the ATP subsystem is monitored train operation by the limited speed on the limited speed curve; judge according to limited speed whether train current driving speed exceeds the speed limit, if hypervelocity is then carried out overspeed protection.

In the prior art, the main method that adopts is: calculate the acquisition velocity curve according to 4 formula repeatedly, and according to the control of the limited speed in this velocity curve speed of a motor vehicle.

But in the process that realizes according to the control of the limited speed in the above-mentioned velocity curve speed of a motor vehicle, there are the following problems at least in the prior art:

The floating number that contains a lot of complexity in this method is calculated, and this scheme is to calculate according to uniformly retarded motion, accumulated error easily, and then cause train out of control.

Summary of the invention

Embodiments of the invention provide a kind of car controlling method and device of train control system, reduce the difficulty of obtaining car controlling data methods such as limited speed, improve the car controlling ability of train control system.

For achieving the above object, embodiments of the invention adopt following technical scheme:

A kind of car controlling method of train control system comprises:

Obtain fitting parameter according to the train driving correlation factor, described train driving correlation factor is: parameter or the coefficient relevant with the train driving process;

Obtain the car controlling data according to described fitting parameter and described train driving correlation factor.

A kind of car controlling device of train control system comprises:

Parameter acquiring unit is used for obtaining fitting parameter according to the train driving correlation factor, and described train driving correlation factor is: parameter or the coefficient relevant with the train driving process;

Data capture unit, the fitting parameter and the described train driving correlation factor that are used for obtaining according to described parameter acquiring unit are obtained the car controlling data.

The technical scheme of the embodiment of the invention has following beneficial effect: the method for car controlling data is obtained in employing according to fitting parameter and train driving correlation factor, can avoid complicated floating point arithmetic, not only can reduce equipment requirement to train control system, but also can improve the efficient of obtaining the car controlling data, so reduced train danger out of control, improved the car controlling ability of train control system, and need not the too much formula of repeated use in scheme calculates, therefore need not to take a large amount of train daily records and preserve the intermediate data that a plurality of formula produce, need not hypothesis calculates by uniformly retarded motion, therefore can reduce the generation of accumulated error, and then effectively improved the accuracy of car controlling data, strengthened the car controlling performance of train control system.

Description of drawings

In order to be illustrated more clearly in the embodiment of the invention or technical scheme of the prior art, to do to introduce simply to the accompanying drawing of required use in embodiment or the description of the Prior Art below, apparently, accompanying drawing in describing below only is some embodiments of the present invention, for those of ordinary skills, under the prerequisite of not paying creative work, can also obtain other accompanying drawing according to these accompanying drawings.

Fig. 1 is the velocity curve scheme drawing of a kind of row control method in the prior art;

Fig. 2 is the schematic flow sheet of the car controlling method of train control system in the embodiment of the invention 1;

Fig. 3 is the schematic flow sheet of the car controlling method of train control system in the embodiment of the invention 2;

Fig. 4 is the schematic flow sheet of the car controlling method of train control system in the embodiment of the invention 3;

Fig. 5 is the structural representation of the car controlling device of train control system in the embodiment of the invention 4;

Fig. 6 is the structural representation of the car controlling device of another train control system in the embodiment of the invention 4.

The specific embodiment

A kind of row control method comprises: suppose that every 1m distance is uniformly retarded motion, and adopt following formula computation speed curve:

$v_n=\sqrt{2a_{n-1}s+v_{n-1}^2}---\left(1\right);$

Wherein, Vn represents the locational limited speed of n point, and s represents stopping distance; a _N-1Represent the locational deceleration/decel of n-1 point, and a _N-1=a _N-1'+a _N-1"

Wherein, the deceleration/decel a of the pure braking force initiation of the locational train of n-1 point _N-1' calculate by following formula

a _n-1′＝kv _n-1+l；(2)；

In the formula (2), v is the moving velocity of train on the n-1 point position; K, l are coefficient, the value of k, l and type of train and velocity correlation.

The deceleration/decel a that basic resistance and gradient resistance cause on the n-1 point position _N-1" calculate by following formula

${a_{n-1}}^{\′\′}=\frac{(w_{n-1}+i_j)*g*{10}^{-3}}{1+r}---\left(3\right);$

Formula (3) w _N-1It is the locational train unit of n-1 point basic resistance; i _jAcceleration grade thousand marks for the braking low side; G is an acceleration due to gravity, and g ≈ 9.81m/s ²R is a train rotating mass coefficient.

W in the formula (3) _N-1Calculate by following formula:

w _n-1＝av _n-1 ²+bv _n-1+c(4)；

v _N-1It is the moving velocity of train on the n-1 point position; A, b, c are coefficient, and the value of a, b, c is relevant with type of train.

