A train operation situation simulation method based on dynamic model

Abstract

The present invention discloses a train operation situation deduction method based on a dynamic model, comprising: constructing and solving a train dynamics equation based on parameter data of a train running on a line to obtain a train dynamics model; dividing the train operation line into multiple sub-intervals based on different location areas and speed limits; calculating a target speed pattern curve deduction model for each sub-interval based on the distribution of train dynamics model parameters within each sub-interval to obtain an interval line parameter model; loading speed limit values ​​for all sub-intervals within a given interval from a line parameter knowledge base; determining the numerical calculation direction for each sub-interval; and performing numerical calculation in a specified direction in each sub-interval using the interval line parameter model based on the determination result of the numerical calculation direction to perform train operation situation deduction. The present invention is suitable for real-time calculation of complex systems, accurately analyzing and predicting the operation status of trains, and more accurately reflecting the physical laws of train operation.

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 dynamic model. Background Art

[0002] Train operation status prediction technology analyzes dynamic train information and static line information to monitor and predict train operation status in real time over a period of time. Specifically, by analyzing dynamic information such as train speed, acceleration, and position, as well as static information such as line conditions and emergencies, this technology predicts the train's operation status over a period of time, including its location, speed, transit time, interval travel time, and arrival delay time.

[0003] In CTC (Centralized Traffic Control) and TDCS (Train Dispatching Command System), train operation status simulation technology predicts train operation time by building a data-driven train dynamics model, while considering the impact of train speed and conversion conditions on train operation plans in the event of delays, thereby achieving 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 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 operational level of railway transportation.

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

[0005] From the perspective of railway signals, existing technologies have accuracy issues and can only describe the state of the train at discrete time points or spatial points. They cannot directly describe the dynamic behavior of the train in continuous time, which limits the analysis and prediction of the train's operation status. Differential equations are theoretically the basis of train dynamics models. When existing technologies use differential equations to deduce the operation status of high-speed trains or trains under complex line conditions, they 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 status deduction. In addition, from the perspective of mathematical methods, if the step size setting of the differential equations in existing technologies is unreasonable, the deduction of the train operation status 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 deduction 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 existing technology 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 pattern curve estimation models of different sub-intervals are calculated to obtain an 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 perform train operation status deduction.

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

[0016] Calculate the unit force c=μ on the train when 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 that simulates the change of train braking level.

[0017] Calculate the additional resistance of the unit Where l is the train length, I i is the slope of the i-th slope 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 i-th 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 net 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: Where 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 as follows: by constructing and solving the train dynamics equations, a detailed train dynamics model is established. By considering the acceleration, speed, and position changes during the train's 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 slopes, curves, and tunnels is fully considered. Through detailed modeling of each sub-interval, the additional resistance of the train during operation is carefully calculated. According to the dynamic characteristics of the train and the line conditions, the target speed pattern curve inference model is used to dynamically calculate the target speed pattern 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 equations, 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 slope 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] The technical benefits of this system include dividing the line into multiple subsections based on slopes, curves, tunnels, areas without electrical phase separation, and speed limits. This division, informed by actual line characteristics, ensures that train dynamics parameters within each section accurately reflect the complexity of the line. Speed ​​limits are determined based on a variety of factors, including line structure, blocking method, speed limit commands, and weather conditions. This ensures that train speed control within each subsection meets actual operating requirements and adapts to the impact of external conditions on 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 pattern curve deduction model of each sub-interval is calculated based on the distribution of train dynamic model parameters in each sub-interval to obtain the section line parameter model, including:

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

[0032] The target speed pattern curve estimation model includes the traction state segment l of the train operation t , cruise state segment l r , Coasting state segment l c and brake status segment l b .

[0033] Each of the subintervals contains multiple calculation points, which record the speed, time and position attributes of the train. The interval between adjacent calculation points is the 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 used in cruise mode.

[0035] Substitute the control variables 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 function, y(l r ) is the cruise 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 precise target speed pattern curve inference model, dynamic control quantity combination, high-precision calculation of the fourth-order Runge-Kutta method, and 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 loading of the speed limit values ​​of all the sub-intervals within a given interval from the line parameter knowledge base includes:

[0041] The line parameters corresponding to all the sub-intervals within the given interval are retrieved from a 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 subinterval 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, and sudden changes in speed are avoided.

