A train speed measurement model parameter setting method

Abstract

A train speed measurement model parameter setting method periodically collects train wheel rotation speed, train radar speed and vehicle satellite positioning system speed, when satellite positioning speed measurement is effective, satellite positioning speed measurement data is used to set wheel / train speed ratio adjustment model parameters and train radar speed adjustment model parameters; when satellite positioning speed measurement is invalid, the train radar speed adjustment model parameters obtained by the previous setting are used to calculate new train radar speed adjustment model parameters according to a given expression, the adjusted radar speed adjustment model parameters are used to set wheel / train speed ratio adjustment model parameters, and then train speed, creep rate change rate, creep and train wheel set speed change rate are obtained according to the wheel / train speed ratio adjustment model. The method combines the advantages of high accuracy of satellite positioning speed measurement and good real-time performance and long-term normal operation of radar speed measurement, and improves the accuracy and reliability of measuring various train speed related quantities.

Description

[0001] The present patent application is a divisional application, the original application number is 202310472872.7, the application date is April 27, 2023, and the invention name is a train self-adaptive adhesion control system. TECHNICAL FIELD

[0002] The present application belongs to the technical field of train traction control, in particular to a train speed measurement model parameter setting method. BACKGROUND

[0003] Due to creep, especially the existence of wheelset idling, the train wheelset speed is not consistent with the actual train speed, and when judging whether wheelset idling occurs, calculating creep rate, creep degree and other data, train wheelset speed and train speed need to be measured separately and cannot be replaced by train wheelset speed. Train speed is commonly measured by radar and satellite positioning. Satellite positioning measures the running speed and position of the train in real time, and then transmits the data to the train control end for processing to obtain the train speed. Satellite positioning can overcome the errors caused by train wheelset idling and slipping, but the satellite positioning capability is greatly affected by weather and terrain, and cannot achieve speed measurement for 100% of the time. There is a delay in data transmission, and the transmission delay time is not fixed due to changes in distance and ionosphere, affecting the real-time performance of speed measurement. The radar speed measuring device is generally installed on the train bottom, and the radar antenna emits radar waves at a certain angle with the ground When the train moves relative to the ground, the received radar waves will produce frequency shift, and the train speed can be obtained by solving the data such as radar wavelength, frequency shift, angle between radar and ground, radar installation height, etc. However, the angle between radar and ground, radar installation height, etc. may produce time shift fluctuation, and the train road surface conditions are not consistent, and the radar installation height may also change with the road surface conditions, thereby affecting the accuracy of radar speed measurement.

[0004] Train operation is realized through the interaction between wheel and rail. Only when the effective adhesion between wheel and rail is ensured, the power of traction motor can be further utilized. The adhesion between wheel and rail is not only related to the train itself and the material of wheel and rail, but also related to a series of uncertain factors changing with time and space, such as the condition of line and the cleanliness of rail surface. If the traction force during the operation of locomotive is greater than the available adhesion between wheel and rail, the excess traction force will accelerate the wheel to form a wheel slip, the relative slip speed will increase rapidly, and the available adhesion will decrease rapidly, which will cause the wear and even damage of wheel and rail, increase the maintenance cost of railway operation, and threaten the safe operation of locomotive. Due to the changing conditions of locomotive operation, the wheel slip cannot be completely avoided during traction, the change of driver's operation or the deterioration of rail surface condition. At present, the combined correction method is mainly used for anti-slip control of domestic AC / DC locomotive. Firstly, the wheel acceleration is judged. When the wheel acceleration exceeds a certain threshold value, it indicates that the wheel slip is relatively serious, and the driving torque of the locomotive is quickly and deeply reduced, that is, the traction force of the locomotive is reduced. If the wheel acceleration does not exceed the threshold value, the creep speed is judged. When the creep speed exceeds the threshold value, the driving torque is adjusted by a large amplitude. Otherwise, it is determined as normal operating condition. The combined correction method used at present uses two or more single threshold conditions to judge whether the wheel slip occurs. When the wheel slip does not occur, the comprehensive judgment of the risk of wheel slip cannot be realized. When the wheel slip has occurred, the comprehensive judgment of the degree of wheel slip cannot be realized. SUMMARY

[0005] The present application aims at the defects of the prior art, and provides a train speed measurement model parameter setting method. The train wheel rotation speed V(h), the train radar speed W(h) and the vehicle satellite positioning system speed U(k) are periodically collected. The collection period of the vehicle satellite positioning system speed is T U , and the collection periods of the train wheel rotation speed V(h) and the train radar speed W(h) are both T V . T U is greater than T V . The train is an electric locomotive hauled train.

[0006] The vehicle satellite positioning system speed is periodically judged to be effective. When the vehicle satellite positioning system speed is judged to be effective, the wheel / train speed ratio adjustment model parameter and the train radar speed adjustment model parameter are set according to U(k), that is, according to the formula

[0007]

[0008] The current wheel / train speed adjustment coefficient P V (k) and the radar speed variable ratio coefficient P W (k) are set, and the radar speed adjustment coefficient P W is equal to P W(k); V(k) is the train wheel rotation speed collected at the time point synchronized with the satellite positioning system speed U(k), and W(k) is the train radar speed collected at the time point synchronized with the satellite positioning system speed U(k).

[0009] When the satellite positioning system speed is determined to be invalid, the radar speed model parameters are adjusted, i.e., according to the formula

[0010]

[0011] The current radar speed variable ratio coefficient P is calculated according to the formula W (k), m is greater than or equal to 3; and according to the formula

[0012]

[0013] The radar speed adjustment coefficient P is calculated according to the formula W ; the radar speed weighting coefficient μ W (k-i) satisfies the formula

[0014] and the radar speed weighting coefficients satisfy the relationship that the smaller i is, the larger the radar speed weighting coefficient is; according to the formula

[0015]

[0016] The radar synchronous adjustment speed W * (k) is calculated; according to W * (k), the wheel / train speed ratio adjustment model parameters are adjusted, i.e., according to the formula

[0017]

[0018] The current wheel / train speed adjustment coefficient P V (k) is adjusted.

[0019] According to the formula

[0020]

[0021] The whole wheel / train speed ratio coefficient U V (k) of the wheel / train speed ratio adjustment model is calculated; the variable ratio weighting coefficient μ V (k-i) satisfies the formula

[0022] and the variable ratio weighting coefficients satisfy the relationship that the smaller i is, the larger the variable ratio weighting coefficient is.

[0023] Further, the train speed V C (h) is calculated according to the formula

[0024]

[0025] Carrying out calculation; taking train speed V as current train speed V C (h); according to formula

[0026]

[0027] Calculating creep degree x2; according to formula

[0028]

[0029] Calculating creep degree change rate x1; according to formula

[0030]

[0031] Calculating train wheel set speed change rate x3.

[0032] The method for judging whether the speed of the on-board satellite positioning system is valid is to periodically read the positioning state information X(k) in the on-board satellite positioning system data; when the positioning states in the positioning state information X(k) and X(k-1) are both valid positioning, and the number of satellites currently used for position solution in the positioning state information X(k) and X(k-1) is both greater than or equal to δ, the speed of the on-board satellite positioning system is valid, otherwise, the speed of the on-board satellite positioning system is invalid; δ is greater than or equal to 4.

[0033] The τth train wheel rotation speed collection time point before the sampling time point of the speed of the on-board satellite positioning system Y(k) is the synchronization collection time point of the speed of the on-board satellite positioning system U(k), and τ is the number of delay interval periods; the number of delay interval periods τ is the collection period T converted from the time lag value by which the obtaining time point of the speed of the on-board satellite positioning system is lagged behind the obtaining time points of the train wheel rotation speed and the train radar speed. V times.

