CN116238549B

CN116238549B - Adhesion control system of electric locomotive

Info

Publication number: CN116238549B
Application number: CN202310472883.5A
Authority: CN
Inventors: 严钦云; 陈刚; 凌云; 孔玲爽; 聂辉; 汤彩珍
Original assignee: Hunan University of Technology
Current assignee: Hunan University of Technology
Priority date: 2023-04-27
Filing date: 2023-04-27
Publication date: 2024-06-14
Anticipated expiration: 2043-04-27
Also published as: CN116238549A

Abstract

According to the adhesion control system of the electric locomotive, an adhesion coefficient expert control model obtains an adhesion coefficient setting value by adopting a direct reasoning calculation method according to the ambient temperature, the weather state and the track adhesion state to adaptively adjust the adhesion coefficient, so that the maximum traction limit of the locomotive can be changed in real time along with the change of road conditions of a road section, and locomotive traction is carried out under the condition that wheelset idle running does not occur as much as possible. When the wheel set cannot be prevented from idling, a nonlinear mathematical model is adopted to calculate an idling risk value, and a plurality of single threshold judgment conditions of the idling of the traditional wheel set and a weighting judgment condition under the condition that the single threshold condition is not satisfied are integrated, so that multi-factor integrated judgment is realized, and the idling judgment and the idling traction control are more comprehensive and accurate.

Description

Adhesion control system of electric locomotive

Technical Field

The invention belongs to the technical field of locomotive traction control, and particularly relates to an electric locomotive adhesion control system.

Background

Locomotive (train) operation is realized through interaction between wheel tracks, and the power of a traction motor can be further utilized only on the premise of ensuring effective adhesion between the wheel tracks. The track adhesion characteristics are related not only to the locomotive itself and the track material, but also to a series of uncertainty factors that vary over time, such as line conditions, track surface cleanliness, etc. If the traction force is greater than the available adhesive force between the wheel tracks in the running process of the locomotive, the excessive traction force accelerates the wheels to form idling, the relative sliding speed is increased quickly, the available adhesive force is reduced quickly, the abrasion and even damage of the wheel tracks can be caused, the maintenance cost of railway operation is increased, and the safe running of the locomotive can be threatened. Due to the ever-changing conditions of locomotive operation, changes in driver handling or deterioration of rail surface conditions during traction, lost motion cannot be completely avoided; at present, a domestic alternating-current and direct-current locomotive mainly adopts a combined correction method to carry out anti-idle and anti-skid control, firstly, the acceleration of wheels is judged, when the acceleration exceeds a certain threshold value, the phenomenon of idle sliding is severe, and the driving torque of the wheels is rapidly and deeply reduced, namely the traction force of the locomotive is reduced; if the acceleration of the wheel does not exceed the threshold value, the creep speed is judged, and when the creep speed exceeds the threshold value, the driving torque is adjusted to a larger extent, otherwise, the normal running condition is judged. Judging whether idling occurs by adopting 2 or more single threshold conditions in the used combination correction method, and when the idling does not occur, not realizing comprehensive judgment of idling risk; when the idling has occurred, comprehensive judgment of the idling degree cannot be achieved.

Disclosure of Invention

The invention aims to overcome the defects of the prior art and provide an electric locomotive adhesion control system for realizing the self-adaptive control of the maximum traction force. The electric locomotive adhesion control system comprises an adhesion coefficient expert control model, a traction limiting self-tuning module and an idle traction control module; the input of the adhesion coefficient expert control model is the ambient temperature, the weather state and the track adhesion state, and the output is the adhesion coefficient setting value; the traction limiting self-setting module sets an adhesion coefficient empirical calculation model according to the adhesion coefficient setting value, and outputs the traction limiting self-setting model after upper limit limiting control is carried out on the input locomotive traction; the idling traction control module judges whether the locomotive wheel pair idles according to the creep degree change rate, the creep degree and the locomotive wheel pair speed change rate, and determines whether to carry out idling traction control on locomotive traction subjected to upper limit limiting control according to an idling judgment result.

The model is based on the setting of the adhesion coefficient

Wherein V is the locomotive speed, mu _j is the calculated adhesion coefficient output by the model, and a ₁、a₂、a₃、a₄、a₅ is the empirical formula parameter for calculating the adhesion coefficient; and xi is the setting value of the adhesion coefficient output by the adhesion coefficient expert control model. The upper limit control of the input locomotive traction force is that

Wherein P _μ is the calculated adhesion weight, mu _j·P_μ is the maximum traction limit value, and F ₁、F₂ is the input traction and the output traction of the traction limiting self-tuning module respectively.

And the adhesion coefficient expert control model carries out direct reasoning calculation on the environmental temperature, the weather state and the track adhesion state which are input in real time to obtain an adhesion coefficient setting value.

The adhesion control system of the electric locomotive further comprises a climate rail surface monitoring module; the weather rail surface monitoring module comprises an ambient temperature measuring unit, a weather state measuring unit and a rail surface image acquisition and recognition unit; the environment temperature measuring unit measures and outputs the current environment temperature, the weather state measuring unit measures and outputs the current weather state, the rail surface image acquisition and recognition unit acquires the real-time rail surface image for recognition processing, and the current rail object state is output.

The method for judging whether the locomotive wheelset idles by the idling traction control module is that when the idling risk value E is more than or equal to 1, the locomotive wheelset idles; idle risk value E is in accordance with

Calculating, wherein x ₁ is the creep change rate, and theta ₁ is the creep change rate threshold; x ₂ is the creep degree, θ ₂ is the creep degree threshold; x ₃ is the locomotive wheel set speed change rate, and θ ₃ is the wheel set speed change rate threshold; gamma ₁、γ₂、γ₃ is a nonlinear weighting control factor, and gamma ₁≥10、γ₂≥10、γ₃ is more than or equal to 10. The creep degree change rate x ₁, the creep degree x ₂ and the locomotive wheel set speed change rate x ₃ are all non-negative values.

The idle traction control module realizes idle traction control by controlling an idle traction control ratio theta, wherein the idle traction control ratio theta is a ratio between locomotive traction output by the idle traction control module and locomotive traction input by the idle traction control module, and is more than or equal to 0 and less than or equal to 1. The idle traction control process of the idle traction control module is:

A step (I) of reducing the idling traction force, wherein the idling traction force is started from the continuous increase of the idling risk value E which is greater than or equal to 1 to the continuous decrease of the idling risk value E from the continuous increase to the continuous decrease; in the process I, the control theta starts to decrease with the slope d ₁, and the value of theta at the end of the process I is the lowest maintenance value; the minimum maintenance value of θ is not less than 0;

a step II of maintaining the minimum maintenance value of the idle traction force, wherein the idle traction force is started from the end of the step I to the end when the idle risk value E is smaller than 1; in the process II, the idling risk value E is continuously reduced, and the control theta is equal to the minimum maintenance value;

A step III of recovering the idle traction force, wherein the idle traction force recovery step starts from the end of the step II to the end when theta is increased to be equal to 1; in process III, the idle traction control module controls θ to start increasing with a slope d ₂ until θ equals 1. The rate of decrease of slope d ₁ is greater than the rate of increase of slope d ₂.

In an idle traction control process II of the idle traction control module, if the idle risk value E is changed from continuous decrease to continuous increase, returning to the process I for idle traction control; in the idle traction control process III of the idle traction control module, if the idle risk value E increases again to 1 or more, the process I is returned to perform the idle traction control.

The electric locomotive adhesion control system also comprises a locomotive speed measurement adjusting module (or device) which is used for measuring and adjusting locomotive speed amounts such as the creep degree, the creep degree change rate, the locomotive wheel pair speed change rate and the locomotive speed of the locomotive, namely periodically collecting the locomotive wheel rotation speed, the locomotive radar speed and the vehicle satellite positioning system speed, and calculating to obtain the locomotive speed, the creep degree change rate and the locomotive wheel pair speed change rate. The locomotive speed measurement and adjustment module comprises a speed adjustment calculation unit, a locomotive wheel rotation speed acquisition unit, a locomotive radar speed acquisition unit and a vehicle satellite positioning system speed acquisition unit, wherein the locomotive wheel rotation speed acquisition unit periodically acquires locomotive wheel rotation speed V (h), the locomotive radar speed acquisition unit periodically acquires locomotive radar speed W (h), and the vehicle satellite positioning system speed acquisition unit periodically acquires vehicle satellite positioning system speed U (k) and positioning state information X (k). The period for collecting the speed and the positioning state information of the vehicle satellite positioning system is T _U, and the period for collecting the radar speed of the locomotive and the rotating speed of wheels of the locomotive is T _V;T_U which is larger than T _V. The speed adjustment calculation unit carries out the setting calculation on the wheel/vehicle speed ratio adjustment model parameters according to the speed U (k) of the vehicle satellite positioning system and the positioning state information X (k), or carries out the setting calculation on the wheel/vehicle speed ratio adjustment model parameters according to the radar synchronous adjustment speed output by the locomotive radar speed adjustment model. The wheel/vehicle speed ratio adjustment model outputs locomotive speed, creep degree change rate and locomotive wheel pair speed change rate. And setting the calculated wheel/vehicle speed ratio adjustment model parameters and the locomotive radar speed adjustment model parameters by adopting an iterative calculation mode, wherein the iterative calculation period is the same as the acquisition period T _U of the vehicle-mounted satellite positioning system speed acquisition unit.