[0049] In a preferred embodiment of the present invention, in the 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 subinterval is the last subinterval, the direction is determined again at the end of the given interval, with the end speed v of the entire given interval as the value. E As the current speed v cur .

[0052] Its technical effects are: by accurately setting the initial speed value, calculation deviation or error accumulation caused by inaccurate initial conditions during the deduction process is avoided; by re-judging 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 used in the first sub-interval and the last sub-interval respectively, which ensures that the speed change in the deduction process is consistent with the actual situation and avoids unreasonable speed mutation 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, performing numerical calculation in a specified direction in each of the sub-intervals using the section line parameter model according to the determination result of the numerical calculation direction, and performing train operation status deduction includes:

[0054] When the determination result of the numerical calculation direction is positive, the train operation process under the traction working 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 size h, sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve, substitute into the interval line parameter model, and 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 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 the braking condition and the 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 size h, sub-interval parameters, braking condition control quantity combination, train weight and train characteristic curve, substitute 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 number of steps 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 There is a current velocity v in {v'} that is greater than the current velocity v of 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 line starts 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 coordinate 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 deducing the traction working condition through forward calculation and deducing the braking working condition and coasting working condition through reverse calculation. In the process of forward and reverse calculation, the train can smoothly transition under different working conditions, especially between the traction state and the coasting state. By setting the traction end position and the coasting start position, it ensures that the train will not be unstable in the transition process from acceleration to deceleration or coasting. By recording the duration of the traction state, cruising state, coasting state and braking state in the position coordinates, the system can conduct a comprehensive analysis of the entire train operation process, providing rich data support for optimizing train operation, helping dispatchers or control systems to further optimize the train scheduling and control strategies based on actual operation data and improve operation efficiency.

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

[0067] The numerical calculation method of the present invention is more efficient than the differential equation method and is more suitable for the complex computational requirements of train operation situation deduction. By calculating differential equations to accurately describe the physical laws of train operation, it 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 following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

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

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

[0071] Figure 3 Schematic diagram of the target speed pattern curve calculation model in the train operation situation deduction method based on the dynamic model of the present invention;

[0072] Figure 4 Schematic diagram of a method for determining the calculation direction of a subinterval in a train operation situation deduction method based on a dynamic model of the present invention;

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

[0074] Figure 6 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 Schematic diagram of speed limit loading of a sub-section within section line 1 in the train operation status deduction method based on a dynamic model of the present invention;

[0076] Figure 8 Schematic diagram of the numerical calculation direction determination of section line 1 in the train operation situation deduction method based on the dynamic model of the present invention;

[0077] Figure 9 Schematic diagram of the train operation situation deduction result of subinterval 1 in the train operation situation deduction method based on the dynamic model of the present invention;

[0078] Figure 10 Schematic diagram of the train operation situation deduction result of subinterval 2 in the train operation situation deduction method based on the dynamic model of the present invention;

[0079] Figure 11 Schematic diagram of the train operation situation deduction result of subinterval 3 in the train operation situation deduction method based on the dynamic model of the present invention;

[0080] Figure 12 This is a schematic diagram of the train operation status deduction results of subinterval 4 in the train operation status deduction method based on the dynamic model of the present invention. DETAILED DESCRIPTION

[0081] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only 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 herein 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 running on the line to obtain a train dynamics model; dividing the train operation line into multiple sub-intervals according to different location areas and speed limits; calculating the target speed pattern 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 using the interval line parameter model to perform numerical calculation in a specified direction in each sub-interval according to the judgment result of the numerical calculation direction to perform train operation status deduction.