[0034] The train speed V is used for upper limit amplitude control of train traction force, and the method is to determine the train speed V according to

[0035]

[0036] The calculation adhesion coefficient μ is calculated j , a1, a2, a3, a4, a5 are empirical formula parameters for calculating the adhesion coefficient. The sample data of the train speed V, the track state C1 and the corresponding adhesion coefficient limiting value μ m are collected, and the calculation adhesion coefficient μ j corresponding to the train speed V is calculated; the train speed V, the track state C1 and the calculation adhesion coefficient μ j corresponding to the train speed V are taken as inputs, and the adhesion coefficient limiting value μ mAs output, the adhesion coefficient neural network adjustment model is trained using sample data, and after the training is completed, the model parameters are fixed to obtain the adhesion coefficient neural network adjustment model; after the modeling is completed, the adhesion coefficient neural network adjustment model inputs the real-time collected and calculated train speed V, track state C1, and calculated adhesion coefficient μ j , and outputs the adhesion coefficient limiting value μ m , according to the formula

[0037]

[0038] The upper limit amplitude control of the train traction force is performed, F1 is the train traction force before the upper limit amplitude control, F2 is the train traction force after the upper limit amplitude control; P μ is the calculated adhesion weight of the train traction locomotive.

[0039] The creep degree change rate x1, the creep degree x2, and the train wheel set speed change rate x3 are used to judge whether the train wheel set is in idle running, and the method is to calculate the idle running risk value E according to the formula

[0040]

[0041] ; wherein θ1 is the creep degree change rate threshold, θ2 is the creep degree threshold, and θ3 is the wheel set speed change rate threshold; τ is a nonlinear weighting index, γ1, γ2, and γ3 are nonlinear weighting factors, and τ≥10, γ1≥1, γ2≥1, and γ3≥1.

[0042] The method for judging whether the train wheel set is in idle running is that when the idle running risk value E is greater than or equal to 1, the train wheel set is in idle running.

[0043] Idle running traction force control is realized by controlling the idle running traction force control ratio θ, the idle running traction force control ratio θ is the ratio between the train traction force output by the idle running traction force control module and the input train traction force, and 0≤θ≤1; the idle running traction force control process is:

[0044] Process I, idle running traction force reduction process, starting from when the idle running risk value E is greater than or equal to 1 and continuously increases, and ending when the idle running risk value E changes from continuously increasing to continuously decreasing; in process I, θ is controlled to start to decrease with a slope d1, and the value of θ at the end of process I is the lowest maintenance value; the lowest maintenance value of θ is not less than 0;

[0045] Process II, idle running traction force lowest maintenance value maintenance process, starting from the end of process I, and ending when the idle running risk value E is less than 1; in process II, the idle running risk value E continuously decreases, and θ is controlled to be equal to the lowest maintenance value;

[0046] Process III, idling traction force recovery process, starts from the end of process II and ends when θ increases to equal 1; in process III, the idling traction force control module controls θ to start increasing with a slope d2 until θ equals 1; the descending rate of slope d1 is greater than the ascending rate of slope d2.

[0047] In the idling traction force control process II, if the idling risk value E changes from continuously decreasing to continuously increasing, return to process I for idling traction force control; in the idling traction force control process III, if the idling risk value E again increases to be greater than or equal to 1, return to process I for idling traction force control.

[0048] The calculation method of the delay interval period number τ is to satisfy the relationship

[0049] The delay interval period number τ is calculated when the latest continuous m1 times of judging the vehicle satellite positioning system speed are all valid; wherein β(k-i) is the latest m1 times of train acceleration change rates, and ε is an acceleration change threshold greater than 0; the train acceleration change rate is calculated according to the formula

[0050]

[0051] ; wherein α(k) is the latest collected train acceleration, and α(k-1) is the train acceleration collected last time; the train acceleration is calculated according to the formula

[0052]

[0053] ; wherein U(k-1) is the vehicle satellite positioning system speed collected last time when U(k) is collected.

[0054] Let the parameter to be optimized be the delay interval period number τ * and the radar speed adjustment coefficient p W * ; when the delay interval period number is τ * , the train radar speed collected at the synchronous collection time point corresponding to U(k-i) is W * (k-i), and the minimum value optimization objective function is

[0055]

[0056] Take the delay interval period number τ * satisfying the optimal value Q as the delay interval period number τ; the value range of τ * is an integer greater than 0 and less than 2 / T V , the value range of p W * is greater than or equal to 0.8 and less than or equal to 1.2; m1 is greater than or equal to 10.

[0057] The track state C1 is a 2-parameter vector, and the 2 parameters C 11 , C 12 are respectively the track dryness and the track slope.

[0058] The train speed measurement model parameter setting method is realized by a train speed adjustment processing module, and is used for a train adaptive adhesion control system. The train adaptive adhesion control system comprises an adhesion coefficient empirical calculation model, an adhesion coefficient neural network adjustment model, a traction force limiting module, an idling traction force control module, a track surface monitoring module and a train speed adjustment processing module. The train traction force output by a train speed controller is subjected to upper limit limiting control by the adhesion coefficient limiting value through the traction force limiting module, and then whether the train wheelset is idling is judged by the idling traction force control module, and the train traction force is subjected to idling traction force control. The track surface monitoring module comprises a track surface image acquisition unit, a track surface image recognition unit and a slope acquisition unit. The track surface image recognition unit recognizes and processes the real-time track surface image acquired by the track surface image acquisition unit and outputs the current track dryness. The slope acquisition unit outputs the track slope. The train speed adjustment processing module periodically acquires the train wheel rotation speed, the train radar speed and the vehicle-mounted satellite positioning system speed, and calculates the train speed, the creep degree, the creep degree change rate, the train wheelset speed change rate.

[0059] The beneficial effects of the present application are as follows: when the satellite positioning speed measurement is effective, the wheel / train speed ratio adjustment model parameters and the train radar speed adjustment model parameters are set by the satellite positioning speed measurement data; when the satellite positioning speed measurement is ineffective, the new train radar speed adjustment model parameters are calculated according to the given expression based on the train radar speed adjustment model parameters set previously, the wheel / train speed ratio adjustment model parameters are set by the adjusted radar speed adjustment model parameters, and then the train speed, the creep degree change rate, the creep degree and the train wheelset speed change rate and other train speed related quantities are calculated according to the wheel / train speed ratio adjustment model. This method combines the advantages of high accuracy of satellite positioning speed measurement and good real-time performance and long-term normal operation of radar speed measurement, and improves the accuracy and reliability of measuring various train speed related quantities.

[0060] The most significant factor influencing the adhesion coefficient, especially the nonlinear factor, is train speed. The input to the adhesion coefficient neural network adjustment model includes actual train operating condition data such as train speed, track moisture content, and track gradient, as well as the calculated adhesion coefficient related to train speed, obtained based on the empirical formula for calculating the adhesion coefficient. This allows the training of the adhesion coefficient neural network adjustment model to only require adaptive corrections for train speed based on the nonlinear empirical formula for calculating the adhesion coefficient, while simultaneously adjusting the adhesion coefficient based on actual train operating condition data such as track moisture content and track gradient, reducing the modeling difficulty of the adhesion coefficient neural network adjustment model. With the joint participation of the empirical formula for calculating the adhesion coefficient based on a large amount of experimental data, the system can take into account the actual train operating section and changing operating conditions, enabling the maximum traction force limit of the train to change in real time with changes in road conditions, and to perform train traction as little as possible without wheelset slippage. When adhesion conditions worsen, even with upper limit restrictions, the locomotive traction force (wheel circumferential tangential force) on the wheel axle still exceeds the wheel-rail adhesion force, making wheelset slippage unavoidable. To quickly restore normal train traction, this invention employs a nonlinear mathematical model to calculate the slippage risk value. It integrates multiple individual threshold judgment conditions for traditional wheelset slippage and weighted judgment conditions when none of the individual threshold conditions are met into a single whole, simplifying the judgment criteria. Even when none of the individual threshold conditions are met, it quantifies multiple factors and performs weighted calculations to achieve a comprehensive judgment of multiple factors, making slippage judgment more comprehensive and accurate. The selection of a nonlinear mathematical model minimizes the possibility of misjudgment by weighted judgment conditions when none of the individual threshold conditions are met. Furthermore, the magnitude of the weighted judgment conditions and the relative magnitude of each weighted term can be set and adjusted through parameters, making this nonlinear mathematical model-normalized train wheelset slippage judgment method applicable to different locomotive types and operating conditions. Attached Figure Description

[0061] Figure 1 This is a schematic diagram of the train adaptive adhesion control system.