According to above-mentioned each formula, if at stopping distance is 32km, and one-period is under 20 milliseconds the situation, the distance of every 0.5m all needs using formula (1), (2), (3), (4) calculate quartic curve, as shown in Figure 1, comprise 1 EBP (Emergency Braking Profile, the emergency braking curve), article 3, NBP (Normal Braking Profile, the service braking curve), for every curve, existing scheme is from terminal EOA (End of Authority, driving permission terminal point) begins to calculate, the limited speed of this point is V_EOA (v1 when being n=1), get step-length s=0.5m, utilize formula (1), (2), (3), (4) turn left iteratively to calculate and release v2, the v1 that is the current v2 as a result that calculates when next time calculating, each reckoning all will be carried out formula (1), (2), (3), (4), 32km then needs to calculate altogether more than 60,000 time so, existing four curves, then need to calculate the individual point of more than totally two ten ten thousand (4*3.2/0.5=25.6 ten thousand), be equivalent to weekly have in the phase more than 20 ten thousand times floating number to take advantage of, remove, open calculating such as radical sign, and need whole daily records 92% with on preserve the data message of these more than 20 ten thousand points.

To be described as the basis that obtains the car controlling data in conjunction with a kind of iunction for curve that is used for train control system in various embodiments of the present invention, this iunction for curve is in conjunction with formula

$v_n=\sqrt{2a_{n-1}s+v_{n-1}^2}---\left(1\right);$

a _n-1′＝k _v-1+l；(2)；

${a_{n-1}}^{\′\′}=\frac{(w_{n-1}+i_j)*g*{10}^{-3}}{1+r}---\left(3\right);$

w _N-1=av _N-1 ²+ bv _N-1+ c (4) obtains by differential equation, uses in following each embodiment for the ease of this iunction for curve, introduces the derivation of this iunction for curve earlier at this.

Be located at the k in the formula (2) in the acceleration/accel of a certain scope, l is constant, and following formula is promptly arranged:

$\mathrm{ds}=\frac{\mathrm{ds}}{\mathrm{dv}}\mathrm{dv}$

$=\frac{v}{a^{\′}+a^{\′\′}}\mathrm{dv}$

(5)

Obtain formula (2), (3) substitution formula (5) as follows

$=\frac{v}{\frac{({\mathrm{av}}^2+\mathrm{bv}+c+i_j+x)*g*{10}^{-3}}{1+r}+\mathrm{kv}+l}\mathrm{dv}---\left(6\right)$

If: $u=\frac{1+r}{g*{10}^{-3}},$ $p=\frac{b+\mathrm{ku}}{a},$ $q=\frac{c+i_j+\mathrm{lu}+x}{a};$ $v+\frac{p}{2}=m,$ $\sqrt{q-\frac{p^2}{4}}=n;$

So $s=\frac{u}{a}\∫\frac{v}{v^2+\mathrm{pv}+q}\mathrm{dv}---\left(7\right)$

$=\frac{u}{a}\∫\frac{m-\frac{p}{2}}{m^2+n^2}\mathrm{dm}$

$=\frac{u}{a}(\∫\frac{m}{m^2+n^2}\mathrm{dm}-\frac{p}{2}\∫\frac{1}{m^2+n^2}\mathrm{dm})$

$=\frac{u}{a}\left\{\frac{1}{2}\mathrm{In}\right|m^2+n^2|-\frac{p}{2n}\mathrm{arctg}\frac{m}{n}+C\}$

With the m of following formula, n expression formula substitution promptly obtains

$s=\frac{u}{a}\left\{\frac{1}{2}\mathrm{In}\right|v^2+\mathrm{pv}+q|-\frac{p}{2\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}C\}---\left(8\right)$

In (8) formula, C is a constant, because terminal velocity v _EOAKnown, and this moment s be 0, so can get:

$C=\frac{p}{2\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\frac{1}{2}\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|---\left(9\right)$

With above-mentioned formula (9) substitution formula (8), obtain:

$s=\frac{u}{2a}\left\{\begin{array}{c}\mathrm{In}|v_{\mathrm{start}}^2+{\mathrm{pv}}_{\mathrm{start}}+q|-\frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{start}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}+\\ \frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|\end{array}\right\}---\left(10\right)$

Wherein, s represents stopping distance, v _StartRepresentative driving starting velocity, v _EOARepresentative driving permission terminal velocity, a represents the first type of train coefficient of correlation, and u represents first fitting parameter, and p represents second fitting parameter, and q represents the 3rd fitting parameter.

