TWI277549B - Automatic fixed-position stop control device for train - Google Patents

Abstract

The objective of the present invention is to achieve energy-saved operation capable of reducing energy loss that occurs during traveling. To solve the problem, there is provided an automatic fixed-position stop control device for train, which makes the train stop at a fixed position. The automatic fixed-position stop control device is characterized in including: a braking feature data storage part for storing braking feature data such as deceleration for each braking level of the train, delay time of switching braking level and response delay time; a current train data obtaining means for obtaining current train data such as current speed, current position and current braking level; a deceleration control plan making means for making a deceleration control plan with a purpose of using a plurality of braking levels to make the train stop at a specific position according to the braking feature data stored in the braking feature data storage part and the current train data obtained from the current train data obtaining means; a deceleration control command extracting means for extracting the deceleration control command at each time point from the deceleration control plan planned by the deceleration control plan planning means; and a deceleration control command output means for outputting the deceleration control command extracted by using the deceleration control command extracting means to a braking device.

Description

1277549 (1) TECHNICAL FIELD The present invention relates to a train position stop automatic control device that does not require a driver to stop an automatic operation of a train at a specific time at a specific time. [Prior Art] φ Automatic The train running device (hereinafter referred to as "ΑΤΟ") automatically performs the inter-station operation of the train, and the train is stopped at a specific stop position of the lower station at a specific time. Fig. 47 is a diagram showing an example of the system configuration of the electric train having such a crucible. The automatic train control device (ATC) not shown on the figure inputs a speed limit signal to the automatic train running device 1, and the data bank 3 inputs the route conditions such as the slope and curvature, the vehicle condition, and the running time table to the automatic train running device 1. And stored information such as driving resistance. Further, the automatic train φ operating device 1 calculates the current vehicle position based on the vehicle position detected by the ground sub-detector 10 and the vehicle speed detected by the speed detector 9, and inputs a thrust command F cmd to the drive brake device 2, Indicates the thrust that should be provided at that point in time. At this time, the thrust command Fcmd in this specification is defined as both the traction force command at the time of vehicle acceleration and the braking force command at the time of vehicle deceleration. The traction command is the thrust command F cmd > 0, and the thrust force is the thrust command Fcmd < 0. The drive brake device 2 is composed of a VVVF (variable voltage, variable frequency) variable frequency transformer inverter 4, a main motor 5, a brake control device 6, and a mechanical 煞 1277549 (2) vehicle 8. The main motor 5 and the wheel 7 running on the rail 11 are connected to each other, and in the arrangement of the mechanical brake 8, the wheel 7 can be mechanically braked. The action from the thrust command F cmd to the actual thrust is different depending on the traction force and the braking force. When the traction is obtained, the thrust command F cmd ( > 〇 ) is input to the variable frequency inverter 4 . The variable frequency variable voltage inverter 4 controls the torque of the main motor 5 to obtain the traction force corresponding to the thrust command F cmd . At this time, the brake control device 6 and the mechanical brake 8 do not perform an operation. When the braking force is obtained, the thrust command F cmd ( < 〇 ) is input to the brake control device 6 instead of the inverter variable voltage inverter 4. First, the brake control device 6 outputs a thrust command, that is, a brake force command, to the variable frequency inverter 4 . The inverter variable voltage inverter 4 feeds back the electric vehicle force F elec outputted via the main motor 5 to the brake control device 6. In order to obtain the thrust command F cmd, that is, the braking force of the braking force command, the brake control device 6 will first act on the electric vehicle force F elec, and compensate the electric braking force by the mechanical braking force F mech of the mechanical brake 8 . Part of the way to control the mechanical brakes 8. Therefore, the mechanical braking force F mech is as follows. F mech = F cmd - F elec (1) As shown in Fig. 48, the automatic train running device 1 includes a tentative driving plan unit 12, an optimum driving plan unit 13, and a thrust command generating unit 14. The tentative driving plan 12 will generate a tentative driving mode (F0(x), V〇(x)) as the initial 为 for the purpose of producing the best driving mode. At this time, the driving mode indicates the position -6 - 1277549 (3) on the route, the thrust Fn (x) of x, and the speed Vn (x) in a manner corresponding to a series of positions. The Best Driving Plan 13 will plan the train's best driving mode FI ( X ) according to the fish tentative driving mode (FO (X), V0 (X)) and the storage information of the database 3. In the optimal driving mode FI (X) generated, the thrust command generating unit 14 outputs a thrust command F cmd to the variable frequency variable voltage inverter 4 according to the detected position of the train, the detection speed, and the speed limit signal of the ATC. The thrust that should be output should not be used at that time. When planning the best driving mode for a train, in general, there are countless φ possible driving modes. In particular, it is different from the morning and evening when the train schedule is too small. During the day, morning, or late night when the number of trains running is small, the train has a long interval, so there is a large margin on the plan. There are fewer restrictions. Japanese Laid-Open Patent Publication No. Hei 8-2 1 6885 and Japanese Laid-Open Patent Publication No. Hei No. 5-1 93502 disclose the best driving plan for energy saving as an evaluation item. However, the energy savings of these known examples are not considered from the standpoint of energy loss caused by the driving/braking control of trains such as the drive unit and the brake unit. In this regard, "Basic Review of the Effective Use of Recycling Energy Changed by the Brake Mode" (September 7 of the Suimoto Railway Technology Joint Conference) and "Review of the Practicalization of Pure Electric Vehicles" (National Electric Society National Convention 5 In -244), the braking control of the train, especially the driving mode of the energy loss caused by the mechanical braking caused by the braking, is reviewed. However, the energy loss caused by the driving brake control of the train is also generated during the drive control. In addition, when the brake is controlled, there are other factors that cause energy loss in addition to the mechanical brake. Therefore, the minimum of 1277549 (4) of integrated energy loss cannot be achieved. [Problem to be Solved by the Invention] The object of the present invention is to comprehensively evaluate the energy loss caused by train driving brake control, to minimize the energy loss between stations, and to realize energy saving. Therefore, the following is a brief description of the energy loss of the present invention. The loss caused by φ train driving will change due to the driving mode, and the machines that may cause losses can be divided into the following two categories. The first is the energy loss of the power inverter such as the variable frequency inverter 4 of the driving device and the main motor 5. These losses can be expressed as a function of thrust and speed. The second is the energy loss caused by the mechanical brakes. Observing the acceleration and deceleration of the train from the point of view of energy flow, and ignoring the energy loss and driving force of the above-mentioned electric machine, in the running acceleration, the inverter is not converted by the unillustrated wiring, and the inverter is used. The electric motor 5 or the like drives the electric energy to be converted into the kinetic energy of the vehicle, and in the deceleration of the electric vehicle, the kinetic energy of the vehicle is converted into electric energy and the power is generated again. In this ideal state, there is no energy loss. However, in the deceleration of the electric vehicle, when the braking force command of the electric vehicle or the driver exceeds the braking force that can be output by the electric machine, the mechanical braking device 8 compensates for the insufficient braking force, and the deceleration is maintained at a specific speed. When the mechanical brake 8 performs this action, the kinetic energy of the vehicle is consumed in a thermal manner, which is energy loss. In the present invention, the portion of the loss caused by the execution of the mechanical brake is defined as the vehicle loss. -8- 1277549 (5) This brake loss occurs when the brake force command exceeds the load of the electric machine, that is, the drive to the valley, and the load on the power supply side does not exist in accordance with the regenerative power. In the latter case, if the driving device obtains the braking force command, the inverter variable voltage inverter 4 is controlled so that the main motor 5 outputs the braking force corresponding thereto. At this time, the kinetic energy of the vehicle is converted into the regenerative energy of the power source. However, if there is no load corresponding to the regenerative power on the power supply side, that is, if there is no train in the acceleration, excess regenerative power is generated, thus causing the wiring. The electric pressure rises. Therefore, in order to suppress the rise of the overhead voltage, the drive device performs control for suppressing the braking force. This is called light load regeneration control. In this light load regeneration control operation, the main motor 5 outputs a braking force smaller than the braking force command. At this time, the lack of power will be compensated by the power of the mechanical brakes. · , -. . · When implementing energy-saving operation, it is important to plan an appropriate driving mode plan and to actually implement driving according to the driving mode. A means for realizing the operation in accordance with the driving mode, an automatic train running device (ΑΤΟ), and a self-rotating train stop Ih device (TASC), which are automatically generated without a driver, are well known. With these devices, it is possible to smoothly push the actual thrust to achieve the best driving mode. However, the system is complicated and costly because it directly targets the driving brake device of the vehicle and requires the ground device for the purpose of position detection. On the other hand, by using the thrust of the driver to indicate the best plan, it is possible to expect a train that is close to the planned driving mode by the skill of the driver. This is the operation support device. When such a running support device is used, the energy saving effect is better than the use of-9- 1277549 (6), AT Ο and ΤΑ SC due to the driver's reaction delay, etc. However, it is only necessary to give instructions to the driver. However, the driving brake device of the vehicle is not directly related, so it has the advantage of simplifying the system. Further, since the operation of the driver is relied on in the end, the ground device or the like for the purpose of position detection can be removed or simplified. In this way, the cost can be reduced, and the cost is better. In addition, in recent years, there has been a concern that the driver's driving technique is lowered due to degeneration. Therefore, when the operation support device is used, the thrust must be adjusted at any time according to the judgment of the driver, so that there is no problem that the driving technique is lowered. Further, the automatic train running device has been put into practical use as a speed limit that can follow the speed limit of the train and a certain degree of margin with the speed limit. However, because of the error tracking control such as ΡΙ control, there are quite a lot of places that depend on the characteristics of trains and routes. In the current situation, it is necessary to adjust the characteristics of each train and each route to control the parameters. Time and labor. In addition, the driving plan is proposed and the train is executed according to it: the automatic train φ car running device is also considered. When planning a driving plan, the simple train driving model is sometimes used. The simplest is the way in which the trains of its objects can be represented in the following simple physical form. F - Fr = Μ · a ... ( 7 ) At this time, F system runs traction or braking force, Fr series vehicle driving resistance, Μ series vehicle weight, α system acceleration (including negative acceleration, ie deceleration) ). The resistance generated by the train driving resistance Fr series vehicles is usually only considered for the convenience of calculation. Starting resistance: resistance at the start of the line-10- 1277549 (7) Air resistance: air resistance when the train is driving 1 Slope resistance: slope of the line resistance curve resistance: curve of the curve resistance resistance of the tunnel: resistance generated when driving in the tunnel If the air resistance considers the resistance between the wheel tread and the track surface, the speed is usually used twice. In general, the train running resistance Fr is usually considered for the resistance formed by the slope resistance, φ air resistance, curve resistance, tunnel resistance, and starting resistance. Here, it is considered when driving a train other than a tunnel, so only slope resistance, air resistance, and curve resistance are considered. At this time, the slope resistance, the air resistance, and the curve resistance can be obtained by the following equations (8), (9), and (10) (for example, refer to the document "Operation Theory (DC AC Power Vehicle)" Edit). (a): slope resistance

Frg=- s ... ( 8 ) Lu Frg: slope resistance [kg weight / ton] s: slope [%G] (positive on uphill, negative on downhill) (b) air resistance

Fra= A + Bv + Cv2 ( 9 )

Fra: air resistance [kg weight / ton] A, B, C: coefficient v: speed [km / h] (c) curve resistance

Frc = 800/r r 1 π ^ -11 - 1277549 (8)

Frc: curve resistance [kg weight / ton] r: radius of curvature [m] When using the model shown in equation (7) for automatic train operation, even for the automatic train operation mode based on the driving plan, train characteristics and route characteristics, etc. The characteristics also have a great impact on ride comfort and stopping accuracy. [Disclosed] φ [Means for Solving the Problem] The present invention is based on the premise that a train stops at a specific time at a specific time when the train is traveling between stations, and an object thereof is to provide an automatic train running device and a train running support device. Energy-saving operation is achieved by reducing the energy loss caused by driving. Moreover, the object of the present invention is to provide an automatic train running device, which can reduce the time and labor required for adjustment, and can automatically perform characteristic learning after driving, thereby further improving ride comfort, and at the same time increasing Stop accuracy. Further, it is an object of the present invention to provide a device for performing a necessary data collecting operation for the operation of a running device only when the train travels back and forth on a specific route. Further, an object of the present invention is to provide an automatic train running device which can realize: first, to eliminate the influence of chasing when the train is automatically operated, and to improve the effect of saving energy; and secondly, to obtain the delay time, Improve the stop accuracy of the target position; third, it can improve the poor ride comfort caused by the change of the speed control command during the execution level operation. -12- 1277549 (9) Further, the object of the present invention is to provide a train position stop automatic control device which can ensure stop accuracy without frequent switching of the level and does not require a long adjustment period. In order to achieve the above object, the train position stop automatic control device of the present invention automatically stops the train at a specific position, and has the following features: a deceleration for storing each brake level of the train, a delay time for switching the brake level, and a response delay. For the information on the current characteristics of the train, the current position, the current brake level, etc., the information on the train's current data acquisition means: based on the information stored in the "Motor Vehicle Characteristics Data Storage Department" Information on the characteristics of the vehicle and the "deceleration control plan" for the deceleration control plan for the purpose of stopping the train at a specific position by a plurality of trains. The deceleration control plan proposed by the "Deceleration Control Plan Drawing Means" outputs the "Deceleration Control Command Precipitation Means" of the deceleration control command at each time point, and the deceleration control command that is released by the "Deceleration Control Command Precipitation Means" is output to the brakes. "Deceleration control command output means" of the device. [Embodiment] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. Fig. 1 is a block diagram showing a schematic configuration of an automatic train operating device according to a first embodiment. Therefore, the embodiment is particularly relevant to the optimal driving plan of the automatic train running device, and the other parts are omitted. The best driving plan unit 13 shown in Fig. 1 is compensated by driving mode-13-1277549 (10) index calculating unit 15 , driving mode compensating unit 19 , driving distance compensating unit 20 , 定时 and timing The determination unit 21 is configured. The driving mode compensation index calculation unit i 5 is composed of a loss index calculation unit 16 , an overload indicator calculation unit 17 , and an adder 18 . The loss index calculation unit 16 calculates the loss index CPL ( x ) of the train position x based on the tentative driving mode (F() (X ), V0 ( X )). At this time, the CPL is Co st of Power Loss. At this time, the driving mode is expressed by the thrust Fn(x) and the speed Vn(x) of a certain position X. φ Figures 2 and 3 are examples of various loss indicators. Figure 2 shows the loss index for the trip, and Figure 3 shows the loss indicator for the brake deceleration. Further, in more detail, Fig. 2(a) is a machine loss index, Fig. 2(b) is a total loss indicator, Fig. 3(a) is a machine loss indicator, and Fig. 3(b) is a brake loss indicator. Figure 3 (c) is the total loss indicator. Here, the machine loss indicator refers to the loss index of the electric machine. Specifically, it is the addition indicator (frequency conversion transformer) loss indicator and the motor (main motor) loss indicator. φ These indicators are expressed as a function of velocity v and thrust F and are calculated by multiplying the loss [W] of a certain operating point (v, F) by the reciprocal of velocity [m/s]. By multiplying the reciprocal of the speed, a formal evaluation can be performed on the loss caused by the slight change Δ v[m/s] at the speed vl [m/s] of an operating point. The total loss indicator CPL (X) is calculated by multiplying the weight loss factor W1 by the total of the machine loss indicator and the braking loss indicator. The weighting factor W1 is set from the viewpoint of the degree of loss reduction effect that can be obtained, or is set to be balanced with other indicators. That is, the loss index CPL (X) -14 - 1277549 (11) = Wlx (machine loss index + brake loss indicator). (2) The overload indicator calculation unit 17 is based on the tentative driving mode (FO (X), V0 (X)) Calculate the overload indicator COL (X) of the train position X. COL is the Cost of Over Load 〇 Machine loss is the sum of converter losses and motor losses. Fig. 4 (a) and (b) are examples of converter loss [W] and motor loss [W] at each operating point. According to the tentative driving mode (FO (X), V0 (X)), the corresponding converter loss [W] and motor loss [W] are integrated respectively, and the converter loss plus the time between stations can be calculated [J ] and motor loss [J]. If it is overloaded beyond the specification 値 [W], the corresponding overload indicator is calculated. For example, if the weighting factor is W2, the converter loss indicator COLC (X) can be COLC (X) = W2x {converter loss [J] / (travel time + stop time) • a converter specification [W]}x The converter loss indicator (Fig. 5(a)) is calculated. COLC system Cost of Over Load in Converter 〇 Similarly, the motor loss indicator COLM ( X ) can be obtained using the motor loss indicator shown in Figure 5 (b) (however, the weighting factor is W3, which can be set separately). The overload indicator COL(x) can be added to these metrics and is obtained by 1277549 (12) COL(x) = COLC(x) + COLM(x) ... ( 4 ). COLM is the Cost of Over Loss in Motor. Adder 1 8 adds the loss indicator CPL ( x ) and the overload indicator COL(x) to