[0062] Figure 2 Flowchart of the method for establishing a neural network adjustment model for the adhesion coefficient;

[0063] Figure 3 This diagram illustrates the traction control function of the traction control module when train wheelsets experience idling. Figure 1 ;

[0064] Figure 4 This diagram illustrates the traction control function of the traction control module when train wheelsets experience idling. Figure 2 ;

[0065] Figure 5 Structure diagram of train speed adjustment processing module;

[0066] Figure 6 Flow chart of train speed measurement model parameter setting method;

[0067] Figure 7 Structure diagram of wheel / vehicle speed ratio coefficient first-order fitting straight line;

[0068] Figure 8 Flow chart of calculating delay interval period number;

[0069] Figure 9 Structure diagram of vehicle-mounted satellite positioning system speed acquisition delay, train acceleration, train acceleration change rate;

[0070] Figure 10 Structure diagram of train wheel rotation speed and train radar speed synchronous acquisition time point of vehicle-mounted satellite positioning system speed. DETAILED DESCRIPTION

[0071] The present application is further described below in conjunction with the accompanying drawings. The train is an electric locomotive-pulled train.

[0072] Figure 1 Structure diagram of train adaptive adhesion control system, including adhesion coefficient neural network adjustment model 10, traction force limiting module 11, idling traction force control module 12, train speed adjustment processing module 13, track surface monitoring module 14 and adhesion coefficient empirical calculation model 15. F1 is the train traction force before upper limit limiting control output from the train speed controller (i.e. locomotive speed controller), F2 is the train traction force after upper limit limiting control, μ m Adhesion coefficient limiting value output by the adhesion coefficient neural network adjustment model, the traction force limiting module limits the adhesion coefficient according to μ m Upper limit limiting control is performed on the input F1, specifically

[0073]

[0074] In formula (1), P μ Calculated adhesion weight of the train traction locomotive, which is a constant after the locomotive model of the traction train is determined; μ m ·P μ Is the maximum traction force limiting value. P μ The units of each traction force F1, F2 are kN; when needed, the traction force can also be converted into corresponding torque.

[0075] The adhesion coefficient empirical calculation model is

[0076]

[0077] In formula (2), V is the train speed, μ j is the calculated adhesion coefficient of the model output, a1, a2, a3, a4, and a5 are empirical formula parameters of the calculated adhesion coefficient, the values of which are related to the model of the electric locomotive, for example, a1=0.24, a2=12, a3=100, a4=8, and a5=0 are respectively taken for each type of domestic electric locomotive; a1=0.189, a2=8.86, a3=44, a4=1, and a5=0 are respectively taken for 6K type electric locomotive; a1=0.28, a2=4, a3=50, a4=6, and a5=-0.0006 are respectively taken for 8G type electric locomotive; and so on. The unit of the train speed V is km / h.

[0078] The method for training and modeling of the adhesion coefficient neural network adjustment model is to collect sample data of the train speed V, the track state C1, and the corresponding adhesion coefficient limit value μ m , calculate the calculated adhesion coefficient μ j corresponding to the train speed V, take the train speed V, the track state C1, and the calculated adhesion coefficient μ j corresponding to the train speed V as inputs, and take the adhesion coefficient limit value μ m as output, train the adhesion coefficient neural network adjustment model using the sample data, fix the model parameters after the training is completed, and obtain the adhesion coefficient neural network adjustment model. After the training and modeling are completed, the adhesion coefficient neural network adjustment model inputs the real-time collected and calculated train speed V, track state C1, and calculated adhesion coefficient μ j , and outputs the adhesion coefficient limit value μ m , and the traction force limiting module performs upper limit amplitude control on the train traction force according to the output adhesion coefficient limit value μ m .

[0079] Figure 2 The flow chart method for establishing the adhesion coefficient neural network adjustment model is shown in the figure. First, the structure of the neural network adjustment model is determined. The track state output by the track monitoring module is a 2-parameter vector, that is, C1=(C 11 , C 12 ), and the two parameters C 11 and C 12 are respectively the track dryness and the track slope, therefore, the input quantity of the adhesion coefficient neural network adjustment model is the track dryness C 11 , the track slope C 12 , the train speed V, and the calculated adhesion coefficient μ j , and the output quantity is the adhesion coefficient limit value μ mThe adhesion coefficient neural network adjustment model can select a 3-layer BP neural network structure of 4-7-1, or a 3-layer BP neural network structure of 4-9-1, or a 3-layer BP neural network structure of 4-11-1. The adhesion coefficient neural network adjustment model can also select a 3-layer radial basis function neural network with a structure of 4-5-1, or 4-6-1, or 4-7-1.

[0080] Secondly, sample data is collected and calculated. Train speed, track dryness, track slope and the corresponding adhesion coefficient limit value under different scenarios are collected as a first sample data set; the input of the first sample data set is train speed, track dryness and track slope, and the output is the adhesion coefficient limit value; the calculation adhesion coefficient corresponding to the train speed in the first sample data set is calculated according to formula (2), and the calculated calculation adhesion coefficient is inserted as input into the first sample data set to obtain a second sample data set; the input of the second sample data set is train speed, calculation adhesion coefficient, track dryness and track slope, and the output is the adhesion coefficient limit value.

[0081] Sample data of train speed, track state and the maximum traction limit value corresponding to the train speed and track state of different types of electric locomotives are collected. When a1, a2, a3, a4 and a5 of the adhesion coefficient empirical calculation model are the empirical formula parameters of domestic electric locomotives, the train speed, track state and the corresponding adhesion coefficient limit value sample data of domestic electric locomotives are collected as a first sample data set, and the calculation adhesion coefficient and the second sample data set are obtained by using the calculation adhesion coefficient empirical formula of domestic electric locomotives; when a1, a2, a3, a4 and a5 of the adhesion coefficient empirical calculation model are the empirical formula parameters of 6K type electric locomotive, the train speed, track state and the corresponding adhesion coefficient limit value sample data of 6K type electric locomotive are collected as a first sample data set, and the calculation adhesion coefficient and the second sample data set are obtained by using the calculation adhesion coefficient empirical formula of 6K type electric locomotive; when a1, a2, a3, a4 and a5 of the adhesion coefficient empirical calculation model are the empirical formula parameters of 8G type electric locomotive, the train speed, track state and the corresponding adhesion coefficient limit value sample data of 8G type electric locomotive are collected as a first sample data set, and the calculation adhesion coefficient and the second sample data set are obtained by using the calculation adhesion coefficient empirical formula of 8G type electric locomotive.

[0082] Thirdly, the adhesion coefficient neural network adjustment model is trained. The second sample data set collected and calculated is used to train the adhesion coefficient neural network adjustment model, and after the training is completed, the model parameters are fixed to obtain the adhesion coefficient neural network adjustment model.

[0083] The track surface image acquisition unit in the track surface monitoring module acquires real-time images of the track surface, and the track surface image recognition unit recognizes and processes the real-time track surface images acquired by the track surface image acquisition unit and outputs the current track dryness C 11 , 0≤C 11 ≤100%, and C 11 =0% represents that the track surface is completely dry, and C =100% represents that the track surface is completely wet. The track surface image recognition unit recognizes and processes the real-time track surface images and outputs the current track dryness as a conventional technology of image processing. The slope acquisition unit measures the slope of the current track in real time and outputs; the slope acquisition unit can use various integrated slope measurement devices, inclination measurement devices, or slope sensors, inclination sensors; the measurement and output range of the track slope C 12 is -3% (-1.67°) to +3% (+1.67°), and a positive value is an uphill, and a negative value is a downhill.