The locomotive speed measurement adjusting module periodically reads the locomotive wheel rotation speed V (k) and the locomotive radar speed W (k) acquired at the synchronous acquisition time point and performs iterative calculation, wherein k is the current iterative calculation substitution.

And in the kth iterative computation, the locomotive speed measurement adjusting module judges whether the speed of the vehicle-mounted satellite positioning system is effective. When the speed of the vehicle satellite positioning system is judged to be effective, the current wheel/vehicle speed adjustment coefficient P _V (k) is according to the following formula

And (3) setting, wherein U (k) is the vehicle satellite positioning system speed acquired last time, and V (k) is the locomotive wheel rotation speed acquired at the U (k) synchronous acquisition time point.

When judging that the speed of the vehicle-mounted satellite positioning system is effective, the current radar speed transformation ratio coefficient P _W (k) is according to the following formula

And (3) performing tuning, wherein W (k) is the locomotive radar speed acquired at the U (k) synchronous acquisition time point. When the vehicle satellite positioning system speed is valid, the radar speed adjustment factor P _W is equal to P _W (k).

When the speed of the vehicle satellite positioning system is invalid, the current radar speed transformation ratio coefficient is obtained by fitting and calculating the previous radar speed transformation ratio coefficient, the method is that m points (k-1, P _W(k-1))、(k-2,P_W(k-2))、......、(k-m,P_W (k-m)) are subjected to linear fitting to obtain a radar speed transformation ratio first-order fitting straight line, the value P _W ^* (k) on the points (k, P _W ^* (k)) on the radar speed transformation ratio first-order fitting straight line is taken as the current radar speed transformation ratio coefficient P _W(k).P_W(k-1)、P_W(k-2)、......、P_W (k-m), and the current radar speed transformation ratio coefficient P _W(k).P_W(k-1)、P_W(k-2)、......、P_W (k-m) is sequentially the m radar speed transformation ratio coefficients obtained by setting and calculating in the previous m iterative calculation processes of the locomotive speed measurement and adjustment module. The radar synchronous regulation speed W ^* (k) is according to

Calculating; the current wheel/vehicle speed adjustment coefficient is in accordance with

And (5) setting.

The wheel/vehicle speed ratio coefficient U _V (k) is obtained by fitting and calculating wheel/vehicle speed ratio coefficients, m points (k, P _V (k)), (k-1, PV k-1, k-2, PV k-2,) and K-m+1, and PV k-m+1 are linearly fitted to obtain a wheel/vehicle speed ratio coefficient first-order fitting straight line, a value U _V (k) on the point (k, U _V (k)) on the wheel/vehicle speed ratio coefficient first-order fitting straight line is taken as a value U _V(k).P_V(k-1)、P_V(k-2)、......、P_V (k-m+1) and sequentially represents m-1 wheel/vehicle speed ratio coefficients obtained by setting and calculating in the previous m-1 iterative calculation processes of the locomotive speed measurement and adjustment module, and m is an integer which is more than or equal to 3.

The current locomotive speed V _C (h) is in accordance with

The calculation is performed with the same calculation period as the sampling period T _V. Taking locomotive speed V as current locomotive speed V _C (h).

According to

The current creep degree x ₂ (h) is calculated, the calculation period is the same as the sampling period T _V, and the creep degree x ₂ is equal to the current creep degree x ₂ (h).

The rate of change of creep x ₁ is in accordance with

The calculation is performed with the same calculation period as the sampling period T _V. x ₂ (h-1) is the current creep level obtained in the previous calculation of the creep level with the sampling period T _V.

The vehicle satellite positioning system speed U (k) is characterized in that the tau-th locomotive wheel rotation speed acquisition time before the sampling time is U (k) synchronous acquisition time point, tau is a delay interval period number, and the locomotive wheel rotation speed acquired at the tau-th locomotive wheel rotation speed acquisition time point is V (k). Similarly, the tau-th locomotive radar speed acquisition time before the sampling time of the vehicle-mounted satellite positioning system speed U (k) is the U (k) synchronous acquisition time point, and the synchronous acquisition time points of the locomotive radar speed W (k) and the locomotive wheel rotation speed V (k) are consistent. The delay interval period number tau is a value of the acquisition period TV which is converted from a time lag value of the acquisition time of the speed of the vehicle satellite positioning system, which lags behind the acquisition time of the rotation speed of the locomotive wheel and the radar speed of the locomotive. When the speed U (k) of the vehicle-mounted satellite positioning system is invalid, the sampling time of the vehicle-mounted satellite positioning system still exists, namely, the synchronous acquisition time point of the U (k) still exists. When meeting the requirements

When the vehicle-mounted satellite positioning system speed is judged to be effective for the last continuous m ₁ times, calculating the delay interval period number tau, wherein m ₁ is more than or equal to 10; an acceleration change threshold value of ε greater than 0, in particular, the value of ε may beTo/>Is selected within the numerical range of/>Is the average acceleration of the locomotive. In the above formula, beta (k) is beta (k-i) when i is equal to 0, and is the calculated last locomotive acceleration change rate; i is equal to 1,2 respectively, beta (k-i) at m ₁ -1 is the rate of change of locomotive acceleration at the nearest m ₁ -1 times.

Locomotive acceleration rate of change is in accordance with

Calculating; wherein alpha (k) is the last acquired locomotive acceleration, and alpha (k-1) is the last acquired locomotive acceleration.

Locomotive acceleration is measured and collected by an accelerometer. Alternatively, locomotive acceleration is in accordance with

Calculating; wherein U (k-1) is the vehicle satellite positioning system speed of the last acquisition of U (k).

The method for calculating the lag interval period number tau is to set the parameters to be optimized as the lag interval period number tau ^* and the radar speed proportional coefficient p _W ^*. When the delay interval period number is tau ^*, the rotation speed of the locomotive wheel acquired at the synchronous acquisition time point corresponding to U (k-i) is V ^* (k-i), the locomotive radar speed acquired at the synchronous acquisition time point corresponding to U (k-i) is W ^* (k-i), namely, the rotation speed of the locomotive wheel acquired at the synchronous acquisition time point corresponding to U (k) and the locomotive radar speed are V ^*(k)、W^* (k-i) respectively, the rotation speed of the locomotive wheel acquired at the synchronous acquisition time point corresponding to U (k-1) and the locomotive radar speed are V ^*(k-1)、W^* (k-1) respectively, the rotation speed of the locomotive wheel acquired at the synchronous acquisition time point corresponding to U (k-2) and the locomotive radar speed are V ^*(k-2)、W^* (k-2) respectively, and so on. The minimum optimization objective function is

Taking the lag interval period number tau ^* meeting the optimal value (namely Q is the minimum value) Q as the lag interval period number tau; the value range of τ ^* is an integer greater than 0 and less than 2/T _V, and the value range of p _W ^* is greater than or equal to 0.8 and less than or equal to 1.2.

In the locomotive speed measurement adjusting module, the collected locomotive wheel rotation speed is obtained after the sampled locomotive wheel rotation speed is filtered; filtering the sampled locomotive radar speed to obtain the collected locomotive radar speed; and filtering the sampled vehicle-mounted satellite positioning system speed to obtain the acquired vehicle-mounted satellite positioning system speed. Before acquiring the first vehicle-mounted satellite positioning system speed, the method comprises the following steps of

Wherein i=1, 2.

The beneficial effects of the invention are as follows: the main factors affecting the adhesion coefficient include the rail surface condition and the surrounding environment condition in addition to the locomotive speed. The input of the adhesion coefficient expert control model comprises the main factors influencing the speed of the adhesion coefficient locomotive such as the ambient temperature, the weather state, the track adhesion state and the like, an adhesion coefficient setting value is obtained by adopting a direct reasoning calculation method, and then the adhesion coefficient empirical calculation model parameters reflecting the influence of the speed of the locomotive are set by the adhesion coefficient setting value, so that the system can adaptively adjust the adhesion coefficient according to road condition data of the actual operation of the locomotive such as the weather severity, the track pollution degree and the like, and under the condition of the joint participation adjustment of the adhesion coefficient expert control model, the system can consider the actual operation road section and the changed operation road condition of the locomotive based on a large amount of experimental data, so that the maximum traction limit of the locomotive can be changed in real time along with the change of the road condition of the road section, and the locomotive traction can be carried out under the condition that the wheel pair idle running does not occur as much as possible. When the adhesion condition is poor and even if the upper limit limiting is carried out, the locomotive traction force (wheel circumference tangential force) of the wheel axle is still larger than the wheel track adhesion force, and the normal traction of the locomotive cannot be recovered as soon as possible when the wheel set idles. The non-linear mathematical model is selected, so that the possibility of misjudgment of the weighting judgment conditions under the condition that the single threshold condition is not met can be avoided as much as possible. Meanwhile, the action size of the weighting judgment conditions can be set and adjusted through parameters, and the relative action size of each weighting term can also be set and adjusted through parameters, so that the locomotive wheel idle rotation judgment method based on the nonlinear mathematical model normalization can be suitable for different locomotive types and running conditions.

