Train operation situation deduction method based on kinetic model

Abstract

The invention discloses a train operation situation deduction method based on a kinetic model, which comprises the following steps: constructing and solving a train kinetic equation according to parameter data of a train in line operation to obtain a train kinetic model; dividing a train running line into a plurality of sub-intervals according to different position areas and speed limitations; calculating target speed mode curve calculation models of the different sub-intervals according to the parameter distribution conditions of the train dynamics model in the different sub-intervals to obtain interval line parameter models; loading the speed limit values of all sub-intervals in the given interval from the line parameter knowledge base; judging the numerical calculation direction of each subinterval; and according to a judgment result of the numerical calculation direction, executing numerical calculation in a specified direction in each sub-interval by using an interval line parameter model, and deducing the train operation situation. The method is suitable for real-time calculation of a complex system, accurate analysis and prediction of the running state of the train, and can reflect the physical rule of train running more accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of train motion behavior, and in particular to a train operation situation deduction method based on a dynamics model. Background Art

[0002] The deduction technology of train operation status is a technology that monitors and predicts the operation status of trains in the future by analyzing the dynamic information of trains and the static information of lines. Specifically, the deduction technology of train operation status analyzes the dynamic information such as speed, acceleration, position of trains, as well as the static information such as line conditions and emergencies, and deduces the operation status of trains in the future, including the operation position, speed, passing time, interval operation time and arrival delay time.

[0003] In CTC (Centralized Traffic Control) and TDCS (Train Dispatching Command System), the train operation status simulation technology predicts the train operation time by building a data-driven train dynamics model, while considering the impact of train speed and conversion conditions on the train operation plan in the case of delays, to achieve real-time monitoring and prediction of train operation status. The integrated application of train operation status simulation technology provides decision support functions for CTC and TDCS systems, enabling CTC and TDCS systems to more accurately grasp the train operation status, assist dispatchers in formulating more scientific and reasonable dispatching strategies, optimize train operation efficiency, improve train punctuality, reduce train delays and reduce traction energy consumption, and improve the overall operation level of railway transportation.

[0004] In the existing technical solutions, differential equations are the core link for deducing the train operation status. The continuous train operation process is converted into a series of discrete time points and space points, and the corresponding differential equations are established according to the differential equations in the train dynamics model. By calculating the changes in the train state between adjacent time points or space points, the differential equation can approximately describe the dynamic behavior of the train. At the beginning of the deduction of the train operation status, the differential equation needs to be initialized according to the initial state of the train, and then the state of the train at discrete time points or space points, including position, speed, acceleration, etc., is calculated by recursive calculation. The existing technology predicts the operation status of the train in the future period of time based on the calculated train state, including information such as the operation position, speed, passing time, interval operation time and arrival delay time. In the above manner, the deduction of the train operation status is realized through differential equations, providing decision support for dispatchers and train drivers.

[0005] From the perspective of railway signals, the existing technology has accuracy problems. It can only describe the state of the train at discrete time points or spatial points, and cannot directly describe the dynamic behavior of the train in continuous time, which limits the analysis and prediction of the train operation situation. Differential equations are theoretically the basis of train dynamics models. When the existing technology uses differential equations to deduce the train operation situation of high-speed trains or complex line conditions, it may not be able to fully capture the subtle changes that can be described by differential equations, thereby affecting the accuracy of the train operation situation deduction. In addition, from the perspective of mathematical methods, if the step size setting of the differential equations in the existing technology is unreasonable, the train operation situation deduction may have numerical instability problems, resulting in the model's prediction results significantly deviating from the actual situation. Summary of the invention

[0006] In view of this, the purpose of an embodiment of the present invention is to provide a train operation status simulation method based on a dynamic model, which can be applicable to real-time calculation of complex systems, accurately analyze and predict the operation status of trains, more accurately reflect the physical laws of train operation, and solve the problems existing in the prior art in terms of accuracy and numerical stability.

[0007] The embodiment of the present invention is achieved as follows:

[0008] A train operation situation deduction method based on a dynamic model, comprising:

[0009] According to the parameter data of the train running on the line, the train dynamics equation is constructed and solved to obtain the train dynamics model.

[0010] The train operation line is divided into multiple sub-sections according to different location areas and speed restrictions.

[0011] According to the distribution of train dynamics model parameters in different sub-intervals, target speed mode curve estimation models of different sub-intervals are calculated to obtain the interval line parameter model.

[0012] The speed limit values ​​of all the sub-intervals within a given interval are loaded from a line parameter knowledge base.

[0013] The numerical calculation direction of each subinterval is determined.

[0014] According to the determination result of the numerical calculation direction, the section line parameter model is used to perform numerical calculation in the specified direction in each sub-section to deduce the train operation status.

[0015] In a preferred embodiment of the present invention, in the above-mentioned train operation situation deduction method based on the dynamic model, the train dynamics equation is constructed and solved according to the parameter data of the train running on the line to obtain the train dynamics model, which includes:

[0016] Calculate the unit force c = μ on the train when it is running on the line t f t -μ b bwr, the unit is N / kN, where t is the train running time, f t is the unit traction force, b is the unit braking force, w is the unit basic resistance, r is the unit additional resistance, μ t is the control variable simulating the change of train traction level, μ b It is the control quantity to simulate the change of train braking level.

[0017] Calculate the unit additional resistance Where l is the length of the train, I i is the slope of the i-th ramp covered by the train in thousandths, l i is the length of the ramp covered by the train at the i-th ramp, l ri is the calculated length of the train covering the i-th curve, R i is the curve radius of the train covering the i-th curve, ω si is the unit tunnel resistance of the ith tunnel covered by the train, l si is the tunnel length of the i-th tunnel covered by the train.