[0084] The idling traction control module uses the established nonlinear mathematical model to calculate the idling risk value E, and the idling risk value E is calculated according to the formula

[0085]

[0086] The calculation is performed. In formula (3), x1 is the slip rate change rate, θ1 is the slip rate change rate threshold; x2 is the slip rate, θ2 is the slip rate threshold; τ is a nonlinear weighting index, γ1, γ2 are nonlinear weighting factors, and τ≥10, γ1≥1, γ2≥1. The slip rate change rate x1 and the slip rate x2 are both non-negative values. The idling judgment condition is that when E≥1, it is judged that the train (locomotive) wheel set has idling. In combination with formula (3) and the idling judgment condition, the idling judgment logic obtained by decomposition is that there are three cases (or one of the three conditions is met) that can be judged as the train (locomotive) wheel set idling, which are, ① when the slip rate change rate x1 is greater than or equal to the threshold θ1; ② or when the slip rate x2 is greater than or equal to the threshold θ2; ③ or when the slip rate change rate x1 is less than the threshold θ1, the slip rate x2 is less than the threshold θ2, and the idling risk value E is greater than or equal to 1. The first two conditions ① and ② are single threshold conditions, that is, when x1≥θ1 is met alone, or x2≥θ2 is met alone, both of which meet the condition that E is greater than or equal to 1, that is, the idling judgment condition is met. Condition ③ is a weighted judgment condition in the case where the single threshold condition is not met. The greater the value of τ, the greater the factor of the single threshold judgment, and the smaller the role of the weighted judgment of condition ③. For example, when γ1 and γ2 are both 1 and τ is equal to 100, if x1 / θ1 and x2 / θ2 are both equal to 0.84, the idling risk value E is equal to 0.957, which does not meet the idling judgment condition; if x1 / θ1 and x2 / θ2 are both equal to 0.85, the idling risk value E is equal to 1.002, which meets the idling judgment condition. When τ has a small value, the role of condition ③ is greater. For example, when γ1 and γ2 are both 1 and τ is equal to 10, if x1 / θ1 and x2 / θ2 are both equal to 0.7, the idling risk value E is equal to 1.002, which meets the idling judgment condition. The nonlinear weighting factors γ1 and γ2 are used to determine the relative roles of the weighted terms, and do not affect the judgment condition of the single threshold value. The greater the value of γ1 and γ2, the smaller the weighting role of the corresponding judgment term; on the contrary, the smaller the value of γ1 and γ2, the greater the weighting role of the corresponding judgment term. For example, γ1 is small and γ2 is large, so in the calculation of the idling risk value E of condition ③, the role of x1 / θ1 is greater than that of x2 / θ2, but the role of the single threshold condition ① and ② remains unchanged. As long as any one of ① and ② reaches or exceeds the threshold value, the idling judgment condition is still met.

[0087] The idling risk value E is calculated according to formula

[0088]

[0089] The calculation is performed. In formula (4), x1 is the creep rate change rate, θ1 is the creep rate change rate threshold; x2 is the creep rate, θ2 is the creep rate threshold; x3 is the train wheel set speed change rate, θ3 is the wheel set speed change rate threshold; τ is a nonlinear weighting index, γ1, γ2, γ3 are nonlinear weighting factors, and τ≥10, γ1≥1, γ2≥1, γ3≥1. The creep rate change rate x1, the creep rate x2, and the train wheel set speed change rate x3 are all non-negative values. The idling judgment condition is that when E≥1, it is judged that the train (locomotive) wheel set has idling. In combination with formula (4) and the idling judgment condition, the idling judgment logic obtained by decomposition is that there are four conditions (or one of the four conditions is met) that can be judged as idling, which are, ① when the creep rate change rate x1 is greater than or equal to the threshold θ1; ② or when the creep rate x2 is greater than or equal to the threshold θ2; ③ or when the train wheel set speed change rate x3 is greater than or equal to the threshold θ3; ④ or when the creep rate change rate x1 is less than the threshold θ1, the creep rate x2 is less than the threshold θ2, the train wheel set speed change rate x3 is less than the threshold θ3, and the idling risk value E is greater than or equal to 1. The first three conditions ①②③ are single threshold conditions, that is, single x1≥θ1, or single x2≥θ2, or single x3≥θ3, all of which satisfy E greater than or equal to 1, that is, the idling judgment condition is met. Condition ④ is a weighted judgment condition in the case where the single threshold condition is not met; the greater the value of τ, the greater the single threshold value judgment factor, and the smaller the weighted judgment effect; for example, γ1, γ2, γ3 are all 1, and τ is equal to 100, if x1 / θ1, x2 / θ2, x3 / θ3 are all equal to 0.76 at this time, the idling risk value E is equal to 0.99, which does not meet the idling judgment condition; if x1 / θ1, x2 / θ2, x3 / θ3 are all equal to 0.77 at this time, the idling risk value E is equal to 1.04, which meets the idling judgment condition. When τ has a small value, the weighted effect of condition ④ is greater, for example, γ1, γ2, γ3 are all 1, and τ is equal to 10, at this time x1 / θ1 and x2 / θ2 are both equal to 0.7, and x3 / θ3 is equal to 0, which can also meet the idling judgment condition, or for example, x1 / θ1, x2 / θ2, x3 / θ3 are all equal to 0.53 at this time, the idling risk value E is equal to 1.01, which meets the idling judgment condition. The nonlinear weighting factors γ1, γ2, γ3 are used to determine the relative effect of the weighted items, and do not affect the judgment condition of each single threshold value; the greater the value of γ1, γ2, γ3, the smaller the weighted effect of the corresponding judgment item; on the contrary, the smaller the value of γ1, γ2, γ3, the greater the weighted effect of the corresponding judgment item. For example, γ1 is small, and γ2, γ3 are large, so in the calculation of the idling risk value E in condition ④, the role of x1 / θ1 in the weighted calculation is greater than that of x2 / θ2 and x3 / θ3, but the role of the single threshold condition ①②③ remains unchanged. As long as any one of ①②③ reaches or exceeds the threshold value, the idling judgment condition is still met.

[0090] The value range of the foregoing θ2 is between 0.005 and 0.05; the value range of θ1 is between 0.0001 / s and 0.005 / s; and the value range of θ3 is between 3 m / s and 30 m / s. 2 2 The units of x1, x2 and x3 are the same as those of θ1, θ2 and θ3 respectively.

[0091] Each of the 2 terms in formula (3) and the 3 terms in formula (4) includes a function term in the form of

[0092] e = γ (ρ-1) (5)

[0093] wherein ρ is x1 / θ1, x2 / θ2 and x3 / θ3 respectively, and γ is γ1τ, γ2τ and γ3τ respectively; when 0≤ρ<1, that is, when the value to be judged is smaller than the corresponding threshold value, the closer the value to be judged is to the corresponding threshold value, the greater the influence of the change of the value to be judged on the function term. For example, in the case of comparison between x1 and θ1, the closer x1 is to θ1, the smaller change of x1 can also cause greater change of e. This characteristic amplifies the effect of the change of the value to be judged (i.e. x1, x2 and x3) near the threshold value, and is more sensitive near the threshold value; on the contrary, when the value to be judged is far away from the threshold value, the sensitivity is reduced to avoid the possibility of false judgment of the weighted judgment condition when none of the threshold conditions is met.

[0094] The creep rate and the creep rate change rate are included in the nonlinear mathematical model formula (3) and formula (4) for calculating the idle risk value E. The creep rate is the relative difference between the train wheelset speed and the train speed, and its value directly reflects how far the train wheelset is from the idle state or how far the train wheelset has gone from the idle state; the creep rate change rate is the speed of change of the creep rate, and its value is related to the train wheelset speed change rate and the train speed change rate, and the greater the value, the higher the risk of idle. The train wheelset speed change rate is also included in formula (4), and the greater the value, the higher the risk of idle, but the train wheelset speed change rate is independent of the train speed change, and the addition of this term can be beneficial to the prediction of idle when the train speed is high. When calculating the idle risk value E, formula (3) or formula (4) can be selected as needed; when formula (4) is selected, the effects of the creep rate change rate and the train wheelset speed change rate should be considered when determining the values of γ1 and γ3.