Drawings

FIG. 1 is a schematic diagram of an adhesion control system for an electric locomotive;

FIG. 2 is a method of direct reasoning calculation of an adhesion coefficient expert control model;

FIG. 3 is an idle traction control schematic diagram 1 of an idle traction control module when an idle of a locomotive wheelset occurs;

FIG. 4 is an idle traction control schematic diagram 2 of an idle traction control module when an idle of a locomotive wheelset occurs;

FIG. 5 is a schematic diagram of a locomotive speed measurement adjustment module;

FIG. 6 is a flow chart of a locomotive speed adjustment method;

FIG. 7 is a schematic diagram of an embodiment of a first order fitted line of radar speed-to-transformation ratio coefficients;

FIG. 8 is a schematic diagram of an embodiment of a first order fit straight line for a wheel/truck speed ratio coefficient;

FIG. 9 is a flowchart for calculating the number of stall cycles;

FIG. 10 is a schematic diagram of a vehicle satellite positioning system speed acquisition delay, locomotive acceleration, and locomotive acceleration rate of change;

FIG. 11 is a schematic diagram of a synchronous acquisition time point of the rotational speed of a locomotive wheel and the speed of a locomotive radar for the speed of a vehicle-mounted satellite positioning system.

Detailed Description

The invention is further described below with reference to the accompanying drawings.

Fig. 1 is a schematic diagram of an adhesion control system of an electric locomotive, which includes an adhesion coefficient expert control model 10, a traction force limiting self-tuning module 11, an idle traction force control module 12, a locomotive speed measurement adjustment module 13 and a weather rail surface monitoring module 14.F ₁ is locomotive traction force output from locomotive speed controller, and ζ is adhesion coefficient setting value output by adhesion coefficient expert control model, and traction force limiting self-setting module is used for setting adhesion coefficient empirical calculation model according to adhesion coefficient setting value, specifically including

In the formula (1), V is the locomotive speed, μ _j is the calculated adhesion coefficient output by the adhesion coefficient empirical calculation model, a ₁、a₂、a₃、a₄、a₅ is an empirical formula parameter for calculating the adhesion coefficient, and the value of the parameter is related to the model of the electric locomotive, for example, a ₁＝0.24、a₂＝12、a₃＝100、a₄＝8、a₅ =0 is taken for each domestic electric locomotive; a ₁＝0.189、a₂＝8.86、a₃＝44、a₄＝1、a₅ =0 is taken from the 6K type electric locomotive respectively; taking a ₁＝0.28、a₂＝4、a₃＝50、a₄＝6、a₅ = -0.0006 of the 8G type electric locomotive respectively; etc. The unit of locomotive speed V is km/h.

The traction force limiting self-tuning module simultaneously carries out upper limit limiting control on the traction force F ₁ of the locomotive before upper limit limiting according to the calculated adhesion coefficient output by the adhesion coefficient empirical calculation model, namely

In the formula (2), P _μ is the calculated adhesion weight of the locomotive, and the value of the calculated adhesion weight is constant for the determined model of the electric locomotive; mu _j·P_μ is the maximum traction limit; and F ₂ is locomotive traction after upper limit limiting. The unit of P _μ and each locomotive tractive effort F ₁、F₂ is kN; traction can also be converted into a corresponding torque when needed.

In the embodiment of fig. 1, the climate rail surface monitoring module comprises an ambient temperature measurement unit, a weather status measurement unit and a rail surface image acquisition and identification unit. C ₁₁ is the ambient temperature measured and output by the ambient temperature measuring unit, and the output is in the range of-15 ℃ to +50 ℃; when the ambient temperature is lower than-15 ℃, enabling C ₁₁ to be equal to-15 ℃; when the ambient temperature is higher than +50℃, let C ₁₁ equal +50℃. C ₁₂ is the weather state measured and output by the weather state measuring unit, and the current weather state comprises snowing, light rain, medium rain, heavy rain and no rain and snow, and the total weather states are 5 types; the rain-free mist state is classified into a light rain state. C ₁₃ is the track object state of track surface image acquisition and identification unit acquisition discernment and output, including snow, fallen leaves, dust, clean, totally divide 4 kinds of track object states. The environment temperature measuring unit measures and outputs the environment temperature, the weather state measuring unit measures and outputs the weather state, and the rail surface image acquisition and recognition unit acquires and recognizes and outputs the rail object state, which are all conventional technologies in the field.

In an embodiment, a method 1 for reasoning an environmental temperature, a weather state and a track adhesion state input in real time in a running process of a locomotive by using an adhesion coefficient expert control model and obtaining an adhesion coefficient setting value ζ is shown in fig. 2, and specifically includes:

Reasoning 1, and reasoning to obtain a weather environment factor value xi ₁ according to the environment temperature and the weather state, specifically, when the environment temperature is less than 0 ℃ and the weather state is not free of rain and snow, xi ₁＝b₁₁; when the ambient temperature is greater than or equal to 0 ℃ and the weather state is light rain or snowing, xi ₁＝b₁₂; when the ambient temperature is greater than or equal to 0 ℃ and the weather state is moderate rain, xi ₁＝b₁₃; when the ambient temperature is greater than or equal to 0 ℃ and the weather state is heavy rain, xi ₁＝b₁₄; when the ambient temperature is less than 0 ℃ and the weather state is sleet-free, ζ ₁＝b₁₅; when the ambient temperature is greater than or equal to 0 ℃ and the weather state is sleet-free, ζ ₁＝b₁₆. In reasoning 1, the requirement of b ₁₁＜b₁₂＜b₁₃＜b₁₄＜b₁₅＜b₁₆ is satisfied, and the specific value is determined according to the running state of the locomotive and the experience of an expert, for example, a value combination is that b ₁₁＝0.3,b₁₂＝0.4,b₁₃＝0.65,b₁₄＝0.7,b₁₅＝0.9,b₁₆ =1.

Reasoning 2, and reasoning to obtain a track object factor value xi ₂ according to the track object state, specifically, when the track object state is snow, xi ₂＝b₂₁; when the track object state is fallen leaves, xi ₂＝b₂₂; when the track object state is dust, xi ₂＝b₂₃; when the track landing state is clean, ζ ₂＝b₂₄. In reasoning 2, the requirement of b ₂₁＜b₂₂＜b₂₃＜b₂₄ is satisfied, and the specific value is determined according to the running state of the locomotive and the experience of an expert, for example, a value combination is that b ₂₁＝0.5,b₂₂＝0.6,b₂₃＝0.8,b₂₄ =1.

And 3, calculating to obtain an adhesion coefficient setting value xi according to weather environment factor xi ₁ and orbit object factor xi ₂, namely, xi=xi ₁·ξ₂.

The method 2 for reasoning the environmental temperature, the weather state and the track object state which are input in real time in the running process of the locomotive by the adhesion coefficient expert control model and obtaining the adhesion coefficient setting value zeta comprises the following steps of firstly reasoning and obtaining the initial adhesion coefficient setting value zeta ₀ according to the table 1.

TABLE 1

	Snow or rain	Medium or heavy rain	No rain or snow
				Snow cover	b₀	b₁	b₂
Fallen leaves	b₃	b₅	b₆
				Dust	b₄	b₇	b₉
Clean water	b₈	b₁₀	1

The specific content of reasoning according to the table 1 is that when the weather state is snowy or rainy and the track object state is snow, the xi ₀ is equal to b ₀; when the weather state is medium rain or heavy rain and the track object state is snow, making xi ₀ equal to b ₁; when the weather state is free of rain and snow and the track object state is snow, making xi ₀ equal to b ₂; when the weather state is snowing or rainy and the track object state is fallen leaves, making xi ₀ equal to b ₃; when the weather state is snowing or rainy and the track object state is dust, making xi ₀ equal to b ₄; when the weather state is medium rain or heavy rain and the track object state is fallen leaves, making xi ₀ equal to b ₅; when the weather state is free of rain and snow and the track object state is fallen leaves, making xi ₀ equal to b ₆; when the weather state is medium rain or heavy rain and the track object state is dust, making xi ₀ equal to b ₇; when the weather state is snowing or rainy and the track object state is clean, making xi ₀ equal to b ₈; when the weather state is free of rain and snow and the track object state is dust, making xi ₀ equal to b ₉; when the weather state is medium rain or heavy rain and the track object state is clean, making xi ₀ equal to b ₁₀; when the weather state is rain and snow free and the track object state is clean, zeta ₀ is equal to 1. For example, the values 0.4, 0.45, 0.5, 0.6, 0.7, 0.8, 0.85, 0.9, and 0.95 are sequentially and respectively required to satisfy b₀＜b₁＜b₂＜b₃＜b₄≤b₅＜b₆＜b₇≤b₈≤b₉＜b₁₀;.