[0018] Constructing the train dynamics equations Among them, v is the train speed, x is the train position, ξ is the train acceleration coefficient, and c is the unit force acting on the train.

[0019] The train dynamics equations are solved by numerical calculation using the fourth-order Runge-Kutta method. The calculation method is: Among them, f is the standard form of the first-order ordinary differential equation, x0 and y0 are the initial conditions of the differential equation, and h is the step size. The train dynamics model is obtained Among them, v=v(s) is the target speed curve, v=v(t) is the speed time curve, and t=t(s) is the time distance curve.

[0020] Its technical effect is: by constructing and solving the train dynamics equation, a detailed train dynamics model is established, and by considering the acceleration, speed, and position changes during the train operation, the train's motion behavior under different working conditions is accurately described. When calculating the unit additional resistance, the influence of complex line conditions such as ramps, curves, and tunnels is fully considered. Through detailed modeling of each sub-interval, the additional resistance of the train in operation is carefully calculated. According to the dynamic characteristics of the train and the line conditions, the target speed mode curve inference model is used to dynamically calculate the target speed mode curve of each sub-interval, and combined with the actual line parameters, a complete interval line parameter model is formed. By using the fourth-order Runge-Kutta method to numerically solve the train dynamics equation, it has higher calculation accuracy and can effectively reduce error accumulation, thereby ensuring the accuracy of the train operation situation deduction.

[0021] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the unit basic resistance w is obtained according to an empirical formula.

[0022] The unit traction force f t Calculated according to the traction characteristic curve.

[0023] The unit braking force b is obtained by conversion according to the braking characteristic curve.

[0024] The train running acceleration coefficient ξ value is 120.

[0025] In a preferred embodiment of the present invention, in the above-mentioned train operation situation deduction method based on the dynamic model, the train operation line is divided into multiple sub-intervals according to different location areas and speed limits, including:

[0026] The train operation line is divided into multiple sub-sections according to ramp areas, curve areas, tunnel areas, non-electric phase separation areas and speed restrictions.

[0027] The speed limit is determined by the line configuration, blocking method, speed limit orders and weather.

[0028] When the train runs in the same sub-section, the parameter data of the train running on the line remains unchanged.

[0029] Its technical effect is that the line is divided into multiple sub-sections according to the ramp area, curve area, tunnel area, non-electric phase separation area and speed limit. The division method based on the actual line characteristics ensures that the train dynamic parameters in different areas can accurately reflect the complexity of the line. The speed limit is determined by multiple factors such as line structure, blocking method, speed limit command and weather, ensuring that the speed control of the train in each sub-section meets the actual operation requirements and can adapt to the impact of the external environment on the speed.

[0030] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the target speed mode curve deduction model of different sub-intervals is calculated according to the distribution of train dynamic model parameters in different sub-intervals to obtain the interval line parameter model, which includes:

[0031] In each of the sub-intervals, train dynamics model parameters of the sub-interval are called from a line parameter knowledge base to construct a target speed mode curve estimation model.

[0032] The target speed mode curve estimation model includes the traction state segmentation of the train operation. t , Cruise state segment l r , Idling state segment l c and brake status segment l b .

[0033] Each of the subintervals contains a plurality of calculation points, which record the speed, time and position attributes of the train, and the interval between adjacent calculation points is a step length h.

[0034] In the sub-interval, the control quantity combination corresponding to the traction state is The control quantity combination corresponding to the cruise state is The control quantity combination corresponding to the coasting state is (0,0), and the control quantity combination corresponding to the braking state is in, The maximum traction level used for traction state, The minimum traction level used for cruising. The maximum braking level used for the braking state, The minimum braking position for cruising.

[0035] Substitute the control quantity combinations corresponding to the traction state, the cruising state, the coasting state and the braking state into the train dynamics model to obtain the section line parameter model Among them, y(l t ) is the traction state operation function, y(l r ) is the cruising state operation status function, y(l c ) is the running state function of the idling state, y(l b ) is the braking state operation status function.

[0036] Its technical effect lies in: in different sub-intervals, through the sophisticated target speed mode curve extrapolation model, the combination of dynamic control quantities, the high-precision calculation of the fourth-order Runge-Kutta method, and the comprehensive use of the line parameter knowledge base, it brings significant technical effects such as high-precision deduction, global coordinated optimization, and adaptation to changing line conditions.

[0037] In a preferred embodiment of the present invention, in the above train operation situation deduction method based on the dynamic model,

[0038] When r+w>0,

[0039] When r+w<0,

[0040] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the speed limit values ​​of all the sub-intervals in a given interval are loaded from the line parameter knowledge base, including:

[0041] The line parameters corresponding to all the sub-intervals within the given interval are retrieved from the line parameter knowledge base.

[0042] Load the speed limit value of the sub-interval.

[0043] In a preferred embodiment of the present invention, in the above-mentioned train operation situation deduction method based on the dynamic model, the determination of the numerical calculation direction of each sub-interval includes:

[0044] Take the speed value of the last calculation point of the previous subinterval as the estimated speed v d .

[0045] Take the velocity value of the first calculation point of the current subinterval as the current velocity v cur .

[0046] If v d <v cur , then the current sub-interval uses the estimated speed v d Perform forward calculation.

[0047] If v d >v cur , then the previous subinterval uses the current speed v cur Perform reverse calculation.

[0048] The technical effect is that through the above-mentioned judgment logic, a smooth transition of speed between different sub-intervals is ensured to avoid sudden changes in speed.

[0049] In a preferred embodiment of the present invention, in the above train operation status deduction method based on the dynamic model, if the current sub-interval is the first sub-interval, the initial speed value v0 of the first sub-interval is taken as the estimated speed v d .

[0050] If the current subinterval is the last subinterval, the speed value of the last calculation point of the last subinterval is taken as the estimated speed v d .