C(x) = CPL ( x ) + COL ( x ) to find the total index C ( x) of the train position x. The driving mode compensation unit 19 adds the total index C (X) to the thrust mode F0 (X) of the tentative driving mode, and outputs the first compensation driving mode F01 (X) (in this stage, the speed mode VO (X) does not Will change). The first compensation driving mode F01 (X) does not match the specific 値 because the thrust mode is only compensated. In order to make the driving distance X and the specific 値 coincide, the driving distance compensation unit 20 performs the first compensation driving mode (F01 (X), V0 (X)) according to the route condition stored in the database 3, the vehicle condition, and the driving resistance. Compensation, and output the second compensation driving mode (F02 (X), V02 (X)) and the travel time T run. The distance compensation can be implemented by, for example, adjusting the taxi time. However, the distance compensation method is not limited by this. The timing determination unit 21 determines whether or not the travel time T run is within the allowable error for the specific 値. When the travel time T run is outside the tolerance,

-16- 1277549 (13) The second compensation driving mode (F02 ( x ), V02 ( x )) is regarded as the new tentative driving mode (FO'( X ), V0' ( X )), and the calculation is re-executed. When the travel time T run is within the tolerance, it is then output as the best driving mode (FI ( X ), V1 ( X )). With the above configuration, the tentative driving mode (FO (X), V0 (X)) can use the loss index CPL (X) and the overload indicator COL (X) to make the thrust of the position X significantly compensated. For example, Fig. 6 is a result of the driving mode of the present invention. At this time, it is assumed that the overload condition has not been reached, so the overload indicator has no effect. The "original mode (A)" is a tentative mode. The "Applicable indicator (B)" is the first compensation driving mode (F01 (X), V01 (X)). The higher the loss index is, the more the thrust compensation is implemented, and the weaker the braking force will be. On the other hand, although the running acceleration side is small, it also compensates for the traction force corresponding to the loss indicator. The "level quantization (C)" does not appear in the embodiment of the present invention. However, if the level is only 6 segments, if the continuous thrust cannot be output, the jp is selected for the thrust F01 (X) of the first compensation driving mode. The thrust with the lowest thrust error. "Distance adjustment (D)." is the second compensation driving mode (F02 (X), V02 (X)) for compensating the driving distance to a specific 値1 300m for the "Level Quantization (C)" mode. The loss of the 2070 [kJ] ’ second compensation driving mode relative to the pre-compensation driving mode is 1 650 [kJ], which reduces considerable energy loss. In terms of driving time, the latter is 84.5 [sec] compared to the former, and the latter is increased by 8 4.9 [sec]. By repeating the calculations until the specific travel time becomes a specific time, the drive brake can be generated while ensuring the timing and the positional stopability. 17-1277549 (14) The optimal driving mode F 1 (x) for minimizing the energy loss on the control ). With this formula, you can achieve the best energy saving effect while ensuring timing and positional stoppage. When only the driving mode in which the total energy loss is minimized is performed, the energy loss caused by the electric machine such as the inverter variable voltage inverter 4 (converter) and the main motor 5 (motor) included in the driving brake device 2 may be increased. . The operating range of the power machine is limited by the specifications. When the operating condition exceeds the specification, the temperature is increased due to heat, and the protection action or malfunction or burnout occurs. The overload index calculation unit 17 determines the degree of overload of each machine for the tentative driving mode. When the result of the judgment is overloaded, the purpose of suppressing the energy loss of the electric machine is to compensate the thrust of the overload indicator. Since the thrust compensation is performed from a region where the energy loss is large, the overload state can be effectively avoided. In this way, it is possible to prevent the power machine from being overloaded and causing the operation to stop and malfunction, and to improve the reliability of the system. # Because the best driving plan is also implemented in the train, the position and speed of each moment can be used as In the initial condition, and in the case of ensuring the timing and stop position of the stop to the next station, the optimal saving can be achieved. That is, when the current driving mode is deviated due to the speed limit of the ATC or the like, the optimal energy saving driving mode can also be obtained from this state. If you follow the original driving mode, you may increase the loss and not the energy loss. Therefore, even in the event of an accident that deviates from the original driving mode, optimal energy saving can be achieved from that point in time. In this embodiment, the position and speed are used as initial conditions, and it is ensured to

1277549 (15) Timing of the next stop • When the position is stopped, the best energy-saving driving mode is generated, so it can be applied not only to the automatic train running device (ΑΤΟ) that implements the automatic train operation between stations. It can be applied to the train automatic stop control device (TASC) that implements the fixed position parking control only in the braking section. Further, in the present embodiment, it is assumed that the driving distance and the specific 値 are matched, and the configuration is such that the calculation of the compensation of the driving mode φ is performed until the traveling time reaches a certain 値, and conversely, the configuration may be The travel time is the same as the specific 値, and the calculation of the compensation of the driving mode is implemented until the driving distance reaches a certain level. Figure 7 is a block diagram showing a schematic configuration of an automatic train running device according to a second embodiment, and the same portions as those in the first embodiment are denoted by the same reference numerals, and the description thereof will be omitted. Instructions are given. The database 3 inputs the operation schedule to the loss index calculation unit 16, and the database 36 inputs the operation load amount to the loss index calculation unit 16. The operating load of φ in the database 36 is stored, and the running power of each train in a certain time is the running load of the train. The loss index calculation unit 16 extracts the corresponding operational load from the log table of the running schedule and the running load. As mentioned above, since the loss of the brake will change due to the running load, the loss index corresponding to the running load is calculated. Others are the same as in Figure 1. From the above, the following effects and effects can be obtained. Corresponding to the predicted operational load, the adjustment loss indicator CPL ( X ) ′ is especially the braking loss indicator. For example, Figure 3 (b) has a full operating load.