[0083] In a preferred embodiment of the present invention, in the above-mentioned train operation status deduction method based on the dynamic model, the train dynamic equation is constructed and solved according to the parameter data of the train running on the line to obtain the train dynamic model, which includes: calculating the unit net 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 the homogeneous rod model, and the calculation of the unit additional resistance needs to take into account the conditions of slopes, curves and tunnels. Where, l is the train length (in meters), I i is the thousandth of the slope of the i-th slope covered by the train (unit: ‰), 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 i-th 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 Where 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 net force on the train. The train dynamic equation is solved by numerical calculation using the fourth-order Runge-Kutta method. The calculation method is: Where 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 for train operation status deduction processes the parameters of the train's operation within the section into data points, each of which contains position, speed, and time information. These series are then calculated using the fourth-order Runge-Kutta method. By loading the characteristic curves, vehicle weights, and line parameters of different train types, the numerical calculation method for 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), enabling accurate analysis of train operation status.

[0085] Its technical effect is as follows: by constructing and solving the train dynamics equations, a detailed train dynamics model is established. By considering the acceleration, speed, and position changes during the train's 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 slopes, curves, and tunnels is fully considered. Through detailed modeling of each sub-interval, the additional resistance of the train during operation is carefully calculated. According to the dynamic characteristics of the train and the line conditions, the target speed pattern curve inference model is used to dynamically calculate the target speed pattern 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 equations, 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 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 formula used for w of different train types is different; the unit traction force f t It is obtained by converting the traction characteristic curve; the unit braking force b is obtained by converting the braking characteristic curve; the train running acceleration coefficient ξ is taken as 120.

[0087] In a preferred embodiment of the present invention, in the above 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, block mode, 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] The technical benefits of this system include dividing the line into multiple subsections based on slopes, curves, tunnels, areas without electrical phase separation, and speed limits. This division, based on actual line characteristics, ensures that train dynamic parameters (such as additional resistance and speed limits) within each section accurately reflect the complexity of the line. Speed ​​limits are determined based on a variety of factors, including line structure, block method, speed limit commands, and weather conditions. This ensures that train speed control within each subsection meets actual operating requirements and can adapt to the impact of external conditions (such as weather changes) on 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, and the interval line parameter model is obtained, which includes: in each sub-interval, calling the train dynamic model parameters of the sub-interval from the line parameter knowledge base, and constructing 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 , Coasting state segment l c and brake status segment l b ; Each of the subintervals 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 deduction, the numerical calculation method will call the line parameters of the slope, 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 subinterval, 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 function, y(l r ) is the cruise 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 variables for traction, cruising, coasting, and braking conditions are combined and substituted into the unit resultant force formula for the train while running on the line. The unit resultant force formula and the unit additional resistance formula are then 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 precise target speed pattern curve inference model, dynamic control quantity combination, high-precision calculation of the fourth-order Runge-Kutta method, and 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.; and loading the speed limit values ​​of the sub-intervals.

[0094] In a preferred embodiment of the present invention, in the train operation status deduction method based on the dynamic model, the determination of the numerical calculation direction of each subinterval includes: taking the speed value of the last calculation point of the previous subinterval 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] A single subinterval may not have a calculation direction, or may have both forward and reverse calculation directions. In the calculation direction determination step, the present invention traverses the intersection of each subinterval to determine the calculation direction of each subinterval. This step needs to be performed at the beginning and end of each subinterval 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, and sudden changes in speed are avoided.

[0097] In a preferred embodiment of the present invention, in the 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 subinterval is the last subinterval, 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] Its technical effects are: by accurately setting the initial speed value, calculation deviation or error accumulation caused by inaccurate initial conditions during the deduction process is avoided; by re-judging 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 used in the first sub-interval and the last sub-interval respectively, which ensures that the speed change in the deduction process is consistent with the actual situation and avoids unreasonable speed mutation 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 size h, sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve, substitute into the interval line parameter model, and 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 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 deduction; if Figure 6 As shown, when the result 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 coordinate, the distance of a single step length h is moved from the position of the current data point, according to the current speed v of the current data point i , step size h, sub-interval parameters, braking condition control quantity combination, train weight and train characteristic curve, substitute 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 number of steps 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 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, substitute into the section line parameter model, calculate the speed value of the previous data point until the coasting start position, and obtain the speed estimated value {v'} of all data points between the coasting start position and the end position; 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 There is a current velocity v in {v'} that is greater than the current velocity v of 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 line starts 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 at the position coordinate l t , the duration of the cruise state in the position coordinate 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 deducing the traction working condition through forward calculation and deducing the braking working condition and coasting working condition through reverse calculation. In the process of forward and reverse calculation, the train can smoothly transition under different working conditions, especially between the traction state and the coasting state. By setting the traction end position and the coasting start position, it ensures that the train will not be unstable in the transition process from acceleration to deceleration or coasting. By recording the duration of the traction state, cruising state, coasting state and braking state in the position coordinates, the system can conduct a comprehensive analysis of the entire train operation process, providing rich data support for optimizing train operation, helping dispatchers or control systems to further optimize the train scheduling and control strategies based on actual operation data and improve operation efficiency.