[0051] If the current sub-interval is the last sub-interval, the direction is determined again at the end of the given interval, with the end speed v of the entire given interval. E As the current speed v cur .

[0052] The technical effects are as follows: by accurately setting the initial speed value, calculation deviation or error accumulation caused by inaccurate initial conditions is avoided during the deduction process; by re-determining the speed value under the termination condition, the deduction result at the termination position is ensured to be consistent with the actual situation, and sudden deceleration or acceleration caused by errors is avoided; the initial speed value and the termination speed value are respectively used in the first sub-interval and the last sub-interval, which ensures that the speed change in the deduction process is consistent with the actual situation and avoids unreasonable speed mutations during the deduction process.

[0053] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the train operation status deduction is performed by using the section line parameter model to perform numerical calculation in a specified direction in each sub-section according to the judgment result of the numerical calculation direction, and includes:

[0054] When the determination result of the numerical calculation direction is positive, the train operation process under the traction condition is deduced.

[0055] Move a single step h from the position of the current data point, according to the current velocity v of the current data point i , step length h, sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the estimated speed value v of the next data point i+1 .

[0056] If the estimated speed v d is less than the current speed v of the next data point cur , then the estimated speed v d Set to the current velocity v of the next data point cur , v i+1 =v cur =v d .

[0057] If the estimated speed v d is greater than the current speed v of the next data point cur , then the position of the current data point is recorded as l t, and stop the forward calculation.

[0058] When the determination result of the numerical calculation direction is reverse, the train operation process under braking condition and coasting condition is deduced.

[0059] In the direction of decreasing position coordinates, move a single step length h from the position of the current data point, according to the current velocity v of the current data point i , step length h, sub-interval parameters, braking condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the speed estimation value of the previous data point

[0060] Reverse calculation moves n in the direction of decreasing position coordinates c step length, the step number n corresponds to the train's coasting transition distance l c , satisfying the relationship n c = l c / h.

[0061] Each single step h is moved according to the current velocity v of the current data point i , step size h, sub-interval parameters, coasting condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model, and the speed value of the previous data point is calculated until the coasting start position, and the speed estimated value {v'} of all data points between the coasting start position and the end position is obtained.

[0062] like and {v'} are both less than the current velocity v of the data point at the corresponding position i , then and {v'} is set to the current velocity v of the corresponding data point cur .

[0063] like and {v'} has a current velocity v greater than the data point at the corresponding position i , then v i The location information of the data point is recorded as l s -l b , record the position information of the data point where the idle start position is located as l s -l b -l c , l s is the subinterval length and stops the reverse calculation process.

[0064] Record the duration l of the traction state in the position coordinate t , the duration of the cruise state in the position coordinates l r , the duration of the coasting state in position coordinates lc and the duration l of the braking state in the position coordinate b .

[0065] Its technical effect is that it can accurately simulate the acceleration, deceleration and coasting process of the train according to the different states of the train in actual operation by forward calculation and deduction of traction conditions, and reverse calculation and deduction of braking conditions and coasting conditions. In the process of forward and reverse calculation, the train can smoothly transition under different conditions, especially between the traction state and the coasting state. By setting the traction end position and the coasting start position, it is ensured that the transition process from acceleration to deceleration or coasting of the train will not be unstable. By recording the continuous length of the traction state, cruising state, coasting state and braking state on the position coordinates, the system can comprehensively analyze the entire train operation process, provide rich data support for optimizing train operation, and help dispatchers or control systems further optimize the dispatching and control strategies of trains according to actual operation data to improve operation efficiency.

[0066] The beneficial effects of the embodiments of the present invention are:

[0067] The numerical calculation method of the present invention has higher calculation efficiency than the differential equation method and is more suitable for the complex calculation requirements in the process of train operation situation deduction. The physical laws of train operation are accurately described by calculating differential equations, which has advantages over the differential equation method in terms of accuracy and numerical stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0069] Figure 1 It is a flow chart of the train operation situation deduction method based on the dynamic model of the present invention;

[0070] Figure 2 It is a schematic diagram of the distribution of train dynamics model parameters at different positions of the section line in the train operation situation deduction method based on the dynamic model of the present invention;

[0071] Figure 3 It is a schematic diagram of a target speed mode curve calculation model in a train operation situation deduction method based on a dynamic model of the present invention;

[0072] Figure 4 It is a schematic diagram of a calculation direction determination method of a sub-interval in a train operation situation deduction method based on a dynamic model of the present invention;

[0073] Figure 5 It is a schematic diagram of detailed steps of forward calculation in the train operation situation deduction method based on the dynamic model of the present invention;

[0074] Figure 6 It is a schematic diagram of detailed steps of reverse calculation in the train operation situation deduction method based on the dynamic model of the present invention;

[0075] Figure 7 It is a schematic diagram of the speed limit value loading situation of the sub-section in the section line 1 in the train operation situation deduction method based on the dynamic model of the present invention;

[0076] Figure 8 It is a schematic diagram of the direction discrimination of the numerical calculation of the section line 1 in the train operation situation deduction method based on the dynamic model of the present invention;

[0077] Fig. 9 It is a schematic diagram of the train operation situation deduction result of sub-interval 1 in the train operation situation deduction method based on the dynamic model of the present invention;

[0078] Fig.10 It is a schematic diagram of the train operation situation deduction result of sub-interval 2 in the train operation situation deduction method based on the dynamic model of the present invention;

[0079] Fig.11 It is a schematic diagram of the train operation situation deduction result of sub-interval 3 in the train operation situation deduction method based on the dynamic model of the present invention;

[0080] Fig.12 It is a schematic diagram of the train operation status deduction results of sub-interval 4 in the train operation status deduction method based on the dynamic model of the present invention. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0082] Please refer to Figures 1 to 6The first embodiment of the present invention provides a train operation status deduction method based on a dynamic model, which includes: constructing and solving the train dynamics equation according to the parameter data of the train when it is running on the line to obtain the train dynamics model; dividing the train operation line into multiple sub-intervals according to different location areas and speed limits; calculating the target speed mode curve deduction model of different sub-intervals according to the distribution of train dynamics model parameters in different sub-intervals to obtain an interval line parameter model; loading the speed limit values ​​of all the sub-intervals in a given interval (the maximum train operation speed allowed in the sub-interval) from the line parameter knowledge base; judging the numerical calculation direction of each sub-interval; and according to the judgment result of the numerical calculation direction, using the interval line parameter model to perform numerical calculation in a specified direction in each sub-interval to deduce the train operation status.

