CN118323206B

CN118323206B - Locomotive wheel rail adhesion coefficient setting value setting method

Info

Publication number: CN118323206B
Application number: CN202410633125.1A
Authority: CN
Inventors: 聂辉; 严钦云; 凌云; 陈刚; 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: 2026-04-14
Anticipated expiration: 2043-04-27
Also published as: CN118323206A; CN116238549B; CN116238549A; CN118419076A

Abstract

A method for setting the locomotive wheel-rail adhesion coefficient involves first inferring weather environmental factor values based on ambient temperature and weather conditions, then inferring track coating factor values based on track coating conditions, and finally taking the product of the weather environmental factor value and the track coating factor value as the locomotive wheel-rail adhesion coefficient setting value. This adhesion coefficient setting value is used to calculate the adhesion coefficient, and then the calculated adhesion coefficient is used to control the upper limit of the locomotive traction force, ensuring that the maximum traction force limit of the locomotive can be changed in real time according to changes in road conditions, minimizing the occurrence of locomotive wheel slippage.

Description

Locomotive wheel rail adhesion coefficient setting value setting method

The invention relates to a patent application, which is a divisional application, the original application number is 202310472883.5, the application date is 2023, 4 and 27 days, and the invention is named as an adhesion control system of an electric locomotive.

Technical Field

The invention belongs to the technical field of locomotive traction control, and particularly relates to a locomotive wheel rail adhesion coefficient setting value setting method.

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. The method comprises the steps of judging the acceleration of wheels, rapidly and deeply reducing the driving torque of the wheels when the acceleration exceeds a certain threshold value and reducing the traction force of the locomotive, judging the creep speed if the acceleration of the wheels does not exceed the threshold value, and greatly adjusting the driving torque when the creep speed exceeds the threshold value, otherwise, judging the normal running condition. In the combined correction method, 2 or more single threshold conditions are adopted to judge whether idling occurs, and when idling does not occur, comprehensive judgment of idling risk cannot be realized, and when idling occurs, comprehensive judgment of idling degree cannot be realized.

Disclosure of Invention

Aiming at the defects of the prior art, the invention provides a locomotive wheel rail adhesion coefficient setting value setting method, which uses an adhesion coefficient expert control model to infer the real-time input environment temperature, weather state and track adhesion object state and obtain an adhesion coefficient setting value xi, and comprises the following specific steps:

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 environment temperature is greater than or equal to 0 ℃ and the weather state is light rain or snowy, xi ₁＝b₁₂, when the environment temperature is greater than or equal to 0 ℃ and the weather state is medium rain, xi ₁＝b₁₃, when the environment temperature is greater than or equal to 0 ℃ and the weather state is heavy rain, xi ₁＝b₁₄, when the environment temperature is less than 0 ℃ and the weather state is free of rain and snow, and when the environment temperature is greater than or equal to 0 ℃ and the weather state is free of rain and snow, xi ₁＝b₁₆, and b ₁₁<b₁₂<b₁₃<b₁₄<b₁₅<b₁₆ is required to be satisfied;

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 object state is clean, xi ₂＝b₂₄, and meeting the requirement b ₂₁<b₂₂<b₂₃<b₂₄;

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.

Preferably ,b₁₁＝0.3,b₁₂＝0.4,b₁₃＝0.65,b₁₄＝0.7,b₁₅＝0.9,b₁₆＝1;b₂₁＝0.5,b₂₂＝0.6,b₂₃＝0.8,b₂₄＝1.

Empirical calculation model for setting adhesion coefficient based on adhesion coefficient setting value ζ, in particular based on

The calculated adhesion coefficient mu _j is calculated, wherein V is the locomotive speed, and a ₁、a₂、a₃、a₄、a₅ is the empirical formula parameter for calculating the adhesion coefficient.