[0095] ​The nonlinear mathematical model for calculating the idling risk value E, namely Equation (3) or Equation (4), and the corresponding idling judgment conditions, integrate the traditional single-threshold judgment conditions for multiple wheelset idling and the weighted judgment conditions when none of the single-threshold conditions are met into a whole, simplifying the judgment basis. Furthermore, when none of the single-threshold conditions are met, multiple factors are quantified and weighted to achieve a comprehensive judgment of multiple factors, making the idling judgment more comprehensive and accurate. The selection of the nonlinear mathematical model can minimize the possibility of misjudgment by the weighted judgment conditions when none of the single-threshold conditions are met. At the same time, the magnitude of the weighted judgment conditions can be set and adjusted by parameters, and the relative magnitude of each weighted term can also be set and adjusted by parameters, making the train wheelset idling judgment method normalized by this nonlinear mathematical model applicable to different locomotive types and operating conditions.

[0096] Figure 3 This diagram illustrates the traction control function of the traction control module when train wheelsets experience idling. Figure 1 The traction control ratio θ is the ratio between the train traction force output by the traction control module and the input train traction force. In other words, the traction control ratio θ is the ratio between the train traction force F3 after traction control and the train traction force F2 before traction control. F3 and F2 satisfy the following condition:

[0097] F3=θ·F2 0≤θ≤1 (6)

[0098] The relationship. Figure 3 Before t1, the slip risk value E is less than 1, the train wheelset does not slip, and the slip traction control ratio θ is equal to 1. The slip traction control process of the slip traction control module is as follows:

[0099] Process I, the process of decreasing traction force during idling; starting from when the idling risk value E is greater than or equal to 1 and continues to increase, until the idling risk value E changes from continuously increasing to beginning to decrease, that is, from... Figure 3 The process begins at time t1 and ends at time t2. During process I, the idling traction control module controls θ to decrease at a slope d1, and the value of θ at the end of process I is the minimum maintenance value. The minimum maintenance value of θ is not less than 0.

[0100] Process II involves maintaining the minimum idling traction force value; starting from the end of Process I, the idling risk value E continuously decreases until it falls below 1, at which point the process ends. Figure 3 The process begins at time t2 and ends at time t3; during process II, the idling traction control module controls θ to be equal to the minimum maintenance value.

[0101] Process III, the traction recovery process during idling; it begins at the end of Process II and ends when θ increases to equal 1, i.e., from...Figure 3 at time t3 to time t4; in process III, the idle traction control module controls θ to increase with slope d2 until θ equals 1.

[0102] When the idle risk value E increases from less than 1 to greater than or equal to 1, the condition that the idle risk value E is greater than or equal to 1 and continuously increasing is met. When θ equals 1 and the idle risk value E continuously is less than 1, the idle traction control module does not implement idle traction control.

[0103] Figure 4 When the idle risk value E increases from less than 1 to greater than or equal to 1, the condition that the idle risk value E is greater than or equal to 1 and continuously increasing is met. When θ equals 1 and the idle risk value E continuously is less than 1, the idle traction control module does not implement idle traction control. Figure 2 In process II, if the idle risk value E changes from continuously decreasing to continuously increasing, return to process I to implement idle traction control; for example, Figure 4 In process III, if the idle risk value E again increases to be greater than or equal to 1, return to process I to implement idle traction control; for example, Figure 4 In process III, if the idle risk value E again increases to be greater than or equal to 1, return to process I to implement idle traction control; for example,

[0104] The descending rate of slope d1 is selected to be between 0.3 / s and 2 / s, for example, the descending rate of slope d1 is selected to be 0.5 / s, then θ is reduced by 50% in 1s, which can be from 100% to 50% in 1s, or from 80% to 30% in 1s, etc. The ascending rate of slope d2 is selected to be between 0.05 / s and 0.5 / s, for example, the ascending rate of slope d2 is selected to be 0.2 / s, then θ is increased by 20% in 1s, which can be from 40% to 60% in 1s, or from 50% to 70% in 1s, etc. When determining d1 and d2, the descending rate (absolute value) of slope d1 should be greater than the ascending rate (absolute value) of slope d2.

[0105] In the current commonly used combination correction method in China, the unloading strategy of the moment is fixed regardless of the degree of idling, and the wheel-rail adhesion state during unloading is not considered; firstly, the unloading depth is not enough, and the idling is not completely suppressed; secondly, the unloading depth is too large, causing the loss of locomotive traction; thirdly, the unloading will only stop when the acceleration or creep rate is less than the set threshold, which is easy to cause the consequence of too large unloading depth. The idling traction control module of the present application controls the idling traction according to the idling risk value which realizes the comprehensive judgment of multiple factors, and the degree and process of the reduction of the locomotive traction are controlled by the idling risk value reflecting the wheel-rail adhesion state, which can avoid the situation that the unloading depth is not enough, the idling is not completely suppressed, or the unloading depth is too large, causing the loss of locomotive traction; the unloading will stop when the idling risk value changes from increasing to decreasing, which can avoid the consequence of too large unloading depth. The nonlinear characteristics of the idling risk value can make the risk-biased judgment item play a relatively more obvious control role.

[0106] Figure 5 The train speed adjustment processing module structure, or the train speed adjustment system structure schematic diagram, is used to realize the train speed adjustment method. The train wheel rotation speed acquisition unit 101 outputs the collected train wheel rotation speed V(h) (including V(k)) to the speed adjustment calculation unit 104, and the train radar speed acquisition unit 103 outputs the collected train radar speed W(h) (including W(k)) to the speed adjustment calculation unit 104; the vehicle-mounted satellite positioning system speed acquisition unit 102 collects and outputs the vehicle-mounted satellite positioning system speed U(k) and the positioning state information X(k) to the speed adjustment calculation unit 104; the speed adjustment calculation unit 104 adjusts and calculates the wheel / vehicle speed ratio adjustment model parameters and the train radar speed adjustment model parameters according to the input information, and outputs the train speed, the creep degree, the creep degree change rate, and the train wheel set speed change rate. Specifically, the combination switch SW1 in the speed adjustment calculation unit 104 is controlled by the positioning state information X(k) input from the terminal 5; when it is judged that the vehicle-mounted satellite positioning system speed is valid according to X(k), the terminal 1 of the combination switch SW1 is connected with the terminal 2 and the terminal 3, and the wheel / vehicle speed ratio adjustment model and the train 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 train radar speed adjustment model is not used at this time, i.e., W * (k) is not used at this time. When it is judged that the vehicle-mounted satellite positioning system speed is invalid according to X(k), the terminal 4 of SW1 is connected with the terminal 2, the train radar speed adjustment model recursively calculates the parameters of the train radar speed adjustment model according to the given method, and the train radar speed adjustment model adjusts the train radar speed value W(k) at the synchronous collection time point of the train radar speed value W(h) to obtain the radar synchronous adjustment speed W* (k), the radar speed W is adjusted synchronously * (k) to tune the parameters of the wheel / vehicle speed ratio adjustment model; terminals 1 and 3 are left open, i.e. the vehicle satellite positioning system speed U(k) is not used (or is invalid) at this time, and the parameters of the train radar speed adjustment model are not tuned by external signals. The wheel / vehicle speed ratio adjustment model performs adjustment calculation based on the input train wheel rotation speed V(k) and train radar speed W(k), and outputs to the train speed V of the adhesion coefficient empirical calculation model and to each train speed related quantity C2 of the free rolling traction force control module, wherein when the free rolling risk value E is calculated according to formula (3), each train speed related quantity C2 includes the creep rate change rate x1 and the creep rate x2; when the free rolling risk value E is calculated according to formula (4), each train speed related quantity C2 includes the creep rate change rate x1, the creep rate x2 and the train wheelset speed change rate x3. Figure 5 The combination switch SW1 in the formula (3) is a schematic switch, and its meaning is to control the direction of the signal flow according to X(k). In digital control, the method of program branching is usually used to realize it.