[0083] In a preferred embodiment of the present invention, in the above-mentioned train operation situation deduction method based on the dynamic model, the train dynamics equation is constructed and solved according to the parameter data of the train when running on the line to obtain the train dynamics model, which includes: calculating the unit resultant force c=μ t f t -μ b bwr, the unit is N / kN, where t is the train running time, f t is the unit traction force, b is the unit braking force, w is the unit basic resistance, r is the unit additional resistance, μ t is the control variable simulating the change of train traction level, μ b To simulate the control quantity of the train braking level change; the train dynamics model adopts a homogeneous rod model, and the calculation of the unit additional resistance needs to take into account the conditions of ramps, curves and tunnels. Where, l is the length of the train (in meters), I i is the thousandth of the slope of the i-th ramp covered by the train (in ‰), l i is the length of the ramp covered by the train at the i-th ramp (in meters), l ri is the calculated length of the train covering the i-th curve (in meters), R i is the curve radius of the train covering the i-th curve (in meters), ω si is the unit tunnel resistance of the ith tunnel covered by the train (in N / kN), l si is the length of the i-th tunnel covered by the train (in meters); construct the train dynamics equation Wherein, v is the train speed (in km / h), x is the train position (in km), ξ is the train acceleration coefficient, and c is the unit resultant force on the train. The train dynamics equation is solved by numerical calculation of the fourth-order Runge-Kutta method, and the calculation method is: Among them, f is the standard form of the first-order ordinary differential equation, x0 and y0 are the initial conditions of the differential equation, and h is the step size. The train dynamics model is obtained Among them, v=v(s) is the target speed curve, v=v(t) is the speed time curve, and t=t(s) is the time distance curve.

[0084] Specifically, the numerical calculation method of train operation status deduction processes the parameters of the train when it is running in the interval into data points, each of which contains position, speed and time information, and then calculates these series through the fourth-order Runge-Kutta method. By loading the characteristic curves, vehicle weights and line parameters of different types of trains, the numerical calculation method of train operation status deduction can calculate the target speed curve v=v(s), speed time curve v=v(t) and time distance curve t=t(s) of the train operation, and realize accurate analysis of the train operation status.

[0085] Its technical effect is: by constructing and solving the train dynamics equation, a detailed train dynamics model is established, and by considering the acceleration, speed, and position changes during the train operation, the train's motion behavior under different working conditions is accurately described. When calculating the unit additional resistance, the influence of complex line conditions such as ramps, curves, and tunnels is fully considered. Through detailed modeling of each sub-interval, the additional resistance of the train in operation is carefully calculated. According to the dynamic characteristics of the train and the line conditions, the target speed mode curve inference model is used to dynamically calculate the target speed mode curve of each sub-interval, and combined with the actual line parameters, a complete interval line parameter model is formed. By using the fourth-order Runge-Kutta method to numerically solve the train dynamics equation, it has higher calculation accuracy and can effectively reduce error accumulation, thereby ensuring the accuracy of the train operation situation deduction.

[0086] In a preferred embodiment of the present invention, in the above train operation status deduction method based on the dynamic model, the unit basic resistance w is obtained according to an empirical formula, and the empirical formulas used for w of different train types are different; the unit traction force f t It is obtained by converting according to the traction characteristic curve; the unit braking force b is obtained by converting according to the braking characteristic curve; the train running acceleration coefficient ξ value is taken as 120.

[0087] In a preferred embodiment of the present invention, in the above-mentioned train operation situation deduction method based on the dynamic model, the train operation line is divided into multiple sub-intervals according to different location areas and speed limits, including: the distribution of train dynamic model parameters at different locations of the interval line is as follows: Figure 2 As shown, the train running line is divided into multiple sub-sections according to the slope area, curve area, tunnel area, non-electric phase separation area and speed limit; the speed limit is determined according to the line structure, blocking method, speed limit command and weather; when running in the same sub-section, the parameter data of the train running on the line remains unchanged.

[0088] Its technical effect is that the line is divided into multiple sub-sections according to the ramp area, curve area, tunnel area, non-electric phase separation area and speed limit. The division method based on the actual line characteristics ensures that the train dynamic parameters (such as additional resistance, speed limit, etc.) in different areas can accurately reflect the complexity of the line. The speed limit is determined by multiple factors such as line structure, blocking mode, speed limit command and weather, ensuring that the speed control of the train in each sub-section meets the actual operation requirements and can adapt to the impact of the external environment (such as weather changes) on the speed.