[0101] Please refer to Figures 7 to 12, the second embodiment of the present invention provides a method for performing train operation status simulation on section line 1. Section line 1 has multiple ramps, 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] Subinterval 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 train running in section line 1 is 0 km / h, and the terminal speed is 0 km / 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 values ​​of the subintervals from the line parameter knowledge base and loads the speed limit values ​​as the speed information of the data points in the corresponding subintervals. That is, the speed information loaded for the 300 data points in subinterval 1 is 160 km / h, the speed information loaded for the 300 data points in subinterval 2 is 60 km / h, the speed information loaded for the 700 data points in subinterval 3 is 140 km / h, and the speed information loaded for the 300 data points in subinterval 4 is 160 km / 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 of 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 as follows Figure 8 shown.

[0109] (3) Numerical calculation

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

[0111] The numerical knowledge base obtains the traction working condition control quantity combination, train weight, train traction characteristic curve, and the numerical calculation method of train operation situation deduction from the starting point of sub-interval 1. The unit basic resistance of the line, train length, and the slope of the sub-interval are substituted 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 velocity 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, coasting condition control quantity combination, train weight, train braking characteristic curve, unit basic resistance of the train, train length, and sub-interval slope in thousandths 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 speed of the braking condition and the coasting condition is greater than the current speed value of the data point at the corresponding position. The train operation status deduction results of sub-interval 1 are as follows: Figure 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 start and end of sub-interval 2. The result of train operation situation deduction is as follows Figure 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 sub-interval slope 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 velocity v of the next data point cur The train operation status simulation results of sub-section 3 are as follows: Figure 11 As shown. The starting position of the traction state of subinterval 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 cruise, coasting and 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 sub-interval slope 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 velocity 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, coasting condition control quantity combination, train weight, train braking characteristic curve, unit basic resistance of the train, train length, and sub-interval slope in thousandths 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 speed of the braking condition and the coasting condition is greater than the current speed value of the data point at the corresponding position. The train operation status deduction results of sub-interval 4 are as follows: Figure 12 As shown, since the estimated speeds of the braking and coasting conditions are lower than the estimated results of the traction condition, the estimated results of the forward calculation are overwritten by the estimated results of the reverse calculation. The duration of the traction state in subinterval 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 cruise state in subinterval 1, so l t 、l r is 0.

[0115] It should be understood that the above-described specific embodiments of the present invention are merely illustrative or illustrative of the principles of the present invention and do not constitute limitations 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 within the scope of protection of the present invention. In addition, the appended claims are intended to cover all variations and modifications that fall within the scope and metes and bounds of the appended claims, or equivalents thereof.

Claims

1. A train operation situation deduction method based on a dynamic model, characterized in that: include: Based on 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 route into multiple sub-sections according to different location areas and speed restrictions; Calculating target speed pattern curve estimation models for different sub-intervals according to the distribution of train dynamics model parameters in different sub-intervals 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 numerical calculation in the specified direction is performed in each sub-interval using the section line parameter model to perform train operation status deduction; Calculating target speed pattern curve estimation models for different sub-intervals according to the distribution of train dynamics model parameters in different sub-intervals to obtain the section line parameter model includes: 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 pattern curve estimation model; The target speed pattern curve estimation model includes the traction state segmentation of the train operation , cruise state segmentation , Idling state segmentation and brake status segmentation ; Each subinterval contains multiple calculation points, which record the speed, time and position attributes of the train. The interval between adjacent calculation points is the step length. ; 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 , 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 variables 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. ,in, To run the situation function in the traction state, Run the attitude function for the cruise state, Run the posture function for the idle state, is the braking state running status function, where is the train running position, To simulate the control quantity of the train traction level change, is the unit traction force, is the unit basic resistance, is the slope of the i-th slope covered by the train in thousandths, is the train running speed, To simulate the control quantity of the train braking level change, is the unit braking force; 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 ; Take the speed value of the first calculation point of the current subinterval as the current speed ; like < , then the current sub-interval uses the estimated speed Perform forward calculations; like > , then the previous subinterval uses the current speed Perform reverse calculation.