And $u=\frac{1+r}{g*{10}^{-3}},$ $p=\frac{b+\mathrm{ku}}{a},$ $q=\frac{c+i_j+\mathrm{lu}+x}{a};$

Wherein, each letter is the coefficient of train in operational process, and r represents train rotating mass coefficient, and g represents acceleration due to gravity; A, b, c represent first, second, third type of train coefficient of correlation respectively, and a, b, and the value of c is relevant with type of train; K, l represent first, second type of train velocity correlation coefficient respectively, and the value of k, l and type of train and velocity correlation; i _jRepresent the gradient thousand marks; X represents track resistance other factors coefficient, can be 0 or the expression formula formed of a plurality of resistance variable; Need to prove: this track resistance other factors coefficient is meant and removes above-mentioned k, l, and a, the factor coefficient of the track resistance beyond the b, c for example may need the tunnel resistance factor coefficient considered in the car controlling process afterwards, perhaps bend blocking-up factor coefficient etc.

Need to prove that the iunction for curve that the embodiment of the invention adopts includes, but are not limited to formula (10), can also be other iunction for curve, for example: with the functional form behind formula (10) the process equivalent transformation, as shown in the formula

$s=\frac{u}{a}\left\{\begin{array}{c}\frac{1}{2}\mathrm{In}|v_{\mathrm{start}}^2+{\mathrm{pv}}_{\mathrm{start}}+q|-\frac{p}{2\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{start}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}+\\ \frac{p}{2\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|\end{array}\right\}.$

Or be based on above-mentioned theory of the present invention and obtain iunction for curve, but with above-mentioned a, b, coefficients such as c are with other letter or the curvilinear function represented of expression formula etc.Concrete form is not given unnecessary details at this, still, because these conversion all are based on the scheme that the foregoing description content can be known or derive and, therefore also should be in protection scope of the present invention.

Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.And, below each embodiment be possibility of the present invention, embodiment put in order and the numbering execution sequence preferred of embodiment with it irrelevant.

Embodiment 1

Present embodiment provides a kind of car controlling method of train control system, and as shown in Figure 2, this method comprises:

Step 101 is obtained fitting parameter according to the train driving correlation factor;

Wherein, this fitting parameter is meant the parameter that is used for the train control system iunction for curve that provides in the present embodiment is provided.And this fitting parameter comprises first fitting parameter, second fitting parameter, and the 3rd fitting parameter.

In present embodiment and following each embodiment, this train driving correlation factor comprises a kind of in following or several combination:

Train rotating mass coefficient r, gravity acceleration g, the first type of train coefficient of correlation a, the second type of train coefficient of correlation b, the 3rd type of train coefficient of correlation c, k represent the first type of train velocity correlation coefficient, the second type of train velocity correlation coefficient l, the gradient thousand mark i _j, track resistance other factors coefficient x, driving starting velocity v _Start, driving permission terminal velocity v _EOA, stopping distance s.

Step 102 is obtained the car controlling data according to described fitting parameter and described train driving correlation factor.

Wherein, described control data can be the stopping distance s of train, also can be to be used for the limited speed v whether definite train running speed exceeds the speed limit on a certain position _Start

The method that present embodiment provides is to obtain the car controlling data according to fitting parameter, therefore need not to carry out complicated floating number calculates, reduce the difficulty of obtaining car controlling data methods such as limited speed, also reduced obtaining the equipment requirement of control datas such as limited speed, improve the accuracy of control data, and then strengthened the car controlling ability of train control system.

Embodiment 2

When present embodiment specifically is stopping distance in conjunction with this control data, a kind of car controlling method of train control system.As shown in Figure 3, this method comprises:

Step 201 is obtained fitting parameter according to the train driving correlation factor;

Wherein, the train driving correlation factor comprises a kind of in following or several combination:

Train rotating mass coefficient r, gravity acceleration g, the first type of train coefficient of correlation a, the second type of train coefficient of correlation b, the 3rd type of train coefficient of correlation c, k represent the first type of train velocity correlation coefficient, the second type of train velocity correlation coefficient l, the gradient thousand mark i _j, track resistance other factors coefficient x, driving starting velocity v _Start, driving permission terminal velocity v _EOA, stopping distance s.

According to the difference of the fitting parameter that obtains, need the train driving correlation factor of utilization also different, specifically comprise:

First fitting parameter that obtains according to the train rotating mass coefficient in the described train driving correlation factor and acceleration due to gravity.

For example: this first fitting parameter can obtain by following formula (11):

$u=\frac{1+r}{g*{10}^{-3}}---\left(11\right)$

Wherein, u represents first fitting parameter, and r represents train rotating mass coefficient, and g represents acceleration due to gravity.

According to first, second type of train coefficient of correlation in the described train driving correlation factor, second fitting parameter that the first type of train velocity correlation coefficient and described first fitting parameter obtain.