-19- 1277549 (16) The hourly vehicle loss index, because of the limitation of the capacitance of the inverter transformer 4, the higher the speed of the high-speed high-speed vehicle, the greater the loss index. Fig. 8 shows the vehicle loss index when the load is fully operated (125 kW / main motor). At this time, because the running load is insufficient, it is impossible to output the area of the electric vehicle force equal to the thrust command F cmd . That is, the loss indicator will start to increase from the lower speed. Therefore, the energy loss caused by the load state can be reliably predicted, and more efficient energy saving driving can be realized. Φ Fig. 9 is a block diagram showing a schematic configuration of an automatic train running device according to a third embodiment, and the same portions as those in Fig. 16 are denoted by the same reference numerals, and the description thereof will be omitted. Be explained. The apparatus of Fig. 9 is provided with a database 34 and a driving mode separating unit 35 for replacing the tentative driving plan unit 12 and the optimal driving plan unit 13 of Fig. 48. The data bank 34 stores the driving mode at the time of driving between the stations of each train. The driving mode precipitation unit 35 deposits the driving mode FI (X) corresponding to the current inter-station driving from the database 3 storing the running time table. The driving mode stored in the database 34 can be realized by the following method, that is, the best driving plan shown in the first embodiment is executed in advance, and the final driving mode of the result is stored. According to the above configuration, the following effects and effects can be obtained. In the generation of the optimal driving mode, since the convergence calculation is repeatedly performed to execute the optimal plan, it takes some time to calculate. Therefore, when the driving plan of the next station is implemented in the parking at the departure station, the calculation time is sometimes limited and the optimum is not sufficient. Implementing these plans in advance avoids the limitation of computing time and gets the best driving mode. In this way, you can increase the energy saving effect by stepping into -20- 1277549 (17). In addition, the driving mode is calculated in advance, and the driving mode can also be confirmed. In this way, the abnormal mode can be eliminated and the reliability of the system can be improved. 10 is a schematic block diagram of a train system having a train operation support device according to a fourth embodiment, and the same portions as those in FIG. 47 will be denoted by the same reference numerals, and the description thereof will be omitted. . Here, there is provided a train operation support device 22 in place of the automatic train operation device φ of the first embodiment. The train operation support device 22 performs the same processing as the automatic train operation device 1 of the first embodiment, and generates and outputs a thrust recommendation 値 Free. That is, the train operation support device 22 outputs a thrust recommendation 値 Free for replacing the thrust command F cmd of the automatic train running device 1. This thrust recommendation 値Free is input to the thrust indicating device 24 provided to the main controller 23. The main controller 23 outputs a thrust command F cmd corresponding to the angle or position of the main controller to the drive brake device 2. The configuration of the thrust indicating device 24 is as shown in Fig. 1 for example. The thrust indicating device φ 24 is composed of an angle command calculating unit 25, an impedance controller 26, a servo amplifier 27, a servo motor 28, and an encoder 29. The servo motor 28 and the main controller 23 are mechanically coupled. The thrust recommendation 値Free outputted by the train operation support device 22 is input to the angle command computing unit 25. The angle command computing unit 25 calculates the angle of the main controller corresponding to the thrust recommendation 値Free of the input, and outputs it as an angle command Θ cmd. The impedance controller 26 inputs the angle command Θ cmd and the actual main controller angle 检测 detected by the encoder 29, and outputs the servo amplifier 27 so that the latter (angle Θ) and the former (angle command Θ cmd) 1277549 (18) Torque command T cmd for the purpose. The servo amplifier 27 drives the servo motor 28 ° in such a manner that the output torque of the servo motor and the torque command T cmd are driven. The impedance controller 26 applies a torque To ope to the main controller 23 for the driver to form a desired The servo motor 28 is controlled by the impedance (the moment of inertia J, the damping D, and the stiffness K). The block diagram of the control system is as shown in Fig. 2, and the rotor of the servo motor 28 and the main controller 23 are equal. The effect moment, φ gl and g2 are equivalent to the cutoff frequency of the filter for the purpose of removing interference. When the angle command Θ cmd is zero, the torque applied to the main controller 23 from the outside, that is, the torque applied by the driver to the main controller 23 reaches the main controller angle Θ, the transfer function Θ ( s ) If the interference cutoff filter is ignored, it can be expressed by the following equation, so that the desired impedance (J, D, K) can be obtained. 〇e^7.s^.s+KmTope (6) The above components have the following effects: effect. The thrust indicating device 24 controls the angle of the main controller 23 with the servo motor 28 to obtain a thrust command F cmd which is calculated by the train operation support device 22 and which is free. In this manner, when the driver operates the main controller 23, the impedance of the impedance controller 26 is controlled to make the driver feel that the desired impedance (J, D, K) has been reached. That is, the driver can obtain the thrust command F cmd corresponding to the thrust recommendation 値Free without the main controller 23 being touched. On the other hand, when the driver operates the main controller 23, -22- 1277549 (19) can withstand the thrust from the servo motor 28 to the thrust direction 値 Free direction, and can be set at any angle, that is, the thrust command F Cmd. That is, the driver can also delegate the driving to the train operation support device 22, and if necessary, the driver operates the main controller 23 and controls the thrust command according to the consciousness. The angle Θ of the main controller 23 for the purpose of energy-saving operation can be detected by the reaction force from the main controller 23, and the driving can be performed with the realization of the energy-saving position stop mode. Therefore, in addition to profitable φ, the driver's operation is used to realize energy-saving driving and stop driving at a fixed position, and in the event of an unexpected situation, countermeasures can be quickly taken. The thrust command F cmd for driving the brake device 2 is not directly controlled by the train operation support device 22, but is provided by the angle Θ of the existing main controller 23, and the system can be simplified. Further, in the case of the train operation support device, it is necessary to pass the driver afterwards, and it is not necessary to require the train operation support device 22 to have strict positional stop accuracy, so that the device can be simplified. In this way, the reliability and cost of the system can be improved. φ In addition, the train operation support device needs the driver after all, so the driver's operation technology needs to be required at any time. According to this embodiment, it is possible to avoid the problem that, in the case of a system having an automatic train running device, it is possible to cope with an unexpected problem due to a decrease in the operating technique of the driver. Figure 13 is a block diagram showing a schematic configuration of a train operation support device according to a fifth embodiment. Since the configuration of the thrust indicating device 24 is different from that of the fourth embodiment in the present embodiment, the different portions will be described here. However, in the present embodiment, the thrust command is accelerated by six stages (P1 to P6), the vehicle is decelerated by six stages (B1 to B6), and the neutral position (N), and the emergency 煞-23-1277549 (20) vehicle (EB) ) The main controller level mode is adopted. ^ The grade here refers to the model used to drive the speed versus thrust and is used in the current tram drive control. The number of segments can range from a few segments to more than 30 segments, depending on the system. Further, the main controller 23 of Fig. 13 is a schematic configuration when viewed from above. The thrust indicating device 24 is composed of a recommended level indicating the control unit 30 and the lamp group 3 1 . In the embodiment shown in the figure, the lamp group 31 is composed of six lamps corresponding to the φ stages P1 to P6 such as the running acceleration, six lamps corresponding to the brake deceleration levels B1 to B6, lamps corresponding to the neutral level N, and corresponding It consists of an emergency brake class EB lamp, which consists of 14 lights. The recommended level indicates that the control unit 30 receives the recommended level command N rec of the train operation support device 22, and executes the control in which the corresponding lamp is lit. According to the above configuration, the following effects and effects can be obtained. The driver can use the lighting to confirm whether or not the level is set to achieve the goal of saving energy in the case of ensuring the timing and the stop position. φ For example, the content of the recommended level command N rec is the running acceleration level P6, and its corresponding lamp will illuminate, and for the brake deceleration level B3, the corresponding lamp will illuminate. The driver observes the condition of the lighting, performs the level operation of the corresponding main controller 23, and realizes the energy saving operation for suppressing the energy loss. There is no direct electrical/mechanical connection between the thrust indicating device 24 and the driving brake control system, and the driver's operation is required. Therefore, in the event of an unexpected situation, the driver can quickly respond according to the judgment of the driver, and the system is improved. Trustworthiness. The lamp and the display device using the LED (light-emitting diode) are easier to implement and improve the reliability of the system, compared with the servo mechanism of the main controller 23 of the fourth embodiment. The cost of the device can be further reduced. Fig. 14 is a block diagram showing a schematic configuration of a train operation support device according to a sixth embodiment. In the present embodiment, the configuration of the thrust indicating device 24 is different from that of the fifth embodiment. Therefore, only the different portions will be described. The thrust indicating device 24 of the present embodiment is represented by a recommended level indicating the control unit 32, And the sound output unit 33 is configured. When the recommended level command N rec is received from the train operation support device 22, the recommended level display control unit 32 controls the sound output unit 33 to output the corresponding voice. For example, when the recommended level is B3, a voice such as "Block 3" will be issued. With the above configuration, the following effects and effects can be obtained. The driver can know by voice the level of energy saving driving in the case of ensuring timing and positional cessation. According to this aspect, the same effects and effects as those of the fifth embodiment can be achieved. When the recommended level is indicated by a lamp in the fifth embodiment, the driver's attention will be focused on the expression, and as a result, an accident may occur due to failure to pay attention to the front. In contrast to this, using the instructions of the voice, this problem can be avoided and the reliability of the system can be improved. Fig. 15 and Fig. 16 show an embodiment of the automatic train running device of the present invention. The automatic train running device (ΑΤΟ) placed on the train 0 shown in the figure receives the speed limit data from the automatic train control device (ATC) 102 of the ground system, and acquires the route condition from the database (DB) 103 in the train 0. (Slope angle and radius of curvature of the curve, etc.), vehicle conditions (train-25-1277549 (22) number of cars, weight, etc.), and operating conditions, etc., will also obtain the departure signal from the driving platform 104, from the load The device 105 obtains the load signal, obtains the train speed signal from the speed detector 1 〇6, and obtains a signal of the train position from the ground-up sub-detector 107 that responds to the ground-level sub-segment that is appropriately placed on the route. The above-ground sub-systems that are appropriately placed on the route are used to confirm the train position. Here, DB103 indicates that it is placed in the train 0, and may be a ground system located outside the train, and may be distributed φ in the train 0 or on the ground. In addition to the data processing means 180 and the automatic train operation means 181 for performing on-line data processing, the ΑΤ0 108 includes estimation means and learning means represented by the pre-employment characteristic estimating means 124 and the post-business characteristic learning means 134 which will be described later. The data processing means 180 will process the train speed signal, in addition to the processing of the train speed, the train position (time integral of the speed 値), the train acceleration (the differential 速度 of the speed), and the train travel distance (the time of the absolute speed) Points 値) Bin Shi continuous operation Xin. From the train position to the train driving distance, moderate compensation will be implemented according to the train position signal of the ground sub-detector. The data processing means 18 0 performs a specific calculation based on each input signal, and provides the measurement data necessary for the learning and automatic train operation described later. The necessary measurement data for the automatic operation of the train will be provided to the train automatic operation means 81. The train automatic operation means 1 8 1 outputs an operation command to the drive unit 9 or a deceleration command to the speed reduction device 110 in accordance with the result of calculation using each input data. The drive unit 109 includes a main motor for the purpose of towing the train, and a power converter for controlling the same. Moreover, the reduction gear unit 110 usually has both a mechanical brake and an electric -26- (23) 1277549 brake. The AT01 08 is placed on the train, and the pre-business characteristic estimation means 124 and the post-business characteristic learning means related to the learning of the present invention are provided! The part of 34 is shown in detail in Fig. 16. It is the pre-business driving judgment means 〇2, the pre-business characteristic initial setting means 1 2 1 , the pre-operating test driving automatic train means 122, The driving result storage means 123, the pre-service characteristic estimating means 124, the estimation result compensating means 125, the characteristic estimating means storage means 126, the learning characteristic database (learning characteristic DB) 1 30, the characteristic initial setting means 131, and the automatic train running means 132. The post-business driving result storage means 133, the post-business characteristic learning means 134, and the learning result compensation means 135 are comprised. The means 121 to 126 are processing means for the purpose of driving the vehicle before the driving test, and the means 13 1 to 135 are the processing means for the purpose of the business trip and the rear of the vehicle, and the driving determination means 1:20 and the learning characteristic DB 130 It is not related to the business before and after the business, but is set in a way that is shared by both. In Fig. 16, the data processing means 180 and the automatic train operation means 18 1 which are originally used as the automatic train running device φ ΑΤΟ 108 are omitted. Next, the operation of the devices of Figs. 15 and 16 will be described. In Fig. 15, ΑΤ0108 obtains the speed-restricted data from the ATC 102 in advance, obtains the pre-acquisition information such as the route condition, the vehicle condition, and the operating conditions from the DB 103, and simultaneously obtains the speed, and then performs a specific operation to generate a run command. Or the control command formed by the deceleration command, and realize the automatic operation of the train 如 as described above. ΑΤ0108 receives the departure signal from the bridge 1〇4 and starts the automatic operation using the automatic operation of the train -27- 1277549 (24). After the departure, the load information obtained from the load carrying device 105, the speed data obtained from the speed detector 1〇6, and the ground sub-detection information obtained from the above-ground sub-detector 1〇7 are used. The load information is used as the weight related information of the train, and the ground sub-test information is used for the compensation of the position information. Using this information, the ATO 108 can formulate train control commands (running commands/deceleration commands). When the operation command is prepared as a control command, the operation command is output, and the train is operated by the φ drive unit 109. In addition to the running torque (running traction) command, the running command has a running level command and the like when driving. Further, when the deceleration command is prepared as a control command, a deceleration command is output, and the deceleration device 1 10 is used to decelerate the train. The deceleration command is the braking force command, and when the class is driving, it is the braking class command. Next, a detailed description of the action of A T 0 1 0 8 will be carried out with reference to Fig. 16. When receiving the departure signal from the bridge 104, first, the pre-business test driving means 120 or the determination of the driving after the business is performed by the pre-employment driving determination means 120. The method of judging at this time may be a method using a flexible flag such as "testing when the flag is not set", "having a business trip when the flag is established", and a method of setting the result by using a rigid switch. When the pre-business driving determination means 120 determines that the pre-business test is being carried out, the pre-business characteristic initial setting means 1 2 1 sets the initial characteristic parameters at the time of the pre-business test driving. The method of setting can be considered by using the human-machine interface to manually implement the setting method before the start of driving. In addition, it is only necessary to input the characteristic parameters from the information that can be obtained in advance, such as the specifications and route characteristics of the train. -28- 1277549 (25) Next, using the characteristic parameters set by the pre-business characteristic initial setting means 121, the test driving of the train using the automatic operation is carried out by the pre-service test train automatic operation means 1 22 . In terms of the method of automatic train operation, such as formulating the best driving plan when parking by the station, re-planning the driving plan or using the control command according to the automatic operation of the station and the large deviation from the optimal driving plan. Method of compensation for error feedback. In addition, in the case of the pre-operating pre-vehicle, for example, when the train φ is used for the purpose of driving, the test vehicle for the purpose of the characteristic estimation is carried out, and the vehicle for the purpose of the characteristic estimation is executed. Next, the result of the automatic operation is performed by the automatic train operation means 1 2 2 before the pre-operating test, and the storage result storage means 1 2 3 is used for storage. When storing, the target driving plan, speed information and location data measured during driving are regarded as electronic files stored on the hard disk (HD): etc. Next, the calculation of the characteristic parameters is performed by the pre-business characteristic estimating means 124 by using the test driving result stored by the driving result storing means 1 2 3 . Predicted characteristic parameters such as weight, acceleration characteristics, and deceleration characteristics should be implemented before the business. In terms of the weight of the number of trains, the number of trains is based on the test drive before the operation. Therefore, no passengers can ride, and the acceleration or deceleration during taxiing and the driving resistance of the train can be used to estimate. Here, a case where the train of the object is expressed by the simple physical form of the equation (7) is considered. In terms of train running resistance, calculations can be performed using the formulas of the route characteristics such as slope and curvature, air resistance, and frictional resistance. In addition, for the calculation of the running resistance of the train -29 - 1277549 (26), please refer to the book "Operation Theory (DC AC Power Station)". In general, the train running resistance Fr can be expressed by the following formula.