The calculated adhesion coefficient mu _j is obtained according to the setting of the adhesion coefficient setting value xi and is used for carrying out upper limit limiting control on the traction force of the locomotive, so that the maximum traction force limit of the locomotive can be changed in real time along with the change of road conditions of road sections, and the locomotive traction is carried out under the condition that the wheelset idle running does not occur as much as possible, and the method is as follows

And performing upper limit limiting control on locomotive traction, wherein F ₁ is locomotive traction before upper limit limiting, F ₂ is locomotive traction after upper limit limiting, P _μ is calculated adhesion weight of the locomotive, and mu _j·P_μ is a maximum traction limiting value. The locomotive is an electric locomotive.

When the adhesion condition becomes poor, even if the upper limit limiter is performed, the idling risk value E cannot be calculated by using a nonlinear mathematical model when the wheel set is idling. Specifically, judging whether the locomotive wheelset idles according to the change rate of the creep degree, the creep degree and the change rate of the locomotive wheelset speed, and determining whether the locomotive traction subjected to the upper limit control is subjected to idle traction control according to the idle judgment result

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

The control of the idle traction is realized by controlling the idle traction control ratio theta, wherein the idle traction control ratio theta is the ratio between the locomotive traction output by the idle traction control module and the 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 process I, wherein the idle traction force reducing process is started from the idle running risk value E being more than or equal to 1 and continuously increasing to the idle running risk value E ending when continuously decreasing from continuously increasing to continuously decreasing, and the process I is used for controlling theta to start decreasing by a slope d ₁, and the value of theta at the end of the process I is the lowest maintenance value, wherein the lowest maintenance value of theta is not less than 0;

In the process II, the idling risk value E is continuously reduced, and the control theta is equal to the minimum maintenance value;

And in the process III, the idle traction control module controls the theta to start to increase with the slope d ₂ until the theta is equal to 1. The rate of decrease of slope d ₁ is greater than the rate of increase of slope d ₂.

In the 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, the idle traction control is performed in the return process I, and in the idle traction control process III of the idle traction control module, if the idle risk value E is increased to 1 or more again, the idle traction control is performed in the return process I.

The method for setting the adhesion coefficient of the locomotive wheel rail is realized by an electric locomotive adhesion control system comprising an adhesion coefficient expert control model, wherein the input of the adhesion coefficient expert control model is the environment temperature, the weather state and the track adhesion state, and the output is the adhesion coefficient setting value.

The adhesion control system of the electric locomotive further comprises a traction force limiting self-tuning module, an idling traction force control module and a locomotive speed measurement adjusting module. The traction limiting self-setting module sets an adhesion coefficient empirical calculation model according to an adhesion coefficient setting value, performs upper limit limiting control on the input locomotive traction and outputs the traction limiting self-setting model, and 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 the locomotive traction subjected to the upper limit limiting control is subjected to idling traction control according to an idling judgment result.

The locomotive speed measurement adjusting module measures and adjusts 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 collects the locomotive wheel rotation speed, the locomotive radar speed and the vehicle satellite positioning system speed, and calculates 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 adjusting coefficient according to the following formula

And (5) setting.

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

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 numerical value which is converted into an acquisition period T _V times by a time lag value of the acquisition time of the speed of the vehicle satellite positioning system, wherein the acquisition time lag value lags the acquisition time of the rotation speed of the locomotive wheels 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 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, epsilon is an acceleration change threshold value larger than 0, specifically, the value of epsilon can be 0.05m ₁ Up to 0.3m ₁ Is selected within the numerical range of (a),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, and beta (k-i) when i is equal to 1,2, and m ₁ -1 is the last locomotive acceleration change rate m ₁ -1.

Locomotive acceleration rate of change is in accordance with

And calculating, wherein alpha (k) is the locomotive acceleration acquired last time, and alpha (k-1) is the locomotive acceleration acquired last time.

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

And calculating, wherein U (k-1) is the vehicle-mounted satellite positioning system speed acquired before U (k) is acquired.

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

The delay interval period number tau ^* meeting the optimal value (namely Q is the minimum value) Q is taken as the delay interval period number tau, the value range of tau ^* is an integer which is more than 0 and less than 2/T _V, and the value range of p _W ^* is more than or equal to 0.8 and less than or equal to 1.2.