[0107] In the embodiment of the train speed adjustment system, the collection period T of the train wheel rotation speed collection unit is 32 ms, and the collection period T of the vehicle satellite positioning system speed collection unit is 60 s. V U ​is equal to 4. When the train wheel rotation speed V(h) and the train radar speed W(h) are output, the corresponding speed acquisition unit has carried out corresponding filtering processing in the speed sampling and data processing link according to the specific circumstances; for example, if the train wheel rotation speed V(h) is sampled by using a pulse rotation speed sensor (encoder), then the jitter interference of the pulse edge and the high-frequency interference in the pulse transmission process are filtered out; if the train wheel rotation speed V(h) and the train radar speed W(h) directly output analog or digital quantities, then low-pass filtering, smoothing filtering, Kalman filtering and other filtering means can be used alone 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 includes one or more receiving terminals in the 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 includes a corresponding receiving and processing module; the receiving and processing module receives information such as the number of satellites currently used for solving the position, the ground speed (vehicle-mounted satellite positioning system speed), whether the positioning state is valid and the like of one or more receiving terminals; or also includes receiving information such as the longitude, latitude, UTC time and altitude of one or more receiving terminals, and calculating the vehicle-mounted satellite positioning system speed according to the information. The technical means adopted in the train wheel rotation speed acquisition unit, the train radar speed acquisition unit and the vehicle-mounted satellite positioning system speed acquisition unit is a conventional technical means in the art.

[0108] Figure 6 In the train speed adjustment system, the flowchart of the train speed measurement model parameter setting method is as follows, and the iteration calculation period is the same as the acquisition period of the vehicle-mounted satellite positioning system speed acquisition unit. The specific steps in each iteration calculation are as follows:

[0109] Step 1: reading the vehicle-mounted satellite positioning system data at the kth iteration calculation time (equivalent to kT U sampling time), including the vehicle-mounted satellite positioning system speed U(k) and the positioning state information X(k);

[0110] Step 2: reading the train wheel rotation speed V(k) and the train radar speed W(k) collected at the synchronization acquisition time point of the vehicle-mounted satellite positioning system speed U(k);

[0111] Step 3: judging whether the vehicle-mounted satellite positioning system speed is valid; if the vehicle-mounted satellite positioning system speed is valid, going to step 4; if the vehicle-mounted satellite positioning system speed is invalid, going to step 5;

[0112] Step 4: setting the wheel / train speed ratio adjustment model parameter and the train radar speed adjustment model parameter according to U(k), that is, according to the formula

[0113]

[0114] Set current wheel / vehicle speed adjustment factor P V (k) and radar speed ratio factor P W (k); set radar speed adjustment factor P W equal to P W (k), go to step 6;

[0115] Step 5, calculate adjustment radar speed model parameters, i.e. according to the formula

[0116]

[0117] Calculate current radar speed ratio factor P W (k); and according to the formula

[0118]

[0119] Calculate radar speed adjustment factor P W ; according to the formula

[0120]

[0121] Calculate radar synchronization adjustment speed W * (k); according to W * (k) set wheel / vehicle speed ratio adjustment model parameters, i.e. according to the formula

[0122]

[0123] Set current wheel / vehicle speed adjustment factor P V (k); go to step 6;

[0124] Step 6, calculate adjustment wheel / vehicle speed ratio adjustment model wheel / vehicle speed ratio factor, calculate each train speed related quantity. Calculate adjustment wheel / vehicle speed ratio factor U V (k) has two embodiments; calculate adjustment wheel / vehicle speed ratio factor U V (k) embodiment 1, according to the formula

[0125]

[0126] Calculate adjustment wheel / vehicle speed ratio factor U V (k). Calculate adjustment wheel / vehicle speed ratio factor U V (k) embodiment 2, for m points (k, P V (k)), (k-1, P V (k-1)),..., (k-m+1, P V(k-m+1)) to obtain a first-order fitting straight line of the wheel / vehicle speed adjustment coefficient, and taking a point (k, U V (k)) on the first-order fitting straight line of the wheel / vehicle speed adjustment coefficient as the wheel / vehicle speed adjustment coefficient. V (k) is a wheel / vehicle speed ratio coefficient. Figure 7 FIG. 4 is a schematic diagram of a first-order fitting straight line of the wheel / vehicle speed adjustment coefficient. Figure 7 In FIG. 4, m is equal to 4, and the four "+" 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), and the "o" point on the first-order fitting straight line of the wheel / vehicle speed adjustment coefficient is point (k, U V (k). Figure 7 FIG. 4 is a schematic diagram, the coefficient values of the four "+" points are not actual data, the error is intentionally marked larger for clear display, and the slope of the first-order fitting straight line is also intentionally marked larger.

[0127] The positioning state information X(k) includes information of whether the positioning state is valid positioning or invalid positioning, and the number of satellites currently used to calculate the position. In step 3 of the train speed adjustment method of the embodiment, the method for judging whether the speed of the satellite positioning system on the vehicle is valid is that when the positioning state in the positioning state information X(k) is valid positioning, the speed of the satellite positioning system on the vehicle is valid, otherwise, the speed of the satellite positioning system on the vehicle is invalid. The method for judging whether the speed of the satellite positioning system on the vehicle is valid is that when the positioning states in the positioning state information X(k) and X(k-1) are both valid positioning, the speed of the satellite positioning system on the vehicle is valid, otherwise, the speed of the satellite positioning system on the vehicle is invalid. The method for judging whether the speed of the satellite positioning system on the vehicle is valid is that when the positioning state in the positioning state information X(k) is valid positioning, and the number of satellites currently used to calculate the position in the positioning state information X(k) is greater than or equal to δ, the speed of the satellite positioning system on the vehicle is valid, otherwise, the speed of the satellite positioning system on the vehicle is invalid. The method for judging whether the speed of the satellite positioning system on the vehicle is valid is that when the positioning states in the positioning state information X(k) and X(k-1) are both valid positioning, and the number of satellites currently used to calculate the position in the positioning state information X(k) and X(k-1) are both greater than or equal to δ, the speed of the satellite positioning system on the vehicle is valid, otherwise, the speed of the satellite positioning system on the vehicle is invalid.

[0128] X(k-1) is the read vehicle satellite positioning system data at the previous iteration, i.e. at time k-1. In the embodiment, the vehicle satellite positioning system speed acquisition unit comprises a GPS system receiving terminal and a corresponding receiving processing module. When the method for judging whether the vehicle satellite positioning system speed is valid uses the number of satellites currently being used to solve the position in the positioning state information X(k), it is required that the value of δ is greater than or equal to 4, and the preferred value is 5.

[0129] P in step 4-6 V P when i equals 0 V P when i equals 1, 2, …, m-1 V P when i equals 1, 2, …, m-1 V P when i equals 1, 2, …, m-1 V P when i equals 1, 2, …, m-1 W P when i equals 0 W P when i equals 1, 2, …, m W P when i equals 1, 2, …, m W P when i equals 1, 2, …, m W P when i equals 1, 2, …, m W μ when i equals 0 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1

[0130] μ when i equals 1, 2, …, m-1 W μ when i equals 0 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1 W μ when i equals 1, 2, …, m-1

[0131] Step 6 calculates the wheel / vehicle speed ratio coefficient U V μ when i equals 0 V μ when i equals 1, 2, …, m-1V (k-2),..., μ V (k-m+1) is the variable ratio weighting coefficient corresponding to P V (k-1), P V (k-2),..., P V (k-m+1), satisfying the relationship of

[0132]

[0133] The variable ratio weighting coefficient takes a value satisfying the relationship that the smaller i is, the larger the variable ratio weighting coefficient is, that is, from large to small, respectively, μ V (k-1), μ V (k-2),..., μ V (k-m+1) take values, for example, when m is equal to 4, μ V (k), μ V (k-1), μ V (k-2), μ V (k-3) are respectively equal to 0.4, 0.3, 0.2, 0.1, or are respectively equal to 0.55, 0.27, 0.13, 0.05, and the like.

[0134] In step 6, the train speed related quantities include train speed V, creep degree change rate x1, creep degree x2, and train wheel set speed change rate x3. The current train speed V C (h) is calculated according to the formula

[0135]

[0136] The calculation period and the sampling period T V are the same. V(h), V(k), W(h), W(k), U(k), W * (k), V C (h) are in units of m / s; T V , T U are in units of s. The train speed V is taken as the current train speed V C (h); the train speed V is in units of km / h, and after converting the units of m / s to km / h, the value of the train speed V is equal to 3.6 times the value of V C (h).