Fr= Frg+ Fra+ Frc =s + (A+Bv+Cv2 ) + 800/r (11) However, Fr is the train resistance [kg weight / ton], Frg is the slope resistance [kg weight / ton] (on The slope is positive and the downhill is negative), Fra is the driving resistance [kg weight / ton], Frc is the curve resistance [kg weight / ton], and s is the slope [%. ], A, B, and C are coefficients, v is the train speed, and r is the radius of curvature. Considering the above items right, the child can be calculated by the variant of equation (7). M = ( F - Fr) / ά ... ( 1 2 ) In the formula (12), when the vehicle is coasting, the running traction force F may be set to 0 (zero). Further, in terms of acceleration (or deceleration) α, the calculation can be performed using the measurement result (train travel speed) by the minimum level method or the like. In the above process, the weight Μ can be derived. After the calculation of the weight Μ is completed, the weight estimation 値 can be used to estimate the running characteristics and the braking characteristics. First, use the weight estimation 値Me st, the acceleration α ace at the running time, and the train running resistance Fr to estimate the operating characteristics (the operating level and the relationship of the running -30-1277549 (27) traction force, etc.). The acceleration a acc and the train resistance Fr in operation can be obtained by the same processing as the aforementioned weight calculation. Using the weight and weight calculation, the running traction force F can be estimated by the following formula. F = Mest a acc + Fr (13) When the train is operated with the grade, the running traction of each grade of φ can be calculated by the equation (1 3 ). It can also be used to estimate the relationship between the operating level and the running traction. In addition, the braking force characteristics can be estimated by using the weight estimation 减, the deceleration during deceleration, and the train running resistance. The deceleration at the time of deceleration and the train resistance can be obtained by the same processing as the above weight calculation. Using this and the weight calculation 値, the braking force F can be estimated by the following formula. F= Mesta dec+ Fr ... (14) However, a dec is the deceleration (negative acceleration). When a train that performs a brake operation is used, the brake force of each grade can be estimated by the formula (1 4 ). This result can be used to estimate the relationship between the brake class and the braking force. These calculations are best calculated after the station is driving or when the vehicle is parked. However, it is also possible to perform calculations in the train and confirm the calculation results in the train. In this way, the calculation of the weight, the running characteristics, and the braking characteristics can be implemented. For the error of the number of trains to be compiled for each train, the adjustment can be completed in a shorter period of time than before the business trip -31 - 1277549 (28). Then, the estimated value calculated by the pre-business characteristic estimating means 124 is used to calculate the 値, and the calculation result compensation means 125 performs the compensation. When performing compensation, it should be set within the allowable range of theoretically achievable characteristic parameters and must be corrected to this tolerance. For example, if the characteristic estimation is beyond the allowable range, it is conceivable to use the setting of the calculation beforehand, or the restriction within the allowable range. If it is too large to deviate from this allowable range, φ must be re-executed for operation such as test driving. Next, the characteristic calculation of the compensation by the estimation result compensation means 125 is performed, and the characteristic estimation calculation means 126 is stored in the learning characteristic DB 130. The method of storing can be the same as the above-described driving result storage means 123. The learning characteristic DB 130 can store the characteristic learning results obtained after the business trip described later, in addition to the characteristic calculation results obtained by the trial driving before the driving. The case where it is determined by the pre-vehicle driving determination means 120 that it is driving after the business is described below. When driving, the initial parameters of the characteristic parameters are set first by the characteristic initial setting means 1. 3 1 . At the time of the initial business trip, the characteristic parameters (characteristic estimation results) stored in the storage means 126 are estimated using the utilization characteristics obtained from the learning characteristic DB13 0. When learning is carried out at the same time as the business travels, the characteristic parameters (characteristic learning results obtained from the learning results can be used), and the characteristic parameters set by the characteristic initial setting means 1 31 are used, and the train automatic operation means 132 Perform automatic running of the train. -32- 1277549 (29) In terms of the automatic operation of the train, basically, it is the same as the automatic train operation means 122 for the pre-operating test train. When there is an unspecified number of passengers, the weight will change. Therefore, the initial weight of the station must be estimated when running from the station. The weight calculation method can also utilize the load if the load can be obtained. When the load should not be used, the weight can be estimated by performing the same function as the pre-service characteristic estimating means 124 and the estimation result compensation means 125 at the initial stage of operation after the departure of the station. When the result of the φ estimation and the characteristic initial setting means 1 3 1 are different, the processing of the driving plan and the like must be performed again. Figure 17 is a summary of the weight calculations performed during the initial operation from the station. In Fig. 17, the horizontal axis represents the distance from the departure station to the next station, and the vertical axis represents the speed of each position in the speed mode. According to the characteristics of the departure station, the optimal driving mode is calculated according to the characteristics of the departure. 13 1 (fine dotted line) After the execution of the driving, the actual driving result according to the initial running interval 1 3 0 is the actual driving mode 1 32 (thick solid line) The weight calculation is performed, and based on the weight calculation, the driving mode 132 (thick broken line) is calculated by recalculating and implementing the compensation, and the actual running operation is performed accordingly. Then, the result of the automatic operation performed by the automatic train operation means 32 is stored by the post-business driving result storage means 33. The method of storing ' can be the same as the above-described driving result storage means 23. Next, the characteristic learning is performed by the post-business characteristic learning means 134 using the driving result stored by the post-business driving result storage means 133. Regular learning aspects of this feature are implemented in the following manner. (1) Learning based on the results of the inter-station driving -33- 1277549 (30) (2) Learning based on the results of the whole route ~ (3) Learning based on the results of the 1 day driving (4) Learning based on the results of several days of driving (5) Learning based on the results of several months of driving The following are explained separately for the above (1) to (5). (1) Learning based on the results of the inter-station driving φ Perform the learning based on the inter-station driving results obtained after the inter-station driving, and reflect the learning results in the next station. For example, when it starts to rain, learn the correspondence when the braking force is reduced. It is judged that an example of learning must be carried out for the driving result of one station, for example, the corresponding reduction in the vehicle power in the rainy day. In rainy days, if the train uses air brakes, the rain will reduce the friction of the brake blocks and reduce the braking force (deceleration performance). At this point, after the start of rain, you should find that the deceleration performance is reduced. Just learn the characteristics of the braking force based on this result. The learning result at this time is usually temporary, so it can be stored separately and used as a temporary characteristic parameter. ' ' . . . , (2) Learning based on the results of the whole route According to the driving results from the first to the last of the route, the learning results are reflected in the driving of the next route. For example, when there is almost no shortage of the target stop position (deviation amount) at the end of a route, in order to eliminate the amount of deviation, it is sufficient to perform the learning of the vehicle force characteristic corresponding to the deviation amount. For example, when the target stop position is exceeded, the setting of the braking force characteristic should be slightly larger than the actual 値. That is, since -34- 1277549 (31) is greater than the actual braking force, the assumed deceleration cannot be obtained. At this time, it is only necessary to carry out the learning that the setting of the braking force characteristic is slightly smaller. (3) Learning based on the results of the one-day driving The learning is performed based on the results of the one-day driving, and the learning results are reflected on the next day's driving. For example, when observing the results of the driving on the 1st (for example, the driving result of several trips of the entire route), it can almost be said that Φ will find that the parking between stations is more than the same as the target stop position. The degree is likely to be an error in the setting of the route characteristic parameters such as the slope and curve between the stations. In this case, it is sufficient to perform the learning of the route characteristic parameters such as the slope and the curve with a slight adjustment of the corresponding driving result. (4) Learning based on several-day driving results Store the results of several days of driving and perform learning based on the stored results. For example, if you observe the driving results of several days, if there is a deviation of the driving plan only when it is taken on Monday, it should be affected by some factors, and only the running traction characteristics or braking force characteristics of the time zone are in Deviate from the actual situation. If there is no deviation in other time zones, the characteristic parameter itself should not deviate from the actual situation, so only the characteristics of the object time zone are compensated, and then the compensation can be corrected by learning. (5) Learning based on several months of driving results When storing the driving results for several months, the learning is performed based on the stored results. For example, based on the results of the storage of the maintenance check, etc., perform the study -35 - 1277549 (32) j For example, observe the results of the three months of driving, you can find that 3 months ago, 2 months ago, and 1 month The vehicle power in the front will gradually decrease as time passes. In this situation, it is difficult to judge by studying the results of several days of driving. When using an air brake, it is likely that friction causes the brake block to wear. Therefore, it is necessary to change (learn) the characteristic parameters or take measures such as the replacement of the brake block according to the result. In addition, measures to change the aging time such as the wheel diameter can be used. For learning above φ, you can selectively use the examples shown in the flowchart of Figure 18 to implement the learning. In Fig. 18, the pre-business driving judgment means 1 20 is used as the pre-business test driving or the business driving after the business (step 1 5 1 ), and the judgment result is the former (pre-business test driving). The pre-business test driving is carried out (step 152), the initial parameter estimation is performed (step 153), and the processing is terminated. If the result of the determination in step 151 is "business driving", one of the five types of learning corresponding to the driving content is implemented. In other words, it is determined that the vehicle is in the end of the driving mode (step 154), and if it is the end of the station, the "(1) learning based on the inter-station driving result" is performed (step 155), and if the entire route is completed, Implement "(2) Learning based on the results of the entire route" (step 156). In step 154, if the driving is completed for one day, the data of how many days are stored is further determined (step 157) 'According to the result of the judgment, if one day of data has been stored, "(3) is based on one day. "Learning of driving results" (Step 158), if it is already stored for a number of copies of the data, implement "(4) Learning based on the results of several days of travel" (Step 159), if it is already stored for several months, then "( 5) Learning based on the driving results of several months" (step I60). -36- 1277549 (33) - However, each of the learning steps 155, 156, 158, 159, and 160 indicated by thick lines in Fig. 18 performs learning only when the driving result exhibits a tendency to learn as shown below. . That is, a) when the deviation of the same tendency is continuously exhibited (for example, when the same degree of target stop position is exceeded in all the stations in the whole route driving result), and b) when a significant deviation occurs. φ Learning can be considered as a method of increasing or decreasing a certain characteristic parameter by a certain ratio. For example, as mentioned above, in the whole route driving result, when all the stations have the same degree of target stop position exceeding, the setting of the braking force should be slightly larger than the actual braking force, so the braking force characteristic is reduced by a certain ratio. The setting of learning. In particular, depending on the learning aspects of the results of the inter-station driving, there are few cases where deviations of the same tendency occur. Therefore, at this time, learning should be implemented. That is, • Automatic train operation mode: When there is a considerable deviation in the driving plan and the actual measurement, the automatic train operation mode for the compensation of the control command (running level command, brake level command, etc.) is implemented corresponding to the deviation. • Learning method: When there is a deviation between the driving plan and the actual measurement, the learning is performed in response to the compensation of the control command. Take the braking force characteristic as an example. For example, when braking, -37- (34) 1277549, if there is a control command compensation that is greater than the plan, it should be a deceleration that is not assumed. In this case, the setting of the braking force characteristic should be too large. Therefore, it is only necessary to implement the learning of setting the braking force characteristic at a certain ratio. If there is a control command that compensates for the brake level to be less than the plan, the opposite is true, as long as the setting of the brake force characteristic is expanded to a certain extent. The judgment of the difference between the estimated characteristics and the actual , is based on the acceleration and deceleration obtained in the form of measurement data, using the characteristics of the train, the characteristics of the route shape (slope, curve, etc.), the weight, the running traction or the braking force. To determine whether the formula (7) is satisfied. As described above, the learning result compensation means 135 performs compensation for the result of the learning by the post-business characteristic learning means 134. The method of compensation can be the same as the above-mentioned estimation result compensation means 1 2 5 . This compensation result is stored as a characteristic learning result and stored in the learning characteristic DB130. As shown above, learning is carried out even during business operations, and business operations are performed while adjusting the characteristic parameters. Most of the above studies are automatically learned on the line during train stop at the time of the station. However, the calculation of the weight of the runtime is automatically calculated on the line in the vehicle. In this way, the automatic operation of the train can be carried out by using the continuous implementation of learning and calculation, and the automatic operation can be carried out in a situation where the number of trains is different and the timeliness changes are well matched. As described above, the automatic train running device -38-(35) 1277549 of the seventh embodiment can be used to calculate the weight, running traction, and braking force before the driving. For the number of trains of different trains, it can be adjusted in a shorter period of time than before, and the characteristic parameters can be learned after the operation, so even if the characteristic parameters change, the ride comfort and stopping accuracy can be achieved. Automatic operation. In addition, after the business, the learning of the inter-station driving section and the route driving section can be distinguished based on the period of use of the data, so that it is possible to learn more realistically. In addition, in the calculation before the business and in the post-business study, compensation for the calculation and learning results will be implemented. In the event of an impossible result, it can be compensated without using the impossible characteristic parameters. Implement calculations and learning. In the above way, as the characteristics learning progresses, an effective driving plan can be developed. In addition, if there is a large learning in the train, the learning will be triggered, and the driving plan will be redesigned to realize the automatic train operation that satisfies the ride comfort, the stopping accuracy of the target stop position, and the driving time. In the seventh embodiment, most of the learning is automatically learned on the line in the train stop when the train arrives at the station, and the weight calculation at the time of the train is automatically calculated on the line in the train. However, if there is a human-machine interface in which the learning progress can be confirmed during train driving, online automatic learning can be performed in the driving, and the system using the learning result can be realized at the judgment of the driver. At this time, it is also possible to use only the learning means as a separate device and use it as a support device for automatic train operation. Fig. 19 is a view showing an essential part of the automatic train running device of the ninth embodiment. In this embodiment, the post-business characteristic learning means includes an automatic characteristic learning means 1341, an automatic characteristic learning means 1342, an automatic characteristic learning means 1343, an automatic characteristic learning means 1344, and an automatic characteristic learning means 1342, each request item - 39-1277549 (36). And the automatic characteristic learning means 1 3 45, and further, the learning result comparison means 1 3 6 having the learning result obtained by inputting the automatic characteristic learning means, and the comparison result according to the learning result comparison means 1 36 are performed to compensate the learning result Learning result compensation means 1 3 7. The automatic characteristic learning means 1 3 4 1 to 1 3 4 5 performs the characteristic learning as described in the description of the embodiment φ 7 . The learning result comparison means 136 accepts the learning results of the automatic characteristic learning means 1341 to 1345, compares the learning results, and checks whether there is a large contradiction between them. In the automatic characteristic learning means 1341 to 1 3 45, there is a considerable difference in the interval of learning during the learning period, and basically, the result of one of the longer periods of the learning period can be checked according to the result of one of the shorter periods of the learning period. For example, if the learning result of the automatic feature learning means 1 345 is obviously η times, for example, 10 times, of the learning result of the automatic feature learning means 1 3 44 of the same time zone, it is judged to be a significant abnormality, and the automatic characteristic learning is performed. The learning result of means 1 3 45 can be regarded as the result of significant contradiction. Further, the inspection is performed by the complex result in the automatic characteristic learning means 1 34 1 to 1345, and the inspection accuracy can be further improved. Next, the learning result compensation means 137 compares the significant contradiction in the learning result comparison means 1 36. Perform compensation. In the method of compensation, the easiest method is to directly use the learning result of the automatic feature learning method with a short learning period (learning interval). However, when using the multi-learning results of the automatic characteristic learning means 1341 to 1345, it is also possible to consider the average 値 of these learning results using -40-1277549 (37). Moreover, if the learning results of most of the automatic characteristic learning means 1341 to 1345 are contradictory, or the learning results of the automatic characteristic learning means 1341 to 1345 have large errors, the average value may be considered. . The automatic feature learning means 134 can perform characteristic learning using an adaptive observer. If the target device has been modeled as shown in equation (7), the adaptive observer uses the observable (detection) 値 to identify the parameter. The system identification can also be carried out by type, and the automatic train operation means 181 can utilize the identification result of the adaptive observer at any time to form an adaptive control system. In the case of equation (7), the adaptive observer can be used to observe the acceleration and deceleration of the crucible (calculated from the detection speed of the speed detector 106), and the operating traction or braking force of the control command, to identify the weight and the train running resistance at any time. In order to adapt to the calculus of the observer, the method of expanding the least squares, the singular Kalman observer, and the adaptive observer can be used. (For details, please refer to "Introduction to Strong Adaptive Control" (Taiji Manju, Jin Jing Xi Meixiong, OHMSHA) Chapter 2, "Inferred and Adaptive Observers for Unknown Devices" P.47~87, or "System Control Series 6 Best Filtering" (Xishan Jinglu, Peifeng) Section 3.3 "Adapting to the Observer" P.50 ~57). As shown above, the comparison of several automatic characteristic learning means with different learning periods (learning intervals) is carried out to eliminate the contradictory learning results, and a more accurate characteristic learning result can be obtained. In the eleventh embodiment, the automatic characteristic learning means 134 can also perform the characteristic learning using the interference observer, and the interference observer can use the motion control to identify the interference (for details, please refer to "Using MATLAB-41 - 1277549 (" 38) Design of Control System (edited by Noda Kenji, Nishimura Hideo and Hirata Hiroshi; Tokyo University of Electrical Engineering Publishing House) Section 4.4, "Interference Observer for Motion Control" P.99~102). The train running resistance of the formula (1) is regarded as the force disturbance of the motion control, and the interference of the train can be estimated at any time by using the interference observer. Using this calculation result to implement learning, you can perform learning with higher precision. A detailed description of the first embodiment of the present invention will be made with reference to the drawings. Fig. 20 is a view showing the configuration of the automatic train running device 1 and the data storage unit 201. The φ automatic train running device 1 is composed of a train characteristic learning device 207 of a train characteristic learning means and an automatic operation control unit 208 of an automatic train operating means. The train characteristic learning device 207 obtains the characteristic data (train resistance, delay time (described later)) and route information of the train in the train. The data acquired by the train characteristic learning device 207 is stored in the data storage unit 201. The data acquired by the train characteristic learning device 2 to 7 and stored in the data storage unit 20 1 is output to the automatic operation control unit 208. The automatic operation control unit 2Ό8 formulates a driving plan based on the data acquired by the train characteristic learning device 207 and stored in the data storage unit 201. The train will operate automatically according to this driving plan. The train characteristic learning device 207 is a train storage capacity calculation unit 209, a train weight calculation unit 209 that operates the traction force deviation detection means, a train resistance calculation unit 209 that operates the traction resistance calculation means, and a brake force calculation means and The braking force calculation unit 2 1 1 and the delay time calculation unit delay time calculation unit 2 1 2, and the boarding rate calculation unit's boarding rate calculation unit 2 1 3, detecting the train speed to constitute 0 - 42- 1277549 (39) The output of the data storage unit 201 is input to the train weight calculation unit 209, the train resistance calculation unit 210, the braking force calculation unit 2n, the boarding rate calculation unit 213, and the automatic operation control unit 208. The output of the train weight calculation unit 209 is input to the data storage unit 201. The output of the train resistance calculating unit 210 is input to the data storage unit 201. The output of the braking force calculation unit 211 is input to the data storage unit 201. The output of the delay time calculation unit 212 is input to the data storage unit 201. The output of the ride rate calculation unit 213 is input to the data storage unit 2〇1. The output of the operation control unit 8 is input to the train weight calculation unit 209, the braking force calculation unit 211, the delay time calculation unit 212, and the ride rate calculation unit 213. When the train acceleration operation is performed, the data storage unit 201 inputs the train resistance 値 and the automatic operation control unit 208 to the train weight .F and the train speed V at the current point to the train weight calculation unit 209. The train weight calculation unit 209 calculates the train weight 以 using the braking resistance 値Fi*, the running bow [force 値F, and the train speed V by Equation 15. The @train weight 求 obtained by the train weight calculating unit 209 stores the data storage unit. In Equation 15, Μ is the train weight, F is the running traction 値, Fr is the train resistance 値, and α is the train acceleration. The train acceleration α can be obtained by using the train speed V. (15) M = (F - Fr) la The train weight calculating unit 209 can also be used as the running traction deviation detecting means for the running traction force 値F, and the train weight Μ calculated by the train weight calculating unit 2〇9 can be used as the speed V After that, and calculate the train weightΜ