[0089] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the target speed pattern curve deduction model of different sub-intervals is calculated according to the distribution of train dynamic model parameters in different sub-intervals to obtain the interval line parameter model, which includes: in each sub-interval, calling the train dynamic model parameters of the sub-interval from the line parameter knowledge base to construct the target speed pattern curve deduction model, and the target speed pattern curve deduction model is as follows: Figure 3 The target speed mode curve estimation model includes the traction state segment l of the train operation t , Cruise state segment l r , Idling state segment l c and brake status segment l b ; Each of the sub-intervals contains multiple calculation points, which record the speed, time and position attributes of the train, and the interval between adjacent calculation points is a step length h; when executing the train operation status simulation, the numerical calculation method will call the line parameters of the ramp, curve, tunnel, speed limit and non-electric phase separation area from the line parameter knowledge base for calculation. The line parameter knowledge base not only stores the interval line parameters, but also stores the traction characteristic curve, braking characteristic curve, unit basic resistance formula and train gross weight data to assist the numerical calculation process; in the sub-interval, the control quantity combination corresponding to the traction state is The control quantity combination corresponding to the cruise state is The control quantity combination corresponding to the coasting state is (0,0), and the control quantity combination corresponding to the braking state is in, The maximum traction level used for traction state, The minimum traction level used for cruising. The maximum braking level used for the braking state, The minimum braking level used in the cruising state; the control quantity combination corresponding to the traction state, the cruising state, the coasting state and the braking state is substituted into the train dynamics model to obtain the section line parameter model Among them, y(l t ) is the traction state operation function, y(l r ) is the cruising state operation status function, y(l c ) is the running state function of the idling state, y(l b ) is the braking state operation status function.

[0090] Specifically, the control quantity combination of traction, cruising, coasting and braking conditions is substituted into the unit resultant force formula that the train is subjected to when running on the line, and then the unit resultant force formula and the unit additional resistance formula are substituted into the train dynamics equation to obtain the constructed derivative function of the fourth-order Runge-Kutta method, which is the interval line parameter model.

[0091] Its technical effect lies in: in different sub-intervals, through the sophisticated target speed mode curve extrapolation model, the combination of dynamic control quantities, the high-precision calculation of the fourth-order Runge-Kutta method, and the comprehensive use of the line parameter knowledge base, it brings significant technical effects such as high-precision deduction, global coordinated optimization, and adaptation to changing line conditions.

[0092] In a preferred embodiment of the present invention, in the above train operation situation deduction method based on the dynamic model, Set by the line parameter knowledge base; when r+w>0, When r+w<0,

[0093] In a preferred embodiment of the present invention, in the above-mentioned train operation status simulation method based on the dynamic model, the loading of the speed limit values ​​of all the sub-intervals within a given interval from the line parameter knowledge base includes: calling the line parameters corresponding to all the sub-intervals within the given interval from the line parameter knowledge base, including factors affecting the speed limit such as ramps, curves, tunnels, etc.; loading the speed limit values ​​of the sub-intervals.

[0094] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the determination of the numerical calculation direction of each sub-interval includes: taking the speed value of the last calculation point of the previous sub-interval as the deduced speed vd ; Take the velocity value of the first calculation point of the current subinterval as the current velocity v cur ;like Figure 4 As shown, if v d <v cur , then the current sub-interval uses the estimated speed v d Perform forward calculation; if v d >v cur , then the previous subinterval uses the current speed v cur Perform reverse calculation.

[0095] Among them, a single sub-interval may not have a calculation direction, or may have both forward calculation and reverse calculation directions. In the calculation direction determination step, the present invention traverses the intersection of each sub-interval to determine the calculation direction of each sub-interval, and needs to be performed at the beginning and end of each sub-interval to ensure the accuracy of the calculation direction.

[0096] The technical effect is that through the above-mentioned judgment logic, a smooth transition of speed between different sub-intervals is ensured to avoid sudden changes in speed.

[0097] In a preferred embodiment of the present invention, in the above train operation status deduction method based on the dynamic model, if the current sub-interval is the first sub-interval, the initial speed value v0 of the first sub-interval is taken as the estimated speed v d ; If the current subinterval is the last subinterval, the speed value of the last calculation point of the last subinterval is taken as the estimated speed v d ; If the current sub-interval is the last sub-interval, the direction is determined again at the end of the given interval, with the end speed v of the entire given interval E As the current speed v cur .

[0098] The technical effects are as follows: by accurately setting the initial speed value, calculation deviation or error accumulation caused by inaccurate initial conditions is avoided during the deduction process; by re-determining the speed value under the termination condition, the deduction result at the termination position is ensured to be consistent with the actual situation, and sudden deceleration or acceleration caused by errors is avoided; the initial speed value and the termination speed value are respectively used in the first sub-interval and the last sub-interval, which ensures that the speed change in the deduction process is consistent with the actual situation and avoids unreasonable speed mutations during the deduction process.

[0099] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the train operation status deduction is performed by using the section line parameter model to perform numerical calculation in a specified direction in each sub-section according to the judgment result of the numerical calculation direction, including: Figure 5 As shown, when the result of the numerical calculation direction is positive, the train operation process under the traction condition is deduced; a single step length h is moved from the position of the current data point, and the current speed v of the current data point is calculated. i , step length h, sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the estimated speed value v of the next data point i+1 ; If the estimated speed v d is less than the current speed v of the next data point cur , then the estimated speed v d Set to the current velocity v of the next data point cur , v i+1 =v cur =v d ; If the estimated speed v d is greater than the current speed v of the next data point cur , then the position of the current data point is recorded as l t , and stop the forward calculation; if Figure 6 As shown, when the result of the determination of the numerical calculation direction is reverse, the train operation process under braking condition and coasting condition is deduced; along the direction of decreasing position coordinates, a single step length h is moved from the position of the current data point, and the current speed v of the current data point is calculated. i , step length h, sub-interval parameters, braking condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the speed estimation value of the previous data point Reverse calculation moves n in the direction of decreasing position coordinates c step length, the step number n corresponds to the train's coasting transition distance l c , satisfying the relationship n c = l c / h; Each single step length h is moved according to the current speed v of the current data point i , step size h, sub-interval parameters, coasting condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model, and the speed value of the previous data point is calculated until the coasting start position, and the speed calculation value {v'} of all data points between the coasting start position and the end position is obtained; if and {v'} are both less than the current velocity v of the data point at the corresponding position i , then and {v'} is set to the current velocity v of the corresponding data point cur ;like and {v'} has a current velocity v greater than the data point at the corresponding position i , then v i The location information of the data point is recorded as l s -l b , record the position information of the data point where the idle start position is located as l s -l b -l c , l s is the subinterval length, and stops the reverse calculation process; records the duration of the traction state in the position coordinate l t , the duration of the cruise state in the position coordinates l r , the duration of the coasting state in position coordinates l c and the duration l of the braking state in the position coordinate b Among them, l r Located between the sub-interval traction end position and the coasting start position, l r = l s -l t -l b -l c .