[0137] U V (k) reflects the ratio between the train wheel set speed and the train speed, so the creep degree x2 can be calculated according to the formula

[0138]

[0139] The calculation period and the sampling period T U are the same. Alternatively, according to the formula

[0140]

[0141] The current creep degree x2(h) is calculated, and the calculation period and the sampling period T are the same. V The same, and the creep degree x2 is equal to the current creep degree x2(h).

[0142] The creep degree change rate x1 is calculated according to the formula

[0143]

[0144] The calculation is performed, and the calculation period and the sampling period T are the same. U The same. V (k-1) is the wheel / vehicle speed ratio coefficient obtained by the previous iteration calculation according to the train speed adjustment method. Alternatively, the wheel / vehicle speed ratio coefficient is calculated according to the formula

[0145]

[0146] The current creep degree change rate x1 is calculated, and the calculation period and the sampling period T are the same. V The same. x2(h-1) is the current creep degree obtained by the calculation of the creep degree at the previous sampling period t V The current creep degree obtained by the calculation of the creep degree.

[0147] The train wheel set speed change rate x3 is calculated according to the formula

[0148]

[0149] The calculation is performed, and the calculation period and the sampling period T are the same. V The same. V(h-1) is a sampling value before V(h).

[0150] The creep degree x2 and the creep degree change rate x1 are calculated by using the formula (16) and the formula (18). In order to ensure that a fast response can be obtained when the idle running risk value E is calculated, it is recommended to select the formula (4) to calculate the idle running risk value E. When the creep degree x2 and the creep degree change rate x1 are calculated by using the formula (17) and the formula (19), the formula (3) or the formula (4) can be selected to calculate the idle running risk value E according to the needs.

[0151] Figure 8 The flowchart of the method for calculating the delay interval period number for the train speed adjustment system embodiment is calculated at the same period as the acquisition period of the speed acquisition unit of the on-board satellite positioning system, and the calculation can be performed before or after the iteration calculation of the train speed adjustment method. The specific method is as follows:

[0152] Step 1, obtain the train (locomotive) acceleration change rate β(k) at the current time, i.e., the k time (i.e., kT U sampling time);

[0153] Step ②: Determine whether the conditions for calculating the number of delay interval periods are met. The equation is satisfied.

[0154]

[0155] If the relationship is such that the speed of the vehicle-mounted satellite positioning system has been valid for the most recent m1 consecutive checks, proceed to step ③; otherwise, exit; m1 is greater than or equal to 10. The acceleration change threshold ε can be selected based on the train's acceleration capability and in conjunction with experiments. The value of ε can be determined by... to Choose from within the range of values. This represents the average acceleration of the train upon startup. In the embodiment, T... U Given a time interval of 1 second and m1 equals 20, the average acceleration of a train from 0 to 200 meters can typically reach 0.4 m / s². 2 Then, the value of ε can be selected in the range of 0.4 to 2.4. For example, ε can be taken as 0.75. In equation (21), β(ki) when i equals 0 is the train acceleration change rate β(k) at the current moment; β(ki) when i equals 1 is the train acceleration change rate obtained when calculating the number of delay interval cycles (i.e., iteratively calculating the wheel / vehicle speed ratio coefficient) in the previous calculation; and so on, β(ki) when i equals 1 to m1-1 are the train acceleration change rates obtained when calculating the number of delay interval cycles in the previous m1-1 calculations. The most recent consecutive m1 judgments that the vehicle satellite positioning system speed is valid refer to the following: Figure 6 In the iterative calculation of the train speed adjustment method, in the most recent consecutive m1 iterations, step 3 determined that the speed of the on-board satellite positioning system was valid.

[0156] Step ③: Obtain the number of hysteresis intervals τ. The method is to set the parameter to be optimized as the number of hysteresis intervals τ. * and radar speed proportionality coefficient p W * ;τ * The value of τ is selected within the range that ensures the delay interval is no greater than 2 seconds. * Greater than 0, less than 2 / T V Integer; in the example, T V Equals 32ms, or 0.032s; 2 / T V Equals 62.5, therefore τ * The value of p is greater than 0 and less than or equal to 62. W * The value range is greater than or equal to 0.8 and less than or equal to 1.2. The parameter p to be optimized is... W * Only used in this optimization process. The number of delay interval periods is τ. *At the same time, the train wheel rotation speed collected at the synchronous collection time point corresponding to U(k-i) is V * At the same time, the train radar speed collected at the synchronous collection time point corresponding to U(k-i) is W * At the same time, the train radar speed collected at the synchronous collection time point corresponding to U(k-i) is W

[0157]

[0158] The optimization can adopt genetic algorithm, particle swarm algorithm, etc. The delay interval period number τ satisfying the optimal value (minimum value) q is taken * The delay interval period number τ satisfying the optimal value (minimum value) q is taken

[0159] In step ①, kT U The method for collecting the train acceleration change rate β(k) at the sampling time is that, according to the formula

[0160]

[0161] is calculated, wherein α(k) is the current collected train (locomotive) acceleration, and α(k-1) is the last collected train acceleration. In the embodiment, the current collected train acceleration α(k) is calculated according to the formula

[0162]

[0163] is calculated, wherein U(k) is the current collected vehicle satellite positioning system speed, and U(k-1) is the last collected vehicle satellite positioning system speed. The train acceleration α(k) can also be measured and collected by using an accelerometer. The unit of α(k) is m / s 2 ; the unit of β(k) is m / s 3 .

[0164] Figure 9 The schematic diagram of the vehicle satellite positioning system speed collection delay, train acceleration and train acceleration change rate is shown in the figure, wherein V(t) is the train wheel rotation speed obtained by continuously processing V(h), W(t) is the train radar speed obtained by continuously processing W(h), and U(t) is the satellite positioning system speed obtained by continuously processing U(k); T τ is the delay time of the vehicle satellite positioning system speed collection time point lagging behind the train wheel rotation speed collection time point; the points k-7 to k are each sampling time (k-7)T U to kT U of the vehicle satellite positioning system speed; α(k) and β(k) are respectively the train acceleration and the train acceleration change rate.

[0165] Figure 10This diagram illustrates the synchronized data acquisition time points of the train wheel rotation speed and train radar speed for the onboard satellite positioning system. The sampling time at which U(k) occurs (i.e., kT) is... U The sampling times of V(h-τ), V(h-τ+1), ..., V(h-3), V(h-2), V(h-1), V(h), etc., are the sampling times of the train wheel rotation speed. For example, the time of V(h) is its sampling time hT. V Due to ionospheric delay and other factors, for the acquisition of train speed (including speed from the onboard satellite positioning system and train radar speed) and train wheel rotation speed at the same moment, the acquisition time of the onboard satellite positioning system speed lags behind the acquisition time of the train wheel rotation speed and train radar speed, with a time lag value of T. τ The delay interval τ is the acquisition period T relative to the train wheel rotation speed. V The number of cycles, i.e., the number of hysteresis interval cycles τ, is the time lag value of the acquisition time of the vehicle-mounted satellite positioning system lagging behind the acquisition time of the train wheel rotation speed and the train radar speed, converted into the acquisition period T. V Multiple values. Figure 10 In the above, V(h-τ) is located at the sampling time (h-τ)T. V Let V(k) be the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k). The train wheel rotation speed V(h-τ) acquired at this point is V(k). Specifically, the τ-th train wheel rotation speed acquisition time point (which is also the train radar speed acquisition time point) before the vehicle-mounted satellite positioning system speed U(k) sampling time point is the synchronous acquisition time point of U(k). The acquisition period and time point of the train radar speed and the train wheel rotation speed are the same, and the mutual delay between them is negligible. Therefore, the sampling times of the train radar speeds W(h-τ), W(h-τ+1), ..., W(h-3), W(h-2), W(h-1), and W(h) are the same as the sampling times of the train wheel rotation speeds V(h-τ), V(h-τ+1), ..., V(h-3), V(h-2), V(h-1), and V(h), respectively. The sampling time (h-τ)T of V(h-τ) is... V The sampling time of W(h-τ) is also the synchronous acquisition time point of the vehicle-mounted satellite positioning system speed U(k). The train radar speed W(h-τ) acquired at this point is W(k).