-43- 1277549 (40) When the 値VI used at the time is different, it can be substituted into Equation 15 to obtain the correct running traction force 値F. The train weight calculating unit 209 can also detect the deviation between the running traction force 値F and the operating traction force command 値Fk instructed by the automatic operation control unit 208. The deviation of the running traction command 値Fk& running traction force 値f is output to the data storage unit 201 for storage. Because the deviation of the running traction command 値Fk and the running traction force 値F can be detected, the deviation of the running traction command 値Fk and the running zijin force 値F can be added to the running traction command 値Fk at the time of detection, and the calculation can be calculated as new The traction command 値Fk is run, and with this treatment, a more accurate automatic train operation can be realized. When the train is coasting, the data storage unit 2 〇 1 inputs the train weight Μ and the speed V to the train resistance calculating unit 2 1 0 . The train resistance 値Fr can be calculated by Equation 16 using the train weight Μ and the speed V input from the data storage unit 20 1 . When the train is coasting, the traction force 値F is 0 because there is no running traction. Since the running traction force 値F is 0, the formula 15 can be deformed to derive the formula 1$. The train resistance 値Fr calculated using Equation 16 is output to the data storage unit and stored. In Equation 16, Μ is the train weight, F is the running traction 値, Fr is the column, the vehicle resistance 値, and α is the train acceleration. The train acceleration ^ can be obtained by using the train speed ¥.

Fr=F— Μα = 0— Μα (16) Train resistance 値Fr as shown in the “Operation Theory (DC AC Power Vehicle), Dating Society, etc.), the general train (when there are thousands of differences in high-speed vehicles), the slope Resistance 値Frg, curve resistance 値Frc, and driving resistance 値-44 - 1277549 (41)

The sum of Fra can be expressed by Equation 17. Further, it is understood that the slope resistance 値Frg, the vehicle resistance 値Fra, and the curve resistance 値Frc can also be expressed by Equation 18, Formula 19, and Formula 20, respectively. Since the train resistance 値Fr at the time of coasting can be calculated by the train weight Μ and the speed V, the train resistance calculating unit 210 can also calculate the slope resistance 値Frg and the running resistance 値Fra. The driving resistance 値Fra can be calculated using the speed V. In addition, the curve resistance 値Frc uses the data previously stored in the data storage unit. Since the train resistance 値Fr, the driving resistance 値Fr, and the curve resistance 値Frc can be used as the data, the train resistance calculating unit 2 1 0 can calculate the slope resistance 値Frg using the deformation of the formula i 7 . The slope resistance 値Frg calculated by the train resistance 2 1 0 is output to the data storage unit 201 and stored. In Equation 18, the s-system slope [%] is positive for uphill and negative for downhill. In Equation 19, the coefficients of the A, B, and C systems and the velocity of the V system are [km/h]. In Equation 20, τ is the radius of the curve [m]. The train resistance calculation unit can detect the slope resistance and the train resistance 在 when the train is driving, so the correct data can be detected. In addition, as long as the data is detected by performing a round trip on the planned route, there is a considerable time reduction effect. In Equation 17, Equation 18, Equation 19, and Equation 20, the train resistance F F r , the slope resistance F F Fg , the driving resistance F system Fra, and the curve resistance 値 Frc. A, B, C coefficient, r system curve radius.

Fr = Frg + Fra + Frc (17 ) Frg = s (18 ) Fra = A + B v + Cv2 (19) Frc = 800 / r (20) -45 - 1277549 (42) For the braking force calculation unit 211, automatic The operation control unit 208 inputs the train speed v and the braking command 値Fs, and the data storage unit 2〇丨 inputs the train weight Μ and the train resistance 値f Γ. The braking force calculation unit 2 1 1 calculates the braking force 値Fb using the train speed v, the train weight Μ, and the train resistance 値Fr using Equation 2 1 . The braking force 値Fb calculated by the braking force calculation unit 21 1 is output to the data storage unit 201 and stored. The description is re-implemented using the aforementioned formula. In Equation 21, the braking force is Fb, the weight is Μ, the acceleration is α, and the train resistance 値 is Fr.

Fb=Ma + Fr (21) The braking force calculation unit 2 1 1 can calculate the deviation Fh of the braking force 値Fb calculated by the braking force calculating unit 2 1 1 and the braking command 値Fs as the braking force deviation detecting means (refer to the formula) twenty two ). The deviation Fh between the braking force 値Fb and the braking command 値Fs calculated by the braking calculation unit 21 1 is output to the storage unit 201 and stored in the storage unit 201. When the deviation Fh is detected, the braking command 値Fs is added to the deviation Fh of the braking force 値Fb calculated by the braking force calculating unit 211 and the braking command 値Fs, and a new braking force command 値Fs is obtained, and the calculation method is used. , can provide more correct driving force 値 Fb for the train. In Equation 22, the vehicle braking system Fb., the braking command system Fs, and the deviation system Fh. (twenty two)

Fh = Fs — Fb When the vehicle is braked, the delay time calculation unit inputs the data of the automatic operation control unit -46- 1277549 (43) 208, the data of the time T1 at which the brake command 値Fs is output, and the time Τ2 of the train speed deceleration. The delay time calculation unit 2 1 1 calculates the data of the time Τ1 of the brake command 値 Fs output and the deviation Th of the data of the time Τ 2 of the train speed deceleration (see Equation 23). The deviation Th calculated by the delay time calculation unit 211 is output to the data storage unit 201 and stored. The delay time Th is between φ when the actual brake command from the automatic operation control unit 208 is received until the brake command reaches the drive unit 205 and the brake unit 206 and the operation is performed. By detecting the delay time Th, a driving plan can be drawn up in consideration of the delay time Th, and a more correct and safer driving plan can be obtained. In the formula 23, the timing at which the automatic operation control unit 208 outputs the braking command 値F is T1 ... the time at which the train speed is decelerated is T2, and the delay time is Th.

Th = T2 - ΤΙ ( 2 3 ) The data storage unit 201 inputs the empty vehicle weight φ ί vehicle weight Mk, the current point train weight \1, the number of passengers at full vehicle 1 ^, and the vehicle occupancy rate calculation unit 21 3 The average weight of humans Me. The boarding rate calculation unit 2 1 3 calculates the boarding rate calculation 値Mr ate by using the train weight Mk at the time of the empty train, the train weight 现时 at the current point, the number of passengers N at the time of full vehicle, and the average weight Me of the human being. . The boarding rate calculation 値Mrate calculated by the boarding rate calculation unit 213 is input to the material storage unit 201 and stored in the material storage unit 201. In Equation 24, the weight of the train at the time of empty train is Mk, the train weight at the current point is Μ, the number of passengers at full vehicle is Ν, the average weight of human beings is Me, and the ride rate is calculated as Mrate. -47- 1277549 (44) Μ — Mk

Mr ate - ———— (2 4 ) N kz ′′ In the train characteristic learning device 207 having the above configuration, the train weight calculating unit 209 can calculate the train weight μ during the train operation, and via the data storage unit 20 1 The ride rate calculation unit outputs the train weight μ at the current point. Therefore, the ride rate Mrate between the stations can be estimated. Since the ride rate Mrate between the stations can be estimated, the change in the ride rate of each station can be analyzed, and Further, since the train weight calculating unit 209 can calculate the train weight 现时 at the current point, the correct data of the train resistance 値Fr and the slope resistance 値Frg are also calculated. For the automatic operation control unit 208, Japanese Patent Laid-Open No. 5 - 1 93502 and Japanese Patent Laid-Open No. 6-2845 1 9 use the ground speed, train speed, and elapsed time to detect the current position of the train and according to the automatic train operation mode (refer to Figure 21 (vertical axis). The target speed is determined for the speed and the horizontal axis as the distance (, position >). The train follows the target speed to implement the automatic train operation control. In addition, the distance can be detected by the driving distance and the ground. The control method of the automatic operation control unit is not particularly limited. The operation control unit 208 of the present embodiment has a delay time compensation means, a running traction force deviation compensation means, and a brake force deviation compensation means which are not provided by the conventional automatic operation control unit. The delay time calculation unit 212 inputs the delay time to the delay time compensation unit of the delay time compensation means (not shown). The delay time compensation unit (not shown) calculates the braking force in consideration of the delay time. Or run the traction start time, control the running traction start time. The train weight of the running traction deviation detection means -48-1277549 (45) The quantity calculation part 20 9 will input the running traction deviation into the running traction deviation #payment means (not shown on the figure) The running traction deviation compensation means (not shown) will calculate the new running traction command 値 to control the running traction when considering the running traction deviation. The braking force calculation part will input the braking force deviation compensation 値 to the braking force. Deviation compensation means (not shown on the map). The deviation compensation means (not shown) calculates a new braking force command 値 and controls the braking force in consideration of the braking force deviation compensation 。. The automatic train running device according to the first embodiment of the present invention learns the train characteristics. The device 207 can collect information such as the ride rate, the train weight, the train resistance, and the braking force in the driving, and collects data not only before the implementation of the safe automatic operation, but also when the actual passenger is traveling. In the present embodiment, the train characteristic learning device 207 is used in the train driving... - ' · ' , . After the train is running, it will be processed. Further, in the present embodiment, although only the braking force is indicated, there is no limitation on the method of braking the vehicle, including the level of the vehicle. Further, the train characteristic learning device of the present embodiment can collect the data of the rainy day, the data of each season, the data of each route, and the data of each station. Therefore, it is not limited to performing data collection once for the route. Fig. 22 is a block diagram showing the train construction of the automatic train running device according to each embodiment of the present invention. The train 〇 has a speed detector 302 composed of a pulse generator (PG) or the like mounted on a rotating shaft of the wheel, and an above-ground sub-detector for detecting a ground (inquiry) provided on the track.

(Q-49-1277549 (46) 3 03, further, an automatic train running device 1 for inputting these train detection speed signals and a train detection position signal, and a driving device 305 and braking by which the automatic train running device 1 performs control Device 306. The automatic train control device (ATC), which is not shown, inputs an ATC signal such as a speed limit, an operating condition, and the like to the automatic train operating device 4. The automatic train running device 1 has a database 00 and is implemented when the station is parked. The arithmetic circuit 304A and the inter-station driving calculation circuit 304B perform the arithmetic circuit 309B when the train detection speed signal and the train detection position signal are input to the inter-station driving. The arithmetic circuit 3 04 A is implemented when the station is parked. When the train 0 stops at the station, a specific calculation to be described later is performed, and when the inter-station driving is performed, the arithmetic circuit 307B performs a specific calculation or control to be described later when traveling between the stations of the train 0. Next, the database 300 stores the route conditions. (slope, curvature, etc.), vehicle conditions (restricted speed, vehicle weight, and train characteristics such as acceleration and deceleration) Various characteristics such as time characteristic data and timetable (running table), etc. This database 300 can be a hard disk placed in the automatic train running device 1, or can be carried by the driver recently. Fig. 23 is a block diagram showing the configuration of the automatic train running device 1 according to the thirteenth embodiment of the present invention. The arithmetic circuit 304A is provided with an optimal driving plan preparing means 307 when the vehicle is parked, and the arithmetic circuit is implemented during the inter-station driving. 304B has a driving plan recalculation means 3 08, a control command separating means 3 09, and a control command output means 310. Secondly, the data stored in the database 300 is input to the station to perform the arithmetic circuit 304A and the station. When both driving, the arithmetic circuit 3 04B is implemented, and the detection signals from the speed detectors 3〇50 - 1277549 (47) and the ground sub-detector 303 and the ATC signal are only input to the inter-station driving. The arithmetic circuit 3 04B is implemented. The optimal driving plan drafting means 3 07 will be based on various materials stored in the database 300 to formulate the train to run from one station to the next. The station, and the best driving plan to stop at the target position at the target time. The "best" condition at this time can be various settings. For example, taking the driving time as the highest priority, improving the energy saving efficiency is the highest priority, or Avoiding the ride comfort of rapid acceleration and deceleration is the highest priority. In addition, the example of the method of holding the best driving plan related information of the best driving plan is as follows, such as the speed target corresponding to the time or distance, etc. Control Directives The best driving plan development means 307 is to develop a method of optimal driving plan, for example, a method of predicting train behavior using a mechanical train model (for example, Japanese Patent Laid-Open No. 5-193 502). As shown in Fig. 37, the running curve 'sliding curve' and the reverse running curve are predicted, and the intersection of the sliding curve and the reverse braking curve is used as the starting point of the braking. The φ driving plan recalculation method 3 08 will not only input the driving plan proposed by the best driving plan 3307, but also input the train detecting speed from the speed detector 312 and the ground sub-detector 03. The train detection position and the ATC signal from ATC will perform the recalculation of the driving plan when the error of the proposed driving plan and the actual driving result reaches a certain level or more. The control command depositing means 309 extracts the driving plan and the deceleration command for the current point of the driving device 305 and the braking device 306 according to the driving plan input by the driving plan recalculation means 308, and outputs it to the control-51. - 1277549 (48) Command output means 3 1 0. The control command output means 3 10 0 outputs the acceleration command and the deceleration command input from the control command output means 9 to the drive unit 305 and the brake device 306. Next, the operation of Fig. 22 having the above configuration will be described. Assuming train 0 stops at a station, the best driving plan drafting means 3 07 will refer to the data stored in the database 300 to develop the best driving plan to the next stop. Secondly, when the train starts to run, the driving plan re-calculation means 308 implements the optimal driving plan proposed by the optimal driving plan drafting means 307, and based on the speed detector 302 and the ground sub-detector 303. Comparison of the actual driving results calculated by the train detection speed and the train detection position. When the difference between the two (for example, the speed difference between the speed of the best driving plan and the speed target) is greater than the preset At the time of a critical threshold, the recalculation of the driving plan will be performed. The difference between the two is greater than the critical state, and may occur in addition to the chase phenomenon described above. It may also occur because the ATC enters the limit speed change command because the other trains are stopped in the forward direction. In addition, the recalculation of the driving plan recalculation method 3 0 8 can be performed by considering the actual performance speed at the time of recalculation, the actual performance distance (train position), or the remaining time allowed for the inter-station driving. Next, the control command precipitation means 9 will recalculate the driving plan from the driving plan recalculation means 30 8 to extract a control command such as an acceleration command or a deceleration command, and the control command output means 3 1 0 outputs the outputted control command to the drive. Device 3 05 or brake device 306. With such calculation and control of the automatic train running device 304, the train can stop at the target stop at the target stop at -52-1277549 (49). Thereafter, during the stop of the stop of the train 0 in the next stop, the optimal driving plan drafting means 307 further develops the best driving plan up to the next stop, and performs the same actions as the means 308-310. Moreover, when the error of the best driving plan and the actual driving result of the optimal driving plan 307 is not exceeded, the driving plan recalculation means 3 08 will not perform the recalculation, but will directly calculate the best driving meter. The optimal driving plan of the drawing means 7 is output to the control command discharging means 3〇9. φ In the thirteenth embodiment of the above-mentioned Fig. 23, after the train 0 starts to drive according to the best driving plan proposed by the optimal driving plan, the actual driving result and the driving plan have a certain degree of deviation. In the case of the driving plan recalculation means 308, the recalculation of the driving plan is immediately implemented, and the chasing phenomenon that has occurred in the past can be greatly suppressed, so that the energy saving effect can be improved. Fig. 24 is an automatic train according to the fourteenth embodiment of the present invention. A block diagram of the configuration of the operating device 1. The difference between Fig. 24 and Fig. 23 is that the vehicle recalculation means 3 08 of Fig. 23 employs the cumulative error reference type driving plan recalculation means 3 1 1. The driving plan recalculation means 308 of Fig. 23, because each time the recalculation is made, judges whether the error at that time exceeds the critical value, and sometimes the recalculation with the chasing feeling is implemented due to the influence of the disturbance. Therefore, in this embodiment, the cumulative error reference type driving plan recalculation means 311 performs judgment on an error accumulated to a certain degree (e.g., an error accumulated in '5 minutes). In this way, the above recalculation with chasing sensation can be prevented due to the influence caused by the interference.