For example: this second fitting parameter can obtain by following formula (12):

$p=\frac{b+\mathrm{ku}}{a}---\left(12\right)$

Wherein, p represents second fitting parameter, and u represents first fitting parameter, and a represents the first type of train coefficient of correlation, and b represents the second type of train coefficient of correlation, and k represents the first type of train velocity correlation coefficient.

According to the first, the 3rd type of train coefficient of correlation in the described train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance factor coefficient and described first fitting parameter obtain the 3rd fitting parameter.

For example: the 3rd fitting parameter can obtain by following formula (13):

$q=\frac{c+i_j+\mathrm{lu}+x}{a}---\left(13\right)$

Wherein, q represents the 3rd fitting parameter, and a represents the first type of train coefficient of correlation, and c represents the 3rd type of train coefficient of correlation, i _jRepresent the gradient thousand marks, l represents the second type of train velocity correlation coefficient, and u represents first fitting parameter, and x represents track resistance other factors coefficient.

Need to prove: the acquisition process of above-mentioned first, second, third fitting parameter is order in no particular order, even can carry out the acquisition process of first, second, third fitting parameter simultaneously; And the computing formula of first, second and the 3rd fitting parameter includes but not limited to formula (11), (12), (13), can get access to first, second and the 3rd fitting parameter by some means to the expression formula of formula (11), (12), the equivalent transformation that carries out (13) or distortion, concrete form is not given unnecessary details at this yet.

In addition, because the gradient thousand mark i in formula (13) _jEnvironment with each highway section in the whole rail distance has much relations, so in order to improve accuracy or the degree of safety at the train driving brief acceleration, and do not add under the situation of intensive the i in the formula (13) _j:

One, available its expectation value replaces.Promptly calculate front track distance S ₀The gradient thousand mark i _jExpectation value.Concrete grammar is: according at least one road section length, and the gradient thousand mark expectation values obtained of the gradient thousand marks of corresponding described at least one road section length;

For instance, suppose the place ahead S ₀The highway section of total N the different gradient of regulation distance;

The length in this N highway section is: len _k

To the gradient thousand marks that should the N highway section be: i _Jk

This S then ₀The gradient thousand mark expectation values of regulation distance be:

$E\left[i_j\right]=\underset{k=1}{\overset{N}{\mathrm{\Σ}}}\frac{i_{\mathrm{jk}}*{\mathrm{len}}_k}{S_0}---\left(14\right)$

Wherein, E[i _j] represent the expectation value of the gradient thousand marks, k=1,2,3 ... .N.According to formula (14) it is attached in the formula (13) and gets final product.

Two, available i _jThe gradient thousand mark aviation values replace.Promptly calculate front track distance S ₀The gradient thousand mark i _jAverage.Specifically comprise: the gradient thousand mark averages of obtaining according to highway section quantity and the corresponding wherein gradient thousand marks in each highway section;

For instance, suppose the place ahead S ₀The highway section of total N the different gradient of distance;

To the gradient thousand marks that should the N highway section be: i _Jk

This S then ₀The gradient thousand mark averages of regulation distance be:

$i_{j\_\mathrm{average}}=\underset{k=1}{\overset{N}{\mathrm{\Σ}}}\frac{i_{\mathrm{jk}}}{N}---\left(15\right)$

Wherein, i _{J_average}Represent the gradient thousand calibration averages, k=1,2 ..., N is attached to it in formula (13) according to formula (15) and gets final product.

Three, available i _jThe gradient thousand mark maxims replace.Calculate the gradient thousand mark i of front track distance S0 _jMaxim, concrete grammar comprises: the gradient thousand mark maxims of obtaining according to the gradient thousand marks in each highway section.

For instance, suppose the place ahead S ₀The highway section of total N the different gradient of regulation distance;

To the gradient thousand marks that should the N highway section be: i _Jk

This S then ₀The gradient thousand mark maxims of regulation distance be:

i _{j_max}＝max{i _jk}(16)

Wherein, i _{J_max}Represent the gradient thousand mark maxims, k=1,2 ..., N is updated to it in formula (13) according to formula (16) and gets final product.

Step 202 is obtained stopping distance according to described fitting parameter and described train driving correlation factor.

Under some scene, for example need to do when measuring, train control system need know that the speed of train is from v _StartReach v _EOAThe time stopping distance s value, v in this measurement process so _StartAnd v _EOAJust be known quantity.So this step 202 specifically can realize in the following way:

According to the driving starting velocity v in the described train driving correlation factor _Start, driving permission terminal velocity v _EOAWith the first type of train coefficient of correlation a, and the first fitting parameter u in the fitting parameter, the second fitting parameter p, the 3rd fitting parameter q utilizes the car controlling iunction for curve to obtain stopping distance s.