The locomotive speed measurement adjusting module is used for filtering the sampled locomotive wheel rotation speed to obtain the collected locomotive wheel rotation speed, filtering the sampled locomotive radar speed to obtain the collected locomotive radar speed, and filtering the sampled vehicle satellite positioning system speed to obtain the collected vehicle 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, once again, m-1.

The invention has the beneficial effects that the main factors influencing the adhesion coefficient comprise the surface state of the steel rail and the surrounding environment condition besides 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 wheel pair cannot be prevented from idling, in order to recover the normal traction of the locomotive as soon as possible, the invention adopts a nonlinear mathematical model to calculate an idling risk value, the method has the advantages that the multiple single threshold judgment conditions and the weighting judgment conditions under the condition that the single threshold condition is not met in the conventional wheelset are integrated, the judgment basis is simplified, and the multiple factors are quantized and then weighted calculation is carried out under the condition that the single threshold condition is not met, so that the multiple factors are comprehensively judged, and the idling 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.

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, mu _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, the value of the adhesion coefficient is related to the model of the electric locomotive, for example, a ₁＝0.24、a₂＝12、a₃＝100、a₄＝8、a₅ =0 is respectively taken by domestic electric locomotives, a ₁＝0.189、a₂＝8.86、a₃＝44、a₄＝1、a₅ =0 is respectively taken by 6K electric locomotives, a ₁＝0.28、a₂＝4、a₃＝50、a₄＝6、a₅ = -0.0006 is respectively taken by 8G electric locomotives, and the like. 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, the value of the adhesion weight is constant for the determined electric locomotive model, mu _j·P_μ is the maximum traction limit value, and F ₂ is the locomotive traction after the upper limit limiting. The unit of P _μ and each locomotive tractive effort F ₁、F₂ is kN, and the tractive effort can also be converted to a corresponding torque if desired.

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, the output is in the range of-15 ℃ to +50 ℃, C ₁₁ is equal to-15 ℃ when the ambient temperature is lower than-15 ℃, and C ₁₁ is equal to +50 ℃ when the ambient temperature is higher than +50 ℃. C ₁₂ is the weather state measured and output by the weather state measuring unit, the current weather state comprises snowing, light rain, medium rain, heavy rain and no rain and snow, 5 weather states are divided, and the no-rain fog state is classified into the 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 environment temperature is greater than or equal to 0 ℃ and the weather state is light rain or snowy, xi ₁＝b₁₂, when the environment temperature is greater than or equal to 0 ℃ and the weather state is medium rain, xi ₁＝b₁₃, when the environment temperature is greater than or equal to 0 ℃ and the weather state is heavy rain, xi ₁＝b₁₄, when the environment temperature is less than 0 ℃ and the weather state is free of rain and snow, and when the environment temperature is greater than or equal to 0 ℃ and the weather state is free of rain and snow, xi 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 landing factor value xi ₂ according to the track landing state, specifically, when the track landing state is snow, xi ₂＝b₂₁, when the track landing state is fallen leaves, xi ₂＝b₂₂, when the track landing state is dust, xi ₂＝b₂₃, and when the track landing state is clean, xi 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.