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 based on the parameter data of the train running on the line. Calculate the unit force acting on the train while running on the line , the unit is N / kN, where is the train running time, Add resistance to the unit; Calculate the additional resistance of the unit ,in, is the train length, is the length of the ramp covered by the train at the i-th ramp, is the calculated length of the train covering the i-th curve, is the curve radius of the train covering the i-th curve, is the unit tunnel resistance of the i-th tunnel covered by the train, is the tunnel length of the i-th tunnel covered by the train; Constructing the train dynamics equations ,in, is the train running acceleration coefficient, is the unit force acting on the train; The train dynamics equations are solved by numerical calculation using the fourth-order Runge-Kutta method. The calculation method is: ,in, is the standard form of a first-order ordinary differential equation, and is the initial condition of the differential equation, As the step size, the train dynamics model is obtained ,in, is the target speed curve, is the speed-time curve, 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 basic resistance of the unit According to the empirical formula; The unit traction force Calculated according to the traction characteristic curve; The unit braking force Calculated according to the braking characteristic curve; The train running acceleration coefficient The 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 operation route is divided into multiple sub-sections according to different location areas and speed limits, including: The train operation 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 by the line structure, blocking mode, 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: when hour, ; when hour, .

6. 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 sub-intervals within a given interval from a line parameter knowledge base; Load the speed limit value of the sub-interval.

7. The train operation situation deduction method based on dynamic model according to claim 1 is characterized in that: If the current subinterval is the first subinterval, the initial speed value of the first subinterval is taken. As the estimated speed ; 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. ; If the current sub-interval is the last sub-interval, the direction is determined again at the end of the given interval, and the end speed of the entire given interval is used. As the current speed .

8. The train operation situation deduction method based on dynamic model according to claim 1 is 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 result of the determination of the numerical calculation direction to perform train operation status deduction includes: When the result of the determination of the numerical calculation direction is positive, the train operation process under the traction working condition is deduced; Move a single step from the current data point's location The distance, based on the current speed of the current data point , step length , sub-interval parameters, traction condition control quantity combination, train weight and train characteristic curve, substitute into the interval line parameter model to obtain the estimated speed value of the next data point ; If the estimated speed Less than the current speed of the next data point , then the estimated speed Set to the current speed for the next data point , ; If the estimated speed Greater than the current speed of the next data point , then the position of the current data point is recorded as , and stop the forward calculation; When the result of the numerical calculation direction is reverse, the train operation process under the braking condition and the coasting condition is deduced; Move a single step from the current data point in the direction of decreasing position coordinates The distance, based on the current speed of the current data point , step length , sub-interval parameters, braking condition control quantity combination, train weight and train characteristic curve, substitute into the interval line parameter model to obtain the speed estimation value of the previous data point ; Reverse calculation moves in the direction of decreasing position coordinates Step length, number of steps Corresponding train coasting transition distance , satisfying the relationship ; Each single step , according to the current speed of the current data point , step length , sub-interval parameters, coasting condition control quantity combination, train weight and train characteristic curve are substituted into the section line parameter model, and the speed value of the previous data point is calculated until the coasting start position, and the speed estimated values ​​of all data points between the coasting start position and the end position are obtained. ; like and are all less than the current speed of the data point at the corresponding location , then and Set to the current speed of the corresponding data point ; like and There is a current speed greater than the data point at the corresponding position , then The location information of the data point is recorded as , record the position information of the data point where the idle movement starts as , is the subinterval length, and stops the reverse calculation process; Record the duration of the traction state in the position coordinate , the duration of the cruise state in position coordinates , the duration of the coasting state in position coordinates and the duration of the braking state in position coordinates .

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