-53- 1277549 (50) - Fig. 25 is a block diagram showing the configuration of the automatic train running device 1 according to the fifteenth embodiment of the present invention. The difference between Fig. 25 and Fig. 24 is that a control command compensating means 309 and a control command output means 3 1 0 are provided with a control command compensating means 3 1 2 . The control command compensation means 3 1 2 has a function of determining whether the driving plan output by the driving plan recalculation means 308 and the error of the actual driving result exceeds a critical threshold, and if it is determined to be a critical value or more, the control command is deposited 9 The control instructions for the precipitation are compensated. The control command compensation means 312 is provided to enable the automatic train running device 1 to have a support function. That is, if the train 0 performs the actual driving according to the driving plan of the best driving plan 307 or the driving plan recalculation means 3 08, there is no problem, however, there is sometimes a large deviation from the driving plan. The situation of driving. For example, when one of the plural vehicles has an abnormality. However, in the present embodiment, the control command compensation means 3 12 can also perform the support function, and can appropriately compensate the control command to prevent the stop position and the target position of the train from being largely deviated. Further, in the configuration of the φ 25 diagram, an example of the control command compensation means 3 12 is provided between the control command precipitation means 309 and the control command output means 3 10 of the second figure. Of course, the control command compensation The means 312 can also be provided between the control command means 3 09 and the control command output means 310 of Fig. 24. Figure 26 is a block diagram showing the configuration of an automatic train running device 1 according to a sixteenth embodiment of the present invention. The difference between Fig. 26 and Fig. 25 is that the control command compensation means 3 1 2 of Fig. 25 employs the cumulative error reference type control command compensation means 3 1 3 . Control command compensation means 3 1 2 of Fig. 25, even if the occurrence of one driving plan and the actual driving result error is greater than the critical threshold, the control -54 - 1277549 (51) command compensation means 3 12 will immediately control the command The control command of the precipitation means 309 performs the compensation, and is easily subjected to the control of the chasing feeling by the influence of the disturbance. Therefore, in this embodiment, the cumulative error reference type control command compensating means 313 performs judgment on the accumulated error (for example, the error accumulated in 5 minutes). In this way, it is possible to prevent the above-described recalculation with a chasing sensation due to the influence of the disturbance. Figure 27 is a block diagram showing the configuration of an automatic train running device 1 φ according to a seventeenth embodiment of the present invention. The difference between Fig. 27 and Fig. 26 is that the vehicle calculation recalculation means 3 08 is a recalculation means 3 1 1 of the cumulative error reference type driving plan. Since the other configurations are the same as those in Fig. 6, the detailed description is omitted. Further, in this embodiment, the cumulative error of the driving plan and the actual driving result is determined by the two means 311 and 313. However, the threshold used in the execution of the cumulative error determination by these means can be set to correspond to various conditions. The difference is different. The second block is a block diagram of the automatic train running device 1_ of the eighteenth embodiment of the present invention. The difference between Figure 28 and Figure 27 is that the optimal driving plan for the calculation circuit 3 04 A when the station is parked is 3 _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ The train characteristics data of the library 3 00 contains "delay time" data. In the calculation of the driving plan, the delay time of the train's response to the control command, that is, the time from the output of the control command to the time when the control command affects the actual train, requires a huge computational load to obtain the aforementioned front. There is difficulty in the speed of operation in practical use. Therefore, in the present embodiment, in addition to the delay time first obtained in the train characteristic data stored in the database 00, the optimal driving plan is also "delay time consideration type". The best driving plan 314 is to consider this delay in the development of the best driving plan. In this way, the stop accuracy of the target position of the next parking station can be improved. Figure 29 is a block diagram showing the configuration of an automatic train running device 1 according to a nineteenth embodiment of the present invention. The difference between Fig. 29 and Fig. 28 is the cumulative error reference type vehicle recalculation means 3 1 1 of Fig. 28 for the delay time consideration type driving plan recalculation means 3 1 5. The delay time consideration type driving plan recalculation means 3 1 5 is the same as the delay time considering type optimal driving plan drafting means 314, and the driving plan is implemented by referring to the delay time data contained in the train characteristic data of the database 300 recalculate. In this way, the stop accuracy of the target position of the next parking station can be further improved. Further, in the configuration of the nineteenth embodiment, a combination of the "driving time consideration type j" driving plan recalculation means 315 and the "delay time considering type" optimal driving plan preparing means 314 is employed. The composition of the combination of the general best driving plan for the non-"delayed time consideration" model 3 07, that is, the recalculation of the driving plan of Figures 23 to 27 3 08, 3 1 1 is replaced by the delay time considering the configuration of the vehicle counting recalculation means 315. Figure 30 is a block diagram showing the configuration of an automatic train running device 1 according to a twentieth embodiment of the present invention. The difference between Fig. 30 and Fig. 29 is the late-time-preferred optimal driving plan drafting means 314 in Fig. 29, which is the best predictive driving plan for the forward-predicted type. Previously, the proposed method for predicting the best driving plan is also a kind of "delay time consideration type", which is based on the prediction of the direction of travel of train 0-56-1277549 (53) to stop the train stop. The destination location of the next stop is the intended driving schedule. That is, as shown in Fig. 38, the train behavior prediction of the train traveling direction is calculated and the convergence operation at the target speed through the target point (or the progressive convergence operation from the deceleration start point) is performed, and the retrograde curve can be used without In the case of the situation, the best driving plan is drawn up. If you don't need to consider the delay time, you only need to refer to the target position and the reversed position as the starting point of the brake. It is easy to calculate. However, if the delay time must be considered, the reverse method is used. The operation will be very complicated. Therefore, it takes a lot of calculation time to obtain the start point of the brake, and it may have passed the target position at the time of obtaining the start point of the brake. Further, the method shown in Fig. 38 is to perform the prediction operation of the plurality of traveling directions to obtain the starting point of the braking. Even if the calculation is performed plural times, it is only required to be applied in each specific sampling period. Short time. Figure 31 is a block diagram showing the construction of an automatic train running device 1 according to a twenty-first embodiment of the present invention. The difference between Fig. 31 and Fig. 9 is the delay time considering the driving plan recalculation method in Fig. 29. 3 is the forward direction. Prediction: The recalculation means of the type of vehicle plan is 3 1 7. Previously, the predictive driving plan recalculation means 3 17 and the forward forecasting type optimal driving plan drafting means 3 1 6 phase words, when performing the recalculation of the driving plan, based on the prediction of the traveling direction of the train 0, The calculation is performed for the purpose of stopping the train 0 at the target position of the next parking station. Therefore, the recalculation of the vehicle plan considering the delay time can be implemented in a short time. In addition, the previous recalculation means 317 to the predictive driving plan can replace the delay time considering the driving plan of FIG.

-57- V: 1277549 (54) The new calculation means 315 can also replace the driving plan recalculation means 308, 3 11, 315 of Figs. 23 to 27 and Fig. 30. Figure 32 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-second embodiment of the present invention. The difference between Fig. 32 and Fig. 31 is before Fig. 31. The recalculation means for the predictive driving plan 3 17 is the successive forward predictive driving plan recalculation means 3 1 8 . The recalculation means for predicting the driving plan before the third figure is to perform the recalculation of the driving plan by performing the forward prediction operation according to the predetermined specific control cycle. However, the embodiment is successively performed. The forward prediction type driving plan recalculation means 3 1 8 does not have to perform recalculation in each control cycle. For example, when the sampling control period is 〇·3 seconds, it can be implemented every 1 second, or even every 1 second. Thus 'changing the recalculation cycle can reduce the computational load. Further, it is possible to appropriately determine the calculation cycle by considering the location where the slope of the line changes rapidly: and: limiting the location of the speed change. Figure 33 is a block diagram showing the structure of a turtle of the self-track train operating device according to the twenty-third embodiment of the present invention. The difference between Fig. 33 and Fig. 32 is the recalculation means of the successive forward prediction type driving plan in Fig. 32. The first method is the speed measurement driving type successive forward prediction type driving plan recalculation means 3 1 9 . That is, if the detection sampling period of the speed detector 302 is l[mSec], the operation circuit 304B side of the inter-station driving operation does not directly use the speed detection signal input according to the period, but for the period of 5 to 10 [msec]. The input speed detection signal is subjected to filtering and the like, and then the data update is performed. Secondly, the speed measurement drive type forward forward predictive driving plan recalculation means 319 performs the recalculation of the forward predictive driving plan according to the update period of the data. Profit

A -58- 1277549 (55) In this way, the influence of interference and the like can be suppressed, and the recalculation can be improved: the accuracy of the calculation of the equation. Figure 34 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-fourth embodiment of the present invention. In this embodiment, in addition to the inter-station driving in the 31st diagram, the inter-station driving result storage means 320 is implemented on the arithmetic circuit 304B, and the delay circuit estimating means 21 is added to the arithmetic circuit 304A when the station is parked. Calculate the delay time based on the latest driving results. Therefore, the database 00 of this embodiment may not store the delay time data. That is, after the train departs from a certain station, the inter-station driving result data during the period from the train position, the train speed, the ATC signal, and the like to the next stop station is stored in the inter-station driving result storage means 320. Secondly, after the train arrives at the next stop and stops, during the stop, the delay time estimating means 321 calculates the delay time based on the data stored in the inter-station driving result storage means 3 20, and outputs the estimated result to the delay time. The best driving plan for the type of vehicle planning 3 14 and the forward predictive driving plan recalculation means φ 31 7. The delay time consideration type of the most popular driving plan drafting means 3 1 4 and the forward forecasting type driving plan recalculation means 3 1 7 will be further implemented until the next stop station in consideration of the estimated delay time. Formulation and recalculation of the driving plan. If the method of estimating the delay time by the delay time estimating means 321 is explained, this method does not use a complicated operation, but is a simple method of estimating the signal level change based on the measurement data. For example, when the vehicle is braked, after the brake control command is output and the level operation is performed, the train speed will decrease after a certain period of time. At this time, it can be estimated to decrease to the threshold of the pre-59- 1277549 (56) setting. The time is a delay. Moreover, the delay time stored in the database 3 00 of the above-mentioned 28th to 33rd drawings is particularly complicated because the time is not required to be obtained, so that complicated calculations can be used and the result of the estimation can be stored. The train driving test is carried out, and the delay time estimating means 3 2 1 of this embodiment can be used to obtain data more easily. In this embodiment, the delay time reflecting the latest train characteristics can be obtained, and the optimal driving meter is considered by the delay time. Drawing method 3 1 4 and forward-predicted driving plan recalculation means 3 1 7 The proposed and recalculated driving plan can further improve the reliability. Figure 35 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-fifth embodiment of the present invention. The difference between Fig. 5 and Fig. 34 is to implement an additional line delay time derivation means 322 on the arithmetic circuit 304B when driving between stations, and the forward predictive type driving plan recalculation means 3 17 can be considered In the case where the online delay time estimating means 22 estimates the delay time, the recalculation is performed. That is, in the composition of the 34th figure, the delay time is calculated based on the inter-station driving result of a certain section, and the calculation result is applied to the recalculation of the driving plan of the next section, and the implementation of the 35th figure is implemented. In the form, even if it is driving in the same section, the delay time can be estimated based on a small number of station driving results, so it can also be applied to recalculation. Therefore, the recalculation result of the predictive driving plan recalculation means 3 1 7 before this embodiment is more accurate than the one shown in Fig. 4 to reflect the latest train characteristics. Figure 36 is a block diagram showing the construction of an automatic train operating device 1 - 60 - 1277549 (57) according to a twenty-sixth embodiment of the present invention. In this embodiment, the i-calculation circuit 304B is attached to the temporary predictive parking temporary parking plan calculation means 323 and the driving plan adoption means 324 when the inter-station driving is performed in the 35th drawing. Next, in this embodiment, the driving plan is divided into three types of PI, P2, and P3 in accordance with the train driving time, and the driving plan adopting means 324 adopts the forward predictive type when the train arrives at a specific point on the front side of the target position. Parking Temporary Driving Plan Calculation Means 3 23 Calculated driving plan P3. Hereinafter, the twenty-sixth embodiment φ will be described in detail. First, the definitions of the driving plans PI, P2, and P3 are as follows. P 1 : When the train 1 stops at the station, the best driving plan proposed by the driving plan 3 1 4 (or 3 07, 3 16 may be) is calculated by the driving plan. P2: In the inter-station driving of train 1, the recalculation method 317 (or 3 08, 31 1, 315, 318, 3 19) may be recalculated to recalculate the driving plan. P3 : The calculation method of the temporary pre-measurement parking temporary driving plan after the train station 0 is in the train and the train arrives at the target position φ N meters (for example, N = 300 [m]). 3 23 Proposed temporary parking plan for parking. When the train 〇 reaches the target position N meters before, the temporary driving plan calculating means 3 2 3 formulates the subsequent parking temporary driving plan P3 at a specific cycle (for example, the sampling sampling period of the speed detector 2). This parking use temporary travel plan P3 is designed to use the train detection speed and train detection position at that time to predict the train's parking behavior in consideration of the delay time of the train travel direction. This parking action is, for example, pre-planned 1277549 (58) At the current point of 3, the basic parking action is performed when the train is stopped at a specific braking level position, and is used to stop. Secondly, the prediction of train behavior can also be considered as the physical model of the following formula (25) F - Fr = Μ · α (25) F: running traction or braking force