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; the method comprises the steps of enabling zeta ₀ to be equal to b ₁ when the weather state is medium rain or heavy rain and the track object state is snow, enabling zeta ₀ to be equal to b ₂ when the weather state is no snow and the track object state is snow, enabling zeta ₀ to be equal to b ₃ when the weather state is snow or light rain and the track object state is fallen leaves, enabling zeta ₀ to be equal to b ₄ when the weather state is snow or light rain and the track object state is dust, enabling zeta ₀ to be equal to b ₅ when the weather state is medium rain or heavy rain and enabling zeta ₀ to be equal to b ₆ when the track object state is fallen leaves, enabling zeta ₀ to be equal to b ₇ when the track object state is snow or light rain and enabling zeta ₀ to be equal to b ₈ when the track object state is clean, enabling zeta ₀ to be equal to b ₄ when the track object state is no rain and enabling zeta ₀ to be equal to b ₅ when the track object state is fallen leaves, enabling zeta ₀ to be equal to b ₆ when the track object state is clean, enabling zeta 3626 to be equal to b 438 when the track object state is clean, enabling zeta to be equal to b 438 when the track object state is no dust and enabling zeta to be equal to b 438 when the track object state is clean. 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 degree change rate, theta ₁ is the creep degree change rate threshold value, x ₂ is the creep degree, theta ₂ is the creep degree threshold value, gamma ₁、γ₂ is a nonlinear weighted 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 decomposition by combining the formula (4) and the idle judgment condition is that 3 conditions (or one of the 3 conditions is satisfied) can be judged to be idle, namely ① when the creep change rate x ₁ is larger than or equal to a threshold value theta ₁, ② or when the creep change rate x ₂ is larger than or equal to a threshold value theta ₂, ③ or when the creep change rate x ₁ is smaller than the threshold value theta ₁ and the creep change rate x ₂ is smaller than the threshold value theta ₂ and the idle risk value E is larger than or equal to 1. 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, γ ₁、γ₂ is equal to 100, and if 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, and 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 the nonlinear weighting control factors gamma ₁、γ₂ is used to determine the magnitude of the relative action between the weighted terms without affecting the judgment condition of each item exceeding the threshold, the larger the value of one of gamma ₁、γ₂ is, the smaller the weighting action of the corresponding judgment term is, on the contrary, the smaller the value of one of gamma ₁、γ₂ is, the larger the weighting action of the corresponding judgment term is, for example, gamma ₁ is small, gamma ₂ is large, in the idling risk value E calculation of the condition ③, the effect of the x ₁/θ₁ item in the weighting calculation is larger than the effect of the item x ₂/θ₂, but the effect of the item threshold condition ①② is unchanged as long as any item of ①② reaches or exceeds the threshold, and 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 degree change rate, theta ₁ is the creep degree change rate threshold value, x ₂ is the creep degree, theta ₂ is the creep degree threshold value, x ₃ is the locomotive wheel set speed change rate, theta ₃ is the wheel set speed change rate threshold value, gamma ₁、γ₂、γ₃ is a nonlinear weighted 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 (5) and the idle judgment conditions is that 4 conditions (or one of the 4 conditions is satisfied) can be judged to be idle, namely ① when the creep change rate x ₁ is larger than or equal to a threshold value theta ₁, ② or when the creep change rate x ₂ is larger than or equal to a threshold value theta ₂, ③ or when the locomotive wheel set speed change rate x ₃ is larger than or equal to a threshold value theta ₃, ④ or when the creep change rate x ₁ is smaller than the threshold value theta ₁ and the creep change rate x ₂ is smaller than the threshold value theta ₂ and the locomotive wheel set speed change rate x ₃ is smaller than the threshold value theta ₃ and the idle risk value E is larger than or equal to 1. 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 weighting judgment condition in the case where none of the single threshold conditions is satisfied, the larger the value of γ ₁、γ₂、γ₃ is, the larger the factor occupied by the single super threshold judgment is, the smaller the effect of weighting judgment is, for example, when 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, the idling judgment condition is not satisfied, and 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 judgment 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 the nonlinear weighting control factors gamma ₁、γ₂、γ₃ is used for determining the magnitude of the relative action between the weighted items without affecting the judgment condition of each item exceeding the threshold value, the larger the value of one of gamma ₁、γ₂、γ₃ is, the smaller the weighting action of the corresponding judgment item is, and conversely, the smaller the value of one of gamma ₁、γ₂、γ₃ is, the larger the weighting action of the corresponding judgment item is. 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 the theta ₁ is 0.0001/s-0.005/s, and the value range of the 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)