[0100] Its technical effect is that it can accurately simulate the acceleration, deceleration and coasting process of the train according to the different states of the train in actual operation by forward calculation and deduction of traction conditions, and reverse calculation and deduction of braking conditions and coasting conditions. In the process of forward and reverse calculation, the train can smoothly transition under different conditions, especially between the traction state and the coasting state. By setting the traction end position and the coasting start position, it is ensured that the transition process from acceleration to deceleration or coasting of the train will not be unstable. By recording the continuous length of the traction state, cruising state, coasting state and braking state on the position coordinates, the system can comprehensively analyze the entire train operation process, provide rich data support for optimizing train operation, and help dispatchers or control systems further optimize the dispatching and control strategies of trains according to actual operation data to improve operation efficiency.

[0101] Please refer to Figures 7 to 12, the second embodiment of the present invention provides a method for executing train operation status simulation on section line 1. There are multiple ramps in section line 1, and each ramp is set with a specific speed limit. The units of the starting position and the ending position in Table 1 are km, the unit of the slope is ‰, and the unit of the speed limit value is km / h. According to the parameter distribution of the train dynamics model, section line 1 is divided into 4 sub-sections as shown in Table 1. Sub-section 2 is artificially set with a speed limit of 60km / h according to the speed limit instruction issued by the dispatching section.

[0102] Table 1 Train dynamics model parameters for section line 1

[0103] Sub-interval number Starting position End position Slope per thousand Speed ​​limit 1 0 1.5 3 160 2 1.5 3 -2.5 60 3 3 6.5 2.5 140 4 6.5 8 0.5 160

[0104] The initial speed of the running train in section line 1 is 0km / h, and the terminal speed is 0km / h. The traction characteristic curve, braking characteristic curve, and unit basic resistance calculation method of the running train are derived from a certain type of EMU data and can be retrieved from the line parameter knowledge base. In the embodiment, the step length h is set to 5m, that is, there is a data point every 5m in the section line, and each data point contains position, speed, and time information. Sub-interval 1 contains 300 data points, sub-interval 2 contains 300 data points, sub-interval 3 contains 700 data points, and sub-interval 4 contains 300 data points. The steps of loading the deduced speed limit value, calculating the direction, and calculating the numerical value of the train operation status are described as follows.

[0105] (1) Speed ​​limit value loading

[0106] Section line 1 contains 4 subsections, with speed limits of 160km / h, 60km / h, 140km / h and 160km / h respectively. The specific speed limit loading conditions are as follows: Figure 7 The present invention reads the speed limit value of the sub-interval from the line parameter knowledge base, and loads the speed limit value as the speed information of the data points in the corresponding sub-interval, that is, the speed information loaded for 300 data points in sub-interval 1 is 160km / h, the speed information loaded for 300 data points in sub-interval 2 is 60km / h, the speed information loaded for 700 data points in sub-interval 3 is 140km / h, and the speed information loaded for 300 data points in sub-interval 4 is 160km / h.

[0107] (2) Calculation direction determination

[0108] At the beginning or end of each subinterval, the calculation direction is set according to the estimated speed and current speed of the calculation point. At the beginning of subinterval 1, the estimated speed v d is 0km / h, the current speed of the first calculation point is 160km / h, v d <v cur , subinterval 1 uses the estimated velocity value vd Perform forward calculation. At the beginning of subinterval 2, the velocity v is calculated. d is 160km / h, the current speed of the first calculation point is 60km / h, v d >v cur , subinterval 1 uses v cur Perform the reverse calculation. At the beginning of subinterval 3, the velocity v is calculated d is 60km / h, the current speed of the first calculation point is 140km / h, v d <v cur , subinterval 3 uses the estimated velocity value v d Perform forward calculation. At the beginning of subinterval 4, the velocity v is estimated d is 140km / h, the current speed of the first calculation point is 160km / h, v d <v cur , subinterval 4 uses the estimated velocity value v d Perform forward calculation. At the end of subinterval 4, the velocity v is estimated d is 160km / h, the current speed at the last calculation point is 0km / h, v d >v cur , subinterval 4 uses v cur Execute reverse calculation. The numerical calculation direction of the sub-interval in interval line 1 is determined as follows: Figure 8 shown.

[0109] (3) Numerical calculation

[0110] According to the calculation direction determination result, the present invention performs train situation deduction on section line 1, that is, forward calculation is performed at the beginning of subsection 1 and reverse calculation is performed at the end, forward calculation is performed at the beginning of subsection 3 and at the beginning of subsection 4 and reverse calculation is performed at the end.