Fr : train resistance (driving resistance, slope resistance, curve resistance, tunnel φ resistance, etc.) Μ : Train mass α · Acceleration or deceleration train resistance Fr series resistance when driving, for convenience calculation 'As mentioned above, The composition of driving resistance, slope resistance, curve resistance, and tunnel resistance is usually considered. Therefore, the train resistance Fr can be obtained by the equation (26). • Fr == Frg + Fra + Frc + Frt ( 26) Each of the resistances in equation (26) is obtained using the data stored in the database 300 using the following resistance equations (27) to (30) (see "Operation Theory (DC AC Power Vehicle)", dating agency). • Slope resistance

Frg=s (27)

Frg: slope resistance (kg weight / ton) s: slope (% g ) (positive on the uphill slope and negative on the downhill slope) -62- 1277549 (59) 阻力 Driving resistance. Fra=A+B*v + C.v2 (square of v) (28)

Fra: driving resistance (kg weight / ton) A, B, C: coefficient v: speed (km / h) • curve resistance

Frc= 800/r ...(29 )

Frc: Curvature resistance (kg weight / ton) r: Curve radius [m] • Tunnel resistance type (Because the tunnel resistance will vary greatly depending on the shape and size of the tunnel section and the train speed, etc., it is sometimes used for convenience. The following 値):

Frt= 2 (for single-line tunnel): or =1 (for double-track tunnel) (30) ^ Frt: dangerous road resistance (kg weight/ton) Temporary driving plan calculation means 3 23 due to the use of the physics of the above formula (25) The model, so the temporary parking plan P3 for parking will be repeated after the N-meter location before the target position. By repeating the plan, the parking position of the temporary parking plan for parking P3 is gradually approached to the target position. As shown in Figure 39. Further, the distance N from the target position to the start position of the temporary parking plan for parking can be determined by the formula "travel distance" ± "wide distance". Next, the driving plan of Fig. 3 is explained with reference to the flowchart of Fig. 40.

• 63- 1277549 (60) The action of means 324 is used. When a driving plan for one of PI, P2, and P3 is formulated or recalculated according to a specific cycle, the flow chart is a processing step of one cycle. First, the driving plan employer means 324 to determine whether the current train 0 driving state or the driving time is when the station stops or just after the departure from the station, when the station is driving, and whether it is near the target parking position (step 1). Next, when it is judged as "After the stop at the station or just after the departure from the station", the optimal driving plan P1 (step 2) prepared by the delay φ time consideration type optimal driving plan drafting means 314 is adopted. Thereafter, the driving plan employer means 324 outputs the optimum driving plan P 1 to the control command discharging means 3 〇 9. Further, the operation of the control command precipitation means 3 09 after the input of the driving plan has been described in the above embodiment, and the overlapping description will be omitted. When it is judged at the time of "the inter-station driving" in step 1, the driving plan employs means 3 24 to judge whether or not the recalculation of the driving plan of this cycle has been carried out (step 3). Next, if the recalculation has been carried out, the driving plan P2 is recalculated using the forward pre-reformed line φ braking plan recalculation means 317 (step 4). On the other hand, if it is judged in step 3 that the recalculation of the driving plan of the current cycle is not performed, it is judged whether or not the first driving cycle P 1 has been adopted in the previous cycle, that is, in the previous cycle (step 5). If the best driving plan P1 has been taken at the previous 1 o'clock, the driving plan employer 324 will use the best driving plan P1 (step 2). However, when the best driving plan P1 is not used at the first hour, the current point is the time when the best driving plan P1 has been adopted and the recalculation has been carried out. The first hour is the recalculated driving meter. -64 - 1277549 (61) Painting. Therefore, the driving plan adopts the means 324 to adopt the driving plan adopted at the previous 1 hour (step 6), and the judgment of the step 1 is "near the target parking position", that is, when the target parking position is within N meters, the driving is performed. The plan adoption means 324 inputs the temporary parking plan P3 that has been prepared by the temporary driving plan calculation means 323, and determines whether the parking position is within the "target parking position" "permissible error" (step 7). Next, if the parking position φ is within this range, the parking temporary driving plan P3 is adopted (step 8). However, if it is not within this range, return to step 5, and use the driving plan that performs the recalculation at the first 1 o'clock (or before), and after step 1, repeat the judgment of step 7 until it is located. Up to the scope. As described above, in the twenty-sixth embodiment, the parking temporary parking plan in which the train is stopped in the "target parking position" ± "permissible error" near the target parking position is used, and the train can be stopped at the target with good precision. Parking location. Further, since the train is scheduled to move in the direction of travel, the temporary parking plan for parking is proposed, and it is easy to obtain an automatic train running device which is very convenient in considering the delay time and has a very simple calculation. In addition, in this embodiment, an example in which the parking temporary vehicle planning calculation means 323 is "forward prediction type" will be described. However, the parking temporary vehicle planning calculation means 3 23 is not limited. Must be "forward predictive". The automatic train running device of each of the embodiments described so far is a method of changing the control command in stages by the running level and the braking level of the current general train. However, in the near future, it should be -65- 1277549 (62)

The drive device and the brake device are driven by a continuous control command signal. Therefore, as long as the control command during acceleration is made into a continuous traction command or a running torque command, the optimal driving plan or the recalculation of the driving plan can be implemented, thereby achieving better riding comfort and higher energy saving effect. Automatic operation. In addition, the control command during deceleration can be used as a continuous braking force command, and the optimal driving plan or the recalculation of the driving plan can be implemented, and the automatic riding comfort and the energy saving effect can be realized automatically. Running. Alternatively, both of the above-described continuous control commands during acceleration and deceleration can further achieve automatic operation with better ride comfort and higher energy saving effects. Next, a twenty-seventh embodiment will be described with reference to the drawings. Figure 41 is a schematic diagram showing the configuration of an embodiment of the present invention. The speed position calculation unit 405 calculates the speed and position of the train 0 in the train based on the information of the speed detecting unit 403 such as the tachometer and the information of the ground detecting unit 404 that detects the signal of the ground. The current data acquisition section 412 inputs it to the train position stop automatic control device 410. Further, the map is not indicated, and the information such as the brake level and the stop target position is also input to the train position stop automatic control device 4 1 0 via the train current data acquisition means 4 1 2 . The train position stop automatic control device 410 will determine the current speed, the current position, the current brake level, and the like obtained by the train current data acquisition means 4 1 2, and the respective brake levels stored in the brake characteristic data storage unit 411. The vehicle characteristic data such as the deceleration, the delay time of the brake level switching, and the response delay time are used to formulate the combination of the -66-1277549 (63) in the plural level by the deceleration control plan drafting means 413 to stop the train at the stop target position. Deceleration control plan. For example, when the train is stopped at a specific position by a combination of two levels, the deceleration control plan calculates the time allocation of each brake level. First, after the first brake level is maintained for the specific time determined by the time allocation calculation, Switch to the second brake level and maintain until the train stops. Figure 42 is the simplest example of a deceleration control plan. This example is a deceleration control plan for the location of the remaining distance l〇m, and the level is switched so that the remaining distance is 6m, so that the train stops at the target stop position. For the time allocation, for example, the fake design drawing uses two levels, and the maintenance time of the first braking level is regarded as a variable for the current speed and the remaining distance, and the driving distance at the first braking level deceleration and the second braking level are decelerated. The total distance of the driving distance is equal to the remaining distance , mode, and the maintenance time of the first braking level can be obtained, and the slow time allocation is taken. If there is no solution that satisfies the condition, the combination of the two levels can be changed and the same calculation can be repeated. In the calculation of the driving distance, it is assumed that the deceleration of the level switching delay time after the braking level output command is performed, and the deceleration is performed at the deceleration of the vehicle level before the switching, and the response delay time after the delay time elapses. It will gradually change from the deceleration of the braking level before the switching to the deceleration of the braking level after the switching. After the response delay time elapses, the deceleration will be implemented with the deceleration of the braking level after the switching, and the temporary fixed vehicle will be implemented under the above assumption. For the calculation of the distance, a plan for considering the braking response characteristics when the level is switched is proposed. The deceleration of each brake class is maintained at a safe time. According to the plan switching level proposed in this way, the train can be stopped at a specific position without frequent switching of the level. In addition, when the plan is planned, the first brake level is a level with a large deceleration, and the second brake level is a deceleration rate of -67-1277549 (64) i. When parking at a lower level, the ride comfort can be improved. . : When the deceleration rate of each brake class is changed, for example, when the maintenance time of the first brake level (the level of the large deceleration) is exceeded (the switching schedule time), the prediction when deceleration is performed by the deceleration used in the plan Speed and actual train speed are compared. If the actual speed is small, that is, the deceleration ratio is assumed to be small, do not immediately switch to the second brake level (lower deceleration level), and use the extension of the first brake level. Time to prevent the train φ from exceeding the target stop position. Fig. 43 is an example of adjusting the stop position by changing the switching schedule time. In this example, the actual deceleration is less than the hypothesis, and the deceleration is slower. Therefore, the initial plan is scheduled to be switched between 5m and the deceleration is changed to a level of 3.2m to switch, and the stop position is adjusted. Fig. 44 is a flow chart for adjusting the stop position by changing the switching timing. Extending the maintenance time, for example, calculating the deceleration control plan based on the actual train speed at the switching plan time, and recalculating the deceleration control plan at the time when the first brake level command is recalculated, or based on φ Calculate the deceleration and recalculate the plan to start the switching plan. Also, when formulating the initial deceleration control plan, the maximum preset deceleration is used, and the level switching time can be extended to adjust the stop position regardless of whether the actual deceleration is small or large. Figure 45 is a schematic block diagram of a twenty-eighth embodiment of the present invention. The rest of the configuration is the same as that of the twenty-seventh embodiment except that the deceleration estimating means 416 for estimating the deceleration based on the time series data of the train speed during deceleration is used, and the basic functions are also the same. The deceleration calculation using the deceleration estimation means 41 6 can be obtained by the following method, 68-1277549 (65), for example, after the delay of the level switching and the delay of the response delay time, a specific reduction corresponding to the level At speed, the deceleration is calculated by slowing down the speed that should be caused in a certain period of time. When there is a large error in the train speed data, the moving average of the speed should be taken, and the deceleration should be estimated based on the data after the interference is removed by appropriate filtering. The deceleration estimating means 4 16 is used to estimate the deceleration of the time point, and the deceleration control plan is corrected by the deceleration obtained by the calculation, so that the deceleration of each braking level is generated by the time or speed in the first line of the φ car. When the change is made, the correspondence can also be obtained to ensure the stop accuracy. Figure 46 is a schematic block diagram of a twenty-ninth embodiment of the present invention. The rest of the configuration is the same as that of the twenty-seventh embodiment except for the plan deceleration correcting means 4 1 7 , and the basic function is also the same. The plan deceleration correcting means 417 performs the time points at which the deceleration control plan is decelerated. Or the comparison between the predicted speed of each position and the actual train speed, and the deceleration used by the difference correction deceleration control plan.