The function term in the form shown, wherein ρ is x ₁/θ₁、x₂/θ₂、x₃/θ₃, 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 function term is smaller than the corresponding threshold, the closer the value to be judged is to the corresponding threshold, the larger the influence of the value change on the function term is, for example, when x ₁ is compared with θ ₁, the closer x ₁ is to θ ₁, the smaller the change of x ₁ is, and the larger the change of e (that is, the corresponding judgment term) can be caused. The characteristic amplifies the effect of the change of the value to be judged (namely x ₁、x₂、x₃) near the threshold value, is more sensitive near the threshold value, and conversely, reduces the sensitivity when the value to be judged is far away from the threshold value, so as to avoid the possibility of misjudgment of the weighting judgment condition under the condition that the single threshold value condition is not satisfied 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, the value of the creep degree directly reflects the degree of idling of the locomotive wheel pair or the degree of idling, and the creep degree change rate is the speed of the creep degree change and is related to the speed change rate of the locomotive wheel pair and the speed change rate of the locomotive at the same time, and the larger the value is, the higher the risk of idling is. 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. The idling risk value E can be selected according to the requirement, the formula (4) or the formula (5) is selected, and the formula (5) is selected, so that the effects of the creep degree change rate and the locomotive wheel on the speed change rate have the trend, and the gamma ₁、γ₃ size is determined to be considered.

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

F ₃＝θ·F₂ relationship of 0≤θ≤1 (7). 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:

The method comprises a process I, an idle traction force reducing process, wherein the idle traction force reducing process is started from the idle risk value E which is larger than or equal to 1 and continuously increases to the idle risk value E which is changed from continuously increasing to starting to reducing and ending, namely starting from the time t ₁ in fig. 3 to ending at the time t ₂, and the idle traction force control module controls theta to start reducing at the slope d ₁, wherein the value of theta at the end of the process I is the lowest maintaining value. The minimum maintenance value of θ is not less than 0.

And (2) maintaining the minimum maintenance value of the idle traction, wherein from the end of the process I, the idle running risk value E is continuously reduced to be smaller than 1, namely from the time t ₂ to the time t ₃ in the figure 3, and in the process II, the idle running traction control module controls theta to be equal to the minimum maintenance value.

Process III, idle traction control module controls θ to start increasing with slope d ₂ until θ equals 1, starting from process II to the end of θ increasing to equal 1, i.e., starting from time t ₃ to time t ₄ in fig. 3.

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 process II, if the idle risk value E transitions from continuously decreasing to continuously increasing, the process I is returned to idle traction control, and as in FIG. 4, at time t ₅, 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 increases to 1 or more again, the process I is returned to perform idle running traction control, and as in FIG. 4, at the time t ₆, the idle running risk value E increases to 1 or more again, and the idle running traction control module returns to the process I from the process III immediately.