For example: the car controlling iunction for curve by formula (10) is obtained

$s=\frac{u}{2a}\left\{\begin{array}{c}\mathrm{In}|v_{\mathrm{start}}^2+{\mathrm{pv}}_{\mathrm{start}}+q|-\frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{start}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}+\\ \frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|\end{array}\right\}---\left(10\right)$

Wherein, s represents stopping distance, v _StartRepresentative driving starting velocity, v _EOARepresentative driving permission terminal velocity, a represents the first type of train coefficient of correlation, and u represents first fitting parameter, and p represents second fitting parameter, and q represents the 3rd fitting parameter.

The method that present embodiment provides, can utilize iunction for curve to calculate according to fitting parameter, from the expression formula of this iunction for curve, as can be known, need not to carry out the multiplication and division of floating number, open complex calculations such as radical sign, obviously reduced and obtained the car controlling complexity of data, reduced requirement, and method has been simple train control system equipment, the efficient height, accuracy is strong, and error is not counted in accumulation, can improve the car controlling ability of train control system.In addition,, therefore need not to write down the data message of the hundreds of thousands of point in the computation process,, reduce the technique effect that amount has been printed in daily record so also can obtain the saving log space because the existence of iunction for curve is arranged.

Embodiment 3

When present embodiment specifically is limited speed in conjunction with this control data, a kind of car controlling method of train control system.As shown in Figure 4, this method comprises:

Step 301 is obtained fitting parameter according to the train driving correlation factor;

Wherein, what the train driving correlation factor can be in following is a kind of, or several combination:

Train rotating mass coefficient r, gravity acceleration g, the first type of train coefficient of correlation a, the second type of train coefficient of correlation b, the 3rd type of train coefficient of correlation c, k represent the first type of train velocity correlation coefficient, the second type of train velocity correlation coefficient l, the gradient thousand mark i _j, track resistance other factors coefficient x, driving starting velocity v _Start, driving permission terminal velocity v _EOA, stopping distance s.

According to the difference of the fitting parameter that obtains, need the train driving correlation factor of utilization also different, specifically comprise:

First fitting parameter that obtains according to the train rotating mass coefficient in the described train driving correlation factor and acceleration due to gravity.

According to first, second type of train coefficient of correlation in the described train driving correlation factor, second fitting parameter that the first type of train velocity correlation coefficient and described first fitting parameter obtain.

According to the first, the 3rd type of train coefficient of correlation in the described train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance factor coefficient and described first fitting parameter obtain the 3rd fitting parameter.

But cooresponding formula (11) in the above-mentioned example reference example 2 of specifically obtaining first, second, third fitting parameter, does not give unnecessary details at this (12), (13).

In addition, the gradient thousand mark i in the present embodiment _jEqually also the environment with each highway section in the whole rail distance has much relations, so in order to improve accuracy or the degree of safety at the train driving brief acceleration, and do not add under the situation of intensive the i in the formula (13) _j: available i _jExpectation value, average, or maxim replaces.Does not give unnecessary details at this formula (14), (15), (16) of concrete computation process in can corresponding reference example 2.

Step 302 is obtained limited speed according to described fitting parameter and described train driving correlation factor.

This limited speed promptly is the standard that is used for determining whether the running velocity of train exceeds the speed limit.Train control system prevents that in interior mode train is out of control by running velocity being controlled at limited speed.

In the train driving process, train control system can go out the position of current train by the signal measurement that is used for measuring distance, and this train track distance S that will travel ₀(as leave for the track distance in Shanghai from Beijing) also is known, therefore can calculate stopping distance s.Further, because stopping distance s is to the distance of terminal point, so v when reaching home from the position of current train _EOAValue can calculate by 0.So this step 302 specifically can realize in the following way:

According to the stopping distance s in the described train driving correlation factor, driving permission terminal velocity v _EOAWith the first type of train coefficient of correlation a, and the first fitting parameter u in the described fitting parameter, the second fitting parameter p, the 3rd fitting parameter q obtains limited speed by convergence algorithm.

For example: known s, v _EOA, u, p, q, a is again according to formula (10)

$s=\frac{u}{2a}\left\{\begin{array}{c}\mathrm{In}|v_{\mathrm{start}}^2+{\mathrm{pv}}_{\mathrm{start}}+q|-\frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{start}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}+\\ \frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|\end{array}\right\}---\left(10\right)$

Iunction for curve by convergence algorithms such as secant (Secant) algorithm or dichotomies, can get access to the cooresponding v in position of current train _Start, this v _StartBe the cooresponding limited speed in position of current train.