[0111] The numerical knowledge base obtains the combination of traction condition control quantities, train weight, train traction characteristic curve, and train starting from the beginning of sub-interval 1. The numerical calculation method for train operation status deduction is substituted into the line parameter model of the interval from the unit basic resistance of the line participants, the train length, and the thousandth of the slope of the sub-interval. Get the speed value v of the next data point of the traction condition i+1 , until the estimated speed v d Greater than the current speed v of the next data point curStarting from the end of sub-interval 1, the numerical calculation method of train operation status deduction obtains the braking condition control quantity combination, the coasting condition control quantity combination, the train weight, the train braking characteristic curve, the unit basic resistance of the train, the train length, and the sub-interval slope per thousand from the line parameter knowledge base, and substitutes them into the line parameter model of the interval. Get the estimated speed of the braking condition The section line parameter model The estimated speed values ​​{v'} of all data points between the starting position and the ending position of the coasting condition are obtained until the estimated speeds of the braking condition and the coasting condition are greater than the current speed values ​​of the data points at the corresponding positions. The train operation status simulation results of sub-interval 1 are as follows: Fig. 9 As shown. The starting position of the traction state of subinterval 1 is 0 and the ending position is l t ; The starting position of the coasting state is l t , the end position is l t +l c ; The starting position of the braking state is l t +l c , the end position is l t +l c +l b ; There is no cruise state in subinterval 1, so l r is 0.

[0112] The calculation direction is not set at the beginning and end of sub-interval 2. The result of train operation situation simulation is as follows: Fig.10 There is no traction state, coasting state and braking state in sub-interval 2, so l t , l c , l b Both are 0, the starting position of the cruise state is 0, and the ending position is l s .

[0113] Starting from the beginning of sub-interval 3, the numerical calculation method of train operation status deduction obtains the traction condition control quantity combination, train weight, train traction characteristic curve, unit basic resistance of the train, train length, and the slope of the sub-interval in thousandths from the line parameter knowledge base, and substitutes them into the line parameter model of the interval Get the speed value v of the next data point of the traction condition i+1 , until the estimated speed v d Greater than the current speed v of the next data point cur The train operation status simulation results of sub-section 3 are as follows: Fig.11 As shown. The starting position of the traction state of sub-interval 3 is 0 and the ending position is l s ; The starting position of the coasting state is l t , the end position is lt +l c ; The starting position of the braking state is l t +l c , the end position is l t +l c +l b ; There is no cruising, coasting or braking state in sub-interval 3, so l r , l c , l b Both are 0.

[0114] Starting from the beginning of sub-interval 4, the numerical calculation method of train operation status deduction obtains the traction condition control quantity combination, train weight, train traction characteristic curve, unit basic resistance of the train, train length, and the slope of the sub-interval in thousandths from the line parameter knowledge base, and substitutes them into the line parameter model of the interval Get the speed value v of the next data point of the traction condition i+1 , until the estimated speed v d Greater than the current speed v of the next data point cur Starting from the end of sub-interval 1, the numerical calculation method of train operation status deduction obtains the braking condition control quantity combination, the coasting condition control quantity combination, the train weight, the train braking characteristic curve, the unit basic resistance of the train, the train length, and the sub-interval slope per thousand from the line parameter knowledge base, and substitutes them into the line parameter model of the interval. Get the estimated speed of the braking condition The section line parameter model The estimated speed values ​​{v'} of all data points between the starting position and the ending position of the coasting condition are obtained until the estimated speeds of the braking condition and the coasting condition are greater than the current speed values ​​of the data points at the corresponding positions. The train operation status simulation results of sub-interval 4 are as follows: Fig.12 As shown in the figure, since the estimated speed of the braking condition and the coasting condition is lower than the estimated result of the traction condition, the estimated result of the forward calculation is overwritten by the estimated result of the reverse calculation, and the duration of the traction state in the sub-interval 4 on the position coordinate is 0; the starting position of the coasting state is 0, and the ending position is l c ; The starting position of the braking state is l c , the end position is l c +l b ; There is no traction state and cruising state in sub-interval 1, so l t , l r is 0.

[0115] It should be understood that the above specific embodiments of the present invention are only used to illustrate or explain the principles of the present invention, and do not constitute a limitation of the present invention. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. In addition, the appended claims of the present invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalent forms of such scope and boundaries.

Claims

1. A train operation situation simulation method based on a dynamic model, characterized in that: include: According to the parameter data of the train running on the line, the train dynamics equation is constructed and solved to obtain the train dynamics model; Divide the train operation line into multiple sub-sections according to different location areas and speed restrictions; According to the distribution of train dynamics model parameters in different sub-intervals, target speed mode curve estimation models of different sub-intervals are calculated to obtain an interval line parameter model; Loading speed limit values ​​of all sub-intervals within a given interval from a line parameter knowledge base; Determining the numerical calculation direction of each of the subintervals; According to the determination result of the numerical calculation direction, the section line parameter model is used to perform numerical calculation in the specified direction in each sub-section to deduce the train operation status.

2. The train operation situation deduction method based on the dynamic model according to claim 1 is characterized in that: The train dynamics model is obtained by constructing and solving the train dynamics equation according to the parameter data of the train running on the line, including: Calculate the unit force c = μ on the train when it is running on the line t f t -μ b bwr, the unit is N / kN, where t is the train running time, f t is the unit traction force, b is the unit braking force, w is the unit basic resistance, r is the unit additional resistance, μ t is the control variable simulating the change of train traction level, μ b It is the control quantity to simulate the change of train braking level; Calculate the unit additional resistance Where l is the length of the train, I i is the slope of the i-th ramp covered by the train in thousandths, l i is the length of the ramp covered by the train at the i-th ramp, l ri is the calculated length of the train covering the i-th curve, R i is the curve radius of the train covering the i-th curve, ω si is the unit tunnel resistance of the ith tunnel covered by the train, l si is the tunnel length of the i-th tunnel covered by the train; Constructing the train dynamics equations Where, v is the train speed, x is the train position, ξ is the train acceleration coefficient, and c is the unit force on the train; The train dynamics equations are solved by numerical calculation using the fourth-order Runge-Kutta method. The calculation method is: Among them, f is the standard form of the first-order ordinary differential equation, x0 and y0 are the initial conditions of the differential equation, and h is the step size. The train dynamics model is obtained Among them, v=v(s) is the target speed curve, v=v(t) is the speed time curve, and t=t(s) is the time distance curve.