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

In the conventional combined correction method at home, no matter how the idling degree is, the unloading strategy of moment is fixed, the wheel track adhesion state in the unloading process is not considered, the idling is not completely restrained, the unloading depth is too large, the traction loss of a locomotive is caused, the unloading is stopped only when the acceleration or the creep rate is smaller than a set threshold value, and the result of the excessive unloading depth is easily caused. The idle traction control module controls the idle traction according to the idle risk value for realizing the comprehensive judgment of multiple factors, the load shedding degree and the load shedding process of the locomotive traction are controlled by the idle risk value reflecting the adhesion state of the wheel rail, the situations that the unloading depth is insufficient, the idle is not completely restrained or the unloading depth is overlarge to cause the locomotive traction loss can be avoided as much as possible, the unloading is stopped when the idle risk value is changed from being increased to being reduced, and the effect 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 the acquired locomotive wheel rotation speed V (h) (containing V (k)) to the speed adjustment calculation unit 104, the locomotive radar speed acquisition unit 103 outputs the acquired locomotive radar speed W (h) (containing W (k)) to the speed adjustment calculation unit 104, the vehicle-mounted satellite positioning system speed U (k) and the positioning state information X (k) acquired and output by the vehicle-mounted satellite positioning system speed acquisition unit 102 to the speed adjustment calculation unit, and the speed adjustment calculation unit carries out setting calculation on the wheel/vehicle speed ratio adjustment model parameters and the locomotive radar speed adjustment model parameters according to input information and outputs the locomotive speed, Creep degree, creep degree change rate, locomotive wheel set speed change rate. Specifically, the combined switch SW1 in the speed adjustment calculation unit 104 is controlled by the positioning state information X (k) input by the terminal 5, and when the speed of the vehicle-mounted satellite positioning system is determined 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, 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 synchronization adjustment speed W ^* (k) output by the locomotive radar speed adjustment model is not used at this time, that is, W ^* (k) is not used at this time. 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, the locomotive radar speed adjusting model recursively transmits parameters of the locomotive radar speed adjusting model according to a given method, the locomotive radar speed adjusting model adjusts locomotive radar speed value W (k) of synchronous acquisition time points in locomotive radar speed value W (h) to obtain radar synchronous adjusting speed W ^* (k), and the parameters of the wheel/vehicle speed ratio adjusting model are adjusted by the radar synchronous adjusting speed W ^* (k), the terminal 1, The terminal 3 is suspended, that is, the vehicle satellite positioning system speed U (k) is not used (or is invalid), and the parameters of the locomotive radar speed adjustment model are not set by external signals. The wheel/vehicle speed ratio adjustment model is based on the inputted rotation speed V (k) of the vehicle wheels, The radar speed W (k) of the locomotive is regulated and calculated, and the locomotive speed V sent to the traction limiting self-tuning module and the locomotive speed related quantity C ₂ sent to the idle traction control module are output, wherein when the idle rotation risk value E is calculated according to the formula (4), each locomotive speed related quantity C ₂ comprises a creep degree change rate x ₁ and a creep degree x ₂, and when the idle rotation risk value E is calculated according to the formula (5), each locomotive speed related quantity C ₂ comprises a creep degree change rate x ₁, Creep x ₂ and 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, corresponding speed acquisition units perform corresponding filtering processing according to specific conditions in the speed sampling and data processing links, for example, if a pulse rotation speed sensor (encoder) is adopted for sampling the locomotive wheel rotation speed V (h), jitter interference of pulse edges and high-frequency interference in the pulse transmission process are filtered correspondingly, and 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 the 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, such as 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, wherein the receiving processing module receives information such as the number of satellites of the one or more receiving terminals, the ground speed (vehicle-mounted satellite positioning system speed), whether the positioning state is effective or not, or further comprises information such as the longitude, the latitude, the UTC time and the altitude of the one or more receiving terminals, and accordingly calculates the vehicle-mounted satellite positioning system speed. 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 (k) of the vehicle-mounted satellite positioning system;

Step 3, judging whether the speed of the vehicle satellite positioning system is effective or not, and turning to step 4 when the speed of the vehicle satellite positioning system is effective, and turning to step 5 when the speed of the vehicle satellite positioning system is ineffective;

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), enabling the radar speed adjustment coefficient P _W to be equal to P _W (k), and turning to the step 6;

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

Calculating the synchronous adjustment speed W ^* (k) of the radar, and adjusting the model parameters according to the speed ratio W ^* (k), namely according to the speed ratio

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

and 6, calculating a wheel/vehicle speed ratio coefficient and calculating the speed related quantity of each locomotive. The calculation of the wheel/truck speed ratio coefficient U _V (k) is 2 in total, and the calculation of the wheel/truck speed ratio coefficient U _V (k) is 1 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, a first-order fitting straight line of the wheel/vehicle speed adjustment coefficient was obtained by straight line fitting m points (k, P _V(k))、(k-1,P_V(k-1))、…、(k-m+1,P_V (k-m+1)), and the value U _V (k) of the point (k, U _V (k)) on the first-order fitting straight line of the wheel/vehicle speed adjustment coefficient 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)), respectively, and a point "o" on a first-order fitting straight line of the radar speed ratio coefficient is a point (k, P _W ^* (k)). Fig. 8 is a schematic diagram of an embodiment of a first order fit straight line for a wheel/vehicle speed adjustment coefficient. 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,P_V(k-1))、(k,P_V (k)), respectively, and a point "o" on the first-order fitting straight line of the wheel/vehicle speed adjustment coefficient is a point (k, U _V (k)). Fig. 7 and 8 are schematic diagrams, the coefficient values of the 4 "+" points are not actual data, and for clarity of illustration, the error is purposely identified as being large, and the slope of the first-order fitted line is also purposely identified as being 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 1,2, P _V(k-1)、P_V(k-2)、…、P_V (k-m+1) at m-1, respectively, and is the wheel/vehicle speed adjustment coefficient obtained at the previous m-1 iterative calculations, 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 1,2, P _W(k-1)、P_W(k-2)、…、P_W (k-m) at m, respectively, and is the radar speed-transformation ratio coefficient obtained at 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 (h-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 6, 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, and the unit of T _V、T_U is s. Taking locomotive speed V as current locomotive speed V _C (h), wherein the unit of locomotive speed V is km/h, and after converting unit m/s into km/h, the value of 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).