Wherein, because the formula that has provided in the present embodiment (10), and Secant algorithm or dichotomy be content well-known to those skilled in the art, so obtain v according to formula (10) by Secant algorithm or dichotomy _StartComputation process be that those skilled in the art can be known easily according to foregoing, therefore concrete computation process has not been given unnecessary details at this.

The method that adopts present embodiment to provide, when stopping distance is that 32km test computer hardware environment is:

Computing machine:

Intel(R)Pentium(R)Dual?CPU

[email protected]

2.00GHz, the internal memory of 1.99GB

During physical address extension, the data that go out through this computer testing are referring to table one:

Table one

By the content of table one as can be known, there are serious cumulative errors in the method for prior art, also need to preserve each spot speed on the entire curve, it is many to cause the stack memory cost to arrive 10M, and this test is to draw under the so high hardware case of CPU computing power, but consuming time is to be more than 342 milliseconds, in addition the cpu of ATP is 386 models, the floating point operation performance is quite backward, can't in 20 milliseconds of regulation, finish the calculating of performance graph at all, causing easily can't safe car controlling, and passenger's life security can not get ensureing.But, the method cumulative errors that present embodiment provides are little, substantially can ignore, and method is simple, has avoided complicated floating point arithmetic, calculates consuming timely only to be 0.0012 millisecond, this value also can be ignored substantially, so improved the accuracy of control data, help to provide the car controlling ability of train control system, guarantee the driving safety of train; Further, the method for present embodiment also need not the stack internal memory that each spot speed data is preserved in unnecessary being used for, so saved stack space, has reduced the daily record printing amount.

Embodiment 5

Present embodiment continues to provide when this control data is limited speed, a kind of car controlling method of train control system.The difference of method in the present embodiment and embodiment 3 mainly is, in order further to improve the accuracy of the limited speed of obtaining, can after getting access to fitting parameter, can begin a certain position, as from driving permission terminal point, calculate successively v in every section gradient distance according to formula (10) _Start, iteration calculate when beginning position to current train cooresponding v from driving permission terminal point _Start, i.e. limited speed.

For example: suppose the place ahead S ₀The highway section of total N the different gradient of regulation distance, successively according to the i in each highway section _jSubstitution formula (10) calculates distance v _Start, that is:

The length in this N highway section is: len _k

To the gradient thousand marks that should N highway section be: i _Jk

Earlier according to len _k, i _JkSubstitution formula (10) calculates the initial limited speed v of k section by Secant algorithm or dichotomy _{Start_k}With this v that calculates _{Start_k}Value regard the v in next highway section as _EOA, again according to len _K+1, i _Jk+1, v _{Start_k}(be v _EOA) and formula (10) Secant algorithm or dichotomy calculate v _{Start_k+1}Again with this v _{Start_k+1}V as next highway section _EOA, and the like, can calculate the v of current train position iteratively _Start

Wherein, k=1,2 ..., N.Because it is with one climax following another that railway can not occur in short range, so the value of N is 1～3 as a rule, at this moment, performance can be ignored.

The method that provides by present embodiment can get access to the current train position limited speed of (also can regard as and be destination locations) more accurately, can effectively improve the car controlling ability of train control system, for safe driving brings assurance, and method is simple, need not to carry out complicated floating point arithmetic, low for the train control system requirements on hardware equipment.

Need to prove that the above-mentioned example of respectively executing that the present invention improves all is to be described under the situation of having omited other resistances x, other resistance x can certainly be added in the calculating that this moment, x can use following one:

According at least one road section length, and the track resistance other factors coefficient x expectation value obtained of the track resistance factor coefficient of corresponding described at least one road section length;

The track resistance other factors coefficient x average of obtaining according to highway section quantity and the corresponding wherein track resistance factor coefficient in each highway section;

The track resistance other factors coefficient x maxim of obtaining according to the track resistance factor coefficient in each highway section; That is: the expectation value of x, the average of x, or the x maxim replaces, but in the concrete method of calculating reference example 23 kinds calculate i _jMethod calculate x, detailed process is not given unnecessary details at this.

Embodiment 5

Present embodiment provides a kind of car controlling device of train control system, and as shown in Figure 5, this device comprises: parameter acquiring unit 41, data capture unit 42.

Parameter acquiring unit 41 is used for obtaining fitting parameter according to the train driving correlation factor; Data capture unit 42, the fitting parameter and the described train driving correlation factor that are used for obtaining according to described parameter acquiring unit 41 are obtained the car controlling data.

This is, in another embodiment of the present invention, the car controlling device of train control system as shown in Figure 6, wherein, parameter acquiring unit 41 comprises: first parameter acquisition module, 411, the second parameter acquisition module, 412, the three parameter acquisition module 413.