[0166] Similarly, with Figure 10 For example, when performing the optimization calculation of the number of delay interval periods τ, if τ * If V(h-1) equals 1, then the sampling point where V(h-1) is located is its corresponding synchronous acquisition time point, and its V* (k) equals V(h-1), W * (k) equals W(h-1); if τ * equals 2, the sampling point where V(h-2) is located is its corresponding synchronous acquisition time point, and V * (k) equals V(h-2), W * (k) equals W(h-2); and so on. It should be noted that, for example, τ * equals 1, V * (k) equals V(h-1), and V * (k-1) is not V(h-2); in the embodiment, the vehicle satellite positioning system speed is sampled once, and the train wheel rotation speed is averaged 31.25 times, so if τ * equals 1, V * (k) equals V(h-1), then V * (k-1) can be V(h-32), or V(h-33).

[0167] Due to creep, especially the existence of wheelset idling, the train wheelset speed is not consistent with the actual train speed, and when judging whether wheelset idling occurs, calculating creep rate, creep degree and other data, the train wheelset speed and the train speed need to be measured separately, and the train wheelset speed cannot be used instead of the train speed. The train speed is commonly measured by radar speed measurement and satellite positioning speed measurement. Satellite positioning speed measurement is to track the running speed and position of the train in real time through satellite positioning, and then transmit the information to the train control end for processing, and finally obtain the train speed; satellite positioning speed measurement can overcome the error caused by train wheelset idling and slipping, but the satellite positioning capability is greatly affected by weather and terrain, and cannot achieve speed measurement for 100% of the time; there is data transmission delay, and the transmission delay time is not fixed due to distance and ionosphere conditions, affecting the real-time performance of speed measurement. The radar speed measurement device is generally installed on the train bottom, and the radar antenna emits radar waves in a direction at a certain angle with the ground, when the train has relative motion with the ground, the received radar waves will produce frequency shift, and according to the data of radar wavelength, frequency shift amount, angle radar installation height, etc., the train speed can be calculated; but the angle Data such as radar installation height may produce time shift fluctuation, and train road surface condition is not consistent, and radar installation height may also change with road surface condition, thereby affecting the accuracy of radar speed measurement. In the train speed adjustment system for implementing the foregoing train speed adjustment method, when satellite positioning speed measurement is effective, satellite positioning speed measurement data is used to set and calculate wheel / train speed ratio adjustment model parameters and train radar speed adjustment model parameters; when satellite positioning speed measurement is not effective, the train radar speed adjustment model parameters obtained by being set before are used to calculate new train radar speed adjustment model parameters according to a given expression or by using a first-order fitting straight line method, the wheel / train speed ratio adjustment model parameters are set and calculated by using the adjusted radar speed adjustment model parameters, and then train speed, creep rate change rate, creep and train wheel set speed change rate and other train speed related quantities are calculated according to the wheel / train speed ratio adjustment model. This method combines the advantages of high accuracy of satellite positioning speed measurement and good real-time performance and long period normal operation of radar speed measurement, and improves the accuracy and reliability of measuring train speed related quantities. The train speed adjustment method also judges whether the train is in a variable speed motion state, if the train is in a variable speed motion state, radar speed measurement, satellite positioning speed measurement and train wheel set speed measurement after train variable speed motion are collected to perform optimization calculation of satellite positioning data transmission time, i.e. delay interval period number, so as to obtain accurate real-time satellite positioning data transmission delay time (i.e. delay interval period number), and further ensure the accuracy and reliability of calculating related speed data by using the foregoing train speed adjustment method.

Claims

1. A method for setting parameters of a train speed measurement model, characterized in that Periodically collecting train wheel rotation speed V ( h ), train radar speed W ( h ) and vehicle satellite positioning system speed U ( k ); the period of collecting vehicle satellite positioning system speed is T U , and the period of collecting train wheel rotation speed V ( h ) and train radar speed W ( h ) is T V ; T U greater than T V ; Periodically determining whether the vehicle satellite positioning system speed is valid; when the vehicle satellite positioning system speed is determined to be valid, determining whether the vehicle radar speed is valid according to U k ) setting the wheel / train speed ratio adjustment model parameters and the train radar speed adjustment model parameters, i.e. according to the equations​ Setting current wheel / vehicle speed adjustment coefficient P V ( k ) and radar speed variable ratio coefficient P W ( k ), let radar speed adjustment coefficient P W equal to P W ( k ); V ( k ) for vehicle-mounted satellite positioning system speed U ( k ) at the synchronous acquisition time point, collect the train wheel rotation speed, W ( k ) for vehicle-mounted satellite positioning system speed U ( k ) at the synchronous acquisition time point, collect the train radar speed; When the vehicle speed is judged to be invalid, the radar speed model parameters are adjusted by calculation, i.e. according to the formula Estimating current radar speed variation coefficient P W ( k ), m greater than or equal to 3; and according to the formula Computing radar speed adjustment coefficients P W ; radar speed weighting coefficients μ W ( k − i ) satisfy an equation the relationship that the smaller the value of the radar speed weighting coefficient is, the larger the value of the radar speed weighting coefficient is, and each radar speed weighting coefficient satisfies i the relationship that the smaller the value of the radar speed weighting coefficient is, the larger the value of the radar speed weighting coefficient is, and each radar speed weighting coefficient satisfies Computing radar synchronization adjustment speed W * ( k ) ; according to W * ( k ) set the wheel / vehicle speed ratio adjustment model parameters, i.e. according to the equation Setting current wheel / vehicle speed adjustment coefficient P V ( k ).

2. The method of claim 1, wherein According to the formula calculating the whole wheel / vehicle speed ratio coefficient of the adjustment model of the adjusting wheel / vehicle speed ratio U V ( k ) ; the variable ratio weighting coefficient μ V ( k − i ) satisfies the formula the relationship that the smaller the variable ratio weighting coefficient is, the larger the variable ratio weighting coefficient is, and each variable ratio weighting coefficient value satisfies i the smaller the variable ratio weighting coefficient is, the larger the variable ratio weighting coefficient is; the train speed V C ( h ) according to the formula performing the calculation; taking the train speed V for the current train speed V C ( h ) in accordance with the formula Computing the creep rate x 2; according to the formula Computing a rate of change of creep x 1; according to formula Computing train wheelset speed rate of change x 3.

3. The method of claim 2, wherein The method for judging whether the speed of the vehicle-mounted satellite positioning system is valid is to periodically read the positioning state information in the vehicle-mounted satellite positioning system data X ( k ) ; when the positioning states in the positioning state information X ( k ) and X ( k −1) are all valid positioning, and the number of satellites used for solving the position in the positioning state information X ( k ) and X ( k −1) is all greater than or equal to δ , the speed of the vehicle-mounted satellite positioning system is valid, otherwise, the speed of the vehicle-mounted satellite positioning system is invalid; δ greater than or equal to 4.

4. The method of claim 3, wherein The first train wheel rotation speed acquisition time point before the vehicle satellite positioning system speed sampling time point τ U k τ is the number of delay interval periods; the number of delay interval periods τ is the acquisition period converted from the time lag value by which the vehicle satellite positioning system speed acquisition time point lags behind the train wheel rotation speed and train radar speed acquisition time points T V multiple value.​​​ 5. The method of calibrating parameters of a train speed measurement model according to any one of claims 2-4, characterized in that, creep rate x 1. creep rate x 2. and train wheelset speed rate x 3. for train wheelset free rolling traction force control, the method being to apply the formula Computing an idle risk value E ; wherein θ 1 is a creep rate change rate threshold value, θ 2 is a creep rate threshold value, θ 3 is a wheelset speed change rate threshold value; τ is a non-linear weighting exponent, γ 1, γ 2, γ 3 is a non-linear weighting factor, and τ ≥ 10, γ 1 ≥ 1, γ 2 ≥ 1, γ 3 ≥ 1. The method for judging whether the train wheelset is in the slip is that when the slip risk value is greater than or equal to 1, the train wheelset is in the slip. E The method for judging whether the train wheelset is in the slip is that when the slip risk value is greater than or equal to 1, the train wheelset is in

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