In order to ensure that quick response can be obtained when calculating the idling risk value E, the idling risk value E is calculated by adopting the formula (14) and the formula (16) to calculate the creep degree x ₂ and the creep degree change rate x ₁, and the idling risk value E is calculated by adopting the formula (5) and the formula (17) to calculate the creep degree x ₂ and the creep degree change rate x ₁, the idling risk value E can be calculated by adopting the formula (4) or the formula (5) according to the requirement.

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

And when the latest continuous m ₁ times judge that the vehicle-mounted satellite positioning system speeds are all valid, the method goes to step ③, otherwise, exits, and m ₁ is more than or equal to 10. 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 0.05m ₁ Up to 0.3m ₁ Is selected within the numerical range of (a),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), i is equal to beta (k-i) when 0, and 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), and similarly, i is equal to beta (k-i) when 1 to m ₁ -1, and is the locomotive acceleration change rate obtained when the delay interval period number is calculated in the previous m ₁ -1 times 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.

In step ③, the parameter to be optimized is set to be τ ^* and the value of the radar speed scaling factor p _W ^*;τ^* is selected in the range that the delay interval time is not more than 2s, i.e. more than 0 and less than 2/T _V, in the embodiment, T _V is equal to 32ms, i.e. 0.032s, and 2/T _V is equal to 62.5, so that the value range of τ ^* is more than 0 and less than 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 units of alpha (k) are m/s ² and the units of beta (k) are 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 obtained by serializing V (h), W (T) is a locomotive radar speed obtained by serializing W (h), U (T) is a satellite positioning system speed obtained by serializing U (k), T _τ is a delay time of a vehicle satellite positioning system speed acquisition time lag behind a locomotive wheel rotation speed acquisition time, points k-7 to k are sampling times (k-7) T _U to kT _U of the vehicle satellite positioning system speed, and alpha (k) and beta (k) are locomotive acceleration and locomotive acceleration change rates, 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 (i.e. kT _U) at which U (k) is located is the current time at which the locomotive speed measurement adjusting device implements 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 hT _V thereof. due to the influence of ionosphere delay, etc., the speed of the locomotive (including the speed of the vehicle-mounted satellite positioning system and the speed of the locomotive radar) at the same moment, The acquisition of the rotating speed of the locomotive wheel, the acquisition time of the speed of the vehicle satellite positioning system is delayed from the acquisition time of the rotating speed of the locomotive wheel and the speed of the locomotive radar, the time delay value is T _τ, 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 value which is converted into the acquisition period T _V by converting the time delay value of the acquisition time of the speed of the vehicle satellite positioning system, which is delayed from the acquisition time of the rotating speed of the locomotive wheel and the speed of the locomotive radar. In fig. 11, a sampling time (h- τ) T _V at which V (h- τ) is located is a defined synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U (k), and the rotational speed V (h- τ) of the vehicle wheel acquired at this point is V (k), specifically, a τ -th rotational speed acquisition time (also referred to as a radar speed acquisition time) of the vehicle-mounted satellite positioning system speed U (k) before the sampling time of the vehicle-mounted satellite positioning system speed U (k) is a synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U (k). The acquisition period and the time of the locomotive radar speed are the same as those of the locomotive wheel rotation speed, and the mutual delay between the two is negligible, so that the sampling time of the locomotive radar speeds W (h-W), W (h-tau+1) and the time of the locomotive radar speeds W (h-3), W (h-2), W (h-1) and W (h) are the same as the sampling time of the locomotive wheel rotation speeds V (h-tau), V (h-tau+1), V (h-3), V (h-2), V (h-1) and V (h), the sampling time (h-tau) T _V of the V (h-tau) is the same as the sampling time of the W (h-tau), and the synchronous acquisition time point of the locomotive radar speed W (h-tau) acquired by the point is the W (k).