First parameter acquisition module 411 is used for first fitting parameter that train rotating mass coefficient and acceleration due to gravity according to described train driving correlation factor obtain;

Second parameter acquisition module 412 is used for first, second type of train coefficient of correlation according to described train driving correlation factor, second fitting parameter that the first type of train velocity correlation coefficient and described first fitting parameter obtain;

The 3rd parameter acquisition module 413, be used for the first, the 3rd type of train coefficient of correlation according to described train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance factor coefficient and described first fitting parameter obtain the 3rd fitting parameter.

As shown in Figure 6, data capture unit 42 comprises: speed acquiring module 421, and apart from acquisition module 422.

Speed acquiring module 421, be used for stopping distance, driving permission terminal velocity and the first type of train coefficient of correlation according to described train driving correlation factor, and first fitting parameter in the described fitting parameter, second fitting parameter, the 3rd fitting parameter obtains limited speed by convergence algorithm; And/or

Apart from acquisition module 422, be used for driving starting velocity, driving permission terminal velocity and the first type of train coefficient of correlation according to described train driving correlation factor, and first fitting parameter in the fitting parameter, second fitting parameter, the 3rd fitting parameter utilize the car controlling iunction for curve to obtain stopping distance.

The device that present embodiment provides can obtain the car controlling data according to fitting parameter and train driving correlation factor, and the stopping distance for the optional position all can obtain limited speed by Secant algorithm or dichotomy, need not to carry out complicated floating number multiplication and division, open calculating such as radical sign, there are not cumulative errors, and then effective car controlling performance, and can avoid the frequent phenomenon that weak braking occurs in the train vibration influence process, need not to preserve the data message of each point simultaneously, reduce the daily record printing amount, saved stack space.

Through the above description of the embodiments, the those skilled in the art can be well understood to the present invention and can realize by the mode that software adds essential general hardware platform, can certainly pass through hardware, but the former is better embodiment under a lot of situation.Based on such understanding, the part that technical scheme of the present invention contributes to prior art in essence in other words can embody with the form of software product, this computer software product is stored in the storage medium that can read, floppy disk as computing machine, hard disk or CD etc. comprise that some instructions are with so that an equipment (can be computing machine etc.) is carried out the described method of each embodiment of the present invention.

The above; only be the specific embodiment of the present invention, but protection scope of the present invention is not limited thereto, anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; can expect easily changing or replacing, all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion by described protection domain with claim.

Claims (15)

1. the car controlling method of a train control system is characterized in that, comprising:

Obtain fitting parameter according to the train driving correlation factor, described train driving correlation factor is: parameter or the coefficient relevant with the train driving process;

Obtain the car controlling data according to described fitting parameter and described train driving correlation factor.

2. method according to claim 1 is characterized in that, describedly obtains fitting parameter according to the train driving correlation factor and comprises: first fitting parameter that obtains according to the train rotating mass coefficient in the described train driving correlation factor and acceleration due to gravity.

3. method according to claim 2 is characterized in that, described first fitting parameter that obtains according to the train rotating mass coefficient in the train driving correlation factor and acceleration due to gravity is specially:

$u=\frac{1+r}{g*{10}^{-3}}$

Wherein, u represents first fitting parameter, and r represents train rotating mass coefficient, and g represents acceleration due to gravity.

4. method according to claim 2, it is characterized in that, describedly obtain fitting parameter according to the train driving correlation factor and comprise: according to first, second type of train coefficient of correlation in the described train driving correlation factor, second fitting parameter that the first type of train velocity correlation coefficient and described first fitting parameter obtain.

5. method according to claim 4, it is characterized in that, described according to first, second type of train coefficient of correlation in the train driving correlation factor, the first type of train velocity correlation coefficient and described first fitting parameter obtain second fitting parameter and are specially:

$p=\frac{b+\mathrm{ku}}{a}$

Wherein, p represents second fitting parameter, and u represents first fitting parameter, and a represents the first type of train coefficient of correlation, and b represents the second type of train coefficient of correlation, and k represents the first type of train velocity correlation coefficient.

6. method according to claim 2 is characterized in that, describedly obtains fitting parameter according to the train driving correlation factor and comprises:

According to the first, the 3rd type of train coefficient of correlation in the described train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance other factors coefficient and described first fitting parameter obtain the 3rd fitting parameter.

7. method according to claim 6, it is characterized in that, described according to the first, the 3rd type of train coefficient of correlation in the train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance other factors coefficient x and described first fitting parameter obtain the 3rd fitting parameter and are specially:

$q=\frac{c+i_j+\mathrm{lu}+x}{a}$

Wherein, q represents the 3rd fitting parameter, and a represents the first type of train coefficient of correlation, and c represents the 3rd type of train coefficient of correlation, i _jRepresent the gradient thousand marks, l represents the second type of train velocity correlation coefficient, and u represents first fitting parameter, and x represents track resistance other factors coefficient.