Second, calculating the setting value of the adhesion coefficient zeta based on the initial setting value of the adhesion coefficient zeta ₀ and the measured value of the ambient temperature C ₁₁,

The idle traction control module calculates an idle risk value E by adopting an established nonlinear mathematical model, wherein the idle risk value E is according to the formula

And (5) performing calculation. In the formula (4), x ₁ is the creep change rate, and θ ₁ is the creep change rate threshold; x ₂ is the creep degree, θ ₂ is the creep degree threshold; gamma ₁、γ₂ is a nonlinear weighting control factor, and gamma ₁≥10、γ₂ is more than or equal to 10. The creep change rate x ₁ and the creep x ₂ are all non-negative values. The idling judgment condition is that when E is more than or equal to 1, the locomotive (train) wheel set is judged to idle. The idle judgment logic obtained by combining the expression (4) and the idle judgment condition is: the occurrence of idling can be judged in 3 cases (or in one of 3 conditions is satisfied), and ① is respectively when the creep change rate x ₁ is equal to or greater than the threshold value θ ₁; ② Or, when the creep degree x ₂ is equal to or greater than the threshold value θ ₂; ③ Alternatively, when the creep change rate x ₁ is less than the threshold value θ ₁ and the creep degree x ₂ is less than the threshold value θ ₂ and the idling risk value E is 1 or more. The preceding 2 conditions ①② are single-item threshold conditions, that is, when a single item satisfies x ₁≥θ₁ or when a single item satisfies x ₂≥θ₂, the condition that E is equal to or greater than 1, that is, the condition that idle judgment is satisfied. The condition ③ is a weighted judgment condition in the case where none of the single threshold conditions is satisfied. The larger the values of both γ ₁、γ₂, the larger the factor that the single super-threshold determination takes, and the smaller the effect of the conditional ③ weighting determination. For example, if γ ₁、γ₂ is equal to 100 and x ₁/θ₁、x₂/θ₂ is equal to 0.84 at this time, the idling risk value E is equal to 0.957, and the idling determination condition is not satisfied; if x ₁/θ₁、x₂/θ₂ is equal to 0.85 at this time, the idling risk value E is equal to 1.002, and the idling determination condition is satisfied. When the values of γ ₁、γ₂ are smaller, the greater the condition ③ weighting judgment function is, for example, when γ ₁、γ₂ is equal to 10, then x ₁/θ₁ and x ₂/θ₂ are equal to 0.7, the idling risk value E is equal to 1.002, and the idling judgment condition is satisfied. The relative magnitude between nonlinear weighting control factors gamma ₁、γ₂ is used for determining the magnitude of the relative action between weighted items, and the judgment condition of each item exceeding the threshold value is not influenced; the larger the value of one of gamma ₁、γ₂ is, the smaller the weighting effect of the corresponding judgment term is; conversely, the smaller the value of one of the gamma ₁、γ₂, the greater the weighting effect of the corresponding judgment term; for example, if γ ₁ is small and γ ₂ is large, in the idling risk value E calculation of condition ③, the function of the 1 term x ₁/θ₁ in the weighting calculation is larger than the function of the term x ₂/θ₂, but the function of the single-term threshold condition ①② is unchanged, so long as any one of ①② reaches or exceeds the threshold value, the idling judgment condition is still satisfied.

The risk value of idle E is either in accordance with

And (5) performing calculation. In the formula (5), x ₁ is the creep change rate, and θ ₁ is the creep change rate threshold; x ₂ is the creep degree, θ ₂ is the creep degree threshold; x ₃ is the locomotive wheel set speed change rate, and θ ₃ is the wheel set speed change rate threshold; gamma ₁、γ₂、γ₃ is a nonlinear weighting control factor, and gamma ₁≥10、γ₂≥10、γ₃ is more than or equal to 10. The creep degree change rate x ₁, the creep degree x ₂ and the locomotive wheel set speed change rate x ₃ are all non-negative values. The idling judgment condition is that when E is more than or equal to 1, the locomotive (train) wheel set is judged to idle. The idle judgment logic obtained by combining the expression (5) and the idle judgment condition is that: the occurrence of idling can be judged in 4 cases (or in one of 4 conditions is satisfied), and ① is respectively when the creep change rate x ₁ is equal to or greater than the threshold value θ ₁; ② Or, when the creep degree x ₂ is equal to or greater than the threshold value θ ₂; ③ Alternatively, when the locomotive wheelset speed change rate x ₃ is equal to or greater than the threshold value θ ₃; ④ Alternatively, when the creep change rate x ₁ is less than the threshold value θ ₁ and the creep change rate x ₂ is less than the threshold value θ ₂ and the locomotive wheelset speed change rate x ₃ is less than the threshold value θ ₃ and the idle risk value E is 1 or more. The preceding 3 conditions ①②③ are single-item threshold conditions, that is, when a single item satisfies x ₁≥θ₁, or when a single item satisfies x ₂≥θ₂, or when a single item satisfies x ₃≥θ₃, all satisfy the condition that E is equal to or greater than 1, that is, satisfy the condition of idle running judgment. The condition ④ is a weighted judgment condition in the case where none of the single threshold conditions is satisfied; the larger the value of gamma ₁、γ₂、γ₃ is, the larger the factor occupied by the single super-threshold judgment is, and the smaller the effect of weighting judgment is; for example, if γ ₁、γ₂、γ₃ is 100, if x ₁/θ₁、x₂/θ₂、x₃/θ₃ is equal to 0.76 at this time, the idling risk value E is equal to 0.99, and the idling determination condition is not satisfied; if x ₁/θ₁、x₂/θ₂、x₃/θ₃ is equal to 0.77 at this time, the idling risk value E is equal to 1.04, and the idling determination condition is satisfied. When the values of γ ₁、γ₂、γ₃ are smaller, the greater the weighting effect of condition ④, for example, when γ ₁、γ₂、γ₃ is 10, the idling judgment condition may be satisfied when x ₁/θ₁ and x ₂/θ₂ are equal to 0.7 and x ₃/θ₃ is equal to 0, or when x ₁/θ₁、x₂/θ₂、x₃/θ₃ is equal to 0.53 and the idling risk value E is equal to 1.01, and the idling judgment condition is satisfied. The relative magnitude between nonlinear weighting control factors gamma ₁、γ₂、γ₃ is used for determining the magnitude of the relative action between weighted items, and the judgment condition of each item exceeding the threshold value is not influenced; the larger the value of one of gamma ₁、γ₂、γ₃ is, the smaller the weighting effect of the corresponding judgment term is; conversely, the smaller the value of one of γ ₁、γ₂、γ₃, the greater the weighting effect of the corresponding judgment term. For example, if γ ₁ is small and γ ₂、γ₃ is large, in the idling risk value E calculation of condition ④, the function of the 1 term x ₁/θ₁ in the weighting calculation is larger than that of the term x ₂/θ₂、x₃/θ₃, but the function of the single threshold condition ①②③ is unchanged, so long as any one term ①②③ reaches or exceeds the threshold value, and the idling judgment condition is still satisfied.

The value range of the theta ₂ is 0.005-0.05; the value range of theta ₁ is between 0.0001/s and 0.005/s; the value range of theta ₃ is 3m/s ²～30m/s². The units of x ₁、x₂、x₃ are the same as the units of θ ₁、θ₂、θ₃, respectively.

2 Items in formula (4), 3 items in formula (5), each of which includes, for example

e＝γ^(ρ-1) (6)

A function term of the form shown, wherein ρ is x ₁/θ₁、x₂/θ₂、x₃/θ₃ respectively, and γ is γ ₁、γ₂、γ₃ respectively; when ρ < 1 is not less than 0, that is, when the value to be judged of the judging item is smaller than the corresponding threshold value, the closer the value to be judged is to the corresponding threshold value, the larger the influence of the value change on the function item is, for example, when x ₁ is compared with θ ₁, and when x ₁ is closer to θ ₁, the smaller change of x ₁ can also cause the larger change of e (that is, the corresponding judging item). The characteristic amplifies the effect of the change of the value to be judged (namely x ₁、x₂、x₃) near the threshold value, and is more sensitive near the threshold value; conversely, when the value to be judged is far away from the threshold value, the sensitivity is reduced, so that the possibility of misjudgment of the weighting judgment condition under the condition that the single threshold value condition is not satisfied is avoided as much as possible.

And calculating the non-linear mathematical model type (4) and the non-linear mathematical model type (5) of the idle running risk value E, wherein the creep degree change rate and the creep degree term are respectively arranged. The creep degree is the relative difference between the speed of the locomotive wheel pair and the speed of the locomotive, and the value of the creep degree directly reflects the degree of the difference between the idling of the locomotive wheel pair or the degree of the idling; the rate of change of the creep is the rate of change of the creep, which value is related to both the rate of change of the locomotive wheel set speed and the rate of change of the locomotive speed, the greater the value of which is, the higher the risk of idle running. In the formula (5), the speed change rate item of the locomotive wheel pair is similar to the creep degree change rate, the larger the value of the speed change rate item is, the higher the idle running risk is, but the speed change rate of the locomotive wheel pair is irrelevant to the change of the locomotive speed, and the addition of the speed change rate item can be beneficial to the pre-judgment of the idle running when the locomotive speed is higher. When calculating the idling risk value E, the formula (4) or the formula (5) can be selected according to the need; in selecting equation (5), the determination of gamma ₁、γ₃ should be considered because of the tendency of the rate of change of creep and the effect of the locomotive wheel on the rate of change of speed.