Similarly, taking fig. 11 as an example, when performing the optimization calculation of the delay interval period τ, if τ ^* is equal to 1, the sampling point where V (h-1) is located is the corresponding synchronous acquisition time point, V ^* (k) is equal to V (h-1), 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), W ^* (k) is equal to W (h-2), and so on. It is noted 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 embodiments, 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, 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 a locomotive in real time through satellite positioning, and then transmit the information to a locomotive control end for processing to finally obtain the locomotive speed, the satellite positioning speed measurement can overcome errors caused by the spin and the skid of locomotive wheel pairs, but the satellite positioning capacity is greatly influenced by weather and topography, the speed measurement cannot be realized in 100% of time, the data transmission delay exists, the transmission delay time is not fixed due to the change of the distance and the ionosphere condition, and the real-time performance of the speed measurement is influenced. 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 the radar installation height and the like are solved to obtain the locomotive speed, but the included angle is formedThe 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 speed ratio adjustment model parameters of the calculating wheel/locomotive and the speed adjustment model parameters of the locomotive radar, when the satellite positioning speed measurement is ineffective, the locomotive radar speed adjustment model parameters obtained by the previous adjustment are calculated according to a given expression, or a first-order fitting straight line method is adopted for calculating new locomotive radar speed adjustment model parameters, the adjusted radar speed adjustment model parameters are used for adjusting the speed ratio adjustment model parameters of the calculating wheel/locomotive, and then the locomotive speed related quantities such as locomotive speed, creep change rate, creep degree, locomotive wheel pair speed change rate and the like are obtained according to the wheel/locomotive 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, collecting 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.

Claims

1. The method is characterized in that an adhesion coefficient expert control model is used for reasoning the environmental temperature, weather state and track adhesion state which are input in real time and obtaining an adhesion coefficient setting value xi, and the method comprises the following steps:

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 environment temperature is greater than or equal to 0 ℃ and the weather state is light rain or snowy, xi ₁=b₁₂, when the environment temperature is greater than or equal to 0 ℃ and the weather state is medium rain, xi ₁=b₁₃, when the environment temperature is greater than or equal to 0 ℃ and the weather state is heavy rain, xi ₁=b₁₄, when the environment temperature is less than 0 ℃ and the weather state is free of rain and snow, and when the environment temperature is greater than or equal to 0 ℃ and the weather state is free of rain and snow, xi ₁=b₁₆, and b ₁₁< b₁₂< b₁₃< b₁₄< b₁₅<b₁₆ is required to be satisfied;

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 object state is clean, xi ₂=b₂₄, and meeting the requirement b ₂₁<b₂₂<b₂₃<b₂₄;

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

setting an adhesion coefficient empirical calculation model based on an adhesion coefficient setting value, specifically based on

The method comprises the steps of calculating a calculated adhesion coefficient mu _j, wherein V is the locomotive speed, a ₁、a₂、a₃、a₄、a₅ is an empirical formula parameter for calculating the adhesion coefficient, and calculating the adhesion coefficient mu _j according to setting of an adhesion coefficient setting value zeta for carrying out upper limit limiting control on locomotive traction, so that the maximum traction limit of the locomotive can be changed in real time along with the change of road conditions of road sections.

2. The locomotive wheel rail adhesion coefficient setting value setting method according to claim 1, characterized in that ,b₁₁=0.3,b₁₂=0.4,b₁₃=0.65,b₁₄=0.7,b₁₅=0.9,b₁₆=1;b₂₁=0.5,b₂₂=0.6,b₂₃=0.8,b₂₄=1.