8. method according to claim 6 is characterized in that, the described gradient thousand marks comprise following its

According at least one road section length, and the gradient thousand mark expectation values obtained of the gradient thousand marks of corresponding described at least one road section length;

The gradient thousand mark averages of obtaining according to highway section quantity and the corresponding wherein gradient thousand marks in each highway section;

The gradient thousand mark maxims of obtaining according to the gradient thousand marks in each highway section.

9. method according to claim 6 is characterized in that, described track resistance factor coefficient comprises following one:

According at least one road section length, and the track resistance other factors coefficient expectation value obtained of the track resistance other factors coefficient of corresponding described at least one road section length;

The track resistance other factors coefficient average of obtaining according to highway section quantity and the corresponding wherein track resistance other factors coefficient in each highway section;

The track resistance other factors coefficient maxim of obtaining according to the track resistance other factors coefficient in each highway section.

10. method according to claim 1 is characterized in that, when the car controlling data are restricting data, describedly obtain the car controlling data according to described fitting parameter and described train driving correlation factor and comprises:

According to the stopping distance in the described train driving correlation factor, driving permission terminal velocity and the first type of train coefficient of correlation, and first fitting parameter in the described fitting parameter, second fitting parameter, the 3rd fitting parameter obtains limited speed by convergence algorithm.

11. method according to claim 1, it is characterized in that, when described car controlling data are stopping distance, describedly obtain the car controlling data according to described fitting parameter and described train driving correlation factor and comprise: according to the driving starting velocity in the described train driving correlation factor, driving permission terminal velocity and the first type of train coefficient of correlation, and first fitting parameter in the fitting parameter, second fitting parameter, the 3rd fitting parameter utilize the car controlling iunction for curve to obtain stopping distance.

12. method according to claim 10 is characterized in that, the car controlling iunction for curve of described train control system is:

$s=\frac{u}{2a}\left\{\begin{array}{c}\mathrm{In}|v_{\mathrm{start}}^2+{\mathrm{pv}}_{\mathrm{start}}+q|-\frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{start}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}+\\ \frac{p}{\sqrt{q-\frac{p^2}{4}}}\mathrm{arctg}\frac{v_{\mathrm{EOA}}+\frac{p}{2}}{\sqrt{q-\frac{p^2}{4}}}-\mathrm{In}|v_{\mathrm{EOA}}^2+{\mathrm{pv}}_{\mathrm{EOA}}+q|\end{array}\right\}$

Wherein, s represents stopping distance, v _StartRepresentative driving starting velocity, v _EOARepresentative driving permission terminal velocity, a represents the first type of train coefficient of correlation, and u represents first fitting parameter, and p represents second fitting parameter, and q represents the 3rd fitting parameter.

13. the car controlling device of a train control system is characterized in that, comprising:

Parameter acquiring unit is used for obtaining fitting parameter according to the train driving correlation factor, and described train driving correlation factor is: parameter or the coefficient relevant with the train driving process;

Data capture unit, the fitting parameter and the described train driving correlation factor that are used for obtaining according to described parameter acquiring unit are obtained the car controlling data.

14. device according to claim 13 is characterized in that, described parameter acquiring unit comprises:

First parameter acquisition module is used for first fitting parameter that train rotating mass coefficient and acceleration due to gravity according to described train driving correlation factor obtain;

Second parameter acquisition module is used for first, second type of train coefficient of correlation according to described train driving correlation factor, second fitting parameter that the first type of train velocity correlation coefficient and described first fitting parameter obtain;

The 3rd parameter acquisition module, be used for the first, the 3rd type of train coefficient of correlation according to described train driving correlation factor, the gradient thousand marks, the second type of train velocity correlation coefficient, track resistance factor coefficient and described first fitting parameter obtain the 3rd fitting parameter.

15. device according to claim 13 is characterized in that, described data capture unit comprises:

Speed acquiring module, be used for stopping distance, driving permission terminal velocity and the first type of train coefficient of correlation according to described train driving correlation factor, and first fitting parameter in the described fitting parameter, second fitting parameter, the 3rd fitting parameter obtains limited speed by convergence algorithm; And/or

Apart from acquisition module, be used for driving starting velocity, driving permission terminal velocity and the first type of train coefficient of correlation according to described train driving correlation factor, and first fitting parameter in the fitting parameter, second fitting parameter, the 3rd fitting parameter utilize the car controlling iunction for curve to obtain stopping distance.

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