The nonlinear mathematical model of the idle running risk value E is calculated, namely the formula (4) or the formula (5), and corresponding idle running judgment conditions are combined into a whole, so that the judgment basis is simplified, and the multiple factors are quantized and then weighted under the condition that the single threshold condition is not met, so that the comprehensive judgment of multiple factors is realized, and the idle running judgment is more comprehensive and accurate. The non-linear mathematical model is selected, so that the possibility of misjudgment of the weighting judgment conditions under the condition that the single threshold condition is not met can be avoided as much as possible. Meanwhile, the action size of the weighting judgment conditions can be set and adjusted through parameters, and the relative action size of each weighting term can also be set and adjusted through parameters, so that the locomotive wheel idle rotation judgment method based on the nonlinear mathematical model normalization can be suitable for different locomotive types and running conditions.

Fig. 3 is an idling traction control schematic diagram 1 of an idling traction control module when an idling of a locomotive wheelset occurs. The idle traction control ratio theta is the ratio between the locomotive traction force output by the idle traction control module and the locomotive traction force input, namely, the idle traction control ratio theta is the ratio between locomotive traction force F ₃ after idle traction control and locomotive traction force F ₂ before idle traction control, and the ratio between F ₃ and the traction force F ₂ input meets the following conditions

F₃＝θ·F₂ 0≤θ≤1 (7)

Is a relationship of (3). Before t ₁ in fig. 3, the idle running risk value E is less than 1, the locomotive wheelset is not idling, and the idle traction control ratio θ is equal to 1. The idle traction control process of the idle traction control module is:

A process I, idle traction reduction process; starting from the continuous increase of the idling risk value E equal to or greater than 1, to the end of the continuous increase of the idling risk value E from the continuous increase to the start of the decrease, namely starting from the time t ₁ in fig. 3 to the end of the time t ₂; in process I, the idle traction control module controls θ to begin decreasing with a slope d ₁, and the value of θ at the end of process I is the lowest maintenance value. The minimum maintenance value of θ is not less than 0.

A step II of maintaining the minimum maintenance value of the idle traction; starting from the end of process I, the idling risk value E continues to decrease until the idling risk value E is less than 1, i.e. starting from time t ₂ in fig. 3 and ending at time t ₃; in process II, the idle traction control module controls θ to be equal to the minimum maintenance value.

A process III, an idle traction recovery process; starting from the end of process II, to the end of the increase of θ equal to 1, i.e. starting from time t3 to the end of time t4 in fig. 3; in process III, the idle traction control module controls θ to start increasing with a slope d2 until θ equals 1.

When the idling risk value E increases from less than 1 to 1 or more, the condition that the idling risk value E is 1 or more and continuously increases is satisfied. When θ is equal to 1 and the idle risk value E is continuously less than 1, the idle traction control module does not perform idle traction control.

Fig. 4 is an idling traction control schematic diagram 2 of an idling traction control module when an idling of a locomotive wheelset occurs. In the process II, if the idle running risk value E is changed from continuous decrease to continuous increase, returning to the process I for idle running traction control; as in fig. 4, at time t5, the idle risk value E transitions from continuously decreasing to continuously increasing, and the idle traction control module immediately returns from process II to process I. In the process III, if the idle running risk value E is increased to be more than or equal to 1 again, returning to the process I to perform idle running traction control; as in fig. 4, at time t6, the idle risk value E again increases to 1 or more, and the idle traction control module immediately returns from process III to process I.

The rate of decrease of the slope d1 is selected between 0.3/s and 2/s, for example, when the rate of decrease of the slope d1 is selected to be 0.5/s, then the 1s time decreases θ by 50%, may be the 1s time decreases from 100% to 50%, or the 1s time decreases from 80% to 30%, and so on. The rising rate of the slope d2 is selected between 0.05/s and 0.5/s, for example, when the rising rate of the slope d2 is selected to be 0.2/s, then the 1s time increases θ by 20%, may be 1s time increases from 40% to 60%, or 1s time increases from 50% to 70%, and so on. In determining d1, d2, the rate of decrease (absolute value) of the slope d1 should be made greater than the rate of increase (absolute value) of the slope d ₂.

In the conventional combined correction method at home, no matter how the idling degree is, the moment unloading strategy is fixed, and the wheel track adhesion state in the unloading process is not considered; firstly, the unloading depth is insufficient, and idle running is not completely restrained; secondly, the unloading depth is too large, so that the traction loss of the locomotive is caused; thirdly, unloading is stopped only when the acceleration or the creep rate is smaller than a set threshold value, and the unloading depth is easy to be excessively large. According to the invention, the idling traction control module performs idling traction control according to the idling risk value for realizing the comprehensive judgment of multiple factors, and the locomotive traction load shedding degree and the locomotive traction load shedding process are controlled by the idling risk value reflecting the wheel rail adhesion state, so that the situations that the unloading depth is insufficient, the idling is not completely inhibited or the unloading depth is overlarge and the locomotive traction loss is caused can be avoided as much as possible; the unloading is stopped when the idle running risk value is changed from increasing to decreasing, and the consequence of overlarge unloading depth can be well avoided. The nonlinear characteristic of the idling risk value can enable the judgment item with larger risk to play a relatively more obvious control role.

FIG. 5 is a schematic diagram of a module for measuring and adjusting locomotive speed, or a device for measuring and adjusting locomotive speed, for measuring and adjusting locomotive creep, change rate of locomotive wheel set speed, locomotive speed, etc. The locomotive wheel rotation speed acquisition unit 101 outputs an acquired locomotive wheel rotation speed V (h) (including V (k)) to the speed adjustment calculation unit 104, and the locomotive radar speed acquisition unit 103 outputs an acquired locomotive radar speed W (h) (including W (k)) to the speed adjustment calculation unit 104; the vehicle satellite positioning system speed U (k) and the positioning state information X (k) acquired and output by the vehicle satellite positioning system speed acquisition unit 102 are sent to the speed adjustment calculation unit 104; the speed adjustment calculation unit 104 performs adjustment calculation on the wheel/vehicle speed ratio adjustment model parameters and the locomotive radar speed adjustment model parameters according to the input information, and outputs locomotive speed, creep degree change rate and locomotive wheel pair speed change rate. Specifically, the combination switch SW1 in the speed adjustment calculation unit 104 is controlled by the positioning state information X9 k) input from the terminal 5; when the vehicle-mounted satellite positioning system speed is judged to be effective according to X (k), the terminal 1 of the combined switch SW1 is controlled to be connected with the terminal 2 and the terminal 3, and parameters of the wheel/vehicle speed ratio adjustment model and the locomotive radar speed adjustment model are adjusted by the vehicle-mounted satellite positioning system speed U (k); the terminal 4 is suspended, and the radar synchronous adjustment speed W (k) output by the locomotive radar speed adjustment model is not used at the moment, namely, the W (k) does not work at the moment. When the speed of the vehicle satellite positioning system is judged to be invalid according to X (k), a terminal 4 of the control SW1 is connected with a terminal 2, a locomotive radar speed adjustment model recursively transmits parameters of the locomotive radar speed adjustment model according to a given method, and the locomotive radar speed adjustment model adjusts locomotive radar speed values W (k) of synchronous acquisition time points in locomotive radar speed values W (h) to obtain radar synchronous adjustment speeds W ^* (k), and the parameters of the wheel/vehicle speed ratio adjustment model are set according to the radar synchronous adjustment speeds W ^* (k); the terminals 1 and 3 are suspended, namely, the vehicle satellite positioning system speed U (k) is not used (or is invalid) at the moment, and the parameters of the locomotive radar speed adjustment model are not set by external signals. The wheel/vehicle speed ratio adjustment model carries out adjustment calculation according to the input locomotive wheel rotation speed V (k) and locomotive radar speed W (k), and outputs locomotive speed V sent to the traction limiting self-tuning module and each locomotive speed related quantity C ₂ sent to the idle traction control module, wherein when the idle rotation risk value E is calculated according to a formula (4), each locomotive speed related quantity C ₂ comprises a creep degree change rate x ₁ and a creep degree x ₂; when the idle rotation risk value E is calculated according to equation (5), each locomotive speed related quantity C ₂ includes a creep change rate x ₁, a creep x ₂, and a locomotive wheelset speed change rate x ₃. The combination switch SW1 in fig. 5 is a schematic switch, which means that the signal flow direction is controlled according to X (k), and is usually implemented by a program branching method in digital control.