According to the calculation of the predicted speed at each time point or each position when the deceleration control plan is decelerating, for example, after calculating the brake level used for the plan and the time allocation, the brake level used according to the current train speed and the plan is used. The deceleration, the level switching delay time, and the response delay time are calculated. The predicted speed can be stored in the array mode from the start to the stop of the project, or can be referred to successively. If the memory capacity of the control computer is limited, the train speed of the previous time can also be used. The deceleration of the brake class is calculated successively. When comparing the predicted speed at this time and the actual train speed, when the train speed is small, it should be the actual deceleration -69- (B: 1277549 (66) degrees is greater than the deceleration used in the plan, so the deceleration should be increased' Recalculate the deceleration control plan. Conversely, when the train speed is large, 'the actual deceleration should be less than the deceleration used by the plan, so reduce the deceleration'. Recalculate the deceleration control plan. When changing the deceleration, for example, The error of the predicted speed and the actual train speed is set, and the amount of change in the deceleration is determined corresponding to the time until the error tolerance is reached. The calculation of the predicted speed and the actual train speed is performed by the calculation of the deceleration correction means 4 1 7 and By correcting the deceleration φ, the deceleration control plan can be updated appropriately at any time corresponding to the time variation of the deceleration. Since there is an error in the data of the actual train speed, it is better to use the filtered data or set the deceleration change amount. Measures such as the lower limit to prevent divergence. [Effect of the Invention] The present invention can ensure that the train is special in the train between the train stations. In addition to the conditions of stopping at the stop position, the energy-saving operation for reducing the energy loss caused by driving can be realized. Moreover, the present invention can implement automatic on-line train characteristics, route characteristics, and control parameters in driving. Learning and using the learning result to realize efficient automatic train operation. Further, the present invention can provide a device for collecting the necessary information for operating the operation device when the train travels to and from the scheduled route. The invention realizes the energy saving effect by excluding the influence of chasing when the train is automatically operated. Moreover, by using the specific embodiment, the delay time can be used to improve the stopping of the train at the target position by using -70- 1277549 (67). Accuracy, and other embodiments can also improve the bad ride feeling caused by the change of the speed control command during the level operation. Moreover, the present invention is based on the deceleration of each brake level of the train, the delay time of the brake level switching, and Brake characteristics data such as response delay time, current speed of train, current position The data of the current brake class, etc., is designed to reduce the speed of the train by using a plurality of brake classes to stop the train at a specific position. In this case, it is also possible to formulate a plan for stopping the train at a specific position, and according to the plan, the ride comfort during the deceleration control is improved and the stop accuracy is ensured. Moreover, the present invention utilizes a plurality of brake classes. The combination of the time allocation calculations for each vehicle level for the purpose of stopping the train at a specific position, and the switching timing of the braking level and the braking level used to constitute the deceleration control drawing, in this way, the deceleration variation In time, it is also possible to change the Φ time distribution, and it is possible to adjust the stop position without increasing the level of the ride, thereby improving the ride comfort and ensuring the stop accuracy. Further, the deceleration control plan of the present invention first decelerates The larger brake class performs deceleration, and then switches to the brake class with less deceleration, and the brake class with less deceleration The parking can improve the comfort of the ride. In addition, the present invention implements a comparison between the predicted speed of the switching time at the time of deceleration according to the deceleration control plan and the actual train speed at the time of switching 'When the two are different, the deceleration control meter is changed. Painting, in this way 'very easy is -71 - 1277549 (68) The stomach evaluates the actual train deceleration condition, and the deceleration control plan corresponding to the variation of the deceleration can be recalculated to improve the stopping accuracy. Moreover, the present invention can change the deceleration control plan if the deceleration and the plan used in the proposed plan are different after the deceleration control plan is drawn up. By using this method, the ROUBUST property for the control of the deceleration fluctuation interference can be improved. And make sure to stop the precision. Moreover, according to the time series data of the train speed during deceleration, the present invention pushes the φ calculation deceleration, and formulates the deceleration control plan according to the estimated deceleration, and by using this method, the ROUBUST property for the control of the deceleration variation disturbance can be improved. And ensure that the accuracy is stopped without any hassle adjustments. Further, according to the present invention, the comparison between the predicted speed at each time point or each position and the actual train speed in accordance with the deceleration control plan is performed, and the deceleration used in the difference correction deceleration control 'plan is used, and the correction is performed according to the correction. In the deceleration degree deceleration control plan, the ROUBUST property for the control of the deceleration fluctuation disturbance can be improved, and the stop accuracy can be ensured without the troublesome adjustment. : In addition, according to the speed of the previous time step, the deceleration used in the proposed plan, the level switching delay time, and the response delay time, the present invention successively calculates the time points or positions at the time of deceleration according to the deceleration control plan. In this way, when the memory capacity of the control computer is limited, the ROUBUST performance for the control of the deceleration fluctuation disturbance can be improved, and the stop accuracy can be ensured without complicated adjustment. [Brief Description of the Drawings] - 72 - 1277549 (69) ^ Fig. 1 is a block diagram of an automatic train operating device according to the first embodiment of the present invention. Figure 2 is an example of machine loss indicators and total loss indicators at runtime. Figure 3 is an example of machine loss indicators, brake loss indicators, and total loss indicators for braking operations. Figure 4 is an example diagram of the converter loss indicator and motor loss indicator during operation. Figure 5 is an example diagram of converter losses and motor losses during operation. Fig. 6 is a view showing an example of the driving mode of the first embodiment. Fig. 7 is a block diagram showing an automatic train operating device according to a second embodiment of the present invention. Fig. 8 is a view showing an example of the brake loss when the running load is limited. 〇 Fig. 9 is a block diagram of the automatic train running device according to the third embodiment of the invention. Tenth ffl is a block diagram of a train operation support device according to a fourth embodiment of the present invention. Fig. 11 is a block diagram showing a configuration example of a thrust indicating device according to a fourth embodiment. Fig. 12 is a block diagram showing the control system of the thrust indicating device of Fig. 11. Fig. 3 is a block diagram showing a configuration example of a thrust indicating device of the train operation support device according to the fifth embodiment of the present invention. Figure 14 is a block diagram showing a configuration example of a thrust indicating device of the -73- 1277549 (70) train operation support device according to the sixth embodiment of the present invention. Fig. 15 is a block diagram showing the entire train having the automatic train running device of the present invention. Fig. 16 is a block diagram showing the internal structure of the automatic train running device of Fig. 15. Figure 17 shows the driving mode compensation map based on the weight of the initial operation. φ Figure 18 is a flow chart showing the steps of learning before and after business. Fig. 19 is a block diagram of a compensation means for the purpose of compensating for automatic characteristic learning results according to an embodiment of the present invention. Figure 20 shows the structure of the automatic train running device and data storage unit. Figure 21 is an example of an automatic train operation mode. Fig. 22 is a block diagram showing the construction of a train for the automatic train transport device of the present embodiment. Figure 23 is a block diagram showing the configuration of an automatic train running device 1 according to a thirteenth embodiment of the present invention. Fig. 24 is a block diagram showing the configuration of an automatic train running device 1 according to a fourteenth embodiment of the present invention. Figure 25 is a block diagram showing the configuration of an automatic train running device 1 according to a fifteenth embodiment of the present invention. Figure 26 is a block diagram showing the configuration of an automatic train running device 1 according to a sixteenth embodiment of the present invention. Figure 27 is a block diagram showing the configuration of the automatic train running device 1 according to the seventeenth embodiment of the present invention. Fig. 28 is a block diagram showing the configuration of the automatic train operating device 1 according to the eighteenth embodiment of the present invention. Figure 29 is a block diagram showing the configuration of an automatic train running device 1 according to a nineteenth embodiment of the present invention. Figure 30 is a block diagram showing the configuration of an automatic train running device 1 according to a twentieth embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of the automatic train running device 1 φ according to the second embodiment of the present invention. Fig. 3 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-second embodiment of the present invention. Figure 33 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty-third embodiment of the present invention. The 34th mouse is a block diagram showing the configuration of the automatic train running device 1 according to the 24th embodiment of the present invention. Item 35: The automatic sequence of the twenty-fifth embodiment of the present invention: a block diagram of the configuration of the vehicle operating device 1. Fig. 3 is a block diagram showing the configuration of an automatic train running device 1 according to a twenty sixth embodiment of the present invention. Fig. 3 is a diagram showing an example of the characteristics of the optimum driving plan to be proposed in the embodiment of the present invention. Fig. 3 is a diagram showing an example of characteristics of a driving plan to be formulated or recalculated in the embodiment of the present invention. Fig. 39 is a diagram showing the characteristics of the temporary driving plan proposed in the embodiment of the present invention. Example of the description - 75 - 1277549 (72) • Fig. 40 is a flow chart showing the action of the means 24 of the driving plan of Fig. 36. Fig. 4 is a schematic structural view showing a twenty-seventh embodiment of the train position stop automatic control device of the present invention. Fig. 42 is a schematic diagram showing an example of a deceleration control plan employed by the train position stop automatic control device of the present invention. Fig. 43 is a schematic diagram showing an example of changing the stop position of the train position stop automatic control device of the present invention. Fig. 44 is a schematic diagram showing an example of the step of adjusting the stop position of the switching schedule adjustment stop position of the train position stop automatic control device of the present invention. Fig. 45 is a schematic block diagram showing a twenty-eighth embodiment of the train position stop automatic control device of the present invention. Fig. 4 is a schematic structural view showing a twenty-ninth embodiment of the train position stop automatic control device of the present invention. Φ) Fig. 47 is a block diagram showing a configuration of a general train system having an automatic train running device. Figure 48 is a block diagram of the automatic train running device of the system of Figure 47. [Description of component symbols] 0: Train 1: Automatic train running device (ΑΤΟ) 2: Drive brake device 3: Database-76- 1277549 (73) * 4 : VVVF inverter transformer, 5: Main motor 6 : Brake control device 7 : Wheel 8 : Mechanical brake 9 : Speed detector 1 〇 : Above ground detector Φ φ 11 : Track 1 2 : Provisional driving plan 1 3 : Best driving plan 1 4 : Thrust command generation Part 1 5 : Driving mode compensation index calculation unit 16 : Loss index calculation unit 17 : Overload indicator calculation unit 1 8. Addition unit 19 : Driving mode compensation unit 20 : Driving distance compensation unit 2 1 : Timing determination unit 22 : Train Operation support device 23: Main controller 2 4: Thrust command unit 25: Angle command calculation unit 26: Impedance control unit 27: Servo amplifier-77- (74) 1277549 28: Servo motor 29: Encoder 30: Recommended level control Part 3 1 : Lamp 32 : Recommended level indicating control unit 33 : Sound output unit 3 4 : Database

35: Driving mode precipitation unit 36: database 102: automatic train control device (ATC) 103: database (DB) 104: driver's station 1 0 5 · load device 10: speed detector 107: above ground detector 109: Drive device: deceleration device 120: pre-operating driving determination means 1 2 1 : pre-service characteristic initial setting means 122: pre-operating test driving automatic train means 123: driving result storage means 124: pre-service characteristic estimating means 125: Calculation method compensation means 126: characteristic estimation 値 storage means -78- 1277549 (75)

1 3 Ο : Learning characteristics database (learning characteristic DB) 1 3 1 : Characteristic initial setting means 132: Train automatic operation means 1 3 3 : Driving result storage means 1 3 4 : Post-business characteristic learning means 1 3 5 : Learning result compensation means 1 3 6 : Learning result comparison means 1 3 7 : Learning result compensation means 1 8 0 : Data processing means 1 8 1 : Train automatic operation means 1341~1 345: Automatic characteristic learning means 201: Data storage part 203: above-ground detector 204: speed detector 205: drive device 200: brake device 207: train characteristic learning device 208: automatic operation control unit 209: train weight calculation unit 2 1 0: train resistance calculation unit 2 1 1 : brake Force calculation unit 2 1 2 : Delay time calculation unit 2 1 3 : Travel rate calculation unit 300 : Library - 79 - 1277549 (76) 302 : Speed detector 303 : Above-ground detector 304A : When parking at the station Operation circuit 3 04B: arithmetic circuit 3 05 when driving between stations: drive device 3〇6: brake device 307: optimal driving plan

308 : Driving plan recalculation means 3 09 : Control command precipitating means 3 1 0 : Control command output means 3 1 1 : Cumulative error reference type driving plan recalculation means 3 1 2 : Control command compensating means 3 1 3 : Accumulation Error reference type control command compensation means 3 14 : Delay time consideration type optimal driving plan drafting means 3 1 5 : Delay time consideration type driving plan recalculation means 3 16 : Forward predictive type optimal driving plan drafting means 3 17 : Recalculation of forward-predicted driving plans 3 18 : Recalculation of successive forward-looking driving plans 3 1 9 : Speed measurement-driven progressive forward-predictive driving plan recalculation means 3 20 : Station Driving result storage means 321 : Delay time estimating means 322 : Online delay time estimating means 3 23 : Forward predictive type parking temporary driving plan calculating means - 80 - 1277549 (77) 324 : Driving plan adopting means 402 : braking device 403: speed detecting unit 404: above-ground sub-detecting unit 405: speed position calculating unit

4 1 0 : Train position stop automatic control device 4 1 1 : Brake characteristic data storage unit 4 1 2 : Train current data acquisition means 4 1 3 : Deceleration control plan preparation means 4 1 4 : Deceleration control command precipitation means 4 1 5: deceleration control command output means 416: deceleration estimating means 417: plan deceleration correcting means -81

Claims (1)

1277549 (1) : X. Patent application scope 1 1. A train fixed position stop automatic control device, which automatically stops the train at a specific position, and has the following features: The deceleration of the brake class of the stored train and the switching of the brake class "Brake characteristic data storage unit" of the vehicle characteristic data such as the delay time and the response delay time; The information on the train's current speed, the current position, the current train's level, etc., the means of obtaining the train's current data, the trains obtained from the train's characteristic data storage unit, and the trains obtained by the "train current data acquisition means" For the current data, a deceleration control plan for the deceleration control plan for stopping the train at a specific position with a plurality of brake classes is proposed. The deceleration control plan prepared by the "deceleration control plan development method" is used to analyze the time points. The "deceleration control command output means" of the deceleration control command; and the "deceleration control command output means" which is output by the "deceleration control command discharge means" are output to the "deceleration control command output means" of the brake device. 2. For example, the automatic position control device for stopping the train position in the first application of the patent scope is to calculate the time allocation of each brake class for the purpose of stopping the train at a specific position by using a combination of multiple brake classes, in order to use the brake class and The switching timing of the brake level constitutes a deceleration control plan. 3. For example, the train position stop automatic control device of the second application patent scope, -82- 1277549 (2) - The deceleration control plan first decelerates with the brake class with higher deceleration and then switches to deceleration. Lower brake class. 4. The automatic positioning control device for stopping the position of the train in the second application of the patent scope, wherein the prediction speed of the switching time at the time of deceleration according to the deceleration control plan and the comparison of the actual train speed at the switching time are implemented. Change the deceleration control plan. 5. If the train position stop automatic control device of the patent application scope 1 is applied, after the deceleration control plan is drafted, the deceleration control plan is changed if the deceleration is used when the deceleration is used in the proposed plan. 6. For example, the automatic positioning control device for stopping the train position in the first application of the patent scope includes a “deceleration estimation method” for deceleration based on the time series data of the train speed in deceleration, and formulating the deceleration control according to the estimated deceleration rate. > System planning. 7. For example, the automatic positioning control device for stopping the position of the train in the first application of the patent scope includes "planning deceleration correction means", and the predicted speed of each time point or each position during deceleration according to the deceleration control plan, and The actual train speed is compared, and the deceleration control plan is changed according to the deceleration used in the difference correction deceleration control plan and the correction deceleration calculated based on the "plan deceleration correction means". 8. For example, the automatic positioning control of the train position stop No. 4 of the patent scope is -83-1277549 (3), where V is based on the speed of the previous time, the deceleration used in the proposed plan, the delay of the level switching, And the response delay time, and the predicted speed at each time point or each position at the time of deceleration according to the deceleration control plan is calculated successively.