3. The train operation situation deduction method based on the dynamic model according to claim 2 is characterized in that: The unit basic resistance w is obtained according to an empirical formula; The unit traction force f t Calculated according to the traction characteristic curve; The unit braking force b is obtained by conversion according to the braking characteristic curve; The train running acceleration coefficient ξ value is 120.

4. The train operation situation deduction method based on the dynamic model according to claim 1 is characterized in that: The train running line is divided into a plurality of sub-sections according to different location areas and speed limits, including: The train operation line is divided into multiple sub-sections according to the ramp area, curve area, tunnel area, non-electric phase separation area and speed limit; The speed limit is determined according to the line structure, blocking method, speed limit order and weather; When the train runs in the same sub-section, the parameter data of the train running on the line remains unchanged.

5. The train operation situation deduction method based on dynamic model according to claim 1 is characterized in that: The calculating of target speed mode curve estimation models of different sub-intervals according to the distribution of train dynamics model parameters in different sub-intervals to obtain the section line parameter model comprises: In each of the sub-intervals, train dynamics model parameters of the sub-interval are called from a line parameter knowledge base to construct a target speed mode curve estimation model; The target speed mode curve estimation model includes the traction state segmentation of the train operation. t , Cruise state segment l r , Idling state segment l c and brake status segment l b ; Each of the sub-intervals contains a plurality of calculation points, which record the speed, time and position attributes of the train, and the interval between adjacent calculation points is a step length h; In the sub-interval, the control quantity combination corresponding to the traction state is The control quantity combination corresponding to the cruise state is The control quantity combination corresponding to the coasting state is (0,0), and the control quantity combination corresponding to the braking state is in, The maximum traction level used for traction state, The minimum traction level used for cruising. The maximum braking level used for the braking state, The minimum braking position used for cruising; Substitute the control quantity combinations corresponding to the traction state, the cruising state, the coasting state and the braking state into the train dynamics model to obtain the section line parameter model Among them, y(l t ) is the traction state operation function, y(l r ) is the cruising state operation status function, y(l c ) is the running state function of the idling state, y(l b ) is the braking state operation status function.

6. The train operation situation deduction method based on the dynamic model according to claim 5 is characterized in that: When r+w>0, When r+w<0, 7. The train operation situation deduction method based on dynamic model according to claim 1 is characterized in that: The loading of the speed limit values ​​of all the sub-intervals within a given interval from the line parameter knowledge base includes: Retrieving line parameters corresponding to all the sub-intervals within a given interval from a line parameter knowledge base; Load the speed limit value of the sub-interval.

8. The train operation situation deduction method based on dynamic model according to claim 5 is characterized in that: The step of determining the numerical calculation direction of each subinterval includes: Take the speed value of the last calculation point of the previous subinterval as the estimated speed v d ; Take the velocity value of the first calculation point of the current subinterval as the current velocity v cur ; If v d <v cur , then the current sub-interval uses the estimated speed v d Perform forward calculations; If v d >v cur , then the previous subinterval uses the current speed v cur Perform reverse calculation.

9. The train operation situation deduction method based on dynamic model according to claim 8 is characterized in that: If the current sub-interval is the first sub-interval, the initial speed value v0 of the first sub-interval is taken as the estimated speed v d ; If the current subinterval is the last subinterval, the speed value of the last calculation point of the last subinterval is taken as the estimated speed v d ; If the current sub-interval is the last sub-interval, the direction is determined again at the end of the given interval, with the end speed v of the entire given interval. E As the current speed v cur .

10. The train operation situation deduction method based on dynamic model according to claim 8, characterized in that: The step of performing numerical calculation in a specified direction in each sub-interval using the section line parameter model according to the determination result of the numerical calculation direction to perform train operation status deduction includes: When the determination result of the numerical calculation direction is positive, the train operation process under the traction condition is deduced; Move a single step h from the position of the current data point, according to the current velocity v of the current data point i , step length h, sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the estimated speed value v of the next data point i+1 ; If the estimated speed v d is less than the current speed v of the next data point cur , then the estimated speed v d Set to the current velocity v of the next data point cur , v i+1 =v cur =v d ; If the estimated speed v d is greater than the current speed v of the next data point cur , then the position of the current data point is recorded as l t , and stop the forward calculation; When the result of the determination of the numerical calculation direction is reverse, the train operation process under the braking condition and the coasting condition is deduced; In the direction of decreasing position coordinates, move a single step length h from the position of the current data point, according to the current velocity v of the current data point i , step length h, sub-interval parameters, brake condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model to obtain the speed estimation value of the previous data point Reverse calculation moves n in the direction of decreasing position coordinates c step length, the step number n corresponds to the train's coasting transition distance l c , satisfying the relationship n c = l c / h; Each single step h is moved according to the current velocity v of the current data point i , step length h, sub-interval parameters, coasting condition control quantity combination, train weight and train characteristic curve are substituted into the interval line parameter model, and the speed value of the previous data point is estimated until the coasting start position, and the speed estimated value {v'} of all data points between the coasting start position and the end position is obtained; like and {v'} are both less than the current velocity v of the data point at the corresponding position i , then and {v'} is set to the current velocity v of the corresponding data point cur ; like and {v'} has a current velocity v greater than the data point at the corresponding position i , then v i The location information of the data point is recorded as l s -l b , record the position information of the data point where the idle start position is located as l s -l b -l c , l s is the subinterval length, and stops the reverse calculation process; Record the duration l of the traction state in the position coordinate t , the duration of the cruise state in the position coordinates l r , the duration of the coasting state in position coordinates l c and the duration l of the braking state in the position coordinate b .

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