In the embodiment of the vehicle speed measurement and adjustment device, the acquisition period T _V of the vehicle wheel rotation speed acquisition unit is 32ms, the acquisition period T _U of the vehicle satellite positioning system speed acquisition unit is 1s, and m is equal to 4. When the locomotive wheel rotation speed V (h) and the locomotive radar speed W (h) are output, the corresponding speed acquisition unit performs corresponding filtering processing according to specific conditions in the speed sampling and data processing links; for example, if the locomotive wheel rotation speed V (h) is sampled by adopting a pulse rotation speed sensor (encoder), jitter interference of pulse edges and high-frequency interference in the pulse transmission process are filtered correspondingly; if the locomotive wheel rotation speed V (h) and the locomotive radar speed W (h) directly output analog quantity or digital quantity, low-pass filtering, smooth filtering, kalman filtering and other filtering means can be adopted singly or in combination to filter out high-frequency interference, random interference, white noise interference and the like. The vehicle-mounted satellite positioning system speed acquisition unit comprises one or more receiving terminals in a Global Navigation Satellite System (GNSS), for example, one or more of a GPS system receiving terminal, a Beidou satellite navigation system receiving terminal, a Galileo satellite navigation system receiving terminal and a GLONASS system receiving terminal, and also comprises a corresponding receiving processing module; the receiving processing module receives information such as the number of satellites of the position being resolved, the ground speed (the speed of a vehicle-mounted satellite positioning system), whether the positioning state is valid or not and the like of one or a plurality of receiving terminals; or further comprises the steps of receiving longitude, latitude, UTC time, altitude and other information of one or more receiving terminals, and calculating the speed of the vehicle-mounted satellite positioning system according to the longitude, latitude, UTC time, altitude and other information. The technical means adopted in the locomotive wheel rotation speed acquisition unit, the locomotive radar speed acquisition unit and the vehicle satellite positioning system speed acquisition unit are conventional technical means in the field.

FIG. 6 is a flowchart of a method for adjusting the speed of a locomotive according to the invention, wherein the cycle of iterative calculation is the same as the acquisition cycle of a speed acquisition unit of a vehicle-mounted satellite positioning system, and the specific steps of each iterative calculation are as follows:

Step 1, reading vehicle-mounted satellite positioning system data (equivalent to kT _U sampling time) in kth iterative computation, wherein the vehicle-mounted satellite positioning system data comprises vehicle-mounted satellite positioning system speed U (k) and positioning state information X (k);

step 2, reading the locomotive wheel rotation speed V (k) and the locomotive radar speed W (k) acquired at the synchronous acquisition time point of the speed U (h) of the vehicle-mounted satellite positioning system;

Step 3, judging whether the speed of the vehicle-mounted satellite positioning system is effective; when the speed of the vehicle-mounted satellite positioning system is judged to be effective, the step 4 is carried out; when judging that the speed of the vehicle-mounted satellite positioning system is invalid, turning to step 5;

Step 4, adjusting model parameters according to the U (k) set wheel/vehicle speed ratio and locomotive radar speed, namely according to the model parameters

Setting a current wheel/vehicle speed adjustment coefficient P _V (k) and a radar speed change ratio coefficient P _W (k); let the radar speed adjustment coefficient P _W equal to P _W (k), go to step 6;

Step 5, calculating and adjusting the parameters of the locomotive radar speed adjustment model, namely, performing linear fitting on m points (k-1, P _W(k-1))、(k-2,P_W (k-2, …, k-m, PWk-m) to obtain a radar speed transformation ratio first-order fitting linear, taking the value P _W ^* (k) on the point (k, P _W ^* (k)) on the radar speed transformation ratio first-order fitting linear as the current radar speed transformation ratio coefficient P _W (k). Let the radar speed adjustment coefficient P _W be equal to P _W (k), according to the formula

Calculating a radar synchronous adjustment speed W ^* (k); adjusting model parameters based on W ^* (k) for a given wheel/vehicle speed ratio, i.e. according to

Setting a current wheel/vehicle speed adjustment coefficient P _V (k); turning to step 6;

And 6, calculating a wheel/vehicle speed ratio coefficient and calculating the speed related quantity of each locomotive. Calculating a wheel/vehicle speed ratio coefficient U _V (k) for 2 total embodiments; example 1 for calculating the wheel/truck speed ratio coefficient U _V (k) according to the formula

The wheel/truck speed ratio coefficient U _V (k) is calculated. Calculation of wheel/vehicle speed ratio coefficient U _V (k) example 2, straight line fitting was performed on m points (k, P _V (k)), (k-1, pv k-1, …, k-m+1, pv k-m+1) to obtain a wheel/vehicle speed adjustment coefficient first order fitted straight line, and the value U _V (k) of the point (k, U _V (k)) on the wheel/vehicle speed adjustment coefficient first order fitted straight line was taken as the wheel/vehicle speed ratio coefficient.

Example m is equal to 4. Fig. 7 is a schematic diagram of an embodiment of a first order fitted line of radar speed-transformation ratio coefficients. In FIG. 7, 4 "+" points from left to right are points (k-4, P _W(k-4))、(k-3,P_W(k-3))、(k-2,P_W(k-2))、(k-1,P_W (k-1, point "o" on the first order fitting straight line of radar speed change ratio coefficient is point k, PW x K. FIG. 8 is an embodiment of the first order fitting straight line of wheel/vehicle speed adjustment coefficient is shown in FIG. 8, 4 "+" points from left to right are points (k-3, P _V(k-3))、(k-2,P_V (k-2, k-1, PV k-1, k, PVk, point "0" on the first order fitting straight line of wheel/vehicle speed adjustment coefficient is point k, UN (k)) respectively, FIG. 7, FIG. 8 are schematic diagrams, the coefficient values of the 4 "+" points are not actual data, the error is specially identified as large, and the slope of the first order fitting straight line is also specially identified as large.

The positioning state information X (k) includes information on whether the positioning state is a valid positioning or an invalid positioning, and satellite number information on the position being resolved. In step 3 of the locomotive speed adjusting method, the method for judging whether the speed of the vehicle-mounted satellite positioning system is effective is that when the positioning state in the positioning state information X (k) is effective positioning, the speed of the vehicle-mounted satellite positioning system is effective, otherwise, the speed of the vehicle-mounted satellite positioning system is ineffective. The method for judging whether the speed of the vehicle-mounted satellite positioning system is effective or not is that when the positioning states in the positioning state information X (k) and the positioning state in the positioning state information X (k-1) are effective positioning, the speed of the vehicle-mounted satellite positioning system is effective, otherwise, the speed of the vehicle-mounted satellite positioning system is ineffective. The method for judging whether the vehicle-mounted satellite positioning system speed is effective or not is characterized in that when the positioning state in the positioning state information X (k) is effective positioning and the number of satellites using the calculated positions in the positioning state information X (k) is more than or equal to delta, the vehicle-mounted satellite positioning system speed is effective, otherwise, the vehicle-mounted satellite positioning system speed is ineffective. The method for judging whether the vehicle-mounted satellite positioning system speed is effective or not is that when the positioning states in the positioning state information X (k) and the positioning state information X (k-1) are effective positioning and the number of satellites in the using resolving positions in the positioning state information X (k) and the positioning state information X (k-1) is more than or equal to delta, the vehicle-mounted satellite positioning system speed is effective, otherwise, the vehicle-mounted satellite positioning system speed is ineffective. X (k-1) is the vehicle satellite positioning system data read at the previous iteration calculation, namely the k-1 moment. In an embodiment, the vehicle-mounted satellite positioning system speed acquisition unit comprises a GPS system receiving terminal and a corresponding receiving processing module. When the latter 2 methods are adopted as the method for judging whether the vehicle-mounted satellite positioning system speed is effective or not, and the number of satellites in the position to be solved in the positioning state information X (k) is required to be equal to or greater than 4, the preferred value is 5.

P _V (k) in step 4-6, or P _V (k-i) when i is equal to 0, adjusts the coefficient for the current wheel/vehicle speed. i is equal to P _V(k-1)、P_V(k-2)、…、P_V (k-m+1) when 1, 2, … and m-1 respectively, and is the wheel/vehicle speed adjustment coefficient obtained in the previous m-1 iterative computation respectively. P _W (k) in step 4-5, or P _W (k-i) when i is equal to 0, is the current radar speed transformation ratio coefficient. i is equal to P _W(k-1)、P_W(k-2)、…、P_W (k-m) when 1, 2, … and m are respectively the radar speed transformation ratio coefficients obtained in the previous m iterative calculations.

Step 6 calculation of wheel/Car speed ratio coefficient U _V (k) in example 1, μ _V(k)、μ_V(k-1)、…、μ_V (k-m+1) is a transformation ratio weighting coefficient corresponding to P _V(k-1)、P_V(k-2)、…、P_V (k-m+1), satisfying the formula

Is a relationship of (3). Mu _V(k)、μ_V(k-1)、…、μ_V (k-m+1) was taken from large to small. For example, m is equal to 4, μ _V(k)、μ_V(k-1)、μ_V(k-2)、μ_V (k-3) is equal to 0.4, 0.3, 0.2, 0.1, respectively, or is equal to 0.55, 0.27, 0.13, 0.05, respectively, etc.

In step 5, each locomotive speed related quantity includes locomotive speed V, creep change rate x ₁, creep x ₂, locomotive wheel set speed change rate x ₃. The current locomotive speed V _C (h) is in accordance with

The calculation is performed with the same calculation period as the sampling period T _V. The units of V (h), V (k), W (h), W (k), U (k) and W ^*(k)、V_C (h) are m/s; t _V、T_U is s. Taking locomotive speed V as current locomotive speed V _C (h); the unit of the locomotive speed V is km/h, and after the unit of m/s is converted into km/h, the value of the locomotive speed V is 3.6 times of the value of V _C (h).

U _V (k) reflects the ratio between locomotive wheelset speed and locomotive speed, so that the creep degree x ₂ can be according to the formula

The calculation is performed with the same calculation period as the sampling period T _U. Alternatively, according to the formula

The rate of change of creep x ₁ is in accordance with

The calculation is performed with the same calculation period as the sampling period T _U. U _V (k-1) is the wheel/vehicle speed ratio coefficient obtained by iterative calculation according to the locomotive speed regulation method in the previous time. Alternatively, according to the formula

The current creep change rate x ₁ is calculated, and the calculation period is the same as the sampling period T _V. x ₂ (h-1) is the current creep level obtained in the previous calculation of the creep level with the sampling period T _V.

Locomotive wheelset speed rate of change x ₃ according to

The calculation is performed with the same calculation period as the sampling period T _V. V (h-1) is a primary sample value before V (h).

Calculating the creep degree x ₂ and the creep degree change rate x ₁ by adopting the formulas (14) and (16), and suggesting to select the formula (5) to calculate the idling risk value E in order to ensure that quick response can be obtained when the idling risk value E is calculated; when the creep degree x ₂ and the creep degree change rate x ₁ are calculated by using the formulas (15) and (17), the idling risk value E may be calculated by selecting the formula (4) or the formula (5) as needed.

FIG. 9 is a flowchart of a method for calculating a cycle number of a delay interval according to an embodiment of a speed measurement adjusting device of a vehicle, wherein a calculated cycle is the same as a collection cycle of a speed collection unit of a vehicle satellite positioning system, and the calculation can be performed before or after an iterative calculation of a speed adjustment method of the vehicle, and the method specifically includes:

Step ①, obtaining the locomotive acceleration change rate beta (k) at the current moment, namely the k moment (namely the kT _U sampling moment);

Step ②, judging whether the condition for calculating the delay interval period number is satisfied, and satisfying the formula

If the relation of the vehicle satellite positioning system is determined to be valid for m ₁ times, and if the relation of the vehicle satellite positioning system is determined to be valid for m ₁ times, the step ③ is switched to, otherwise, the step is exited; m ₁ is 10 or more. The acceleration change threshold epsilon may be selected in connection with an experiment based on the acceleration capacity of the locomotive (train). The value of ε may be inTo/>Is selected within the numerical range of/>The average acceleration is started for the locomotive (train). In an embodiment where T _U is 1s and m ₁ is equal to 20, and the average 0-200m acceleration of the locomotive may typically be up to 0.4m/s ², then the value of ε may be selected in the range of 0.4 to 2.4, e.g., ε may be 0.8. In the formula (22), beta (k-i) when i is equal to 0 is the locomotive acceleration change rate beta (k) at the current moment; i is equal to beta (k-i) when 1, and is the locomotive acceleration change rate obtained when the delay interval period number is calculated in the previous time (namely, the wheel/vehicle speed ratio coefficient is calculated in an iterative mode); similarly, i is equal to beta (k-i) from 1 to m ₁ -1, which are the locomotive acceleration rates obtained when the number of stall cycles is calculated for the previous m ₁ -1, respectively. The last m ₁ times of continuous judgment that the vehicle-mounted satellite positioning system speed is effective refers to the last m ₁ times of continuous iterative computation in the iterative computation according to the locomotive speed adjustment method of fig. 6, and the step 3 is judged that the vehicle-mounted satellite positioning system speed is effective.

Step ③, obtaining the delay interval period number tau by setting the parameter to be optimized as the delay interval period number tau ^* and the value of the radar speed proportional coefficient p _W ^*;τ^* to be selected in the range that the delay interval time is not more than 2s, namely an integer more than 0 and less than 2/T _V; in an embodiment, T _V is equal to 32ms, i.e., 0.032s;2/T _V is equal to 62.5, so the range of values of τ ^* is more than 0 and less than or equal to 62. The value range of p _W ^* is more than or equal to 0.8 and less than or equal to 1.2, and the parameter p _W ^* to be optimized is only used in the optimization process. When the delay interval period number is tau ^*, the rotation speed of the locomotive wheel acquired at the synchronous acquisition time point corresponding to U (k-i) is V ^* (k-i), the locomotive radar speed acquired at the synchronous acquisition time point corresponding to U (k-i) is W ^* (k-i), and the minimum value optimization objective function is

The optimization can adopt various optimization algorithms such as genetic algorithm, particle swarm optimization and the like, and the lag interval period number tau ^* meeting the optimal value (minimum value) Q is taken as the lag interval period number tau.

In step ①, the locomotive acceleration change rate beta (k) at the sampling moment of kT _U is obtained according to the formula

And calculating, wherein alpha (k) is the currently acquired locomotive acceleration, and alpha (k-1) is the last acquired locomotive acceleration. In an embodiment, the currently acquired locomotive acceleration α (k) is according to the formula

And calculating, wherein U (k) is the current acquired vehicle-mounted satellite positioning system speed, and U (k-1) is the last acquired vehicle-mounted satellite positioning system speed. The locomotive acceleration alpha (k) can also be measured and acquired by adopting an accelerometer. The unit of alpha (k) is m/s ²; the unit of β (k) is m/s ³.

FIG. 10 is a schematic diagram of a vehicle satellite positioning system speed acquisition delay, a locomotive acceleration, and a locomotive acceleration change rate, wherein V (t) is a locomotive wheel rotation speed after V (h) is continuous, W (t) is a locomotive radar speed after W (h) is continuous, and U (t) is a satellite positioning system speed after U (k) is continuous; t _τ is the lag time of the speed acquisition time of the vehicle satellite positioning system lagging behind the speed acquisition time of the locomotive wheel; points k-7 to k are each sampling time (k-7) T _U to kT _U of the vehicle satellite positioning system speed; alpha (k) and beta (k) are locomotive acceleration and locomotive acceleration change rate respectively.

Fig. 11 is a schematic diagram of a synchronous acquisition time point of the rotation speed of the locomotive wheel and the speed of the locomotive radar of the speed of the vehicle satellite positioning system, wherein the sampling time (kT _U) at which U (k) is located is the current time at which the locomotive speed measurement adjusting device performs iterative computation of the locomotive speed adjustment method, and the sampling time at which V (h- τ), V (h- τ+1), V (h-3), V (h-2), V (h-1), V (h) and the like are each sampling time of the rotation speed of the locomotive wheel, for example, the time at which V (h) is located is the sampling time hTV thereof. The acquisition of the locomotive speed (including the speed of the vehicle satellite positioning system and the speed of the locomotive radar) and the rotation speed of the locomotive wheels at the same moment is influenced by ionosphere delay and the like, and the acquisition moment of the speed of the vehicle satellite positioning system is delayed from the acquisition moment of the rotation speed of the locomotive wheels and the speed of the locomotive radar, so that the time delay value is Ttau; the delay interval period number tau is the period number relative to the acquisition period T _V of the rotating speed of the locomotive wheel, namely the delay interval period number tau is a numerical value which is converted into the acquisition period T _V by a time lag value of the acquisition time lag of the vehicle satellite positioning system speed acquisition time lag behind the rotating speed of the locomotive wheel and the radar speed of the locomotive. In fig. 11, a sampling time (h- τ) T _V at which V (h- τ) is located is defined as a synchronous acquisition time point of the vehicle satellite positioning system speed U (k), and the locomotive wheel rotation speed V (h- τ) acquired at the point is V (k); specifically, the tau-th locomotive wheel rotation speed acquisition time (also locomotive radar speed acquisition time) before the sampling time of the vehicle-mounted satellite positioning system speed U (k) is the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U (k). The acquisition period and time of the locomotive radar speed are the same as the acquisition period and time of the locomotive wheel rotation speed, and the mutual delay between the two is negligible, so the sampling time of the locomotive radar speed W (h-tau), W (h-tau+1) and the locomotive radar speed W (h-3), W (h-2), W (h-1) and W (h) are the same as the sampling time of the locomotive wheel rotation speed V (h-tau), V (h-tau+1), V (h-3), V (h-2), V (h-1) and V (h) respectively, the sampling time (h-tau) T _V of the V (h-tau) is the sampling time of the W (h-tau), the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U (k) is also the synchronous acquisition time point, and the locomotive radar speed W (h-tau) acquired at the synchronous acquisition time point is W (k).

Similarly, taking fig. 11 as an example, when performing the optimization calculation of the delay interval period number τ, if τ ^* is equal to 1, the sampling point where V (h-1) is located is the corresponding synchronous acquisition time point, where V ^* (k) is equal to V (h-1), and W ^* (k) is equal to W (h-1); if τ ^* is equal to 2, the sampling point where V (h-2) is located is the corresponding synchronous acquisition time point, V ^* (k) is equal to V (h-2), and W ^* (k) is equal to W (h-2); and so on. Note that, for example, τ ^* is equal to 1, V ^* (k) is equal to V (h-1), and V ^* (k-1) is not V (h-2); in an embodiment, the vehicle satellite positioning system speed is sampled once and the locomotive wheel rotational speed is sampled 31.25 times on average, so if τ ^* is equal to 1 and V ^* (k) is equal to V (h-1), then V ^* (k-1) may be V (h-32) or V (h-33).

Because of the creeping, especially the idle running of the wheel set, the speed of the locomotive wheel set is inconsistent with the actual locomotive speed, and when judging whether the wheel set runs empty or not and calculating the data such as the creeping rate, the creeping degree and the like, the speed of the locomotive wheel set and the locomotive speed need to be measured separately, and the speed of the locomotive wheel set cannot be used for replacing the locomotive speed. The speed measuring method of the locomotive speed is commonly used for radar speed measurement and satellite positioning speed measurement. The satellite positioning speed measurement is to track information such as the running speed and the position of the locomotive in real time through satellite positioning, and then transmit the information to a locomotive control end through a satellite for processing, so that the locomotive speed is finally obtained; the satellite positioning speed measurement can overcome errors caused by the spin and the slip of locomotive wheelsets, but the satellite positioning capacity is greatly influenced by weather and terrain, and the speed measurement cannot be realized in 100% of time; the data transmission delay exists, and the transmission delay time is not fixed due to the change of the distance and the ionosphere condition, so that the real-time performance of speed measurement is affected. The radar speed measuring device is generally arranged at the bottom of a locomotive, and the radar antenna forms a certain included angle with the groundWhen the locomotive and the ground relatively move, the received radar wave can generate frequency shift according to the wavelength, the frequency shift quantity and the included angle/>, of the radarThe data such as radar installation height and the like are calculated, and the locomotive speed can be obtained; but included angle/>The data such as radar installation height and the like can generate time shift fluctuation, the road surface conditions of the locomotives are inconsistent, and the radar installation height can also change along with the road surface conditions, so that the accuracy of radar speed measurement is influenced. In the locomotive speed measurement adjusting device for realizing the locomotive speed adjusting method, when satellite positioning speed measurement is effective, the satellite positioning speed measurement data is used for adjusting the adjustment model parameters of the reckoning wheel/locomotive speed ratio and the adjustment model parameters of the locomotive radar speed; when the satellite positioning speed measurement is invalid, calculating new locomotive radar speed adjustment model parameters according to a given expression or by adopting a first-order fitting straight line method, calculating wheel/vehicle speed ratio adjustment model parameters by adjusting the radar speed adjustment model parameters, and calculating locomotive speed, creep degree change rate, creep degree, locomotive wheel pair speed change rate and other locomotive speed related quantities according to the wheel/vehicle speed ratio adjustment model. The method combines the advantages of high satellite positioning speed measurement precision, good radar speed measurement instantaneity and long-term normal operation, and improves the accuracy and reliability of measuring the relevant quantity of the speed of each locomotive. The locomotive speed adjusting method further comprises the steps of judging whether the locomotive is in a variable speed motion state, if so, acquiring information obtained by radar speed measurement, satellite positioning speed measurement and locomotive wheel pair speed measurement after the locomotive is in the variable speed motion state, and carrying out satellite positioning data transmission time, namely, optimizing calculation of delay interval period numbers, so as to obtain accurate real-time satellite positioning data transmission delay time (namely, delay interval period numbers), and further guaranteeing accuracy and reliability of relevant speed data calculated by the locomotive speed adjusting method. /(I)

Claims

1. The adhesion control system of the electric locomotive is characterized by comprising an adhesion coefficient expert control model, a traction limiting self-tuning module and an idle traction control module; the input of the adhesion coefficient expert control model is the ambient temperature, the weather state and the track adhesion state, and the output is the adhesion coefficient setting value; the traction limiting self-setting module sets an adhesion coefficient empirical calculation model according to the adhesion coefficient setting value, and outputs the traction limiting self-setting model after upper limit limiting control is carried out on the input locomotive traction; the idling traction control module judges whether the locomotive wheel pair idles according to the creep degree change rate, the creep degree and the locomotive wheel pair speed change rate, and determines whether to carry out idling traction control on locomotive traction subjected to upper limit limiting control according to an idling judgment result;

The model is based on the setting of the adhesion coefficient

Where V is the locomotive speed, mu _j is the calculated sticking coefficient of the model output,、/>、/>、/>、/>Empirical formula parameters for calculating the sticking coefficient; xi is the setting value of the adhesion coefficient output by the adhesion coefficient expert control model;

the upper limit control of the input locomotive traction force is that

Wherein P _μ is the calculated adhesion weight, mu _j•P_μ is the maximum traction limit value, and F ₁、F₂ is the input traction and the output traction of the traction limit self-setting module respectively;

Calculating, wherein x ₁ is the creep change rate, and theta ₁ is the creep change rate threshold; x ₂ is the creep degree, θ ₂ is the creep degree threshold; x ₃ is the locomotive wheel set speed change rate, and θ ₃ is the wheel set speed change rate threshold; gamma ₁、γ₂、γ₃ is a nonlinear weighting control factor, and gamma ₁≥10、γ₂≥10、γ₃ is more than or equal to 10.

2. The adhesion control system of claim 1, wherein the adhesion coefficient expert control model performs direct inference calculation on the environmental temperature, weather state and track adhesion state input in real time to obtain an adhesion coefficient setting value ζ, by:

Firstly, reasoning to obtain an initial adhesion coefficient setting value xi ₀; when the weather state is snowing or raining and the track object state is snow, making xi ₀ equal to b ₀; when the weather state is medium rain or heavy rain and the track object state is snow, making xi ₀ equal to b ₁; when the weather state is free of rain and snow and the track object state is snow, making xi ₀ equal to b ₂; when the weather state is snowing or rainy and the track object state is fallen leaves, making xi ₀ equal to b ₃; when the weather state is snowing or rainy and the track object state is dust, making xi ₀ equal to b ₄; when the weather state is medium rain or heavy rain and the track object state is fallen leaves, making xi ₀ equal to b ₅; when the weather state is free of rain and snow and the track object state is fallen leaves, making xi ₀ equal to b ₆; when the weather state is medium rain or heavy rain and the track object state is dust, making xi ₀ equal to b ₇; when the weather state is snowing or rainy and the track object state is clean, making xi ₀ equal to b ₈; when the weather state is free of rain and snow and the track object state is dust, making xi ₀ equal to b ₉; when the weather state is medium rain or heavy rain and the track object state is clean, making xi ₀ equal to b ₁₀; when the weather state is free of rain and snow and the track object state is clean, making xi ₀ equal to 1; the requirements are satisfied b₀< b₁< b₂< b₃< b₄≤b₅< b₆< b₇≤b₈≤b₉< b₁₀;

Second, according to

Calculating an adhesion coefficient setting value xi; wherein C ₁₁ is ambient temperature, ranging from-15 ℃ to +50 ℃.

3. The adhesion control system of claim 2, wherein the idle traction control module implements idle traction control by controlling an idle traction control ratio θ, the idle traction control ratio θ being a ratio between the locomotive traction output by the idle traction control module and the locomotive traction input, and having 0 +..

4. The electric locomotive adhesion control system of claim 3 wherein the idle traction control process of the idle traction control module is:

A step III of recovering the idle traction force, wherein the idle traction force recovery step starts from the end of the step II to the end when theta is increased to be equal to 1; in process III, the idle traction control module controls θ to start increasing with a slope d ₂ until θ equals 1; the rate of decrease of slope d ₁ is greater than the rate of increase of slope d ₂.

5. The electric locomotive adhesion control system of claim 4 wherein, in the idle traction control process II, if the idle risk value E transitions from continuously decreasing to continuously increasing, then returning to process I for idle traction control; in the idle traction control process III, if the idle risk value E increases to 1 or more again, the process I is returned to perform idle traction control.

6. The adhesion control system of any one of claims 3-5, further comprising a locomotive speed adjustment processing module; the locomotive speed adjusting and processing module periodically collects the locomotive wheel rotation speed, the locomotive radar speed and the vehicle satellite positioning system speed, and calculates to obtain locomotive speed, creep speed and creep acceleration.

7. The electric locomotive adhesion control system of claim 6, further comprising a climate rail surface monitoring module; the weather rail surface monitoring module comprises an ambient temperature measuring unit, a weather state measuring unit and a rail surface image acquisition and recognition unit; the environment temperature measuring unit measures and outputs the current environment temperature, the weather state measuring unit measures and outputs the current weather state, the rail surface image acquisition and recognition unit acquires the real-time rail surface image for recognition processing, and the current rail object state is output.